How Lean IT helped a leading retailer achieve 20% higher conversions and 35% higher average order value
Client Background
The client is a leading omnichannel retailer selling through its website, mobile app, and physical stores. Its catalogue spans thousands of products across several categories, and it serves millions of customers. The company already used Salesforce to manage customer relationships but had not yet applied AI to personalize the shopping experience.
Client Challenge
Strong traffic was not translating into proportionate sales, and four problems stood out:
- High cart abandonment: Many shoppers added items to their carts but never completed the purchase, and there was no timely, personalized way to bring them back.
- One-size-fits-all experience: Every visitor saw the same banners, offers, and product listings, regardless of their interests or purchase history.
- Limited basket growth: Cross-sell and upsell relied on static, manually maintained rules that were slow to update and often irrelevant.
- Disconnected channels: Online, in-store, and service interactions were not linked, so teams lacked a complete view of each customer.
The retailer needed to make better use of the customer data it already held, quickly and without a major platform overhaul.
Lean IT Solution
Lean IT implemented an AI personalization layer on the client’s Salesforce platform using Salesforce Einstein. The solution had four parts:
- Unified customer profiles that brought browsing, purchase, and service data together.
- AI product recommendations across the website, app, and email, tailored to each shopper’s behavior.
- Automated cart recovery journeys that sent personalized reminders and offers to customers who abandoned their carts.
- Smart cross-sell and upsell suggestions that recommended complementary products at high-intent moments.
Implementation
We delivered the program in three phases over roughly 12 weeks.
- Discovery and data readiness (Weeks 1–3): We audited data sources, consolidated customer records, and agreed on the KPIs to track.
- Build and integration (Weeks 4–8): We configured the Einstein recommendation models, connected them to web, app, and email channels, and built the abandoned-cart automation.
- Testing and optimization (Weeks 9–12): We ran A/B tests against the existing experience, tuned the models using live results, and trained the marketing team to run the platform independently.
Outcome and Value Added
Within the first quarter after launch, the retailer saw strong improvements in its core commercial metrics. Conversion rates rose by 20%, as personalized recommendations helped shoppers find relevant products faster. Average order value increased by 35%, driven by smart cross-sell and upsell suggestions that placed complementary items in front of customers at the right moment.
The cart recovery journeys won back around 18% of abandoned carts that would previously have been lost, and personalized email campaigns lifted click-through rates by 27%. Meanwhile, the marketing and merchandising teams cut the time they spent on manual rule-setting by about 40%, freeing them to focus on strategy and customer engagement.
Beyond the numbers, the client now has a unified customer view that marketing, sales, and service teams use every day. Shoppers receive recommendations that feel relevant rather than random, which builds loyalty and repeat purchases. With a scalable AI foundation in place, the retailer can extend personalization to more channels and customer segments as it grows.
Key Takeaway
Retail personalization does not require a multi-year rebuild. By applying Salesforce Einstein to data the client already owned and starting with focused use cases, Lean IT delivered fast, measurable growth in conversions and order value.