How is AI Redefining the Retail Experience?

Retail in a post-pandemic world

Imagine walking into a store, picking up three apples that are almost identical but have different prices, and then walking straight out. You’re not shoplifting, as your digital wallet has automatically been debited. And the correct amount has been charged, thanks to AI-powered cameras trained to detect even the most subtle of differences between products.

This is where the retail industry is headed.

Global retail is undergoing a significant transformation driven by technological advancements and economic growth. Consumers who spent the pandemic years saving on transportation and entertainment are now redistributing their money into a new version of the retail industry.

Frictionless purchases are soon to become the norm, and when consumers want to interact with humans, they can do so knowing that the experience will be as seamless as possible thanks to omnichannel strategies that let consumer information flow seamlessly between online and offline environments; buy online, pick up in-store (BOPIS) has become the new standard for consumers with more weekday flexibility and the expectation of receiving their products on the same day.

Artificial Intelligence (AI) is changing the way retailers do business. The shopping journeys we take online and offline are becoming more personalized as vast amounts of data from multiple sources are fed into complex algorithms designed by humans but optimized by AI. Conversations with chatbots are becoming more human and, thankfully, more useful.

Operations behind the scenes also rely on algorithms that constantly monitor real-time market data, scan customer reviews, and interpret competitive analyses. AI follows these algorithms, learns to make better decisions over time, and eventually improves upon them. This exponential improvement in Machine Learning (ML) is helping retailers to better predict future sales trends and adjust inventory levels based on historical data. These automated processes help retailers avoid overstocking and understocking, which improves overall cost efficiency.

Key AI trends in retail

Blending the online and offline experience

Companies are using AI to provide customers with a seamless shopping experience by integrating all points of customer interaction, which includes in-store interactions, online shopping sites, and dedicated retailer mobile apps. AI can aggregate data from these sources to ensure customers have a consistent experience wherever they shop, from tailored recommendations to timely promotions.

Where traditional multichannel marketing models place products at the heart of strategy, omnichannel models make customers the focal point. They use data from multiple touchpoints to deliver targeted experiences through recommendations, pricing, and other forms of nudges.

Tailoring customer experiences with AI

Dynamic customer profiling and segmentation help deliver highly personalized content at every stage of the customer journey. And it's the vast quantities of data that make this happen.

With its use of algorithms and data points, AI is emerging as the driving force behind applications that provide personalized product recommendations and adaptive advertising through product tagging. Product tagging uses AI to analyze images and text to generate accurate and comprehensive tags that enhance searchability, thus improving the overall shopping experience.

AI-powered brand avatars also extend retailers' digital identities; these virtual characters can manifest themselves as online chatbots or personal shopping assistants, representing a new level of personalization and interactivity.

Optimizing supply and pricing

AI-driven algorithms can create compelling offers for consumers browsing online by analyzing demand, supply, competition, and customer value perception. Real-time adjustments based on changing market conditions and customer responses are becoming the new normal, and it's the increasingly human-like interactions with brand avatars that make these recommendations more persuasive.

Discovering products with AR and VR

The adoption of AI-powered AR (Augmented Reality) and VR (Virtual Reality) is enhancing the customer experience. AR/VR product testing and hybrid physical-digital experiences are surfacing in brick-and-mortar stores. IKEA has been using AR technology for a while now to help customers virtually design and imagine their living spaces; brands like Meller allow consumers to try on sunglasses virtually before buying through a dedicated website and external app integrations.

Shopping sessions could soon be initiated through facial recognition, linking consumers to an existing retail account technology, or by manually scanning QR codes to start an ad hoc session.

Rethinking the checkout process

Computer vision, voice recognition, and biometric technologies now play a more significant role in the checkout experience. AI self-checkout systems use computer vision and machine learning to automate the entire in-store process: 

• Product recognition: cameras and AI models identify products without barcodes.

• Session management: QR codes trigger shopping sessions; cameras track customer actions.

• Automated checkout: the system calculates the total and charges digitals wallet as customers exit.

Though this may seem like a huge step toward avoiding queues, reducing manual stock checks, and generally reducing in-store congestion, there will naturally be concerns over customer tracking, product differentiation, and privacy. This is where we will notice gaps in the learning capabilities of AI models, as well as the ability of humans to create robust policy frameworks to avoid significant ethical and security breaches.

