How Retailers Are Using AI to Solve Their Biggest Digital Problems

TL;DR: Retail digital revenue leaks in five places: unused customer data, one-size-fits-all pages, outdated UX, a desktop store squeezed onto phones, and cart abandonment. Baymard Institute puts the average abandonment rate at about 70%. The fixes are personalization built on behavior you already collect, a mobile-first checkout with few steps, and a modern API layer over legacy inventory and order systems.

The five problems killing retail digital revenue

Working with retail clients at Globalbit, we keep running into the same set of problems. Different company sizes, different product categories, but the same gaps. Here's what we see and what the teams that fix them actually do.

Most retailers are sitting on data they never use

Retailers collect browsing behavior, purchase history, inventory data, pricing trends, and customer demographics. Most of them analyze only a small part of it. The rest sits in a data warehouse costing money to store.

One of our e-commerce clients was losing an estimated $2M a year in missed cross-sell opportunities because its recommendation engine used only purchase history. We built a model that added browsing patterns and time-of-day behavior, and average order value increased 18% in the first quarter.

The fix is not "buy more analytics tools." It usually starts with auditing what data you already have and identifying three to five specific business questions you want it to answer.

Personalization at the individual level is now expected

Treating every visitor to your site the same way means you're optimizing for the average customer, who doesn't exist.

A fashion retailer we worked with showed the same homepage to all visitors. We implemented segment-based personalization: returning customers see reorder suggestions, new visitors see popular categories, and sale-focused visitors see clearance. Conversion rate went from 2.1% to 3.4%, and that single change generated an additional $800K in annual revenue.

From our work. Espresso Club, Israel's #2 coffee brand, used to take orders only by phone. We built its app and website around personalization: each customer sees a different journey, with tailored promotions, subscription logic and one-tap reordering of their usual capsules. Today 350,000+ monthly active users order 5 million cups a month, 24/7. Personalized recommendations and one-click reordering raised customer lifetime value.

The technology to do this exists today — machine learning models that cluster user behavior and serve personalized experiences. The barrier is usually organizational, not technical. Product teams and marketing teams need to agree on what "personalization" actually means for their business.

Your UX is probably outdated (even if it was redesigned recently)

User experience used to mean button placement and color choices. That definition is obsolete. UX in retail now covers the entire customer journey: discovery, comparison, purchase, delivery tracking, returns, and post-purchase support.

When we redesigned the e-commerce experience for WeShoes, Israel's largest shoe retailer, 70% of its traffic came from phones. The old site showed shoes in a grid and did little to help people buy. We designed the product page around the #1 reason people abandon shoe purchases online: fear that the shoe won't fit. A "What's my size?" link sits right above size selection, reviews appear before any choice is made, and a full-width add-to-cart button closes the flow. It took four months from kickoff to development-ready specs. The same thinking applies to any mobile store.

Checkout length matters just as much. We audited a major Israeli retailer's mobile app and found a nine-step checkout, where industry best practice is three to four. Every additional step was costing them roughly 8% of completing users. They reduced checkout to four steps and saw a 23% increase in completed purchases.

If your checkout hasn't been redesigned in the last 18 months, it's probably losing you money.

Mobile shoppers need a mobile-first store

Mobile shoppers behave differently from desktop shoppers. Their sessions are shorter and often interrupted, they navigate with one thumb, and they don't want to type a card number on a small screen. A shrunken desktop site doesn't serve them. Three changes do:

• A three-tap checkout. Review the cart, confirm shipping, pay with biometrics. Offer Apple Pay and Google Pay, and one-tap reorder for returning customers.
• Focused product discovery. A phone shows fewer products per screen, so use browsing history, purchase patterns and context to surface the most relevant ones first.
• Fast pages. Optimize images, lazy-load below-the-fold content, and serve from a CDN with edge locations close to your customers.

Cart abandonment

Baymard Institute's running average, last updated in September 2025, puts the online cart abandonment rate at 70.22%. The number has been relatively stable for years. Most retailers treat the symptom with email reminders. The causes sit in the checkout itself: surprise shipping costs, forced account creation and long forms. Baymard estimates that better checkout flow and design alone could recover $260 billion in lost orders across the US and EU.

Machine learning models can predict which users are likely to abandon and step in before they leave, with a targeted offer, a shorter checkout path or a saved-cart reminder. We've seen this approach reduce abandonment by 12-15% for clients who implement it properly.

The math is worth doing. Say your store completes $10M a year in orders at a 70% abandonment rate. Cutting abandonment to 65% lifts completed orders by about a sixth, roughly $1.7M a year.

Legacy systems slow everything down

Many retailers run on inventory and order management systems designed 15+ years ago. These systems work, but they can't support modern omnichannel experiences. Real-time inventory visibility, dynamic pricing, and unified customer profiles across channels all require modern APIs and data pipelines.

Replacing these systems entirely is risky and expensive. The approach that works: build a modern API layer on top of legacy systems, then gradually migrate functionality as business needs require it.

Frequently asked questions

Where should a retailer start with AI? Start with personalized product recommendations. It has the clearest ROI, the technology is mature, and you can measure results within 60 days.

How much does a retail AI implementation cost? A meaningful recommendation engine or personalization system typically runs $100K-$300K for implementation, depending on data complexity and integration requirements. Payback period is usually 4-8 months.

Should we build AI in-house or use a vendor? For most mid-size retailers, a hybrid approach works best: use a proven ML framework but customize models to your specific catalog and customer behavior. Off-the-shelf solutions rarely capture what makes your business unique.

We've helped retail organizations across grocery, fashion and electronics implement AI-driven personalization, and we've built retail platforms for brands such as Espresso Club and WeShoes. If any of this sounds familiar, we'd like to hear about your challenges.