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 maybe 10-15% of it. The rest sits in a data warehouse costing money to store.
One of our e-commerce clients was losing an estimated $2M annually in missed cross-sell opportunities because their recommendation engine used only purchase history. When we built a model that incorporated browsing patterns and time-of-day behavior, 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. When we implemented segment-based personalization (returning customers see reorder suggestions, new visitors see popular categories, sale-focused visitors see clearance), conversion rate went from 2.1% to 3.4%. That single change generated an additional $800K in annual revenue.
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Your UX is probably outdated (even if it was redesigned recently)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 That definition is obsolete.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.
We audited a major Israeli retailer's mobile app and found that the checkout flow had nine steps. 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.
Cart abandonment — the $18 billion problem
About 70% of online shopping carts are abandoned before purchase. The number has been relatively stable for years because most retailers address symptoms (email reminders) instead of causes (confusing checkout, surprise shipping costs, account creation requirements).
Machine learning models can predict which users are likely to abandon and intervene before they leave — with targeted discounts, simplified checkout paths, or saved-cart reminders. We've seen this approach reduce abandonment by 12-15% for clients who implement it properly.
The ROI calculation is straightforward: if your annual GMV is $10M with 70% abandonment, reducing abandonment by even 10% means an additional $1M in completed purchases.
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. If any of this sounds familiar, we'd like to hear about your challenges.