So, what next?

The improvements we've seen in AI have led experts to believe that technology could reach human-level performance faster than we thought.

Source: McKinsey & Company, "The Economic Potential of Generative AI: The Next Productivity Frontier." Available at McKinsey & Company.

However, there is still some catching up to do when it comes to replicating the social and emotional traits that underpin the human condition; we're still likely to understand the sentiment of customers in written and verbal contexts more consistently than AI.

Source: McKinsey & Company, "The Economic Potential of Generative AI: The Next Productivity Frontier." Available at McKinsey & Company.

As retailers adopt AI, it's crucial to address the various risks associated with its implementation. A significant concern for most companies is ensuring that AI systems produce accurate results in a secure manner.

Inaccuracies in AI predictions can lead to poor business decisions, while security vulnerabilities can expose sensitive customer data to cyber threats. Therefore, rigorous testing, validation procedures, and robust cybersecurity measures are essential to maintain the integrity and reliability of AI applications in retail.

Regulatory compliance is something else that retailers must consider. As AI continues to evolve, so does the regulatory landscape governing its use. Companies must stay informed about current and upcoming regulations to ensure that their AI implementations are compliant. This includes adhering to data privacy laws, ensuring transparency in AI decision-making processes, and being able to explain and justify the outcomes generated by AI systems–this is crucial when looking toward issues of fairness, impartiality, and environmental impact.

Integrating the digital within brick-and-mortar stores.

How can Mendix help retailers?

Mendix offers a comprehensive approach to help retailers integrate and leverage AI effectively through three main strategies:

AI-assisted development

Mendix facilitates AI-assisted application modeling and logic, allowing developers to streamline the creation and management of AI-driven applications. This includes:

• Tools that assist in building and refining applications, ensuring best practices are followed.

• Automatic audits to ensure that the applications meet industry standards and best practices.

• Validation processes that are automated using AI help maintain the quality and accuracy of the applications.

This approach reduces development time and enhances the efficiency of creating robust AI applications that are more reliable and adhere to best practices.

AI-infused apps

Mendix helps users integrate machine learning (ML) models with applications through its ML-Kit. This injects AI capabilities directly into business applications so they can offer predictive analytics, image recognition, and natural language processing to their users. Retailers can enhance their applications by integrating ML models and advanced AI features to improve decision-making and customer experiences.

Connectors

Mendix offers its own integrations for well-established AI suppliers like AWS. These connectors help integrate various AI services such as ChatGPT, Bard, and ABBYY, allowing retailers to leverage novel tools without the need for extensive development. This connectivity ensures that retailers can quickly deploy AI capabilities and scale their solutions using trusted and reliable AI providers.

Retail AI in practice: ML-Kit

Mendix provides an implementation framework and process for using AI in retail contexts, with semantic segmentation as a leading example. Semantic segmentation is a type of image analysis technique in AI and ML where each pixel of an image is classified into a predefined category. In the retail context, semantic segmentation can be used for tasks like product identification, shelf monitoring, and store layout optimization, as covered in the AI trends above.

Video capture: Retailers can set up cameras to capture live video feeds of store shelves and customer interactions, with the webcam feed accessed via a client or server.

Frames pre-processing: The captured video frames are pre-processed with specific Java actions to enhance image quality and prepare the data for analytics.

Object detection: The pre-processed frames are fed into the YOLOv8 model via ML-Kit to detect and classify objects. YOLOv8 (You Only Look Once version 8) is a real-time object detection algorithm designed for high speed and accuracy in identifying and localizing objects within images. This step is crucial for identifying products, monitoring shelf stock levels, and tracking customer movement.

Tracking and analysis: After object detection, the system tracks and counts the identified objects. This helps maintain accurate inventory records and analyze customer behavior patterns.

Output: Detailed analytics and insights are displayed on the Mendix platform, helping managers to make decisions regarding store layouts, inventory, and general customer service.

Retail and CLEVR

CLEVR offers robust AI and data science solutions  to help retailers tackle challenges like poor prediction quality, model degradation, and data corruption. With our expertise in business consultancy, technology implementation, and tailored AI solutions, we help you achieve reliable outcomes.