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  • Wayfinding Your Way To Better Revenue Generation – Retail Edition

    Wayfinding Your Way To Better Revenue Generation – Retail Edition

    The modern shopper has high expectations. They walk into a store wanting a seamless experience, from browsing products to completing a purchase. Central to this experience is wayfinding, the art of guiding customers through a physical space to find what they need.

    Effective wayfinding goes beyond simple signage; it’s about creating an intuitive and engaging layout that fosters positive customer interactions and ultimately boosts revenue.

    What is Wayfinding and Why Does it Matter?

    Wayfinding refers to the art of guiding people through a physical space to reach their desired destination. In the context of retail, it encompasses all the methods a retailer uses to help customers navigate their store.

    This includes physical elements like signage, floor plans, and product displays, as well as digital tools like interactive maps and kiosks. Consider this statistic: according to a PwC, a staggering 73% of shoppers report feeling frustrated when they can’t find what they’re looking for in a store. This frustration can lead to abandoned purchases and a negative brand experience.

    Here’s how retailers incorporate wayfinding into their designs:

    Physical Elements:

    Signage: Clear and concise signs are crucial. This includes using consistent fonts, colors, and symbols throughout the store to create a visual language customers can easily understand. Signs should be placed at appropriate heights and in sufficient numbers so shoppers can readily find their way.

    Floor Plans: Easy-to-read floor plans displayed at entrances and throughout the store can provide a quick overview of the layout and department locations.

    Visual Hierarchy: The store layout itself can be a wayfinding tool. Retailers can use a clear visual hierarchy to direct customer flow. This might involve wider aisles leading to key departments, strategically placed product displays, and even lighting variations to highlight specific areas.

    Digital Tools:

    Interactive Kiosks: These self-service kiosks allow customers to search for specific products, check prices, and even create shopping lists. They’re particularly helpful in large stores with a vast product selection.

    Mobile Apps: Retailer apps with store maps, product information, and personalized navigation can guide customers based on their shopping lists. This allows for a more targeted and efficient shopping experience.

    Accessibility Considerations:

    Effective wayfinding is inclusive. Signage should be clear, concise, and available in Braille or raised lettering for visually impaired customers. Aisles should be wide enough to accommodate wheelchairs and mobility aids.

    Benefits of Effective Wayfinding

    Investing in good wayfinding offers a multitude of benefits for both retailers and customers:

    Improved Customer Experience (CX): When customers can easily find what they’re looking for, they’re more likely to have a positive shopping experience. Studies show that effective wayfinding can lead to a 15% increase in customer satisfaction [Retail TouchPoints]. Happy customers are more likely to return and recommend the store to others.

    Increased Sales & Revenue: When customers spend less time wandering and more time browsing, sales can increase. A University of Cincinnati: [invalid URL removed] study found that a well-designed store layout can lead to a 20% increase in sales. This is because customers are more likely to discover impulse purchases when they are easily guided through different product categories.

    Reduced Operational Costs: Lost and frustrated customers often require assistance from staff. Effective wayfinding can decrease the need for employee intervention, freeing up staff time for other tasks and potentially lowering operational costs.

    Enhanced Brand Image: A well-designed store layout with clear wayfinding creates a sense of professionalism and reflects positively on your brand.

    Challenges of Ineffective Wayfinding

    Retailers face several challenges in creating an effective wayfinding system:

    Complexity of Store Layouts: Modern stores can be complex with multiple floors, departments, and winding aisles. A confusing layout can leave customers feeling lost and disoriented, leading to decreased dwell time (the amount of time a customer spends in a store) and missed sales opportunities.

    Big retailers have a wayfinding challenge. Frustrated customers who cannot locate their desired products may stop their purchasing intentions altogether Pixabay at Pexels

    Accessibility Considerations: Not all customers are able-bodied. Signage that isn’t clear or placed at an accessible height can be a barrier for customers with disabilities. Furthermore, inadequate aisle width or cluttered displays can make it difficult for customers using wheelchairs or mobility aids to navigate the store. Proper wayfinding needs to be inclusive, ensuring everyone feels comfortable and welcome.

    The Rise of Online Shopping: Online shopping has changed customer expectations. Today’s shoppers are accustomed to the ease and efficiency of search functions on e-commerce websites. Retail stores need to offer a comparable level of ease in finding desired products.

    Checklist for Assessing Wayfinding Designs:

    Here’s a checklist for retailers to assess their current wayfinding system:

    Signage: Are signs clear, concise, and easy to read? Are they placed at appropriate heights and in sufficient numbers so customers can easily find their way? Do they use consistent fonts and colors throughout the store?

    Visual Hierarchy: Is there a clear visual hierarchy directing customers towards key departments and products? Are high-traffic areas free from clutter and obstructions?

    Mapping & Navigation: Do you offer physical store maps or digital navigation tools on your website or app? Are these maps easy to understand and navigate?

    Accessibility: Is your wayfinding system accessible to all customers, including those with disabilities? Are signs in Braille or raised lettering? Are aisles wide enough for wheelchairs and mobility aids?

    Employee Training: Are your staff members familiar with the store layout and product locations? Can they effectively answer customer questions and provide directions?

    By scoring your store on these factors, retailers can identify areas for improvement and develop a plan to implement a more effective wayfinding system.

    Examples of Effective Wayfinding in Action:

    Several retailers are leading the way in effective wayfinding:

    IKEA: The company utilizes a one-way path system, strategically showcasing product displays that inspire customers and encourage them to explore different sections. This design, while sometimes criticized for its length, ultimately leads many customers to discover unplanned purchases.

    IKEA makes wayfinding a key component of its customer experience. This enables the furniture chain to engage meaningfully with its customers with inspiring room designs. (Source: YouTube)

    Apple: Apple stores are another prime example. Their clean, minimalist design, large product displays, and knowledgeable staff members create a welcoming and intuitive shopping experience. Interactive product displays allow customers to experiment with technology before making a purchase.

    Apple focuses on usability, fairness, and comfort in designing its stores. Customers are never lost even through the vastness of the space. (Source: YouTube)

    Target: Target uses a grid layout with clear department signage and consistent color schemes. They often employ endcap displays to highlight seasonal or promotional items, drawing customer attention to specific areas of the store.

    What Are Good Wayfinding Related Metrics for Retailers?

    Retailers typically use the following wayfinding-related metrics to track and analyze the effectiveness of their in-store/online navigation system under these three categories customer behavior, sales / operational, engagement, and feedback.

    Customer Behavior Metrics:

    Dwell Time: This measures the average amount of time a customer spends in a store. Increased dwell time in specific departments after wayfinding improvements could indicate better product discoverability.

    Path Analysis: This tracks customer movement patterns within the store. The software can analyze anonymized customer traffic data to identify areas where customers get lost or seem confused. Observing these patterns can reveal weaknesses in the layout or signage that need addressing.

    Heatmaps: Heatmaps visually represent customer density in different areas of the store. These can reveal underutilized spaces or highlight areas where customers tend to congregate. This data can inform product placement and wayfinding adjustments.

    Conversion Rates: Conversion rate refers to the percentage of customers who purchase after entering the store. An increase in conversion rates after wayfinding improvements suggests a more seamless shopping experience.

    Sales and Operational Metrics:

    Sales by Department: Track sales figures for different departments to see if wayfinding changes lead to increased sales in specific areas. This could indicate that customers are now more easily discovering products in those departments.

    Employee Assistance Requests: Monitor the number of times staff is asked for assistance with product location. A decrease in these requests after wayfinding improvements suggests a more intuitive layout.

    Abandoned Carts: For stores with self-checkout options, track the number of abandoned carts. An increase in abandoned carts could be a sign that customers are frustrated with the checkout process or unable to find the products they need, potentially due to wayfinding issues.

    Online Engagement Metrics:

    Click-through Rates (CTRs) on Digital Wayfinding Tools: If your store offers a mobile app with navigation features, track the CTRs on those features. Low CTRs could indicate that customers are unaware of the tool or find it unhelpful.

    Social Media Mentions: Monitor social media for mentions of your store’s layout or navigation. Positive comments about the ease of finding products are a good sign, while negative comments highlight areas for improvement.

    Broken Links Analysis: Monitor whether the key website or application features are accessible and operational. Brands do not want customers to experience a 404 link not found error during the purchase process.

    Click-Through Analysis: Evaluate the entire online customer experience journey for gaps in the process that could lead to purchase disruption (For example, the purchase button does not work across all major browsers).

    Feedback metrics:

    Exit Surveys: Conducting short exit surveys can provide valuable customer feedback. Ask shoppers about their physical or online experience navigating the store and if they were able to find what they needed easily, what made them frustrated, and whether their challenges were resolved by physical and non-physical means.

    The Future of Wayfinding: Technology and Personalization

    Technology is playing an increasingly important role in wayfinding. Here are some exciting trends:

    Interactive Kiosks: Interactive kiosks allow customers to search for specific products, check prices, and even create shopping lists. These kiosks can be particularly helpful in large stores with various products.

    Mobile Apps: Retailer mobile apps can provide customers with store maps, product information, and personalized navigation based on their shopping list. This allows for a more targeted and efficient shopping experience.

    Augmented Reality (AR): Some retailers are experimenting with AR technology, allowing customers to virtually “place” furniture or other items in their homes before purchasing them. This can help customers visualize how a product might look and feel in their own space.

    Effective wayfinding is no longer a luxury, it is a necessity in today’s competitive retail landscape. By prioritizing clear signage, intuitive layouts, and accessible design, retailers can ensure a positive customer experience, boost sales, and build brand loyalty.

    As technology continues to evolve, wayfinding will become even more dynamic and personalized, further blurring the lines between the physical and digital shopping experience. The future of retail belongs to those who can create a seamless and enjoyable journey for every customer who walks through their doors.

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  • Accessibility – Important for Retail Brands to Prioritize

    Accessibility – Important for Retail Brands to Prioritize

    In today’s retail landscape, customer experience is a key differentiator that can set brands apart. While many businesses focus on aspects such as convenience, personalization, and omnichannel experiences, one critical area that is often overlooked is accessibility.

    Retail brands have a responsibility to ensure that their products and services are accessible to all customers, including those with visible and non-visible disabilities. Building accessibility into their customer experience strategy aligns with ethical and legal obligations and presents a significant business opportunity for brands. Retail Mashup explores accessibility in-depth in this article.

    Despite the importance of accessibility, many retail brands fall short in this area. According to a report by the World Health Organization (WHO), approximately 15% of the world’s population lives with some form of disability.

    However, a survey conducted by the Click-Away Pound research initiative found that 71% of disabled customers with access needs would leave a website that they found difficult to use, potentially leading to lost sales and a negative brand image.

    Retail Insider summarized a key Canadian study on accessibility by the Retail Council of Canada back in 2022. In it, the top Canadian retail publication noted that:

    30% of Canadians consider accessibility when looking for a place to shop or do business. Source: Rick Hansen Foundation

    9.1 million people in Canada have a recognized disability. Source: The Global Economics of Disability

    $82.2 billion is the cumulative annual disposable income among Canadians with disabilities Source: The Global Economics of Disability

    2022 RCC Accessibility Guidebook (Source: Retail Council of Canada)

    There are many types of disabilities, which can be broadly categorized into the following categories:

    Physical Disabilities: These disabilities affect a person’s mobility or dexterity. Examples include paralysis, limb loss, and muscular dystrophy.

    Sensory Disabilities: These disabilities affect one or more of the senses. Examples include blindness, deafness, and sensory processing disorders.

    Cognitive Disabilities: These disabilities affect a person’s ability to think, learn, and process information. Examples include intellectual disabilities, learning disabilities, and memory disorders.

    Psychological Disabilities: These disabilities affect a person’s mental health and emotional well-being. Examples include depression, anxiety disorders, and schizophrenia.

