In a world where customer demand is changing rapidly and customer loyalty is becoming volatile, it is crucial for companies to identify their best customers, understand the needs of these costumers and adjust their strategy based on these customer insights. In other words, Customer Analytics is the way forward!

Customer Analytics is about understanding your customer base on an individual level. One important part of customer analytics is the concept of Customer Lifetime Value (CLV). CLV is the expected value that a certain customer brings to the company through future purchases. CLV relies therefore on Predictive Analytics.

By many companies, however, CLV is still used in an aggregated and backward-looking way. These companies compute an average company-wide CLV by looking at the average purchase frequency, the average purchase amount and the average purchasing timespan, based on past information.
A better way to compute CLV, is to predict all future cash flows for each customer and discount them to the present value. This not only includes the monetary value the company is getting from the customer through purchase or subscription, but also acquisition and retention costs that are specific to that customer.

One important element of CLV prediction is the churn rate or, in other words, how long the company expects a specific customer to remain a client. This churn rate defines the lifetime of a customer. The concept of customer churn is closely related to contractual agreements with the customer, meaning that a customer is considered a client while their contract with the company is ongoing. When customers cancel or don’t renew their contract, that’s called the customer churn. This clear costumer lifetime naturally applies to all telecom providers, insurance companies and other services that are subscription/premium based. However, this doesn’t apply to product-centred companies in a similar way, as customers make single purchases and might come back for more in the future, or they might not. In this case, a customer behaviour analysis based on historical data like previous purchases, combined with information about life stages/events and other external data, makes for a good starting point for customer behaviour prediction, including future purchases and expected customer lifetime.

So why is CLV important? It is because it helps companies make better and more efficient management decisions in a data-driven manner. Take customers Anne and Bob for example. Anne is about to make purchase with a value of €20, while Bob will make a purchase with a value of €5. If you base your management decisions on visit value, you will consider Anne as a more valuable customer to your company.

However, if you could predict that Anne is making a one-off purchase while Bob is about to bring in the same value of €5 each month for the following 12 months, you are suddenly aware of the fact that Bob, with an expected value of €60, is a way more valuable customer than Anne.

CLV thus helps companies in segmenting their customer base by identifying their most valuable customers. This is useful in the following ways:

  1. By understanding the characteristics of your most valuable customers, you can target similar potential clients for new customer acquisition, which means targeted business growth.
  2. You can adjust your marketing costs per customer segment, thereby investing more in the most valuable customers and still getting an even higher ROI.
  3. By decomposing CLV into its components, you can make decisions that lead to CLV improvement, such as recommendation models for upselling or loyalty programs for churn improvement.

There are, however, also many challenges along the way of implementing a CLV-driven Customer Analytics strategy. We find that companies most often struggle with one of the following concepts.

Lack of awareness

Many companies have never heard of the concept of Customer Lifetime Value and continue to steer their business in a very old-fashioned way. If that happens to be your case and you want to know where to start, I recommend you to read “The Customer Centricity Playbook: Implement a Winning Strategy Driven by Customer Lifetime Value” by Peter Fader and Sarah Toms.

Lack of capabilities

A lot of companies have not yet integrated a data-driven way of working. While most of these companies do have access to huge amounts of data, they often lack the data science capabilities to extract the customer insights they need to improve their commercial and marketing strategies.

Incorrect CLV implementation

Out of the companies that already implement the CLV concept within their customer analytics framework, many have chosen for a companylevel CLV that is calculated in a backward-looking way. These companies mainly do this to compare themselves with competitors from a benchmarking perspective and steer the change from there on. As explained before, this is an outdated way to use CLV which is best computed on a customer level in a forward-looking fashion. Doing otherwise might even lead to wrong business decisions.

CLV is a customers analytics metric that allows companies to understand their customers on a granular level in order to create high-value personalised experiences for them. This in turn generates more revenues and profits by efficiently allocating marketing budgets and initiatives to the right customers. At Amsterdam Data Collective (ADC), we specialise in helping companies to overcome these challenges. ADC is a full-stack data science agency: we help companies with every step of their data science initiatives. From advising on data-driven strategies to the development of state-of-the-art machine learning algorithms or dashboards that provide real-time management information, we offer help through the following services:

Strategy

ADC helps design and implement corporate data strategies that enhances and empowers companies to reach their business objectives. A key element is that the highest corporate leadership embraces the new data strategy for a full transformation within the whole organisation.

Solutions

A selection of our data solutions:

  • Customer Analytics: How well do you know your customers? What are the characteristics of your best customers? How to attract similar new customers?
  • AI-optimized Online Marketing: Which online marketing campaign delivers the highest ROI? How to draw an advantage on top of the paid marketing platform and beat the competitors?
  • Company Benchmarking: How to build an automatic and self-learning company benchmark that updates itself on the-go?

Education

ADC provides education through workshops and trainings to give your human capital the right skills to operate in a data-driven manner. Topics are:

  • Data Science: What is it? Why is it useful? How can it be beneficial?
  • Customer Analytics: What’s the theory behind it? How to use it? What are the practice examples?

Nizar Es-Skali

Nizar is Amsterdam Data Collective’s Head of Online Retail and Customer Analytics. He has a background in computer science and general management, and more than 15 years of international consulting experience in data analytics and financial risk modelling. By focusing on results and relationships, he guarantees high-quality projects.

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