CASE STUDY

Customer Analytics Assessment Improves Client Acquisition for a Leading Media & Telecom Company

Jun 13, 2017

Every company in the telecom and media industry is exploring ways to increase customer acquisition and profits during a time of stagnant growth. Consumer analytics has become crucial in generating unique market insights for consumer-oriented companies in the industry. Telecom and media companies need to create deep personal experiences and innovative products to meet the ever-changing demands of different customer demographics. Stagnant revenues and declining customer satisfaction have created a need for organizations to leverage data analytics to capture consumers’ attention.

Quantzig’s customer analytics engagement delivers incremental value to the client based on insights from their consumption (STB, VOD, IPTV etc.), online purchases, social media data. The analytics team also recommends personalized offers and determines their viewership and device preferences to ensure that they improve their services and performance based on the same. The assessment analyzes the activity of new customers including their online behavior, interests, purchasing habits, and demographics.

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The Business Challenge

A leading media and telecom company in the US was facing significant challenges in terms of stagnant revenues and declining customer satisfaction. The client wanted improve  on their new customer acquisition numbers based on optimal combination of product features for different customer segments and analysis of customer loyalty and satisfaction.

Our Approach

To meet the specific requirements of the client, Quantzig’s customer analytics team performed a deep dive analysis of sales data, product data, category data, pricing data, customer viewership data, and data from social media metrics.

Our Key Findings:

  • Identified drivers of consumption and content preferences to tailor recommendations and advertisements
  • Measured concurrency of viewership across different devices
  • Performed correlation analysis of program vs. device and concurrent viewership analysis of programs/device w.r.t. time
  • Measured customer stickiness of programs based on RFM analysis across devices
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Predictive Insights from the Customer Analytics Engagement

  • Developed combinations of service features within packages in consultation with the client and conducted online survey on pricing preference for different services and bundles for improving acquisition
  • Developed a complete picture of online and offline user interactions based on demographic and usage behavior to reduce customer call center volumes
  • Measured efforts and defined KPIs in terms of service connections, revenues, customer issues, and first call resolutions.

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