    Neurological Disabilities: These disabilities affect the brain and nervous system. Examples include epilepsy, Parkinson’s disease, and multiple sclerosis.

    Developmental Disabilities: These disabilities affect a person’s physical or mental development. Examples include autism spectrum disorder, Down syndrome, and cerebral palsy.

    It is important to note that disabilities can vary widely in their impact severity and visibility (see below). In addition, individuals with disabilities may have different needs and abilities. Providing inclusive and accessibility considerations can help ensure that people with disabilities can fully participate in the retail environment.

    Visible disabilities are disabilities that are apparent or noticeable to others, often affecting a person’s physical appearance or mobility. Examples include:

    Mobility impairments: Such as difficulty walking or using stairs, often requiring the use of mobility aids like wheelchairs, crutches, or walkers.

    Visible physical disabilities: Such as limb differences, amputations, or disfigurements.

    Visual impairments: Including blindness or low vision that is visibly noticeable through the use of canes, guide dogs, or specialized eyewear.

    Non-visible disabilities, on the other hand, are not immediately apparent to others. These disabilities may impact a person’s physical, mental, or cognitive abilities, but their effects are not visible to the naked eye. Examples include:

    Chronic illnesses: Such as diabetes, fibromyalgia, or chronic pain conditions.

    Mental health conditions: Such as depression, anxiety disorders, or post-traumatic stress disorder (PTSD).

    Cognitive impairments: Such as learning disabilities, autism spectrum disorder (ASD), or attention deficit hyperactivity disorder (ADHD).

    Both visible and non-visible disabilities can have significant impacts on individuals’ lives and may require accommodations or support to enable full participation in daily activities. Retailers need to be aware of and considerate of their customer base and the diverse needs of customers with disabilities, whether visible or non-visible, to ensure that their products, services, and environments are as accessible and inclusive as possible.

    Accessibility goes beyond physical mobility. It encompasses a wide range of disabilities, including visual, auditory, cognitive, and neurological impairments. For example, visually impaired customers may rely on screen readers to navigate websites, while those with cognitive impairments may require simplified language and clear navigation paths. Retail brands must consider these diverse needs when designing their customer experience.

    Several leading retail brands have set the standard for accessibility management. Walmart, for instance, has implemented various initiatives to improve accessibility, such as assisting customers with disabilities in-store and offering accessible shopping options online.

    Similarly, Apple has incorporated features into its products, such as VoiceOver and Magnifier, to assist customers with disabilities in using their devices.

    Apple has many accessibility features embedded in its MacOS, iOS, and beyond. (Source: YouTube)

    Other retail brands that have included accessibility considerations include:
    Toys “R” Us has included sensory-friendly shopping hours and sensory rooms in select stores. These sensory rooms provide a calming environment with sensory-friendly toys and activities for children with autism and other sensory processing disorders.

    Another example is the Manchester Airport in the UK, which has a dedicated sensory room located in Terminal 1 called the “Sunflower Room” for passengers with hidden disabilities. The room provides a quiet and calming space away from the busy airport environment, equipped with sensory toys, soft lighting, and comfortable seating.

    To assess their current level of accessibility, retail brands can conduct accessibility audits. These audits involve evaluating websites, mobile applications, and physical stores for compliance with accessibility standards such as the Web Content Accessibility Guidelines (WCAG). Additionally, brands can engage with customers and advocacy groups to gather feedback on their accessibility efforts.

    In the EU, the leading regulator for accessibility is the European Union Agency for Fundamental Rights (FRA), which works to promote and protect the rights of people with disabilities across the EU. Additionally, the Web Accessibility Directive, adopted in 2016, requires EU member states to ensure that public sector websites and mobile apps are accessible.

    In Canada, accessibility regulations are governed by the Accessibility for Ontarians with Disabilities Act (AODA) in Ontario and the Accessible Canada Act at the federal level. These acts aim to make Ontario and Canada more accessible for people with disabilities by setting standards for accessibility in areas such as customer service, transportation, and information and communications.

    In the US, the Americans with Disabilities Act (ADA) is the primary regulator for accessibility. The ADA prohibits discrimination against individuals with disabilities in all areas of public life, including jobs, schools, transportation, and all public and private places that are open to the general public. This includes requirements for accessible design and accommodations in buildings, facilities, and digital platform

    In Asia, accessibility regulations vary by country. For example, in Japan, the Act for Eliminating Discrimination against Persons with Disabilities sets out requirements for accessibility in areas such as transportation, public facilities, and information and communications. In China, the Regulations on the Accessibility of Urban Environment require that new public buildings and facilities be accessible to people with disabilities.

    Accessibility is not something that can be incorporated in a week. Retail brands need to consider and assess their customer demographics, customer needs, and online/virtual/ physical presence in the entire customer journey before initiating a strategy to build or enhance their current process and procedures.

    In general, brands should include some of the following concepts in their processes:

    Training and Awareness: Educate employees about accessibility and the importance of providing an inclusive customer experience.

    Accessible Design: Ensure that websites, mobile applications, and physical stores are designed with accessibility in mind, including features such as alt text for images, easy-to-read fonts, and clear signage.

    Assistive Technologies: Provide assistive technologies, such as screen readers and magnifiers, to help customers with disabilities access products and services.

    Collaboration: Partner with disability advocacy groups and organizations to gain insights into how to improve accessibility.

    Accessibility consideration in web design is important for all brand online contact and communication (Source: fauxels at Pexels)

    In addition, retail brands can manage specific needs better by categories the different visible and non-visible disabilities:

    Physical Disabilities: Retailers need to consider the accessibility of their physical spaces for customers with physical disabilities. This includes providing wheelchair-accessible entrances, aisles, and restrooms, as well as ensuring that shelves are at a height that is easily reachable for customers using wheelchairs or mobility aids.

    Sensory Disabilities: Retailers can make their stores more accessible to customers with sensory disabilities by minimizing noise and distractions, providing clear signage and wayfinding, and offering alternative formats for information, such as braille or large print.

    Cognitive Disabilities: Retailers can create a more inclusive shopping experience for customers with cognitive disabilities by providing clear and simple signage, logically organizing products, and training their staff to be patient and understanding.

    Psychological Disabilities: Retailers can support customers with psychological disabilities by creating a welcoming and non-judgmental environment, providing quiet spaces for those who may become overwhelmed, and training their staff to recognize and respond appropriately to signs of distress.

    Neurological Disabilities: Retailers can accommodate customers with neurological disabilities by providing clear and consistent communication, offering assistance with tasks that may be challenging, and ensuring that their physical spaces are safe and easy to navigate.

    Developmental Disabilities: Retailers can support customers with developmental disabilities by providing visual supports, such as pictures or symbols, to help them understand information, offering assistance with decision-making, and creating a calm and predictable shopping environment.

    Measuring Accessibility

    Measuring the effectiveness of accessibility efforts is crucial. Retail brands can use metrics such as customer satisfaction scores, website traffic from accessible devices, and feedback from customers with disabilities to gauge their progress. Additionally, brands can benchmark their accessibility performance against industry standards and best practices.

    Other metrics retailers can consider in their quest to manage accessibility include:

    Increased Customer Satisfaction: By providing accessible products, services, and environments, brands can cater to a broader range of customers, including those with disabilities. This inclusivity can lead to increased customer satisfaction and loyalty, as customers feel valued and respected.

    Positive Brand Image: Brands that prioritize their accessible components are often viewed more favorably by customers, both with and without disabilities. This positive brand image can enhance customer trust and loyalty, leading to repeat business and positive word-of-mouth recommendations.

    Expanded Market Reach: By making their products and services accessible, brands can tap into a market segment that is often underserved. According to the World Health Organization, people with disabilities make up approximately 15% of the world’s population, representing a significant consumer base.

    Compliance with Regulations: Many countries have regulations in place that require businesses to ensure people with disabilities are catered for. By complying with these regulations, brands can avoid legal issues and negative publicity, while also demonstrating their commitment to social responsibility.

    Improved Online Presence: Features such as alt text for images, keyboard navigation, and screen reader compatibility can improve a brand’s search engine optimization (SEO) and make its website more user-friendly for all customers, not just those with disabilities.

    Enhanced Customer Engagement: Brands that prioritize accessibility often find that they are better able to engage with customers with disabilities, leading to valuable feedback and insights that can help improve products and services for all customers.

    Innovative Solutions: Drive innovation within a brand, leading to the development of new products, services, and features that benefit all customers, not just those with disabilities.

    Building accessibility into their customer experience strategy is a legal and ethical obligation for retail brands but also a smart business decision. By ensuring that their products and services are accessible to all customers, including those with visible and non-visible disabilities, brands can enhance customer loyalty, improve brand reputation, and tap into a significant market segment. By assessing, enhancing, and measuring accessibility, retail brands can create a more inclusive and welcoming customer experience for everyone.

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  • Powering Intelligent Ad Networks: AI Evolution In Retail (Part 6)

    Powering Intelligent Ad Networks: AI Evolution In Retail (Part 6)

    In the dynamic landscape of retail, advertising plays a pivotal role in driving customer engagement, brand visibility, and ultimately, sales. As the industry evolves, leveraging advanced technologies like Artificial Intelligence (AI) has become crucial for retailers to stay competitive.

    In part 5 of the series, Retail Mashup discussed AI in retail marketing. This part goes further by focusing on how AI can play a role in ad networks in helping brands connect further with consumers.

    An ad network is a platform that connects advertisers with publishers who want to host advertisements. Ad networks serve as intermediaries, facilitating the buying and selling of ad inventory. Advertisers use ad networks to reach a wider audience by displaying their ads on multiple websites or apps within the network.

    Publishers like Google, on the other hand, use ad networks to monetize their websites or apps by displaying ads and earning revenue based on ad impressions or clicks. Ad networks typically use targeting criteria such as demographics, interests, and browsing behavior to deliver relevant ads to users.

    AI has transformed traditional ad networks into intelligent systems capable of delivering personalized, targeted advertisements to consumers. By leveraging machine learning algorithms, these networks analyze vast amounts of data to understand consumer behavior, preferences, and trends. This enables retailers to create highly targeted campaigns that resonate with their target audience, leading to improved conversion rates and return on investment (ROI).

    Here’s a brief timeline highlighting key milestones in the integration of AI into ad networks:

    Early 2000s: The use of AI in ad networks began with basic algorithms for ad targeting and optimization. These early systems laid the foundation for more sophisticated AI applications in later years.

    Mid-2000s: Google introduced its AdSense program in 2003, which used AI algorithms to match ads with relevant content on websites. This marked a significant advancement in contextual advertising powered by AI.

    Late 2000s to Early 2010s: The introduction of real-time bidding (RTB) platforms in the late 2000s and early 2010s led to the integration of AI for ad targeting and bidding optimization. Ad networks began using machine learning algorithms to analyze vast amounts of data in real time to improve ad performance.

    2010s: The 2010s saw a rapid expansion of AI applications in ad networks, driven by advancements in machine learning and data processing technologies. Companies like Google, Facebook, and Amazon invested heavily in AI to enhance ad targeting, personalization, and campaign optimization.

    Mid-2010s: The mid-2010s witnessed the emergence of programmatic advertising, which relies heavily on AI for automated ad buying and selling. AI-powered programmatic platforms revolutionized the ad buying process, making it more efficient and cost-effective.

    Late 2010s to Present: AI continues to play a central role in ad networks, with a focus on advanced targeting, dynamic ad creative optimization, and cross-device targeting. AI-powered ad networks are now capable of delivering highly personalized and targeted ads to users across various channels and devices.

    The adoption of AI in retail marketing is on the rise, driven by the need for more efficient and effective marketing strategies. According to a report by Grand View Research, AI-powered retail marketing is estimated to reach US$40.74 billion by 2030, expanding at a Compounded Annual Growth Rate (CAGR) of 23.9% from 2022 to 2030. This growth is fuelled by the increasing demand for personalized shopping experiences and the need for retailers to optimize and isolate their marketing efforts to specific demographics.

