Retail & Ecommerce

Zola partnered with Pecan AI to predict customer lifetime value earlier and give marketing teams a more precise way to measure acquisition quality and optimize spend. With 95% LTV prediction accuracy, four predictive models, and automated delivery into its existing BI tools, Zola can compare expected customer value directly with acquisition costs and make better channel investment decisions, without building a dedicated machine learning team.

ALTHERR used Pecan AI to predict which customers were most likely to purchase again, to help sales teams test targeted, proactive outreach. Within eight weeks, customers identified by the predictive model accounted for €2.4M in pipeline opportunities and €246K in closed-won value.

See how retailers use predictive analytics to forecast demand, reduce churn, personalize offers, improve ROAS, optimize pricing, and prevent fraud.

With predictive LTV, weekly order insights, and upsell models, Little Spoon improved ROAS, strengthened its subscription flow, and expanded data-driven decision-making across teams.

Nanit improved sales forecasting accuracy, reduced time to insight, and gained clear visibility into demand drivers using Pecan AI, resulting in stronger trust and better decisions.

Drive your growth with five AI use cases for subscription e-commerce.

ShopTJC Ltd. partnered with Pecan to predict the likelihood of customers making additional purchases, enabling them to optimize shipping costs effectively.

With internal data science teams fully engaged with other company projects, this app maker had difficulty building and maintaining an effective demand forecasting model. With Pecan, they transformed their raw sales and inventory data into a powerful, accurate model in just days instead of months.

Predictive analytics recognizes complex patterns in customer data that predict conversions for this customer.