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Predictive modeling vs. customer satisfaction measurement
Customer churn is one of the key challenges facing organizations in today’s highly competitive environment. In order to effectively combat customer churn, companies should find answers to two key questions: which customers are at risk of churning, and what actions can be taken to stem the process.
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Growth or cannibalization? Success or failure of promotion?
Promoting a product, especially one that involves a reduction in its price, almost always results in an increase in sales. But does every increase mean that the promotion was profitable? How often does the promoted product take customers away from other substitute products? How to calculate how much is the actual “incremental” of the action?
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Prediction and segmentation as weapons in the fight against churn
Customer churn is one of the key challenges facing organizations in today’s highly competitive environment. In order to effectively combat customer churn, companies should find answers to two key questions: which customers are at risk of churning, and what actions can be taken to stem the process.
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How do you count the campaign effect so that you can (almost) always declare success?
After a campaign has been carried out, marketers wonder if and what kind of profit their action has brought. Summarizing the projects completed so far and planning future actions, they try to find the answer to this nagging question. In various ways they calculate the effectiveness. Usually, the greater the effect, the less inclined they are to reflect on whether their method of calculating efficiency is correct at all. Is there a foolproof way that clearly tells how the effect of an action should be calculated?
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