    AI-powered retail marketing size by 2030

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    USD$ (millions)

    Quite a few technology companies have embraced AI-powered ad networks to enhance their advertising strategies. Two notable examples are Amazon and Walmart.

    Amazon uses AI algorithms to analyze customer data and behavior to deliver personalized product recommendations and targeted advertisements. This has helped Amazon significantly improve its advertising revenue and drive sales on its platform.

    Walmart uses AI to analyze customer data and optimize its ad campaigns across various channels. By leveraging AI-powered ad networks, Walmart has been able to improve its targeting accuracy and deliver more relevant ads both digitally (online) and physical (at stores) to its customers, leading to higher conversion rates and ROI.

    Beyond the two giant retailers, other examples that created an ad network eco-system include:

    Google: Google is a major player in the ad network space through its Google Ads platform. Google uses AI to improve ad targeting, optimize bids, and enhance ad placements across its network of websites and apps. Google’s AI algorithms analyze user behavior, search history, and other data points to deliver more relevant and personalized ads to users.

    Facebook: Facebook’s ad network, including Facebook Ads and Instagram Ads, uses AI to target ads based on user interests, demographics, and behavior. Facebook’s AI algorithms continuously learn from user interactions to improve ad targeting and performance.

    Microsoft: Microsoft’s ad network, powered by its Bing search engine, Open AI and Microsoft Advertising platform, uses AI to improve ad targeting, ad copy optimization, and bid management. Microsoft’s AI capabilities also extend to its LinkedIn platform, where it helps advertisers target professionals based on their profiles and behavior.

    The Trade Desk: The Trade Desk is a programmatic advertising platform that uses AI for ad buying, audience targeting, and campaign optimization, making it a popular choice among advertisers.

    CVS: The CVS ad network, also known as CVS Media Exchange, is a digital advertising platform operated by CVS Health, one of the largest retail pharmacy chains in the United States. The network allows advertisers to reach CVS customers through targeted digital ads across various channels, including the CVS website, mobile app, email, and in-store displays. With over 9,900 retail locations across the country and millions of customers, CVS offers advertisers a significant reach and the opportunity to engage with a highly valuable audience.

    While Facebook and Google are well known entities , many may not be as familiar with Trade Desk. (Source: YouTube)

    Before getting into the AI-Powered ad networks, brands should consider the following pros and cons:

    Pros:

    Personalization: AI enables retailers to create personalized ad campaigns based on individual customer preferences and behavior, leading to higher engagement and conversion rates.

    Targeting Accuracy: AI algorithms analyze vast amounts of data to identify the most relevant audience segments for a particular campaign, ensuring that ads are shown to the right people at the right time.

    Optimization: AI continuously optimizes ad campaigns based on real-time data, allowing retailers to maximize their ROI and drive more sales.

    Cost-Effectiveness: By targeting the right audience segments, AI-powered ad networks help retailers reduce wasted ad spend and improve the efficiency of their marketing efforts.

    Competitive Advantage: Retailers that leverage AI in their advertising strategies gain a competitive edge by delivering more relevant and engaging ads to their customers.

    Pros and cons of AI-powered ad networks. Google DeepMind at Pexels

    Cons:

    Cost: Implementing and managing AI-powered ad campaigns can be expensive, especially for small and medium-sized retailers with limited budgets. AI technologies require ongoing investment in infrastructure, tools, and talent.

    Complexity: AI-powered ad networks can be complex to set up and manage, requiring expertise in data analysis, machine learning, and digital marketing. Retailers may need to invest in training or hiring skilled professionals to effectively leverage AI in their advertising efforts.

    Data Privacy Concerns: AI-powered ad networks rely on collecting and analyzing large amounts of customer data, raising concerns about data privacy and security. Retailers must comply with regulations such as GDPR and CCPA to protect customer data and ensure ethical use of AI.

    Dependency on Data Quality: AI algorithms depend on high-quality, accurate data to deliver meaningful insights and predictions. Retailers need to ensure that their data is clean, up-to-date, and relevant for AI-powered ad campaigns to be effective.

    Risk of Bias: AI algorithms can inadvertently perpetuate bias if they are trained on biased data or programmed with biased instructions. Retailers must carefully monitor and audit their AI systems to mitigate the risk of bias in their advertising campaigns.

    Measuring the effectiveness of using AI in ad networks involves tracking various metrics that indicate the performance and impact of AI-powered advertising campaigns.

    Here are some key metrics to consider:

    Click-Through Rate (CTR): CTR measures the percentage of people who click on an ad after seeing it. A high CTR indicates that the ad is engaging and relevant to the audience. Goal: Higher is better.

    Conversion Rate: The conversion rate measures the percentage of people who take a desired action after clicking on an ad, such as making a purchase or signing up for a newsletter. AI can help optimize campaigns to improve conversion rates. Goal: Higher is better.

    Return on Ad Spend (ROAS): ROAS measures the revenue generated for every dollar spent on advertising (Revenue from ads divide by Cost from ads). AI can help maximize ROAS by targeting the right audience segments and optimizing ad campaigns for better performance. Goal: Higher is better.

    Cost per Acquisition (CPA): CPA measures the cost of acquiring a customer through advertising. AI can help reduce CPA by improving targeting and optimizing ad spend. Goal: Lower is better.

    Engagement Metrics: Metrics such as time spent on site (time-based), pages per visit, and bounce rate (%) can indicate the effectiveness of ads in engaging the audience and driving them to take action. Goal: Higher is better except for bounce rate.

    Audience Insights: AI can provide valuable insights into audience behavior, preferences, and trends, which can help tailor ad campaigns for better performance. Goal: Higher engagement is preferred and may lead to higher sales

    Ad Placement and Performance: AI can analyze the performance of ads on different platforms and placements to determine which ones are most effective for reaching the target audience. Goal: Similar to conversation rate

    Brand Lift: AI can measure the impact of ads on brand awareness, perception, and recall, providing insights into the overall effectiveness of the advertising campaign. Goal: Higher positive product or brand awareness is preferred

    Incremental Sales: AI can help measure the incremental sales generated by advertising campaigns, taking into account the impact of ads on consumer behavior and purchasing decisions. Goal: Higher is better

    Customer Lifetime Value (CLV): AI can help predict the CLV of customers acquired through advertising, allowing advertisers to optimize campaigns for long-term profitability. Goal: Higher is better

    Applying AI in marketing for ad networks, retailers can improve the effectiveness of their advertising campaigns, drive better engagement with customers, and ultimately increase sales and revenue. Here are some tips for brands looking ahead to build and apply AI in marketing:

    Define Goals: Clearly define your marketing goals and how AI can help achieve them, such as improving ad targeting, increasing conversion rates, or enhancing customer engagement.

    Data Collection and Integration: Collect and integrate relevant data sources, such as customer demographics, behavior, and transactional data, to create a comprehensive view of your customers.

    AI Tool Selection: Choose the right AI tools and platforms for your needs, such as AI-powered ad networks, customer segmentation tools, or predictive analytics platforms.

    Develop AI Models: Develop AI models and algorithms for tasks such as customer segmentation, personalized recommendations, and ad targeting. This may require working with data scientists or AI experts.

    Implement AI in Ad Campaigns: Use AI to optimize ad campaigns by analyzing data, predicting customer behavior, and delivering personalized ads to target audiences.

    Monitor and Analyze Performance: Continuously monitor the performance of your AI-powered ad campaigns using relevant metrics and analytics tools. Analyze the results to identify areas for improvement and optimization.

    Iterate and Refine: Use the insights gained from monitoring and analysis to iterate and refine your AI-powered ad campaigns. Continuously test new ideas and strategies to improve performance.

    Invest in AI Talent: Consider investing in AI talent or partnering with AI experts to help implement and manage your AI-powered marketing initiatives.

    Stay Updated: Keep abreast of the latest trends and advancements in AI and marketing to ensure that your strategies remain relevant and effective.

    Working with trusted established ad networks would be a better option than starting a brand new network. The ultimate goal is to establish a better presence in the marketplace to engage, capture and retain customers.

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  • Perfect Retail Marketing: AI Evolution In Retail (Part 5)

    Perfect Retail Marketing: AI Evolution In Retail (Part 5)

    Retail marketing encompasses retailers’ strategies and tactics to attract customers, drive sales, and build brand loyalty. It involves a range of activities, from traditional advertising and promotions to digital marketing and customer engagement strategies. This is part 5 of the series on using artificial intelligence focusing on retail marketing.

    Retail marketing is what a business performs to attract customers and empower them to buy its products or services. It is about setting the optimal expectations and creating positive experiences that convince would-be or existing customers to choose its brands over the competition. Retail marketing can be physical (e.g., brochures, weekly flyers, catalogs, billboards, etc.), digital (e.g., emails, social media, websites, ad banners, SMS, etc.), or virtual (e.g., in-game avatars, virtual brand ambassadors, etc.)

    Regardless of the format chosen (physical, digital, virtual, or a combination of all three), there are some key concepts brands should consider before building or executing a retail marketing strategy:

    Understanding your customers: What do they need and want? How do they shop?

    Developing a strong brand identity: What makes your store unique?

    Creating a great product assortment: Having the right products in stock that people are looking for

    Pricing your products competitively: Setting prices that are fair to both you and your customers

    Promoting your products and services: Getting the word out about what you have to offer

    Providing excellent customer service: Making sure your customers have a positive experience shopping at your store

    Building customer loyalty: Encouraging customers to come back for more

    Building metrics: Determining and monitoring the metrics for success

    The use of technology in retail marketing has evolved significantly over the years. In the past, retailers relied primarily on traditional forms of advertising, such as print ads, TV commercials, and direct mail, to reach their target audience. However, with the advent of the internet and digital technologies, the landscape of retail marketing has changed dramatically.

    The Hillsdale Shopping Center in San Mateo, California pulled out all the stops to get new customers in the door with auto shows, Christmas shows, etc. Marketing was more physical back in the 1950s) (Source: YouTube)

    Today, retailers have access to a wide range of digital tools and platforms that allow them to target their audience more effectively and measure the impact of their marketing campaigns in real-time. One of the most significant developments in recent years has been the use of artificial intelligence.

    AI is increasingly being used by retailers to enhance marketing engagement and improve the customer experience. AI-powered tools can analyze vast amounts of data to gain insights into customer behavior and preferences, allowing retailers to create more targeted and personalized marketing campaigns.

    Retail brands are using artificial intelligence to maximize their marketing efforts to reach a bigger audience and build a bigger impact

    For example, AI can be used to analyze customer data from online and offline sources to identify patterns and trends. Retailers can then use this information to tailor their marketing messages to individual customers, increasing the likelihood of conversion.

    L’Oreal: L’Oreal uses AI-powered chatbots to provide personalized beauty advice to customers. The chatbots analyze customer data and preferences to offer tailored product recommendations and beauty tips, enhancing the customer experience and driving sales.

    Walmart: Walmart uses AI for various marketing purposes, including dynamic pricing, personalized recommendations, and supply chain optimization. The company’s AI-powered algorithms analyze customer data to offer personalized promotions and improve overall efficiency.

    Best Buy: Best Buy uses AI to personalize the shopping experience for its customers. The company’s AI-powered chatbot helps customers find products, provides recommendations, and answers questions, leading to increased customer satisfaction and loyalty.

    Coca-Cola: Coca-Cola uses AI to create personalized marketing campaigns. The company’s AI algorithms analyze customer data and preferences to deliver targeted advertising and promotions, resulting in higher engagement and brand loyalty.

    Starbucks: Starbucks uses AI for personalized marketing and customer engagement. The company’s mobile app uses AI to analyze customer data and offer personalized recommendations and rewards, driving customer loyalty and increasing sales.

