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Churn Prediction:
Anticipate Customers Likely
to Abandon

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Customer acquisition costs between 5 to 15 times more than customer retention

One of the easiest ways to keep the existing customers is to predict potential churn early and respond fast. Identify the signs of potential churn, understand customer wants and needs and automate campaigns designed to revive and renew loyalty for a solid CRM strategy that minimizes acquisition costs.

  • Companies need to be able to accurately predict churn in order to respond in time
  • Deep knowledge of customers that are likely to abandon is essential to retention strategy
  • Companies need to see the real value of the potential loss of customers
  • When marketing budgets are tight, strategies built around churn and retention are the most cost-efficient
Engineering Field
Engineering Field: Determines the date of last transaction and enable to explore it, in order to define the non-consumption period.

The Solution

  • Fast Identification:
    Advanced Analytics techniques, such as profiling, allow marketers to use variables to easily identify potential churners
  • More Visibility:
    Techniques like Pivot Tables allow marketers to view customer evolution over time and see interactions with other churners
  • Accurate Forecasting:
    Decision Trees enable long-term forecasting and early detection of customer value loss
  • Better Loyalty Strategies:
    Insight into measurable loyalty factors enables personalized retention plans
Workflow: Allows to start an action, a campaign or a strategy, aimed to the customers who are likely to abandon.

Why Big Data Analytics

  • Minimize acquisition costs and increase marketing efficiency
  • Keep customers engaged and loyal over time
  • Decrease the likelihood that competitors will lure existing customers
  • Activate and strengthen the existing customer base
  • Detect customer value loss and react sooner
  • See the value of individual customer loss and create targeted strategies
  • Allow teams to analyze quickly and easily, with no dependence on IT
  • Analyze large, heterogeneous volumes of data quickly and easily

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Decision Tree
Decision Tree: Applies the features of customers who abandon on the rest of customers from the database and makes predictions.

How to Predict and Prevent Customer Churn

Clustering: Classifies the customers according to different levels of churn risk.
Map: Allows to see the geographical nature of churn, verifying it through relative frequency.
Profile: Identifies which variables describe these customers and which do not, in order to know in depth how they are.
Venn Diagram
Venn Diagram: Creates a quantile in the three groups of benefits, to see the value that churners have for the company.

"This product meets a need that we see evolving. We already have good BI, but we want to be able to intuitively drive analysis of client behavior and product performance. The insights Big Data Analytics can give us into who is likely to do what, when and where, will enable a massive improvement in both our product development and the quality of advice we can give our clients. It will also increase the effectiveness of our decision making." Steve Hemsworth, Managing Director,
Big Data Analytics Customers
Why Customers Choose Big Data Analytics?