Predicting Customer Lifetime Value with Unified Data

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Analysts and data scientists are always tuning their algorithms and trying new techniques to make their predictions more accurate. They’re also obsessed with the quality of the data that’s fueling their models, citing the common wisdom of “garbage in, garbage out”.

But what if there is an even bigger opportunity to improve the accuracy of your predictions?

In this paper, we prove the power of more data – including more volume and more variety – in significantly increasing the accuracy of your predictive insights.

What you’ll learn:

  • Accuracy of CLV and churn prediction is predicated on complete data
  • Data types used a wide variety of raw and engineered features from online and offline sources – find out which ones we used for the best results
  • Using this approach, CLV prediction accuracy improved 15.2% and churn prediction accuracy improved 13.4%, on average.

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