    Before heading straight to using AI for their marketing efforts, brands should consider the following pros and cons.
    Pros:

    There are pros and cons for retail brands to use AI in marketing.

    Personalization: AI can help retailers create more personalized marketing campaigns, which can lead to higher engagement and conversion rates (see below for a separate article on personalization)

    Efficiency: AI can automate many marketing tasks, such as data analysis and campaign optimization, saving retailers time and resources.

    Improved Customer Experience: AI-powered tools, such as chatbots, can provide customers with instant support and assistance, enhancing their overall shopping experience.

    Data-driven Insights: AI can analyze vast amounts of data to provide retailers with valuable insights into customer behavior and market trends, helping them make more informed marketing decisions.

    Cons:

    Cost: Implementing AI can be expensive, especially for smaller retailers with limited budgets.

    Complexity: AI technology can be complex and require specialized skills to implement and manage effectively.

    Privacy Concerns: The use of AI in marketing raises privacy concerns, as it involves collecting and analyzing customer data.

    Dependency: Retailers may become overly reliant on AI for marketing decisions, potentially limiting creativity and innovation.

    Most consumers say that misinformation, toxic user bases, fake accounts and bots have degraded the social media experience, and more than 70% expect GenAI to negatively impact social media.

    A Scottish children’s event called “Willy’s Chocolate Experience” became an internet sensation, but for all the wrong reasons. Advertised with dreamlike images of a candy wonderland, the reality fell far short, sparking outrage from attendees and international media attention.

    Held at Box Hub Glasgow, the experience promised a land of vibrant colors, delicious treats, and whimsical oompa loompas. However, ticket holders arrived to find a nearly bare warehouse decorated with a few basic props. Disappointed and frustrated, many demanded immediate refunds. Tickets reportedly cost around £35, according to The Guardian, but the event’s website has mysteriously vanished.

    House of Illuminati, the company behind the fiasco, promised full refunds to all ticket holders.

    AI Marketing failure – The AI-generated images used to advertise the Wonka experience had numerous spelling mistakes and did not depict the wonders of Wonka (Source: Tech.co)

    Building trust will continue to be the number one topic brands should continue as it uses AI to generate content. Quality control should be in place to ensure that copies are reviewed before they go live.

    Often under-utilized, feedback can be a great source of data for retailers to use for generating content with the help of AI. Here are some strategies that could be deployed by retailers.

    Sentiment Analysis: AI can analyze customer feedback, such as reviews and social media comments, to understand the sentiment behind them. Retailers can use this information to create marketing content that resonates with their audience’s emotions and addresses any concerns or issues raised in the feedback.

    Content Personalization: AI can analyze customer data to create personalized marketing content. For example, retailers can use AI to recommend products based on a customer’s purchase history or browsing behavior, creating a more tailored shopping experience.

    Predictive Analytics: AI can analyze past feedback and customer behavior to predict future trends and preferences. Retailers can use this information to create marketing content that anticipates customer needs and desires.

    Optimized Messaging: AI can analyze customer feedback to identify keywords and phrases that resonate with their audience. Retailers can use this information to optimize their marketing messaging for maximum impact.

    Automated Content Creation: AI can generate marketing content, such as product descriptions or social media posts, based on customer feedback and preferences. This can help retailers create a consistent stream of content without the need for manual intervention.

    By leveraging feedback and AI, retailers can create more relevant and engaging marketing content that resonates with their audience, ultimately driving sales and building brand loyalty.

    Measuring the effectiveness of AI in retail marketing requires a combination of metrics that reflect the impact on both marketing performance and business outcomes. Here are some key metrics to consider:

    Conversion Rate: Measure the percentage of website visitors or app users who complete a desired action, such as making a purchase or signing up for a newsletter, as a result of AI-driven marketing efforts.

    Customer Engagement: Track metrics such as click-through rates, time spent on site, and social media interactions to gauge how effectively AI-driven marketing campaigns are engaging customers.

    ROI (Return on Investment): Calculate the return on investment for AI-driven marketing campaigns by comparing the cost of the campaign to the revenue generated. This can help determine the overall effectiveness of AI technology in driving sales and revenue.

    Customer Satisfaction: Use customer surveys and feedback to measure satisfaction levels with AI-powered features, such as chatbots or personalized recommendations. High satisfaction scores indicate that AI is effectively enhancing the customer experience.

    Customer Lifetime Value (CLV): Analyze how AI-driven marketing campaigns impact CLV by increasing repeat purchases, upselling, or cross-selling. A higher CLV indicates that AI is successfully driving long-term customer value.

    Retention Rate: Measure the percentage of customers who continue to engage with your brand over time. AI-driven marketing efforts should contribute to higher retention rates by delivering personalized experiences that keep customers coming back.

    Cost Savings: Evaluate the cost savings achieved through AI automation compared to manual marketing efforts. This can include savings in time, resources, and operational costs.

    Predictive Performance: Assess the accuracy of AI algorithms in predicting customer behavior and trends. Higher predictive performance indicates that AI is effectively leveraging data to drive marketing decisions.

    By monitoring these metrics, retailers can gain valuable insights into the effectiveness of AI in their marketing efforts and make data-driven decisions to optimize performance and drive business growth.

    Looking ahead, applying AI in marketing can be a strategic process that involves several key steps. Here is a general roadmap for retail brands looking to implement AI in their marketing efforts:

    Identify Goals and Objectives: Define clear goals and objectives for using AI in marketing. Whether improving customer engagement, increasing sales, or optimizing marketing campaigns, having specific goals will guide your AI strategy.

    Assess Data Availability and Quality: Evaluate the availability and quality of your data. AI relies on data to generate insights and make predictions, so it’s essential to have access to relevant and reliable data sources.

    Choose the Right AI Tools and Technologies: Select AI tools and technologies that align with your goals and data capabilities. This could include chatbots for customer service, predictive analytics for personalized marketing, or image recognition for visual search.

    Integrate AI into Marketing Channels: Integrate AI into your existing marketing channels and platforms. This could involve implementing AI-powered features on your website, using AI for targeted advertising, or incorporating AI into your email marketing campaigns.

    Test and Iterate: Test your AI-driven marketing campaigns to see how they perform and iterate based on the results. Use A/B testing and other methods to refine your approach and improve effectiveness over time.

    Monitor Performance and Analytics: Continuously monitor the performance of your AI-driven marketing efforts using relevant metrics and analytics. This will help you track progress toward your goals and identify areas for improvement.

    Stay Updated with AI Trends: Keep abreast of the latest trends and developments in AI technology and marketing. AI is a rapidly evolving field, and staying informed will help you make informed decisions and stay ahead of the competition.

    Ensure Compliance and Ethical Use: Ensure that your use of AI in marketing complies with relevant laws and regulations, such as data protection and privacy laws. It’s also important to use AI ethically and responsibly to maintain customer trust.

    By following these steps, retailers can effectively apply AI in their marketing efforts to drive engagement, increase sales, and enhance the overall customer experience.

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  • Maximizing The ChatBots: AI Evolution In Retail (Part 4)

    Maximizing The ChatBots: AI Evolution In Retail (Part 4)

    Chatbots, a form of artificial intelligence (AI), have transformed how retailers engage with customers. These intelligent conversational agents are deployed on various platforms, including websites, messaging apps, and social media, to assist customers with queries, provide product recommendations, and even facilitate transactions.

    Part 4 of the series will explore the definition of chatbots, their evolution in retail, examples of retailers using them, and how AI enhances both chatbots and the customer experience. Additionally, we’ll discuss the pros and cons of using AI for chatbots in retail settings.

    Chatbots are software programs designed to simulate human conversation, allowing users to interact with a computer system using natural language. In the retail sector, chatbots have evolved from basic scripted bots to sophisticated artificial intelligence (AI)-powered assistants capable of understanding and responding to complex queries.

    Early retail adopters to the chatbot technology include:

    Sephora: Sephora launched its chatbot on Kik in 2016, allowing customers to interact with the bot to discover new beauty products and receive personalized recommendations.

    H&M: H&M introduced a chatbot on the messaging app Kik in 2016, enabling users to receive personalized style recommendations and browse products directly within the chat interface.

    eBay: eBay launched a chatbot on Facebook Messenger in 2016, allowing users to search for products, receive recommendations, and track orders using natural language commands.

    1-800-Flowers: 1-800-Flowers introduced a chatbot on Facebook Messenger in 2016, enabling users to order flowers and gifts through a conversational interface.

    H&M was an early adapter to the chatbot revolution. This was a version of the social messaging application called KiK (Source: YouTube)

    In today’s digital age, chatbots are revolutionizing customer interactions with the help of Artificial Intelligence (AI). AI enables chatbots to understand natural language, learn from interactions, and provide personalized responses.

    Natural Language Processing (NLP) allows chatbots to interpret human language, while Machine Learning (ML) helps them improve over time by analyzing data. Natural Language Generation (NLG) enables chatbots to respond in a human-like manner, enhancing user experience.

    AI can make chatbots smarter and provide retailers with more data analytics on tone, perception, purchase intention, feedback, etc.

    AI also enables chatbots to maintain context during conversations, personalize interactions based on user data, and continuously learn from new interactions. Additionally, AI-driven automation allows chatbots to automate tasks, saving time and resources.

    Several AI chatbot companies are considered leaders in the industry today. They include:

    IBM Watson: IBM Watson offers a range of AI-powered chatbot solutions for businesses, including virtual assistants and customer service bots. Watson’s AI capabilities enable chatbots to understand natural language, learn from interactions, and provide personalized responses.

    Google Cloud Dialogflow: Google Cloud’s Dialogflow is a powerful AI platform for building natural and rich conversational experiences. It offers robust NLP capabilities and integration with Google Cloud’s other AI services.

    Microsoft Azure Bot Service: Microsoft Azure Bot Service provides tools and services for building, testing, and deploying intelligent bots. It integrates seamlessly with Microsoft’s AI services, such as Azure Cognitive Services and Azure Machine Learning.

    Amazon Lex: Amazon Lex is a service for building conversational interfaces into any application using voice and text. It powers Amazon’s Alexa and provides advanced NLP and speech recognition capabilities.

    Chatfuel: Chatfuel is a popular AI chatbot platform for creating Facebook Messenger bots. It offers a drag-and-drop interface and integration with AI services like Dialogflow for advanced functionality.

    ManyChat: ManyChat is another platform for building Facebook Messenger bots. It offers a visual bot builder and features like AI-driven conversations, broadcasting, and analytics.

    Rasa: Rasa is an open-source AI chatbot framework that allows developers to build and customize their chatbots. It offers NLP capabilities, dialogue management, and integration with popular messaging platforms.

    IBM makes it very easy for brands to create an AI-driven chatbot (Source: YouTube)

    Many brands are exploring, evaluating, and investing into AI-powered chatbot technologies. Before doing so, brands should consider the following pros and cons:

    Pros of Using AI-Powered chatbot technology:

    Improved Customer Service: According to a study by Oracle, 80% of businesses plan to use chatbots for customer interactions by 2020, highlighting the growing importance of AI-powered chatbots in enhancing customer service.

    Personalized Interactions: A report by Accenture found that 91% of consumers are more likely to shop with brands that provide personalized offers and recommendations, demonstrating the importance of AI in enabling chatbots to offer personalized interactions.

    Cost-Effective: Research by Juniper Research suggests that chatbots could help businesses save over $8 billion per year by 2022, primarily through reduced customer service costs and increased operational efficiency.

    Scalability: With AI, chatbots can handle multiple customer queries simultaneously. For example, Bank of America’s chatbot, Erica, has already handled over 100 million client requests, showcasing the scalability of AI-powered chatbots.

