Customer scoring: definition, method and examples

Customer scoring is an effective method that aims to evaluate and assign a score to each of your customers. This score is based on well-defined criteria, aligned with the specifics of your business (industry, repeat rate, average basket, subscription, etc.)

The goal of customer scoring is to better understand them in order to better segment your customer data base and offer them personalized marketing actions that match their profile (expectations, LTV, etc.). Thus, customer scoring proves to be an indispensable tool for increasing the return on investment (ROI) of your marketing actions and for strengthening the loyalty of your clientele.

But, how do you give a score to your customers? What benefits and applications does customer scoring offer? How do you use your scorings in customer segmentation?

👉🏼 In this article, we will explore the definition, concrete examples, and methods of customer scoring, as well as best practices for deploying it effectively.

This technique is based on two fundamental principles:

The primary goal of customer scoring is to optimize commercial performance by identifying and targeting customers most likely to make repeat purchases, increase their average basket, return frequently, and be fully satisfied (notably with the aim of increasing the Net Promoter Score [NPS]).

⚠️ It is important not to confuse lead scoring and customer scoring. Lead scoring is only used for acquiring new customers and is mainly useful for sales teams. It aims to evaluate prospects based on their level of maturity, interest in products, budget, etc., in order to qualify leads and direct them to sales teams according to their score.

On the contrary, customer scoring is used throughout a customer's lifecycle and allows the marketing team to retain existing customers while promoting upsell and maximizing LTV.

Customer scoring involves ranking customers according to their interest in the offers proposed, their buying behavior, or their willingness to respond positively to solicitations. The scoring criteria, which can include socio-demographic, psychological, behavioral, and consumption data, vary from one company to another and must be aligned with its specific objectives.

Different methods exist for calculating customer scores, relying on mathematical rules and weights attributed to different criteria. Among the standardized approaches is the RFM scoring, which assesses a customer's likelihood of purchase through three factors: Recency (date of last purchase), Frequency (number of purchases), and Monetary value (value of purchases).

To create a personalized scoring model, you must follow these 5 key steps:

After testing and validating this method, it is important to analyze the scores obtained to validate customer segmentation.

⚠️ In some cases, it is relevant to assign a score to a customer not on the scale of the company, but on the scale of products/services. This is particularly relevant when product/service offerings are significantly different from each other.

The RFM model is a widespread technique for customer scoring, appreciated for its ability to segment customers according to their buying behavior through three criteria: Recency, Frequency, and Monetary value. Each criterion is subdivided into classes, allowing the company to assign a 3-digit score to each customer.

For example, an RFM score of 555 signals a loyal customer, while a score of 111 indicates an inactive customer. The RFM model thus serves to categorize customers according to their value to the company and to determine targeted marketing strategies.

💡 In reality, the RFM method can lead to different segmentations. Several segmentation methods have emerged: the 3x3 RFM segmentation of Analytics Vidhya, the RFM segmentation of Putler, or that of Barilliance are just a few examples. It's up to you to determine the right number of segments and the right "labels" depending on your context.

The RFM model is just one method among others for evaluating customers. Other approaches consider various criteria such as socio-demographic, psychological, or navigation data. The LTV model (Lifetime Value), for example, calculates a customer's future value based on their revenue, costs, and lifespan. The NPS model (Net Promoter Score), meanwhile, assesses a customer's satisfaction and loyalty. Each of these models has its advantages and limitations and can complement or compete with others depending on the specifics of each company.

Customer scoring offers substantial advantages to companies eager to refine their marketing and sales strategy. Thanks to customer scoring, it is possible to:

Thus, customer scoring proves to be a valuable decision-making tool, facilitating informed marketing and commercial decisions, based on the score of each individual.

At DinMo, our mission is to democratize access to data for all non-technical teams, whether they are Marketing, Sales, or Customer Success. In a world where everyone claims to be "data-driven," few companies actually analyze and correctly exploit their data. And yet, a huge growth potential is hidden there!

To help our clients, we enrich their prospect and customer data with predictive metrics (future LTV, risk of churn, etc.) and automatically calculate scorings according to their context.

Thanks to our Visual Builder tool, it is then possible to create customer segments based on the generated scorings. These segments can be activated later in any platform (advertising, emailing, CRM, etc.) and used to optimize marketing performance.

💡 A change in the scoring methodology or in the segments is automatically reflected across all platforms, ensuring a homogeneity of strategies in all tools. For example, you can automatically calculate your RFM scores and modify any criterion (recency, frequency, amount, the number of segments, etc.) whenever you wish. Everything will be automatically updated in the Audience Manager.

Customer scoring is an innovative method for evaluating each of your customers according to criteria defined by your sector. This approach provides a deep understanding of your clientele, favoring their segmentation and the development of tailor-made marketing strategies, in line with their profile and potential. Thus, customer scoring presents itself as a key resource for improving the return on investment (ROI) of your marketing campaigns and for strengthening customer loyalty.

Various methodologies, such as the RFM, LTV, NPS models, can be employed to calculate your customers' scores. It is essential to select or design a model that matches your specific ambitions. This selection also involves identifying relevant scoring criteria, choosing the appropriate calculation tools, and implementing targeted marketing actions.