How Metro Turned Product Discovery into a Conversion Engine

Scale brings its own complexity, different cities, different categories, different customer profiles, all behaving differently. What WebEngage allowed us to do is meet that complexity with equal precision. Communication got more contextual, journeys got more relevant, and the team could execute with a clarity that directly showed up in how customers engaged with us.

Shuchi Singh

Vice President, E-Commerce, Metro

Scale brings its own complexity, different cities, different categories, different customer profiles, all behaving differently. What WebEngage allowed us to do is meet that complexity with equal precision. Communication got more contextual, journeys got more relevant, and the team could execute with a clarity that directly showed up in how customers engaged with us.

Shuchi Singh

Vice President, E-Commerce, Metro

120%

Increase in website conversion rate

1.9X

Improvement in click-through rate

60%

Journey revenue driven by two optimised flows

1.7X

Increase in ROAS

Summary

Metro Shoes, one of India’s most trusted footwear brands, was scaling digital traffic across a wide catalog, multiple customer cohorts, and varied regional preferences. As this complexity grew, the focus shifted from simply driving traffic to making product discovery sharper, more contextual, and more conversion-led.

To make this happen, Metro partnered with WebEngage.

Turning Signals into Structure

Using WebEngage, Metro consolidated customer data into a single platform and made it easier to use for day-to-day decision-making. The team could see what customers were browsing, what they were buying, how often they were returning, and which products they were more likely to respond to.

This helped Metro group users based on actual behaviour. Some were high-intent. Some were frequent buyers. Some were price-sensitive. Some had gone inactive. With these segments in place, product discovery could be made more relevant across the funnel.

WebEngage also helped the team identify patterns within categories and across cities. This meant users could be shown products based on what similar customers were actually exploring and buying, instead of relying on broad merchandising rules.

Recommendations also became more relevant. They were informed by past purchases, browsing depth, cross-sell behaviour, and order value. At the same time, channel decisions became more measured, with greater focus on what was driving revenue rather than simply on what was being sent most often.

Metro - Results

The Bigger Picture

For Metro, product discovery became more than a merchandising exercise. By using behavioral intelligence to shape exposure, recommendations, and channel strategy, the brand built a more calibrated path from browse to conversion, where discovery became data-led, journeys became sharper, and channels became more accountable.

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