    The Economist discussed the pros and cons of chatbot technology (Source: YouTube)

    Cons of Using AI-Powered chatbot technology:

    Lack of Human Touch: Despite advancements in AI, some customers still prefer human interaction. A survey by PwC found that 59% of consumers feel companies have lost touch with the human element of customer experience. This is especially important if a retailer is used to interacting with customers through a high time highly physical environment.

    Initial Investment: Implementing AI-powered chatbots can be costly. According to Gartner, the average cost of developing and deploying a chatbot ranges from US$30,000 to $150,000+, depending on complexity. Beyond technology investment, training, maintenance, and continual improvement costs may also need to be factored in.

    Maintenance and Updates: AI systems require regular maintenance and updates to remain effective. According to a report by Forrester, companies spend an average of 40% of their AI budgets on ongoing maintenance and updates.

    Privacy Concerns: AI-powered chatbots collect and analyze customer data, raising privacy concerns. A survey by Edelman found that 81% of consumers are concerned about how much data companies collect on them. Part 2 of this series speaks more at length on privacy concerns.

    High Failure Rates: According to a study by Gartner, up to 80% of chatbot implementations will not deliver the desired outcomes due to various factors such as inadequate understanding of user needs, poor design, and lack of integration with other systems.

    Retailers can use a variety of metrics to measure the effectiveness of using AI in chatbots, particularly focusing on engagement time, issue resolution, and upselling. Here are some key metrics for each category:

    Engagement Time:

    Average Session Duration: This metric tracks the average amount of time customers spend interacting with the chatbot in a single session. A higher average session duration indicates that customers are engaging with the chatbot for longer periods, which can indicate a positive user experience.

    Average Response Time: This metric measures the average time it takes for the chatbot to respond to a customer query. A lower average response time indicates that the chatbot is providing timely and efficient assistance to customers.

    Number of Interactions per Session: Tracking the number of interactions per session can provide insights into how engaged customers are with the chatbot. A higher number of interactions may indicate that customers are finding the chatbot helpful and are actively seeking information or assistance.

    Issue Resolution:

    First Contact Resolution Rate: This metric measures the percentage of customer issues resolved during the first interaction with the chatbot. A higher first-contact resolution rate indicates that the chatbot effectively resolves customer issues without needing escalation.

    Resolution Time: This metric tracks the average time it takes for the chatbot to resolve customer issues. A lower resolution time indicates that the chatbot is efficient at resolving issues, which can lead to higher customer satisfaction.

    Customer Satisfaction (CSAT) Score: While not specific to issue resolution, the CSAT score measures overall customer satisfaction with the chatbot experience. A high CSAT score indicates that customers are satisfied with the chatbot’s ability to resolve their issues.

    Upselling:

    Conversion Rate: This metric measures the percentage of interactions with the chatbot that result in a successful upsell. A higher conversion rate indicates that the chatbot is effective at driving additional sales.

    Average Order Value (AOV): Tracking the AOV of customers who interact with the chatbot can provide insights into the effectiveness of upselling efforts. A higher AOV among chatbot users indicates that the chatbot is successful at encouraging customers to purchase additional items.

    Upsell Success Rate: This metric measures the percentage of upselling attempts that result in a successful upsell. A higher upsell success rate indicates that the chatbot is effective at persuading customers to make additional purchases.

    By tracking these metrics, retailers can assess the effectiveness of using AI in chatbots and make informed decisions to optimize their chatbot strategies for improved engagement, issue resolution, and upselling.

    Incorporating AI in chatbots to further improve customer engagement, issue resolution, and upselling requires a strategic approach. Looking ahead, brands should go on a journey to explore their capabilities, capacities, and general expertise in the following areas:

    1. Determining the Customer Engagement/Communication/Feedback Strategy

    Explore and finalize a strategy for customer engagement and evaluate why AI-powered chatbot technology should be used over other forms of communication

    Determine the touch points where the technology would be maximized

    Determine whether the chatbot would be used in the process (e.g., sales, support, general information, etc.)

    2. Implementing/Enhancing Natural Language Processing (NLP) Capabilities

    Invest in advanced NLP algorithms to improve the chatbot’s ability to understand and respond to complex queries more accurately and efficiently.

    Implement sentiment analysis to gauge customer emotions and tailor responses accordingly, leading to more personalized interactions and improved issue resolution.

    3. Implementing Machine Learning (ML) for Personalization

    Utilize ML algorithms to analyze customer data and behavior patterns, enabling the chatbot to offer personalized product recommendations and upsell opportunities.

    Leverage ML for continuous learning and improvement of the chatbot’s responses based on customer interactions and feedback.

    4. Introducing Chatbot Analytics and Reporting

    Implement robust analytics tools to track key metrics related to engagement time, issue resolution, and upselling.

    Use data-driven insights to identify trends, optimize chatbot performance, and enhance the overall customer experience.

    5. Integrating with CRM and other Platforms

    Integrate the chatbot with customer relationship management (CRM) systems to access customer data and provide more personalized interactions.

    Integrate with e-commerce platforms to facilitate seamless transactions and upselling opportunities directly within the chatbot interface.

    6. Enhancing Multi-channel Support

    Extend chatbot support to additional channels such as voice assistants, social media platforms, and messaging apps to reach customers wherever they are.

    Ensure consistent and seamless experiences across all channels to improve customer satisfaction and engagement.

    Omni-channel support should be mapped out and seamlessly integrated to remove friction.

    7. Implementing Proactive Engagement

    Use AI to analyze customer behavior and predict needs, enabling the chatbot to proactively reach out with relevant information or offers/deals.

    Proactively address potential issues before they escalate, improving customer satisfaction and loyalty.

    8. Integrating Human-Assisted Support

    Implement a seamless handoff between the chatbot and human agents for complex issues that require human intervention.

    Use AI to assist human agents by providing relevant information and suggestions, improving issue resolution and customer satisfaction.

    9. Continuous Monitoring and Optimization

    Regularly review chatbot interactions, feedback, and performance metrics to identify areas for improvement.

    Continuously update and optimize the chatbot’s algorithms and responses based on user feedback and changing customer needs.

    10. Metrics for Success

    Determine what metrics to use to determine success for the technology (i.e., improved sales, customer satisfaction, level 1/2 interactions, etc.)

    Use metrics that may be cross-departmental to maximize its contribution.

    Looking ahead, retail brands have lots to think about before committing to an AI-powered chatbot.

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  • Transforming Inventory Management: AI Revolution in Retail (Part 3)

    Transforming Inventory Management: AI Revolution in Retail (Part 3)

    Artificial intelligence (AI) continues to move rapidly within the retail industry. Part 3 of Retail Mashup’s AI and retail industry series continues with inventory management, demand forecasting, and sales optimization (Looking for Part 1 and Part 2).

    Inventory plays an important role for any retailer big or small. Too much inventory and you are settled with unmanageable costs. Too little and you are losing out on revenue. When it comes to AI and inventory management, we can separate to three integrated sections: inventory management, demand forecasting, and sales optimization.

    Inventory management is the process of overseeing, controlling, and optimizing a company’s inventory of goods and materials. It involves managing the flow of goods from manufacturers to warehouses, and ultimately to retail outlets or customers.

    The primary goal of inventory management is to ensure that the right amount of inventory is available at the right time, to meet customer demand while minimizing storage costs and losses due to obsolescence or spoilage.

    Historically, inventory management is completed using manually intensive methods like inventory counts, estimation, and trend analysis. AI streamlines and optimizes stocks by taking into account more sources of data on a real-time basis.

    Here are some key ways AI is transforming inventory management:

    Optimized Stocking Levels: AI can help determine the optimal stocking levels for each product based on factors such as seasonality, trends, and customer behavior. By maintaining the right amount of inventory, retailers can reduce carrying costs and improve profitability.

    Amazon is a pioneer in using AI for inventory management. The company’s warehouses are equipped with robots that use AI algorithms to autonomously move and organize inventory.

    Inventory Routing and Replenishment: AI algorithms can optimize the routing of inventory between warehouses, distribution centers, and stores to minimize transportation costs and reduce delivery times. AI can also automate the replenishment process based on real-time sales data and inventory levels.

    Inventory Visibility: AI-powered systems provide real-time visibility into inventory levels across the supply chain, enabling retailers to make informed decisions and respond quickly to changes in demand or supply.

    Zara, a fashion retailer, uses AI for demand forecasting and inventory optimization. The company’s AI system analyzes sales data and fashion trends to predict which items will be popular, allowing Zara to adjust its inventory levels and production accordingly.

    Supplier Management: AI can analyze supplier performance data to identify trends, predict future performance, and optimize supplier relationships. This can help retailers ensure timely deliveries and maintain optimal inventory levels.

    Returns Management: AI can analyze customer return patterns to identify the root causes of returns and implement strategies to reduce them. This can help retailers minimize the impact of returns on inventory levels and profitability reporting. Products that are prone to returns could be managed sooner with engagement.

    Amazon uses AI to ensure inventory management is at its optimal level (Source: Amazon)

    Demand forecasting is a process used by businesses to predict future customer demand for products or services. It involves analyzing historical sales data, market trends, and other relevant factors to estimate the likely demand for a product or service over a specific period, such as a month, quarter, or year.

    Demand forecasting helps businesses make informed decisions about production, inventory management, and pricing, enabling them to meet customer demand more effectively and optimize their operations. According to McKinsey, AI can reduce forecasting errors by 20-50%, leading to a 5-15% reduction in inventory costs. This improvement in forecasting accuracy enables retailers to better anticipate customer demand, optimize inventory levels, and improve customer satisfaction.

    AI has enabled retailers to move beyond traditional methods of demand forecasting, which often rely on historical sales data and simplistic models. Instead, AI algorithms can analyze vast amounts of data from various sources, including sales data, market trends, weather patterns, and social media activity, to generate more accurate demand forecasts.

    H&M uses AI for demand forecasting to predict which fashion trends will be popular and adjust its production and inventory levels accordingly. The company analyzes sales data, social media trends, and other factors to predict demand and ensure that its stores are stocked with the latest fashion items.

    By incorporating these additional data points, AI can uncover hidden patterns and correlations that would be impossible for human analysts to identify.

    Cello SCLIS Demand Sensing, with its Brightics AI-based analytics model, enables Samsung Electronics to maximize sales opportunities while avoiding excess inventory to reduce logistics expenses through more accurate and detailed demand forecasting.

    Samsung Electronics uses AI to improve its inventory management/demanding forecasting (Source: YouTube)

    One of the key advantages of AI in demand forecasting is its ability to provide real-time updates. This means that retailers can adjust their inventory levels and marketing strategies on the fly, based on the latest demand trends. For example, if a particular product suddenly becomes popular due to a viral social media campaign, AI can quickly identify this trend and alert retailers to increase their stock levels.

    Lowe’s, a home improvement retailer, uses AI for demand forecasting to predict customer demand for home improvement products. The company analyzes sales data, market trends, and other factors to predict demand and optimize its inventory levels to meet customer needs.

    Furthermore, AI enables retailers to personalize their demand forecasting efforts. By analyzing individual customer behavior and preferences, AI algorithms can predict demand at a more granular level, allowing retailers to offer more targeted products and promotions. This personalized approach not only improves forecasting accuracy but also enhances the overall customer experience.

    Sales optimization, in the context of inventory management and AI, refers to the use of artificial intelligence (AI) technology to improve sales performance by optimizing inventory levels, pricing strategies, and promotional efforts. AI algorithms analyze a variety of data, including historical sales data, market trends, competitor pricing, and customer behavior, to generate insights and recommendations that can help retailers maximize revenue and profitability.

    For example, Sephora, uses AI-powered recommendation engines to personalize product recommendations for customers based on their purchase history, browsing behavior, and preferences. This helps Sephora increase sales by offering relevant products to customers.

    In the context of inventory management, AI-powered sales optimization can help retailers identify which products to stock, when to restock them, and at what price to sell them. By analyzing demand patterns and other factors, AI can help retailers ensure that they have the right products in stock at the right time, minimizing stockouts and excess inventory.

