Self-service analytics dashboards boost agility across teams
Razorpay set out to make data access faster, easier, and more democratic across the organisation. By moving beyond a centrally managed reporting model, the company aimed to give product, sales, and marketing teams the ability to explore insights independently, respond to questions in real time, and act with greater agility.
With Tableau, Razorpay implemented a self-service analytics framework that has put trusted data directly into the hands of business users. Product managers can now independently build dashboards to track user adoption of new features, while sales teams monitor lead conversion and pipeline performance. Marketing teams use the platform to analyse campaign effectiveness in real time, all without needing constant support from data analysts.
Tableau’s intuitive visual interface and seamless integration with Razorpay’s cloud data warehouse make these insights instantly accessible. The improved standardised experience across teams on data lineage, metrics definition and performance ensures that the same language on insights is communicated/perceived by relevant metric/insight owners/consumers. This shift to self-service has improved cross-functional agility. Business users now explore, validate, and act on insights faster, reducing dependencies and accelerating response times.
Real-time tracking sharpens payment performance
In Razorpay’s high-volume payments environment, real-time visibility into success and failure rates across UPI, net banking, and credit cards is key to delivering a seamless customer experience. The company wanted to unify and analyse this data instantly, enabling teams to proactively identify issues and optimise routing strategies for maximum payment success.
Tableau centralises Razorpay’s payment performance data, providing live dashboards that track transaction success rates, latency, and failure trends across all payment instruments. This real-time visibility enables operations and engineering teams to pinpoint underperforming gateways or methods instantly. If a specific bank or provider shows elevated failure rates, teams can quickly reroute traffic or flag it for investigation.
“With Tableau, we’ve been able to identify and track patterns to measure and possibly recommend insights to improve success rates - because now we catch the problem before the customer feels their impact,” says Dhiraj.
With Tableau, Razorpay has been able to proactively identify patterns and insights leading to reduced downtime, improve payment success rates, and enhance the overall payment experience for its merchants. The ability to identify patterns and act quickly means issues are detected and resolved before they escalate.
Behavioural insights power intelligent fraud detection
Razorpay aimed to strengthen its ability to detect fraudulent merchant activity and unusual transaction patterns in real time. By equipping risk teams with advanced visualisation tools, the company set out to spot anomalies early, investigate suspicious behaviour quickly, and track repeat offenders attempting to re-enter the system with new credentials.
Using Tableau, Razorpay now monitors behavioural indicators such as transaction velocity, volume spikes, and chargeback rates in real time. Risk teams can visually analyse trends across geographies, time periods, and user segments to flag high-risk patterns instantly. Tableau also helps build comprehensive merchant profiles that incorporate behavioural and device-level data, enabling the identification of merchants attempting to re-register under different identities.
Fraud detection has become faster, more proactive, and shows increased accuracy. Razorpay can prevent revenue loss, protect merchant trust, and ensure compliance with internal risk controls. Tableau helps operationalise fraud intelligence, transforming risk from a lagging concern into a real-time strategic capability.
Smarter segmentation simplifies access to revenue insights
Tableau empowers Razorpay to segment merchants by industry, transaction volume, and business size. Product and sales teams can now analyze customer lifetime value (CLTV), average revenue per user (ARPU), and acquisition costs within unified dashboards. Tableau also enables dynamic cohort analysis, allowing teams to observe how different customer segments behave over time.
These insights help Razorpay tailor services, optimise pricing, and improve customer targeting strategies. By identifying high-value and at-risk merchants early, the company has enhanced retention and fine-tuned its growth plans. Tableau’s clarity into revenue drivers supports long-term forecasting and strategic decision-making.
Next: Scaling the data-driven impact across Razorpay’s ecosystem
Razorpay now aims to migrate to Tableau Cloud to support greater scalability and higher performance. The team is looking at advanced AI-powered capabilities like Tableau Pulse to deepen fraud prediction and enable real-time merchant monitoring. With every step, their goal remains razor-focused: faster insights, smarter decisions, and a stronger data culture across the business.
