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data-science

How to build a churn prediction model that actually works

The truth is, predicting churn is easy. The hardest part is making it actionable. With this approach you’ll retain only your valuable customers that are about to churn, with a personalized retention action at the right time.

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5 essential ingredients of a successful data strategy

Many organizations struggle with ongoing data issues and are missing the opportunity to use data as a means to profoundly impact their business. What's usually missing is a comprehensive data strategy. Let's have a look at the 5 ingredients a successful long-term data strategy can't do without.

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AI for everyone? It’s no alchemy run by wizards!

We should use AI to make our organization smarter! Chances are you’ve recently heard this statement and see organizations acting to it. Leading companies are using AI across departments to increase productivity. Customer care organizations are using chatbots and speech recognition in their customer contacts. Marketing departments are predicting churn and segmenting their customer base.

Venturing into the field of AI may seem daunting at first. While AI has been hyped immensely in the last decade, it is a deeply technical field. Gartner even made a prediction that 80% of AI will remain alchemy run by wizards whose talents won’t scale in the organization. Your organization probably doesn’t have the scale and technological know-how of Google, Apple or Facebook. This might lead you to conclude that developing AI models isn’t for you. You are right, partially.

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