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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Boost your revenue with pricing analytics

Client: Indian Telecom Major
Time: September 2014


Problem Statement

The client had a rather unusual problem; their data usage was on the up and up but their revenue stream was significantly smaller than last financial year.


Our initial hypothesis led us to believe that pricing plans were not designed with insights in to customer behaviour nor were they optimized to match increased customer uptake. What the company needed was a tool that could help them identify possible revenue loss or gain before a pricing plan rollout. Hence we created one! Our simulator tool helped the client assess the revenue impact of any kind of pricing plan, giving them unprecedented flexibility. Drawing on the findings of the simulator tool, we suggested a new plan for pricing data on a sliding scale which would significantly reduce customers’ anxieties about huge bills while in roaming mode.


The plans suggested were implemented in a pilot project to assess customer response before nationwide rollout. The sales force were trained to convey one single solid message to the customer: “Roam on data without fear”. Thanks largely to the strategy proposed by JSM, the client was able to predict usage behaviour and auto-adjust their plans. Consequently, data revenue for the client soared by over a stellar 20% in the next financial year while new bundled plans were successfully introduced as well.

Karthikayan Visvanathan

An avid watcher, reader and learner of anything new with analytics. Believes success depends on 2 key factors - people and data.

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