The Hidden Problem Shoppers Face in Supermarkets

The Hidden Problem Shoppers Face in Supermarkets

Walk into any supermarket, and you’ll likely see the same scene: aisles packed with choices, shoppers scanning shelves with uncertainty, and frustration building over missing items or confusing layouts. Despite the abundance, product discovery remains a major pain point—both in-store and online.

Shoppers aren’t just looking for any product. They want the right product—quickly, easily, and at the best value. Yet, 78% of consumers are actively seeking the best deals, and nearly 58% expect seamless omnichannel experiences, according to Grocery Doppio. When those expectations aren’t met, satisfaction drops—and so do sales.

Common challenges include:

• Overwhelming choice without clear guidance
• Out-of-stock items disrupting planned purchases
• Poor in-store navigation leading to abandoned trips
• Generic online recommendations that feel irrelevant
• Lack of personalized pricing or promotions

These friction points don’t just annoy customers—they cost retailers. A confusing layout or missing item can push shoppers toward competitors, especially when 47% of grocery retailers are already investing in personalized offers to win loyalty (Grocery Doppio).

Consider Coles, one of Australia’s largest supermarket chains. Before implementing AI-driven tools, customers faced long click-and-collect wait times and impersonal digital experiences. After integrating smarter recommendation systems, Coles saw a 70% reduction in pickup wait times and a +29.6% year-over-year increase in Net Promoter Score (NPS) (Rezolve AI). The fix? Smarter data use, not store redesigns.

What’s clear is that the problem isn’t inventory or foot traffic—it’s discovery. Shoppers struggle to find what they need, want, or didn’t even know they needed. And in an era where personalization drives decisions, generic shelving and one-size-fits-all digital banners fall short.

Supermarkets can’t rely on better signage or loyalty cards alone. The gap between expectation and experience is widening. The solution lies in leveraging real-time data and intelligent systems that guide shoppers—like an invisible assistant at every turn.

The next step? Turning these hidden frustrations into opportunities with AI-powered product discovery—where every interaction feels intuitive, relevant, and rewarding.

AI-Powered Solutions That Drive Real Results

AI-Powered Solutions That Drive Real Results

Shoppers today expect more than aisles of products—they demand personalized, seamless experiences that save time and money. AI is no longer a futuristic concept in supermarkets; it’s a proven driver of real business outcomes.

From boosting average order value to reducing waste, AI-powered solutions are transforming how customers discover products and how retailers operate. The most impactful applications center on personalization, smart discovery, and unified data systems—delivering measurable improvements across sales, efficiency, and loyalty.

• AI increases revenue per visitor by up to 128% (Rezolve AI, Crate & Barrel case study)
• Conversion rates rise by 25–44% with intelligent recommendations (Dynamic Yield, Rezolve AI)
• Average order values grow 8–26% through targeted upselling and cross-selling (Dynamic Yield, Rezolve AI)

These aren’t projections—they’re results already achieved by forward-thinking retailers using AI at scale.

Traditional product discovery relies on static layouts and generic promotions. AI replaces guesswork with precision, using behavioral data to guide shoppers to relevant items—online and in-store.

By analyzing past purchases, real-time browsing, dietary preferences, and even weather patterns, AI delivers hyper-personalized recommendations. For example, when temperatures drop, an AI system can promote soup and tea to customers who previously bought comfort foods.

Key AI-driven discovery tools include: - Smart recommendation engines (e.g., Dynamic Yield)
- Visual search and “Shop the Look” features (Rezolve AI)
- AI-powered mobile apps with geolocation alerts
- Smart carts with real-time suggestions
- Dietary and allergy filters powered by NLP

Such tools turn passive browsing into active engagement, making discovery intuitive and frictionless.

A standout example is Coles, which implemented Rezolve AI’s platform and saw: - 70% reduction in click-and-collect wait times
- +29.6% year-over-year increase in Net Promoter Score (NPS)
- Mobile app engagement up 42.3% (monthly active users)

This wasn’t just a tech upgrade—it was a customer experience transformation powered by AI.

AI’s power depends on data—but only if that data is connected. Siloed systems (POS, e-commerce, loyalty) limit insight. The breakthrough comes when supermarkets unify these streams into single customer profiles.

Oracle Retail emphasizes that AI must integrate: - Point-of-sale transactions
- Loyalty program activity
- Online browsing behavior
- In-store sensor data (e.g., smart carts, cameras)
- External factors like weather and local events

With this holistic view, AI can predict what a customer wants before they search for it.

For instance, if a shopper frequently buys gluten-free items and visits on a rainy evening, the system might recommend a ready-made gluten-free stew—increasing relevance and conversion.

58% of consumers expect seamless omnichannel experiences (Grocery Doppio), and AI makes this possible by synchronizing online and physical touchpoints.

The future belongs to retailers who treat data as a strategic asset—not just for inventory, but for customer-centric decision-making.

Next, we explore how AI enables dynamic pricing and real-time promotions that boost margins while keeping shoppers engaged.

