Growth hacking ecommerce store with 42 million monthly users. Case study on 6% total revenue per user growth for


We increased the average number of PDP pageviews per session, bringing a 6% uplift in the average revenue per user (ARPU) of a marketplace.



Our client is one of the largest marketplaces in Eastern Europe with 60 million sessions per month (according to Similarweb). allows its merchants to sell a wide variety of B2C and B2B products.

Prom’s monetization strategy partly relies on visits to PDPs: every time a user visits a particular PDP, the merchant that owns that PDP is charged for that visit.

Our goal was to increase the ARPU of by increasing the average number of PDP pageviews per session.

Prom’s site is one of the best optimized eCommerce sites in Eastern European and required deep analysis of its revenue structure and user behavior to complete the task.



Our research started with identifying revenue drivers and user behavior that correlates with higher ARPU.

As identified, the key revenue driver is the average number of PDP pageviews per session.
The user behavior that feeds the revenue driver is a click on a product icon on the product listings, which results in a PDP pageview.

Then we decided to identify and evaluate all the product listings on the site that generated PDP pageviews. We found that the majority of PDP visits came from product listings in category pages. However, we also found that a proportion of PDP visits came from clicking on a block with recommended products on PDPs.

Moreover, our funnel analysis indicated that users most commonly end a session on PDPs as this is natural behavior if a user hasn’t found what she/he was looking for. So we decided to concentrate on ways to increase the average number of PDP visits by providing a natural flow from one PDP to the next PDP as an alternative to exiting the site.

We sought to identify factors that define user motivation to engage with the block with recommended products on a PDP. We found that 75% of users never see this block because it was positioned deep on the fifth screen of the page, where only 25% of users scroll to.

We decided to move this block to a more prominent position, increasing its visibility, but struggled to find a place for it in the top two screens because all the elements there were critical for conversion. However, we found a way to place the block with recommended products on the second screen. This required us to move the Buy button to a sticky block at the top of the screen.



Increasing the visibility of the block with recommended products on PDPs by moving it to a more prominent position on PDPs will increase the engagement rate with this block, and as a result increase the average number of PDP pageviews.


Control variation


Alternative variation


Validating the hypothesis

To validate our hypothesis, we conducted an experiment where we compared the control variation with the recommendation block on the fifth screen of a PDP with our alternative variation where we moved the block with recommended products to a more visible position on the page.

The experiment had the following characteristics:

Experiment type: A/B test

Traffic split: 50/50

Device: desktop

Key metric: ARPU

Secondary metrics: user engagement with the block with recommended products

Number of sessions that participated in the experiment: 4.5 million
Number of conversions recorded in the experiment: around 300K conversions per variation.


The alternative variation achieved a 700% increase in engagement with the block with recommended products, resulting in a 6% uplift in ARPU.

The result of the experiment achieved 99% statistical significance.

Key takeaways


1. Fanatically research and identify which elements/factors are the key revenue drivers. Then focus your CRO effort on those elements/factors to increase user engagement with them. A 20/80 rule in practice.


2. Position the site’s UX elements based on their revenue yield potential.


3. Don’t take UX best practices for granted. Use analytics to strengthen your hypotheses before validating them in an A/B test.

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