Which customer is a good customer and which is not, from a business perspective? Which one should we care for more and which one should we “let go”? Is the value of revenue a sufficient determinant? Within these issues, the methods of Customers’ segmentation are helpful...
Analyses – what for? After all, you can say which Customer is the best based on income!
The analytical part, using data from both transactional systems, operational and iterative CRM, Call Center systems, Web sites and others, lets you integrate all data on the Customers and their behavior into one place. Thanks to this, comprehensive analytical material is obtained allowing, among others, to analyze the effectiveness of contacts, marketing campaigns, shopping carts, and finally to analyze the profitability of specific Customers and their appropriate grouping. Often the answers are not so obvious.
The RFM classification system is the primary method used for the segmentation of recipients for whom a history of transactions exists. Introduced by Bob Stone, the system relies on information based on the customer's purchase history and on the assumption that the Customer most valuable for the company is the one who buys more often and for higher values. Technically, it is the method of expressing the value of the Customer by adding the values of the following variables:
- R (Recency) – when did the customer last buy a product from us. In other words, the Customer who has recently made a purchase will be willing to make another purchase. For this variable, the following scoring is assumed: 24 points if the last purchase was made within the last 6 months, 6 points for purchases made within up to the last 9 months and 3 points for purchases made within up to the last 12 months.
- F (Frequency) – how often does the Customer make purchases, i.e. the Customer who makes purchases more often will be willing to make another purchase more eagerly than the Customers buying less frequently. The value of this variable is calculated by awarding 4 points for every transaction made within the last year.
- M (Money) – how much money does the Customer spend. The Customer who spent more money will make a purchase and spend more money more eagerly than the Customer spending less money. Variable M is calculated as 10% of the value of each transaction, but to eliminate any distortion caused by e.g. an unusually high transaction, its value is limited to a maximum of 9 points.
Of course, the higher the indicator, the more attractive the Customer is for the company. Extension of this Customer grouping methodology is the FRAT system. Information about the types of products purchased by Customers (Frequency, Recency, Amount of purchase, Type of merchandise purchased) is added to the above elements. By adopting this type of classification, it is possible to clearly identify groups of contractors who, in the broader time horizon, bring the most benefit to the company. Having identified such groups, one may correlate the marketing and sales policy with the potential of each acquired Customer.
Customer Lifetime Value (LTV)
Acquiring new Customers is five times more expensive than maintaining already acquired ones, but not always such maintenance is justified. The analysis of Customer Lifetime Value (LTV) lets you determine for which recipients investment in their loyalty will be profitable for the company, and for which the investment will not return. The value of the Customer in their lifetime for a company is the projected amount of potential expenses, i.e. the revenues referring to product manufacturing costs and the costs of full customer service. There are many methods of calculating Customer Lifetime Value. Most often, the history of a given contractor is checked and on this basis their future behavior is estimated. Standard measures, as in the case of RFM classification, include: frequency of purchases, average order value, amounts that the customer spends on certain types of products, and the time within which they will make the next purchase of the product. In the case of new contractors, used is a technique of similarities, similar to those already existing in the Customer database. In order to find similarities of this kind, modern techniques of data mining are increasingly used. The result is the creation of three groups of Customers:
- recipients profitable for the company both today and in the future,
- recipients not profitable today but potentially profitable in the future
- recipients not profitable today or in the future
In order to visualize the impact of a customer's lifetime on the company’s profit, the profit cycle curve is used. New, detained and lost Customers are distinguished here.
New and detained Customers create a profit that makes up the total profitability of a given brand over time, whereas the lost Customers are profits possible to achieve.
And so on...
There are many methods of classifying Customers and measures of their profitability for the company. In addition to that mentioned above, extensive analyses of acquisition costs, possible profit margins, profitability analyses in the Customer-Product system and others are used. Thanks to the knowledge of Customers, their grouping, and analyses, companies obtain information on how to manage contacts with the Customer so that these contacts are beneficial to both parties. Regular Customers are some of the most important values of any company. Thanks to the knowledge of the company’s offer and products, they have fewer questions or problems, which makes contact with them faster and more efficient. The profitability of their service is usually higher than in the case of selling products to new Customers. The loss of regular recipients, deviations from the previous characteristics, lower frequency of purchases should be a sign to each company that it is losing a Customer. Many companies tend to notice the loss of products from the store and do not pay attention to the loss of such a basic and long-term company's resource as Customers.