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Business and Technology Insights

Future of Churn Analytics: How Big Data can help

 
April 20, 2016

Customer Churn is a term every business dreads, especially those which rely heavily on customer experience. Todays customers are well informed and armed with multiple options. They want competitive pricing, value for money and, above all, high quality service and wont hesitate to switch providers if they dont find what theyre looking for. As such, it becomes crucial to put in place a sustainable and robust strategy for customer retention to preserve customer lifetime value. Businesses that have a high acquisition low margin model are highly affected by customer churn and need to ensure quick real time decisions to lower the impact.

Current churn analysis techniques rely heavily on Customer Lifetime Value calculations, which are based on certain fixed metrics such as average monthly transactions, average gross margin, monthly retention rate, and so on. All of these metrics are highly number driven and not behavior based. However, in a volatile services market, it is imperative to not overlook the hidden metrics related to customer behavior and modifiers. Identifying the hidden value from vast layers of data through articulate filtering can be a differentiating factor. Thats where a well-defined Big Data analytics structure can be effectively leveraged. The more intelligent and accurate the structure is, the more predictive strategic business decisions would be able to be.

A well-structured Big Data analytics model will help re-define existing predictive churn analytics techniques. Businesses can now tap into non-traditional sources such as social media data analytics, customer touch point feedback, call center feedback and many more to create a holistic analytics model that deals with, not only the monetary value but also the behavioral patterns of the customer. This will allow the businesses to identify potential churning customers and act on preventing the churn. This proactive strategy can deliver better results than waiting for a trigger that indicates an inevitable churn and then trying to re-capture the customer. As such, any analytics model should have :

  • The capacity to accumulate vast unstructured data from both traditional and non-traditional sources
  • The ability to superimpose intelligent filters to reduce noise and false alerts
  • Business specific analytics engines to co-relate data, detect patterns, and generate real-time business insights
  • Agility, through flexible tuning of rules and models for near real time streaming of data

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An intelligent predictive churn analytics model, powered by Big Data analytics will allow businesses to process, analyze, and co-relate traditional and non-traditional metrics to achieve a holistic customer blueprint and effective insights that can trigger an alarm way before real damage is done. A simple example can be that of personalized retention incentives. Businesses can combine the insights from traditional churn analytics models such as average transaction value, monthly discount values, last transaction date, and so on with data from non-traditional sources such as brand or product sentiment on social media, number of complaints in the last month to the call center, competitor offers, and others. They can use this, to predict the churning intent of the said customer and quickly create a customized offer to try and retain them.

Predictive churn analytics is a small step towards automated personalization, which will be a critical business differentiator delivered by full-fledged Big Data adoption. However, businesses will have to start from small use cases, strategizing progressively to encompass complex multi-level use cases to realize the full potential of Big Data analytics. Businesses have to gear up and ensure that they can manage the speed and complexity of Big Data, establish well defined data points, and equip employees with enough training to handle the process complexities. Most importantly, businesses will have to shift the traditional Business Intelligence mindset of reporting and adapt to the real-time action mindset to successfully decipher the holistic customer insights that Big Data analytics is capable of providing.

Ankita Bhattacharjee is a Research Analyst with the Digital Enterprise Group at Tata Consultancy Services (TCS). She has three years of experience in social media management and analytics. She is a part of the TCS Listening Platform Product team, where her responsibilities include supporting product related business insights, report creation, and project deliveries. She holds a Bachelors degree in engineering and a Masters degree in management.