Paytm Analytics is the Spend Analytics feature inside the Paytm wallet — a tool to help India's massive, deeply diverse user base understand their money. We came in as the UI/UX partner before the product had a face; the brief was a name, an idea, and 400 million people.
Designing for that audience means most defaults break. Cultural variance, social stratification, economic disparity, and a wide spread of digital literacy were design inputs from day one — not edge cases bolted on at the end.
Paytm's user base spans the entire spread of digital India — urban tech-fluent power users at one end, first-time wallet users at the other; English speakers, Hindi speakers, dozens of regional languages in between; well-served urban professionals and daily-wage earners using the same app on the same Tuesday.
An analytics surface for that audience can't lean on the usual SaaS analytics conventions. Density that delights an MBA dashboards a casual user out of the feature in three seconds. Charts that make sense in English need to make sense without English.
Universal isn't a tagline here. It's the design constraint.
We worked with Paytm's product managers and data scientists to take Spend Analytics from concept to a feature ready for the wallet. Research went deep — interviews and surveys across user segments — before a single screen was drawn. Then a tight low-fi → high-fi loop with usability testing at each pass.
Spend Analytics launched to the existing Paytm user base and saw rapid adoption — strong engagement, intuitive design feedback, and high ratings on the Play Store and across social channels. The feature wasn't a separate analytics tool; it was an analytics tool that fits the wallet, in the language of the people using it.
Quantitative results available on request under engagement confidentiality.