Step-by-Step: Implementing AI for Product Discovery

AI is no longer a luxury—it’s a necessity for supermarkets aiming to stay competitive. With shoppers demanding personalized, seamless experiences, AI-powered product discovery systems are driving real results: higher conversion rates, increased basket sizes, and stronger customer loyalty.

The good news? You don’t need a data science team or massive IT budget to get started. Thanks to no-code AI platforms, even mid-sized retailers can deploy intelligent recommendation engines in days—not months.

Platforms like Dynamic Yield and AgentiveAIQ allow supermarkets to launch AI-driven personalization without writing a single line of code. These tools offer pre-built templates, drag-and-drop interfaces, and seamless integrations with existing e-commerce and POS systems.

Key benefits of no-code AI: - Rapid deployment (some setups take under 5 minutes) - Lower technical barriers for marketing and merchandising teams - Faster A/B testing and iteration cycles - Scalable across online and in-store channels

According to research, retailers using no-code AI platforms report an average revenue increase of +10% and conversion rate lifts up to +44% (Dynamic Yield, Rezolve AI). This accessibility is leveling the playing field between large chains and regional grocers.

Case in point: Coles, in partnership with Rezolve AI, enhanced its mobile app with AI-driven discovery features—resulting in a +29.6% year-over-year NPS boost and 42.3% growth in monthly active users.

With proven ROI and minimal risk, the first step is clear: select a flexible, secure, no-code platform tailored to retail.

Personalization only works if it’s relevant—and relevance depends on data. To power accurate recommendations, your AI system must pull from multiple real-time sources:

• Customer behavior (app clicks, online browsing, past purchases)
• Loyalty program data
• In-store sensors and smart carts
• Inventory levels and product availability
• External factors (weather, local events, holidays)

Oracle Retail emphasizes that a centralized data infrastructure is foundational. When AI connects POS transactions with digital behavior, it can make powerful predictions—like suggesting soup during a cold snap or barbecue items before a holiday weekend.

Retailers leveraging unified profiles see: - +8% to +26% increase in average order value (AOV)
- +17% improvement in add-to-cart rates
- 58% of consumers report expecting consistent experiences across channels (Grocery Doppio)

Without integrated data, AI remains blind. The transition from siloed systems to a unified customer view is non-negotiable.

Instead of a full rollout, begin with a targeted pilot in high-margin or perishable categories—like organic produce, bakery items, or dairy.

Why start small? - Easier ROI measurement - Lower operational risk - Faster adjustments based on real feedback - Builds internal buy-in from stakeholders

For example, one U.S.-based regional chain piloted AI recommendations in their plant-based foods section, using real-time inventory and dietary preference filters. Within eight weeks, they saw a +22% increase in category sales and reduced spoilage by proactively promoting items nearing expiration.

This phased approach allows teams to refine algorithms, train staff, and scale confidently.

Now that the foundation is set, the next step is enhancing the in-store journey—where AI moves beyond screens and into the aisles.

Best Practices for Sustainable AI Adoption

AI is no longer a futuristic concept in grocery retail—it’s a competitive necessity. To ensure long-term success, supermarkets must adopt AI sustainably by balancing innovation with customer trust, cost efficiency, and scalability. Early adopters like Coles and Crate & Barrel are already seeing results: +29.6% NPS, 128% more revenue per visitor, and 70% faster click-and-collect service (Rezolve AI, Reddit r/RZLV).

But scaling AI across store networks requires more than just technology—it demands strategy.

Consumer skepticism around data use remains a hurdle. A transparent approach to AI not only complies with regulations like GDPR but also strengthens brand loyalty.

Key trust-building actions: - Clearly explain how customer data improves recommendations - Offer easy opt-out options for personalized tracking - Use anonymized data where possible - Display real-time explanations for AI suggestions (e.g., “Recommended because you bought almond milk last week”)

According to Oracle, privacy concerns are among the top barriers to AI adoption in cashierless stores. Yet, 58% of consumers expect seamless omnichannel experiences—creating a delicate balance between personalization and privacy.

Example: When Coles launched its AI-powered mobile app with geolocation and personalized offers, they paired it with an educational campaign explaining data usage—resulting in +22.1% app downloads and +42.3% monthly active users (Rezolve AI).

Transparency isn’t a cost—it’s a conversion catalyst.

High implementation costs deter many mid-sized retailers. The solution? Start small, scale fast.

Phased adoption allows for ROI validation before enterprise-wide deployment: - Begin with high-margin or perishable categories (e.g., bakery, dairy) - Pilot AI in one region or store format - Measure impact on average order value (AOV) and conversion rates - Expand only after achieving positive unit economics

Platforms like AgentiveAIQ and Dynamic Yield offer no-code interfaces, reducing reliance on data scientists and cutting deployment time from months to minutes.

This approach helped Rezolve AI clients achieve an 8–26% increase in AOV and +25% conversion lifts—without costly infrastructure overhauls.

Next, we’ll explore how integrating real-time data unlocks hyper-personalized experiences at scale.