    AI can also help retailers optimize their pricing strategies by analyzing market conditions and competitor pricing to determine the optimal price for each product. By dynamically adjusting prices based on real-time data, retailers can maximize revenue and profit margins.

    Pandora, the jewelry retailer, uses AI for sales optimization by analyzing customer data and trends to optimize its product offerings and pricing strategies. This helps Pandora increase sales by offering products that appeal to its customers.

    While this article showcased many benefits from using AI for inventory management, there are several pitfalls that retail brands should be aware of before they invest significant resources into gaining the appropriate capabilities.

    Employee Management: Some employees may resist the adoption of AI in inventory and demand management, fearing job displacement or changes in their roles. Organizations must provide adequate training and support to help employees adapt to AI-powered systems. In addition, retailers need to hire a mix of data scientists, cross-function specialists, and leaders to manage the process.

    System Integration: Integrating AI systems with existing IT infrastructure can be challenging. Organizations may face compatibility issues, data silos, and the need for additional resources to integrate AI into their operations successfully.

    Privacy Concerns: AI systems require access to large amounts of data, including customer information and sales data, raising concerns about data privacy and security. Organizations must ensure that they comply with data protection regulations and implement robust security measures to protect sensitive information.

    Cost: Implementing AI systems can be costly, requiring investment in technology, infrastructure, and training. Organizations must carefully assess the cost-benefit ratio of AI adoption and ensure that the benefits justify the investment.

    Over-reliance on AI: While AI can improve decision-making, relying too heavily on AI systems without human oversight can lead to errors or misinterpretation of data. Organizations must strike a balance between AI-driven insights and human judgment.

    Data Bias: AI algorithms can be biased if they are trained on biased data. This can lead to unfair or discriminatory outcomes, particularly in areas such as demand forecasting and pricing. Organizations must ensure that AI systems are trained on diverse and unbiased data sets.

    Lack of Transparency: AI algorithms can be complex and difficult to interpret, leading to a lack of transparency in decision-making. Organizations must strive to make AI-driven decisions transparent and understandable to stakeholders.

    Scalability: As organizations grow, the scalability of AI systems can become a challenge. Organizations must ensure that their AI systems can scale to meet the demands of a growing business.

    Measuring the success of AI in inventory management can be tricky as there are many moving pieces and integrated components. Its success is usually to assess its impact on business performance.

    Here are some relevant metrics:

    Inventory Turnover Ratio: This metric measures how efficiently a company manages its inventory by comparing the cost of goods sold to the average inventory level. A higher inventory turnover ratio indicates that inventory is being managed effectively, while a lower ratio may indicate excess inventory or poor inventory management.

    Stockout Rate: The stockout rate measures the percentage of time a product is out of stock when a customer wants to purchase it. AI-powered demand forecasting can help reduce stockout rates by ensuring that inventory levels are aligned with customer demand.

    Forecast Accuracy: Forecast accuracy measures the extent to which actual sales align with forecasted sales. AI algorithms can improve forecast accuracy by analyzing various data sources and generating more accurate predictions.

    Fill Rate: The fill rate measures the percentage of customer demand that is met from existing inventory. A higher fill rate indicates that customer demand is being effectively met, while a lower fill rate may indicate inventory shortages or poor inventory management.

    Inventory Carrying Costs: Inventory carrying costs include expenses such as storage, insurance, and obsolescence. AI-powered inventory management can help reduce inventory carrying costs by optimizing inventory levels and minimizing excess inventory.

    Customer Satisfaction: Customer satisfaction can be measured through surveys, reviews, and feedback. AI-powered demand management can improve customer satisfaction by ensuring that products are available when customers want to purchase them.

    Return on Investment (ROI): ROI measures the financial return on an investment. By comparing the cost of implementing AI in inventory and demand management to the benefits generated, companies can assess the ROI of their AI initiatives.

    Gross Margin Return on Inventory Investment (GMROII): GMROII measures how effectively a company is turning inventory into profit. AI can help improve GMROII by optimizing inventory levels and pricing strategies.

    Looking ahead, brands can take several strategic steps to ensure success in using AI for inventory management. Firstly, investing in AI talent is crucial. By hiring or training AI experts, brands can develop and implement advanced AI algorithms tailored to their inventory needs. Having a dedicated team of AI professionals enables brands to stay at the forefront of AI technology and maximize its potential in inventory management.

    Secondly, integrating AI with existing systems is essential for seamless operations. Brands should ensure that their AI systems for inventory management are integrated with their ERP, CRM, and other systems. This integration enables brands to leverage AI insights across the organization, leading to improved operational efficiency and decision-making.

    Moreover, focusing on data quality is paramount. High-quality data is fundamental for the success of AI in inventory management. Brands should prioritize collecting, cleaning, and maintaining accurate and up-to-date data to ensure that their AI algorithms deliver reliable insights.

    Continuous improvement is another key aspect. Brands should regularly monitor and refine their AI algorithms for inventory management to maintain effectiveness over time. By evaluating performance and making adjustments as needed, brands can improve accuracy and efficiency.

    Collaboration with suppliers and partners is also crucial. Brands should work closely with their suppliers and partners to share data and insights that can improve inventory management. By collaborating, brands and their partners can optimize inventory levels and enhance supply chain efficiency.

    Lastly, adopting a customer-centric approach is essential. By using AI to analyze customer data and preferences, brands can ensure that they have the right products in stock to meet customer demand. This customer-centric approach not only improves inventory management but also enhances customer satisfaction.

    By implementing these strategies, brands can ensure success in using AI for inventory management, gaining a competitive edge, and driving business growth.

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  • Redefining Personalization For Customers – AI Evolution In Retail (Part 2)

    Redefining Personalization For Customers – AI Evolution In Retail (Part 2)

    In recent years, the retail landscape has undergone a profound transformation fueled by advancements in artificial intelligence (AI). This technological revolution has not only reshaped how retailers operate but has also fundamentally altered the way consumers shop. In the first part of the series about AI, we introduced the concept and how the ways it impacts the retail industry. This part continues by focusing on personalization. What are the good, bad and ugly components of using the technology to build the best experience possible for consumers.

    Personalization has long been a key strategy for retailers looking to engage customers and drive sales. However, traditional approaches to personalization often fell short, delivering generic recommendations based on limited data. AI has changed this paradigm by enabling retailers to harness the power of big data and machine learning to deliver highly personalized experiences tailored to individual preferences and behaviors.

    One of the most significant ways AI is revolutionizing personalization in retail is through the use of predictive analytics. By analyzing vast amounts of data, including purchase history, browsing behavior, and demographic information, AI can anticipate customer needs and preferences with remarkable accuracy. This allows retailers to offer personalized product recommendations, promotions, and content that resonate with each individual customer, driving engagement and loyalty.

    For example, according to a study by Salesforce, 62% of consumers expect companies to send personalized offers or discounts based on items they’ve already purchased while 57% of consumers say they’re willing to share personal data in exchange for personalized offers or discounts.

    Personalization vs Data Sharing – 57% of consumers say they’re willing to share personal data in exchange for personalized offers or discounts. (Source: Saleforce)

    AI enables retailers to meet these expectations by analyzing past purchase data to offer personalized discounts and promotions, increasing the likelihood of repeat purchases.

    Another area where AI is redefining personalization is in-store experiences. Technologies such as facial recognition and computer vision are enabling retailers to deliver personalized experiences in real-time. For example, AI-powered mirrors can recommend clothing items based on a customer’s body type and style preferences, while smart shelves can display personalized offers based on a customer’s past purchases.

    A prime example of this is Nike’s in-store experience, where customers can use AI-powered foot scanning technology to receive personalized shoe recommendations based on their unique foot shape and size. This level of personalization not only enhances the customer experience but also increases the likelihood of a purchase.

    Nike’s foot scanning technology improve personalization by using AI to help customers determine their correct shoe size. This technology is available in store or at home (Source: YouTube)

    AI is also playing a crucial role in improving customer service and support. Chatbots powered by AI can provide instant, personalized assistance to customers, answering questions, resolving issues, and even processing transactions. This not only enhances the overall shopping experience but also allows retailers to provide round-the-clock support without the need for human intervention.

    Hyper-personalization refers to the practice of tailoring products, services, content, and marketing efforts to individual customers on a highly granular level. It goes beyond traditional personalization by leveraging advanced technologies, such as artificial intelligence and big data analytics, to create highly individualized experiences for each customer.

    Hyper-personalization relies on collecting and analyzing vast amounts of data about customer behavior, preferences, and demographics to understand their unique needs and preferences. This data is then used to deliver personalized recommendations, offers, and experiences across multiple touch points, such as websites, mobile applications, emails, and in-store interactions.

    Hyper-personalization has its pros and cons. Source: ThisIsEngineering at Pexels

    For example, a retailer practicing hyper-personalization might use AI algorithms to analyze a customer’s past purchases, browsing behavior, and interactions with the brand to recommend products that are likely to be of interest to them.

    They might also personalize the content and layout of their website based on the customer’s preferences and behavior, or send personalized emails with tailored offers and recommendations.

    A big plus is that customers get a digital one-to-one connection with the brand while the big downside is that customers might feel like they are being monitored by big brother.

    Despite the significant advancements AI has brought to the retail industry, challenges remain. One of the biggest challenges is ensuring the ethical use of AI, particularly in areas such as data privacy and algorithmic bias. Retailers must be transparent about how they use customer data and ensure that their AI systems are designed and trained in a way that is fair and unbiased.

    Another challenge is the integration of AI into existing retail systems and processes. Many retailers struggle to effectively implement AI due to a lack of expertise, resources, or a clear strategy. Overcoming these challenges will require a concerted effort from retailers to invest in AI talent, infrastructure, and training to fully realize the benefits of this transformative technology.

    Implementing AI and personalization in retail can be measured using several key metrics to determine success. Here are the top eight most important ones used by the industry today:

    Conversion Rate: Measure the percentage of website visitors or app users who make a purchase. AI-driven personalization should ideally lead to an increase in conversion rates as customers receive more relevant recommendations and offers.

    Average Order Value (AOV): Track the average value of orders placed by customers. AI-driven personalization can help increase AOV by suggesting complementary products or encouraging upsells and cross-sells.

    Customer Lifetime Value (CLV): Measure the total revenue a business can expect from a single customer over their lifetime. Personalization can help increase CLV by improving customer retention and encouraging repeat purchases.

    Customer Engagement: Monitor metrics such as time spent on site, number of pages visited, and frequency of visits. AI-driven personalization should lead to higher levels of engagement as customers find the content and products more relevant to their interests.

    Customer Satisfaction (CSAT) and Net Promoter Score (NPS): Measure customer satisfaction with the personalized experience and their likelihood to recommend the brand to others. Higher CSAT and NPS scores indicate that AI-driven personalization is resonating with customers.

    Return on Investment (ROI): Calculate the ROI of implementing AI-driven personalization by comparing the cost of the technology and implementation with the increase in revenue or cost savings achieved. A positive ROI indicates that the implementation is successful.

    Retention Rate: Measure the percentage of customers who continue to purchase from the brand over time. AI-driven personalization should help improve retention rates by creating more personalized and engaging experiences for customers.

    Personalization Effectiveness: Track the performance of personalized recommendations and offers, such as click-through rates and conversion rates for personalized content. This can help refine personalization strategies to improve effectiveness over time.

    By tracking some or all of these metrics, retailers can assess the impact of AI-driven personalization on their business and make informed decisions to optimize and improve their strategies.

    Beyond metrics, retailers should take an opportunity to educate the public on how they use AI to drive personalization. They can then build and gauge comfort and trust level through feedback and provide additional awareness building, as required. The goal is to make people understand how the technology works for them.

