Losing a customer is a painful and almost impossible to eliminate globally event.
That being said it does not mean that it’s not a manageable one on a portfolio level.
Experience shows that efforts to retain a customer are 5-6x cheaper than to attract a new one. This means: advantage should be taken as much as possible from the application of the former option.
So, we have underlined our pain-point in this Series: not being able to properly manage customer retention (or said reversely – attrition) which is a value driver. But what lies in-between knowing about real business-case and converting it into action and hence value? Well, the approach so far has been to rely on expertise and specialised human resources that evaluate our portfolio on an ongoing basis. This is, of course, a valid solution. But in the modern, hectic and highly-competitive environment we need relay on automation (hence, efficiency and cost reduction), predictability and flexibility (being able to adapt to overall company’s strategy and environment) if we want to maintain our market position and even enhance it and thrive in general.
The approach in the Revolution 4.0: turn your face towards your data and utilise data science to extract stable patterns. Note: experts’ opinion and domain knowledge are not to be neglected but rather used in synergy with modern Machine Learning approaches for optimal solutions.
Here’s a list of general, experience-driven steps for tackling this problem (and similar ones, see the picture above for reference):
1) Understand (and bring to the surface) your pain-point and hence your business case: churn management and improving customer retention with gained business knowledge along the way;
2) Find the right data for this problem (not outlined in the picture above): e.g.: socio-demographic, product features, financial relationship with the customer, etc. All this in the proper historical time-horizon;
3) Exploratory analysis of your portfolio: review your customers through the magnifying glass of data science and business intelligence, and understand patterns, potential segments, etc.;
4) Use advanced predictive analytics to model your pain-point: automate the process of proactively identifying customers that are about to churn. Align the predictive power of the model with the necessary inference level so that understanding of the drivers of these events is achieved (meaning we start understanding our customers better);
5) Integrate the predictive model into your CRM that will provide: a) highly efficient, automated and manageable customers churn scoring system; b) targeted campaign based on improved multilayer understanding of the customer;
6) Bottom-line improvement over time (see picture below for what-if scenario).
As a note it is strongly recommended the results as well as the models are monitored, fine-tuned and every-so-often – redeveloped over time. The business conjucture is constantly changing as well as we are so we need to evolve.
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With this series our aim is to increase data science coverage and to make data-driven decision integral part of more and more companies around the Globe.