Customer Lifetime Value - Data Science determines the true value of customers

from | 4 October 2018 | Basics

In recent years, the digital transformation has led to customers becoming more and more the focus of companies. An important trigger for this was that more and more data about customers, their behaviour and historical transactions became available. As a result, customer lifetime value (CLV) is gaining importance. In this article, we explain the concept and benefits of CLV.

The rising flood of data on customers and their behaviour has led to an appreciation or re-evaluation of data science and the associated possibilities for determining customer value. The calculation of the Customer Lifetime Value (CLV) or the "customer lifetime value" is an important concept in this context, which is in principle relevant for every company.

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The crucial questions that arise from a data science perspective: Can the CLV be calculated? And if so, how, and what consequences can be derived from its calculation? Since there are many different models and methods for calculating customer lifetime value, it is important not to lose sight of the practical benefits for the respective company.

The usefulness of such models stands and falls with the Predictive powerwhether and how often a customer buys a product or uses a service again. In particular through Predictive analytics the possibilities are expanded and new foundations for marketing are created.

Briefly explained: The concept of Customer Lifetime Value

The calculation of the Customer Lifetime Value is one of the most important units in the evaluation of companies and in strategic planning. As a central, value-oriented control instrument, the CLV has concrete effects in the areas of Marketing and Customer management but also in the New product development and the strategic planning.

It is therefore all the more important to briefly consider the significance of the "Customer value" to visualise: The value of a customer to a company in customer lifetime value is equal to all the purchases, interactions and transactions that a customer has made in the course of a joint business relationship with a company and is still likely to do. (Predictive Analytics)

Customer Lifetime Value Curve
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When calculating the customer lifetime value or Customer lifetime value However, there is a fundamental difficulty: not every customer is equally profitable for a company.

For example, the generally formulated goal of aiming for a long customer duration is not sufficient on its own. If a customer is sent the expensive company catalogue every year, but only buys something every 5 years - in the worst case perhaps not even directly, but with a middleman - he only causes costs. An understanding of customer lifetime value based on data science counters difficulties such as these because numerous factors flow into the calculation when segmenting customers.

How Churn Prediction and Churn Prevention Increase CLV

Although the CLV is based on existing data, i.e. usually purchases that have already been made, it is to a great extent a notional valueas it also anticipates future behaviour and takes it for granted. This makes it all the more important to take care of this future customer behaviour. Only in this way can the predicted Customer Lifetime Value actually be realised.

With the Data catalogue and measures to increase the Data quality you create the basis for data science projects.

In concrete terms, this means retaining high-potential customers once they have been acquired for the long term. In order to retain customers and increase the duration of the relationship, models can be used, for example, to calculate the probability of a change of brand.

With Churn Prediction and churn prevention - methods to predict and prevent customer churn - can thus improve the overall CLV values. Depending on whether a customer has been determined to be profitable or non-profitable in the classification of CLV, existing customers can be improved by the Determining the probability of switching be addressed in a targeted manner.

For example, says a Churn prediction algorithm before when a profitable contract customer is likely to switch their mobile phone contract, they can be offered a deal that discourages them from switching. However, in order to prevent customers from switching or dropping out, it is important to precisely identify the causes of switching. The more precisely these are determined, the more targeted marketing instruments can be tailored to the causes.

Value vs. cost: calculating customer lifetime value

Customer loyalty (Retention Rate) is a decisive factor in measuring customer lifetime value. In particular, profitable existing customers ensure repeated sales at comparatively low investment costs.

In contrast, the costs of acquiring a new customer (Customer Acquisition Costs) are much higher. Decisions on whether and how much to invest in marketing are often based on calculating these costs and asking to what extent they are worthwhile. From these considerations, the improvement of the Return on Investment (RoI), the reduction of costs in marketing or the optimisation of marketing measures are the direct result.

In recent years, there has been a Reassessment of the CLV. Customer acquisition costs remain an important component in the calculation of customer lifetime value. However, in data-driven companies, customer lifetime value is increasingly taking on the decisive role, for example in marketing or product development.

This is how the customer lifetime value calculation leads to entrepreneurial success

The overarching goal behind the calculation of customer lifetime value is to increase customer satisfaction in order to ensure the long-term success of a company. Therefore, numerous factors are included in the calculation of customer lifetime value. These include both Monetary as well as non-monetary aspects.

Whether a car customer will buy a car of the same brand again after 10 years depends strongly on the satisfaction with the services and the contacts made with the brand in the meantime. (After Sales) The economic success of a company - and at the same time the customer lifetime value - is measured by repurchases, up-selling, cross-selling and recommendations.

With data science, such as with Algorithms to predict future customer behaviour, this value can not only be determined, but also realised and increased in the long term. Data science services thus provide new foundations for marketing, strategy development, but also the value of a company as a whole.


Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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