    When implementing AI and personalization in retail, it is crucial to consider and comply with relevant privacy laws and regulations depending on where the operations are located.

    Some of the key global and regional laws and regulations to consider include:

    General Data Protection Regulation (GDPR): GDPR is a comprehensive data protection regulation that applies to businesses operating within the European Union (EU) and regulates the processing of personal data of individuals within the EU. GDPR imposes strict requirements on how personal data is collected, processed, stored, and shared, including requirements for obtaining consent, providing transparency, and ensuring data security.

    California Consumer Privacy Act (CCPA): CCPA is a privacy law that applies to businesses operating in California and governs the collection, use, and sharing of personal information of California residents. CCPA grants consumers certain rights over their personal information, such as the right to access, delete, and opt-out of the sale of their personal information.

    Personal Information Protection and Electronic Documents Act (PIPEDA): PIPEDA is a Canadian privacy law that regulates the collection, use, and disclosure of personal information by private sector organizations. PIPEDA requires organizations to obtain consent for the collection, use, and disclosure of personal information and imposes requirements for data security and breach notification.

    Children’s Online Privacy Protection Act (COPPA): COPPA is a U.S. federal law that regulates the online collection of personal information from children under the age of 13. COPPA requires operators of websites and online services directed at children to obtain verifiable parental consent before collecting personal information from children.

    California Privacy Rights Act (CPRA): CPRA is a privacy law that builds upon CCPA and further enhances privacy rights for California residents. CPRA introduces additional requirements for businesses, such as the establishment of a dedicated privacy enforcement agency and the implementation of data minimization and retention requirements.

    Data Protection Directive 95/46/EC: Although superseded by GDPR, the Data Protection Directive (DPD) was the predecessor to GDPR and set out principles for the protection of personal data within the EU. While DPD is no longer in force, it may still be relevant for historical purposes or for organizations operating in countries outside the EU that have adopted similar data protection principles.

    Bonus: Sector-specific regulations: Depending on the nature of the retail business and the data it collects, additional sector-specific regulations may apply (e.g., medical/pharmacy)

    Looking ahead, the future of AI in retail is bright. As AI continues to evolve, we can expect to see even more innovative applications that further enhance personalization, customer experience, and operational efficiency. Retailers that embrace AI and harness its power effectively will be well-positioned to thrive in the ever-evolving retail landscape.

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  • Artificial Intelligence As Retail’s Killer App

    Artificial Intelligence As Retail’s Killer App

    The retail landscape is undergoing a dramatic transformation. Gone are the days of one-size-fits-all shopping experiences. Today’s customers crave personalization, convenience, and a seamless journey across all touch points. This is where artificial intelligence (AI) steps in, poised to redefine the way we shop, how stores operate, and data we will need to improve customer experiences.

    Artificial intelligence (AI) is a branch of computer science focused on creating intelligent machines that can mimic human cognitive functions. It allows computing machines to reason, learn, and solve problems.

    There are different approaches to AI, but a common thread is the use of algorithms that process vast amounts of data. By analyzing this data, AI systems can identify patterns, make predictions, and even adapt their behavior over time.

    Here’s a breakdown of key concepts in AI:

    Machine Learning: This is a type of AI where machines learn from data without being explicitly programmed. They can improve their performance on a specific task as they’re exposed to more data.

    Deep Learning: A subfield of machine learning inspired by the structure and function of the human brain. Deep learning uses artificial neural networks, which are interconnected layers of processing units that can learn complex patterns from data.

    Natural Language Processing (NLP): This field of AI allows machines to understand and process human language. NLP applications include chatbots, virtual assistants, and machine translation.

    Computer Vision: This AI field enables machines to extract information from digital images and videos. It’s used in applications like facial recognition, self-driving cars, and medical image analysis.

    AI is a rapidly evolving field with a wide range of applications. It’s being used in various industries, including healthcare (e.g., how a medication can improve health in different scenarios), finance (e.g., predicting stock price changes based on different inputs), manufacturing (e.g., creating and reviewing designs for flaws), and transportation (e.g., predicting traffic flow). In the retail world, AI can assist in marketing copy testing, personalization, pricing and inventory analysis, and customer experience signal analysis.

    Imagine walking into a store and being greeted by virtual assistants recommending outfits based on your past purchases and style preferences. Or browsing an online store that curates product suggestions tailored specifically to you. This is the magic of artificial intelligence in retail. By analyzing vast amounts of customer data, including purchase history, browsing behavior, demographics, and even social media interactions, artificial intelligence can create a hyper-personalized shopping experience.

    This approach offers a multitude of benefits for both retailers and customers. Customers feel valued and understood, leading to increased satisfaction and loyalty. They discover products they might have otherwise missed, and the entire shopping experience becomes more efficient and enjoyable. For retailers, artificial intelligence-powered personalization translates to higher conversion rates, improved customer engagement, and ultimately, increased sales.

    Artificial Intelligence Assistants: Always There to Help and Create a Personalized Customer Experience

    The days of waiting on hold for customer service are fading fast. Artificial intelligence-powered chatbots and virtual assistants are now available 24/7 to answer questions, provide product recommendations, and even troubleshoot basic issues. These intelligent assistants can handle a significant portion of customer inquiries, freeing up human agents for more complex situations.

    Smarter Search, Faster Discoveries

    Gone are the days of endless scrolling through generic search results. AI can analyze your search queries and past behavior to tailor product suggestions and recommendations. Imagine searching for a dress and seeing not just similar dresses, but also complementary accessories and shoes that complete the look. This intelligent search functionality makes product discovery easier and faster, leading to a more satisfying shopping experience.

    Optimizing Inventory with AI

    AI isn’t just about the front-end customer experience. It can also play a crucial role in optimizing back-end operations. By analyzing sales data and customer trends, AI can predict demand fluctuations and suggest optimal stock levels. This proactive approach helps retailers avoid stock outs that frustrate customers and overstocking that ties up valuable resources.

    The Future of Artificial Intelligence (Source: YouTube Techly Reports Channel)

    Here are some retail brand examples using artificial intelligence today:

    Walmart: This retail behemoth utilizes AI for:

    Smart Inventory Management: Attaching cameras to floor scrubbers allows them to record inventory levels and send data to AI systems. This data is used to optimize stock levels and prevent stockouts.

    Personalized Recommendations: Walmart uses AI to analyze customer purchase history and browsing behavior to deliver targeted promotions and product suggestions through their app and website.

    How Walmart Uses Artificial Intelligence? (Source: YouTube WSJ Channel)

    The North Face: The outdoor apparel brand uses AI-powered chatbots to answer customer questions about product features, sizing, and care instructions. These virtual assistants can also recommend complementary gear based on a customer’s intended activity.

    IBM Watson powers North Face’s chatbot featuring artificial intelligence (Source: YouTube)

    Target: This retail giant implements AI for:

    Augmented Reality (AR) Experiences: Target’s app allows users to virtually place furniture and décor items in their homes to see how they would look before buying.

    Image Recognition: The Target app lets users take a picture of an item they find online or in-store to find similar or identical products within Target’s inventory.

    Ulta Beauty: This cosmetics retailer uses AI for:

    Personalized Beauty Recommendations: Ulta’s app utilizes AI to analyze a customer’s past purchases, skin tone, and makeup preferences to suggest personalized product recommendations.

    Virtual Try-On: Ulta’s app allows customers to virtually try on makeup products using their smartphone camera. This innovative feature allows customers to experiment and find the perfect look without having to physically apply makeup.

    Stitch Fix: This online clothing subscription service leverages AI or stylists (depending on the customer’s preference) to curate personalized clothing selections based on a customer’s style profile, budget, and fit preferences.

    While artificial intelligence offers tremendous potential for improving retail customer experience , it’s important to acknowledge and address potential drawbacks. One major concern is privacy. AI relies on customer data, and retailers need to ensure transparency and build trust with clear data practices. Customers should have control over their data and understand how it’s being used.

    Another consideration is the potential for job displacement as AI chatbots automate customer service tasks. While this may be true to some extent, it’s important to remember that AI is here to augment, not replace, human interaction. The human touch will always be essential for handling complex customer issues and building deeper relationships.

    Finally, there’s the issue of bias. AI algorithms can perpetuate biases present in the data they are trained on. To mitigate this risk, retailers need to ensure their datasets are diverse and representative of their customer base. This helps ensure a fair and unbiased experience for all.

    To successfully leverage artificial intelligence for a superior customer experience, retailers need to make strategic investments in both technology and people. Robust data infrastructure is essential for gathering, storing, and analyzing customer data effectively. This includes data storage solutions, management systems, and powerful analytics tools.

    Hiring AI specialists, data scientists, and engineers is crucial. These skilled professionals will build, maintain, and improve AI models, ensuring they are constantly evolving and delivering the best possible results.

    Shopping using artificial Intelligence could be the way of the futureMichelangelo Buonarroti at Pexels

    However, technology is just one piece of the puzzle. Retailers also need to invest in change management. Employees need training on how to use AI tools effectively and how to integrate them seamlessly into their daily workflows. Fostering a culture of data-driven decision making is also key.

    Finally, robust cybersecurity measures are essential to protect customer data and ensure responsible AI use.

    By strategically implementing AI and addressing potential drawbacks, retail brands can create a seamless and personalized customer experience. This translates to increased customer satisfaction, loyalty, and ultimately, a significant competitive advantage. As AI continues to evolve, we can expect even more innovative applications that will redefine the future of shopping, making it a more personalized, convenient, and enjoyable experience for everyone.

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  • New Community-Based Business Model Powers Retail

    New Community-Based Business Model Powers Retail


    Retail businesses are shifting from selling products to creating experiences. This community-based business model drives customer loyalty and increase spending.

    There is divergence this separation between companies that provide retail services and companies that make and sell the products. While we have always had that dynamic, over the past few years, there has been a real push by retailers to start to do both.

    Retailers starting to create their white-labeled product lines. That has created this dynamic where a retailer is trying to juggle many fronts with promoting in-house and external brands. They are investing time and money into developing a manufacturing supply chain and also creating the experiences that goes along with the products.

    Designing products, managing and developing a manufacturing chain, supply chain, marketing those products, and creating a brand persona around those products is the integration that is required for success. This is on top of many customers using price as a determining factor to their purchase decisions.

    To differentiate themselves, brands are leaning toward a community-based business model that focus more on experiences, community hubs, and products/services that trigger the human senses.

    A community-based business model focuses on building relationships and fostering a sense of belonging among customers, rather than just selling products. It leverages the power of connection to create a loyal customer base and drive sales. Here’s a breakdown of the key aspects:

    Core Components:

    Shared Values & Purpose: The community revolves around a common interest or passion that aligns with the brand’s values. This could be health and wellness (Lululemon), technology (Apple), or outdoor adventure (high-end outdoor equipment store).

    Simple Value Creation & Consumption: The community offers clear benefits to its members, such as educational content, workshops, events, or exclusive product access. These benefits should be easy for members to understand and participate in.

    Instead of being transactional, community-based business model integrate the community needs in its quest to achieve marketplace success. While products or services could be copied, experiences catering to the community is not easy to duplicate without significant infrastructure built to collect relevant data.

    There are benefits and drawbacks to community-based business model. Before shifting and investing into a new business model, brands should consider the following:

    Benefits of the Community-Based Business Model

    Focusing on community needs, the benefits of a community-based business model for retailers are primarily improvement in customer management including engagement and loyalty. The betterment leads to differentiation and improved brand perception.

    Increased customer loyalty: Stronger relationships with customers lead to repeat business and increased customer lifetime value. Feeling like part of a community fosters a sense of belonging that keeps them engaged with the brand.

    Higher customer engagement: Engaged customers spend more and become brand advocates. They follow the brand on social media, attend events, and recommend it to others. The two-way communication fosters a sense of community and active participation.

