To personalize the shopping experience on our fashion e-commerce platform, we knew we needed to understand our customers beyond just their purchase history. We needed to grasp their shopping behavior, motivations, and decision-making patterns to create an experience that felt personalized for each individual. After digging into our data, we noticed four distinct types of shoppers:
- Trendy Shoppers who always purchased from new collections as soon as they were launched.
- Seasonal Buyers who mostly shopped around festivals, holidays, and sales seasons.
- Discount Seekers who actively waited for price drops and special offers.
- Habitual Shoppers who navigated directly to our platform whenever they needed a product, usually shopping for personal occasions/events.
Our goal was simple but ambitious: to make our website and app feel intuitive and responsive to each shopper's behavior. The homepage, product recommendations, and even pricing strategies needed to be adjusted dynamically based on the type of shopper visiting.
The Experiment: Personalized UX & Dynamic Product Listings
To achieve this, we built a Multivariate testing framework that allowed us to experiment with different levels of personalization for different user groups. The first step was correctly identifying who was who. We leveraged customer purchase history, browsing habits, and engagement patterns to train our recommendation engine to categorize each visitor into one of the four shopper personas.
Once we had clear segmentations, we personalized the shopping experience based on their unique behaviors.
- Trendy Shoppers would see the latest collections front and center, along with trending items that were generating buzz.
- Discount Seekers would see banners highlighting upcoming sales and exclusive discount sections tailored to their interests.
- Seasonal buyers were shown festival-themed collections during relevant periods.
- Habitual shoppers were presented with products aligned with their previous purchases and browsing history.
This approach wasn't just limited to product placement and homepage customization. We also adjusted product recommendations, featured items, and even dynamic pricing in some cases. The goal was to guide each shopper toward a buying decision that felt organic and relevant to them.
Over time, as more data was fed into the system, the recommendation engine became smarter, refining its predictions and improving its ability to pair the right customers with the right products at the right time.
Leveraging AI for Upselling & Cross-Selling
Another crucial area where our recommendation engine proved invaluable was in upselling and cross-selling. Once a shopper added an item to their cart, we introduced a section titled "Products You May Like", which wasn't just a generic recommendation list — it was carefully curated based on three critical factors:
- Behavioral Patterns of Similar Customers: The system identified which customer group the shopper belonged to and cross-referenced what other shoppers from the same group typically purchased together.
- Individual Shopping Preferences: The recommendation engine analyzed past purchases to determine what colors, styles, and price points resonated most with each customer.
- Product Matching Algorithms: The system matched complementary items that were frequently bought together, such as a yellow scarf paired with a blue dress, ensuring the suggested products aligned in terms of color, style, and aesthetic. We added a lookbook feature "Book Your Look" based on this recommendation.
This wasn't just a minor tweak — it was a game-changer. By intelligently suggesting products that genuinely aligned with the customer's taste and purchase behavior, we saw a significant increase in cart value and a reduction in abandoned carts.
Abandoned Cart Recovery
If a customer added items to the cart but didn't complete the purchase, our algorithm identified the best timing and communication channel (email, SMS, or push notification) to re-engage them with:
- A reminder notification with an exclusive time-sensitive offer.
- Personalized recommendations based on their cart items and style preferences.
- A nudge showing "only a few left in stock" for urgency.
We converted 12% of these abandoned carts with our personalized marketing effort.
The Impact: How Data-Driven Personalization Boosted Performance
The results spoke for themselves. After running different kinds of tests and continuously improving our recommendation engine, we saw notable improvements across multiple key metrics:
- Reduced Bounce Rate: Visitors engaged with the personalized experience longer and browsed more pages per session.
- Higher Conversion Rates: Customers who received targeted recommendations were more likely to make a purchase.
- Increased Average Order Value (AOV): Smart cross-sell and upsell suggestions led to larger transactions (AOV increased by 18%).
- Improved Ad & Marketing ROI: With clearer customer segmentation, our ad campaigns became more precise, ensuring that Trendy Shoppers were targeted with new collection launches while Discount Seekers were notified about upcoming sales. It reduced 30% customer acquisition costs.
Beyond direct revenue impact, personalization also enhanced customer experience and loyalty. The shopping journey felt more intuitive, which made customers return more frequently.
Expanding Personalization Beyond the Website
We quickly realized that the power of segmentation extended beyond the website and app. We took the learnings from our A/B tests and integrated them into our 360-degree marketing automation system.
- Trendy Shoppers received instant notifications via SMS and email when new collections dropped.
- Discount Seekers were alerted ahead of major sales events, often with an exclusive first look at deals.
- Seasonal Buyers received curated festival-based collections tailored to their past preferences.
- Habitual Shoppers were engaged with personalized emails and SMS links reminding them of their favorite product categories.
By unifying our website personalization, ad targeting, and direct marketing, we created an ecosystem where every interaction with the brand felt relevant and meaningful.
Final Thoughts
A/B testing is not just about incremental changes — it's about building a system that learns, adapts, and evolves.
Building a recommendation engine using a supervised learning model gave us the ability to continuously improve our predictions. The more we fine-tuned the algorithm with real-world data, the smarter it became at predicting customer behavior, and the more it helped manage inventories and sales, thus giving a better utilization of capital.
Customers expect tailored experiences, and when businesses can deliver on that expectation, the rewards are immense.