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A/B testing is a common tool to assess the performance of a new feature in data-driven companies. Yet there are many ways to get it wrong, so we felt it was worth sharing our case and our solution.

At BlaBlaCar, we nurture a marketplace comprising drivers posting rides and passengers booking seats on those rides. The interaction between those two populations makes it dangerous to trust the results of a classic randomized A/B test: so-called interferences arise and create a bias in measurements.

Read on to learn more about the problem and the solution we designed. Lyft Engineering posted a series of blog posts entitled Experimentation in a Ridesharing Marketplace that proved a great entry point into the literature about causal inference and interference. Our experimental design has benefited a lot from what they learned, although the contexts and final solutions differ. …

At ContentSquare, we empower our clients to analyze user interactions with every part of their website, from entire pages down to HTML elements. Aggregating the information in human-readable blocks is key; which is why we ask our clients to cluster URLs into pages (list pages, product pages, home pages, etc) and key HTML elements into zones of interest. Figure 1 shows a product page segmented into different zones: search bar, logo, menu, “add to cart” button, and so on.

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Figure 1. A webpage segmented into zones of interest. The displayed values are metrics associated with those zones.

Segmenting a webpage similar to the one above, is a time consuming operation, regardless of the tools you use. What if we could automatically find zones of interest and segment webpages? …

Julien Dumazert

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