    Differentiation from competitors: Community focus helps businesses stand out in a crowded market. Unique experiences and a sense of belonging create a loyal customer base less likely to be swayed by competitor promotions.

    Improved brand perception: Community-based businesses are seen as destinations and cultural hubs, leading to increased brand awareness and a stronger brand image. The association with community creates a more positive perception of the brand and allows brands to show care and support.

    Improved stock price: In some cases, there is a positive correlation between stock price, company valuation, and investment in community-focus customer experiences. The additional support that draws brand and customer loyalty which has a positive impact to the basket of goods, upsell opportunity, and return purchase.

    Drawbacks of the Community-Based Business Model

    It takes time, resources, and leadership to build a community-based business model. The added complexity, commitment and performance measurement delays could deter brands from moving to this business model.

    Increased complexity: Building and managing a community requires time, resources, and ongoing effort. It involves planning events, managing online forums, and fostering relationships with community members. This can be more complex than the traditional transactional retail model.

    Potential for negativity: While communities can be a source of positive feedback and brand advocacy, they can also be breeding grounds for negativity. The business needs to be prepared to address customer complaints and negative feedback within the community space. Commitment to the community needs to be a long term goal. Taking away community support and care on a whim would have significant negative impact.

    Difficulty in measuring ROI: The return on investment (ROI) for community-building efforts can be difficult to quantify. The benefits may be seen in customer loyalty and brand perception, which can be harder to measure directly compared to traditional sales metrics. Also, the time spent to create, promote and measure success could take a longer time frame which may go against specific business strategy.

    Not suitable for all businesses: This model may not be ideal for all businesses, particularly those with a narrow product focus or limited resources. It works best for businesses that can build a strong community around their products or services and have the resources to invest in community building activities.

    A good example of a retail brand using community-based business model is the movie theater – often seen as a hub or a gathering spot for the community.

    The Barbie movie (Retail Mashup coverage) did a great job of creating experiences around that movie with marketing products and experiences. You had photo ops in the lobby of the cinema. There were write-ups on AMC’s websites, for example, that promoted both Barbie, and Barbie history, and Barbie products. It was creating this ecosystem around a community and cultural experience.

    Barbie pop-up at a local movie theater (Source: YouTube “Mapleshy”)

    Taylor Swift’s ERAS Tour Concert film (Retail Mashup Coverage) achieved an all time record for the genre in its opening weekend and total gross. The hitmaker created a deal with AMC Theaters with exclusive rights and brought scores of “Swifties” to the movie theater chain that was suffering from lack of content in the Fall.

    Experiential marketing music in the lobbies of these theaters, QR codes, and pathways back to purchasing Taylor Swift products.

    It’s creating this rich community and cultural experience that shoppers are responding significantly to, which is a game changer in the retail industry. There are a lot of companies that are doing things in this area, and they tend to be the companies that are rising above the competition.

    Lululemon has a lot of yoga pants and athletic gear, as do a lot of other companies. But Lululemon is still maintaining a market leadership position because they’re also embracing the community. They’re organizing 5K and 10K races in communities. The brand is giving yoga and fitness classes at their stores worldwide.

    Lululemon is a leader in building, promoting, and extending its community-based business model through events including ultra marathon runs (Source: YouTube Lululemon channel)

    Apple is another great example; they are a community destination. Any Apple store you walk by is going to be swarming with people sitting around, chatting with each other, looking at products and learning. Maybe they’ll take a class or go online for a little learning session. They have become good at creating that community experience.

    Inside The First Apple Store showcased the first ever store that was built around the community. (Source: YouTube Apple Explained channel)

    Canada Goose, offers cold rooms, and snow rooms for their customers to try out their products. It’s something engaging and people go there for that.

    As part of its community-based business model, Canada Goose has a cold room that allows customers to try out coats in different temperatures before purchase. (Source: Canada Goose)

    Ritz Carlton, as a hotel chain, enables their employees to spend up trying to impact a guest’s experience. If you see a guest having a bad day, you can send flowers to their room to cheer them up. Somebody’s lost their luggage. You can offer to buy them some clothes or you know, laptop chargers to get them through the hump until they can get themselves fixed up.

    Whether it’s bonding with the retailer or bonding with other customers around the community is the new business model that is becoming meaningful and important. Experiences are important because they move would-be customers to buy from the heart. This is going to motivate them to possibly buy the more expensive item or to buy multiple products rather than being that transactional price-driven shopper.

    Every brand has an awakening moment right now, especially with inflation continuing to drive down profits and potential traffic. Gucci, for example, reported a 20% decrease in revenue in Q4 2023 primarily due to lack of new products and differentiation against other brands.

    Many brands let down their customer by offering products and nothing else. Some are starting to realize that they have the responsibilities to showcase and offer other product components that their customers have not thought about.

    For example, a person going to a chain that sells outdoor equipment would trust the brand to tell them about the outdoor experiences that could best use the gears. If this logic holds true, the outdoor equipment brand has the responsibility to market the experiences and destinations (e.g., partnership with national parks) that showcase the gears in use and allow would-be customers to create testimonials.

    The stores selling only tents and nothing else through product alignment, engagement, and encouragement would lose out on additional selling opportunities across multiple fronts. The question to address, “What is the customer buying the tent for and how can I make that experience the best one possible?”

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  • IKEA Reduces Prices, Gains Happy Customers

    IKEA Reduces Prices, Gains Happy Customers

    In a positive shift its customers, IKEA is implementing strategic price reductions worldwide on furniture following a period of inflationary pressure. As raw material and transportation costs stabilize, IKEA is prioritizing affordability by lowering prices across a range of products. Retail Mashup investigates this phenomenon even when other brands have kept prices higher in a race to manage inflated costs.

    IKEA celebrated its 80th anniversary in 2023 with a fiscal year that cumulated with a revenue increase of 6.6% (7.3% when adjusted for currency impact) to EUR 47.6 billion compared to FY22. This positive result was achieved despite lower sales quantities and lower visitor counts to its online channels.

    Working closely with suppliers on product manufacturing, sourcing, and sustainability, IKEA continued to find ways to prioritize on affordability.

    “Intensification of internal efforts to reduce material and manufacturing costs, led to a decrease in IKEA retail prices at the end of FY23. In challenging times when inflation is high and many people struggle with the cost of living, the need for home furnishing solutions at affordable prices is high. This is where we will continue to do what IKEA has always done – putting customers’ affordability first. Looking further ahead, the three main opportunities we see for IKEA are to become even more affordable, more accessible and more sustainable,” says Inter IKEA Group CEO, Jon Abrahamsson Ring.

    The price reduction initiative extends beyond the United States, with IKEA implementing price cuts in other countries and aiming for further expansion throughout 2024. Its long-term goal is to return furniture prices to pre-pandemic levels, adjusted for inflation, by the end of 2025. This commitment is reflected in markdowns on popular items, such as a 20% reduction on the Billy bookcase in Canada. In the United States, over 10,000 products have their price reduced since September 2023.

    IKEA’s never-ending quest to provide its customers with high quality products that fit the needs of price-conscious customers. IKEA Canada listed over 1,600 products with price reductions as at March 2024. (Source: IKEA Canada)

    News of IKEA reducing its prices strategically gathered positive media attention worldwide.

    Good Morning America reports on the price reduction news (Source: Youtube GMA Channel)

    Inter IKEA Group CEO Jon Abrahamsson Ring discusses the state of global retail and IKEA’s pricing strategy (Source: YouTube Bloomberg Television Channel)

    This initiative creates customer engagement opportunities in the following ways:

    Increased Accessibility: Lower prices make the furniture chain more accessible to a wider range of customers. This is important as inflation reduced affordability overall. Attract new customers who may have previously been priced out and encourage existing budget-conscious customers to consider additional purchases would help turn the tide lower online visitors.

    Trial and Experimentation: With lower prices, customers might be more willing to experiment with new furniture pieces or try out different styles. This can lead to increased browsing, both online and in-store, which can spark further engagement. IKEA has been adding more city stores in countries like Columbia and Canada to cater to urban dwellers who may not want to drive out to the suburbs for their needs. Additionally, more products are placed online.

    Positive Brand Perception: Amid repeated headlines of price increases or product deterioration, the price reduction initiative is a customer-centric move fostering brand loyalty and positive sentiment. This can lead to increased customer engagement through positive word-of-mouth recommendations and online reviews.

    Repeat Purchases: Affordable furniture with proven quality can encourage repeat purchases as customers return to the store to furnish different rooms or update their existing furniture as their needs or tastes evolve.

    Bundled Purchases: Lower prices might incentivize customers to buy additional items, like cushions, throws, or lighting, to complete the look of a newly purchased furniture piece, increasing their overall purchase value.

    Increased Traffic: News of the price decrease can generate excitement and attract customers to IKEA stores and online platforms. This increased traffic can lead to more customer engagement through interactions with sales staff, product displays, and online content.

    IKEA ranks no.1 worldwide in the Home and Garden > Furniture category and continues to lead in categories like Pages/Visit, Average visit duration and Bounce rates after the price reduction news went viral. (Source: Similarweb.com March 1, 2024)

    Headquartered in the Netherlands, the Swedish furniture has always been known for its affordable, ready-to-assemble furniture, and customer experience designs that go beyond competitors.

    For example:

    Immersive experience:

    Showroom-style stores: Stores are designed like giant showrooms, with multiple decorated room settings that showcase how the furniture pieces can be used. This lets customers envision the pieces in their own homes.

    Store layouts that focus on exploration, ease of product discoverability, and fun are driving forces to success (Source: YouTube IKEA India Channel)

    The IKEA Catalogue: The iconic catalogue isn’t just a product list. It is a source of inspiration, support, and imagination. The yearly edition is a collector’s item and features beautifully designed rooms/helpful tips for home furnishing, home automation technologies, and next steps.

    Digital tools: Online tools for kitchen planning and room visualization (using virtual reality) have been a boon for the brand. This allows customers to experiment with different layouts before they buy online or at a store.

    Virtual reality tools that help would-be customers in planning (Source: YouTube IKEA channel)

    IKEA Family reward program: Being a member definitely has its privileges with up to 90 day price protection benefit, specials on food and beverages, free member workshops and events, member-only pricing. Membership is free!

    Convenience across platforms:

    Multiple shopping options: Customers can choose to shop online, in-store, or use a combination of both. They can order online for pick-up or home delivery (with or without installation).

    Clear signage and layout: The stores are designed with a specific layout to guide customers through different product sections, making it easier to find what they need. Lighted arrows on the floor aids navigation around the store. It’s hard to get lost!

    Making it their own:

    DIY assembly: While assembly can be a test for some, it also allows for customization. Customers can personalize furniture by choosing different configurations or adding their own touches.

    Restaurant and Swedish treats: The in-store restaurant is a popular destination in itself, offering Swedish meatballs and other delicacies. It adds to the overall experience and encourages customers to linger longer. IKEA Family reward program members are award with lower everyday prices of select food and beverage items (e.g., In Canada, a medium coffee is free).

    Affordability throughout the journey:

    Cost-conscious approach: Ikea keeps prices low by using efficient manufacturing and packaging. They also offer a variety of financing options to make furniture more accessible.

    Value-added services: The home furnishing company provides childcare services and a marketplace where customers can buy and sell used furniture, extending their affordability focus.

    IKEA’s price reduction strategy demonstrates a focus on customer affordability in a dynamic economic climate. This shift presents a potential opportunity for consumers seeking cost-effective furniture solutions. Combine that investment in customer experience innovation has given the brand a major leg up to the competition. It’s no wonder that the brand has stayed no.1 globally for the past 15 years and more.

    Fun Fact:
    Ingvar Kamprad, IKEA Founder, once said about the brand’s business idea, “To offer a wide range of well-designed, functional home furnishing products at prices so low that as many people as possible will be able to afford them.”

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