Retail's Next Competitive Divide: Turning Data Into Revenue With AI

The retailers that win this year and beyond will act fastest and most accurately on their data using artificial intelligence. Those that have already built their data foundation are now ready to pull away from the pack by extending AI into core commerce functions. As they improve their capacity to drive e-commerce maturity, revenue protection, pricing strategy effectiveness, and product compliance, the competitive and revenue gaps between them and everyone else will grow fast.

Unifying Data for Customer Insights and Process Analysis

Use cases for AI continue to expand as retailers adopt third-party AI data cloud platforms such as Databricks and Snowflake to unify their data for strategic use. This approach can drive revenue growth by creating detailed customer views that support real-time hyperpersonalization to increase conversions. Global retailers with proprietary AI and data resources have shown the way here. Amazon.com, for example, has been using machine learning and AI to personalize not just which products its recommendation engine suggests, but also to personalize product descriptions in real time to match individual customers’ interests and goals. These product descriptions can reference things like a shopper’s dietary preferences, hobbies, or lifestyle for greater engagement and conversion.

On the backend, these kinds of cloud-based data intelligence layers make it easier to identify cost-efficiency improvements in back-office processes and employee productivity. Some leading retailers also use their cloud-based AI data tools to coordinate with suppliers to reduce waste and control costs through optimized planning. For example, Walmart now uses agentic AI to provide real-time inventory visibility across stores and the supply chain so that forecasting, replenishment schedules, and logistics all continuously adapt to real-world conditions.

Detailed customer intelligence can also make marketing teams more effective at acquiring new customers with optimally targeted media spend. After the sale, customer data can inform faster and more effective customer support interactions to encourage loyalty and improve customer lifetime value.

Removing Friction to Capture More Revenue

Ideally, the ultimate goal for retailers is to optimize every step of their e-commerce experience, from product browsing to cart checkout, to drive revenue growth. Using AI analytics and insights based on customer data, retailers can identify and remove friction to shorten the time from site visit or digital channel interaction to conversion, accelerating time to revenue. Customer data insights can also be leveraged to attract new customers and smooth their path to conversion, as well as to boost cross-sell rates among existing customers.

Some retailers already leverage AI-powered data insights to refine their product assortment and display to better align with customer preferences and shopping behaviors. Customer data can also support agentic AI customer assistance for personalized product discovery, feature and cost comparisons, stock availability information, cross-sell and upsell recommendations, and checkout. All of these use cases build toward a mature e-commerce profile that encourages consumers to complete more purchases and return to shop again.

Finding and Stopping Revenue Leaks

Keeping the revenue they earn is a key issue for retailers, which can miss revenue due to a vast number of issues. In particular, many retailers have revenue leakage between order placement and payment reconciliation and may not be aware of the extent of the problem. Auditing and analyzing order management and fulfillment can identify communication gaps between the e-commerce site and the warehouse that result in lost revenue.

Retail is vulnerable to revenue loss through fraud, both at the order and return stages. AI-driven pattern and anomaly detection can help to reduce fraud risks. For example, the National Retail Federation calculated that 9 percent of 2025’s estimated $850 billion in U.S. retail returns was fraud. That represents more than $76 billion in revenue leakage, even before factoring in follow-on expenses that push the cost of each dollar of retail fraud to $4.61, according to Lexis-Nexis data. AI can analyze  customer behavior patterns and fraud risks for specific products to flag high-risk orders for step-up screening.

Invoicing and billing also present opportunities for revenue leakage through poor communication. If customers don’t recognize the name associated with a charge on their bank statements, that can prompt chargebacks that eat into revenue. AI-powered intelligence based on customer, order, and operational data can help retailers identify and address issues like this to reduce revenue leaks.

Leveraging Pricing Data and Competitive Intelligence

AI-driven insights can help retailers optimize pricing and promotions in real time to drive more sales and avoid leaving revenue on the table. For example, AI models can use customer intent and real-time pricing data to help retailers offer customers the right price and value at the right time on their purchasing journey. This kind of real-time offer can lock in a purchase, especially when it’s combined with on-site price comparison data so customers can see they’re getting the best deal available, without having to leave the retailer’s site to compare prices.

Improving Product Compliance With Brand Standards

Having a presence on third-party marketplaces like Amazon and Walmart can help retailers reach a wider audience and capture more sales. The tradeoff can be less control over how products are presented on those platforms compared to the retailer or brand’s own channels. AI analytics tools can review marketplace product listings to ensure that the information there meets the retailer’s and brand’s standards. This approach to product and brand compliance monitoring can prevent returns and chargebacks based on inaccurate product descriptions. Effective compliance also ensures consistency, which builds customer trust.

Maximizing Revenue by Winning Customer Attention and Loyalty

All of the AI strategies discussed here are powered by the kind of data that most retailers already own. What makes these strategies truly competitive is the creativity and speed with which retailers apply them. Winners will use competitive intelligence for more precise pricing. They’ll use customer journey data to identify and remove barriers on the path from intent to purchase. They'll analyze behavioral and operational data to reduce revenue leakage caused by fraud and to build loyalty that competitors can’t breach.

The more of these strategies a retailer can deploy effectively at speed, the larger their share of the $30 trillion global retail market will be. The longer retailers wait to implement AI for these use cases, the harder it will be for them to close the competitive divide.

Manish Sharma is chief revenue officer at eClerx, where he leads global revenue strategy across retail and other key industry verticals.