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The ABC-Analysis

Segmentation and evaluation of customer potential - Part 3

Evaluation of customers and customer relationships has always been a challenge for marketing departments. At the same time, it is an important steering element for communication strategy, media planning and budgeting. In this series of articles, we describe different approaches to customer evaluation and highlight their advantages and disadvantages.

In the third part, we describe the ABC analysis. This is a method for weighting objects, which is also used in customer segmentation.

In times of growing complexity, many people and companies fail to focus on what is essential. Finding out what is actually essential can already be a great challenge. Classification methods that reduce complexity by combining individual objects into groups can be a great support. A well-known and frequently used method is the so-called ABC analysis.

The name of the ABC analysis results from the fact that a set of objects is divided into an A, B and C class. These “objects” can be goods, which are divided into groups according to their value, but also customers can be divided according to the turnover they generate for the company. In this article we will describe the analysis for the purpose of customer segmentation in more detail; the procedure in other areas is analogous. 

In short

  1. With the help of the ABC analysis, customers are divided into three different segments according to the turnover they generate, which are designated with the letters A, B and C.
  2. The A-group designates the most important customers, while the C-group contains the most customers who, however, generate the least turnover.
  3. The A group refers to the most important customers, while the C group contains the most customers who generate the least turnover.
  4. Segmentation can be used to address customers in the segments differently and to define “priority customers”.
    The analysis is simple to perform, but can be extended to include a wide variety of components.

There are further applications in time management (prioritisation of tasks), in personnel management (allocation of employees), in warehousing (items grouped according to access) and in materials management (value of goods).

The classification is based on the so-called Pareto principle, which states that 80% of the results can be achieved with 20% of the total effort. Analogously, the statement can be modified in customer segmentation, i.e. that 20% of the customers are responsible for 80% of the turnover. The analysis wants to elicit exactly these most important customers and classifies them as “A-customers”. A rule of thumb is that another 30% of the customers generate 15% of the turnover (“B-customers”) and the remaining 50% of the customers are responsible for the remaining 5% turnover (“C-customers”). These figures are only guidelines and serve as an example, the classification is company-specific, for this a cluster analysis could be helpful (see point Extensions). An ABC analysis only makes sense if the customer segments are to be treated differently afterwards and there are clear differences in customer behaviour. If the sales are distributed more or less equally among the customers, the ABC analysis does not provide any informative insights.

Evaluation of customers and customer relationships has always been a challenge for marketing departments. At the same time, it is an important steering element for communication strategy, media planning and budgeting. In this series of articles, we describe different approaches to customer evaluation and highlight their advantages and disadvantages.

In the third part, we describe the ABC analysis. This is a method for weighting objects, which is also used in customer segmentation.

In times of growing complexity, many people and companies fail to focus on what is essential. Finding out what is actually essential can already be a great challenge. Classification methods that reduce complexity by combining individual objects into groups can be a great support. A well-known and frequently used method is the so-called ABC analysis.

The name of the ABC analysis results from the fact that a set of objects is divided into an A, B and C class. These “objects” can be goods, which are divided into groups according to their value, but also customers can be divided according to the turnover they generate for the company. In this article we will describe the analysis for the purpose of customer segmentation in more detail; the procedure in other areas is analogous. There are further applications in time management (prioritisation of tasks), in personnel management (allocation of employees), in warehousing (items grouped according to access) and in materials management (value of goods).

The classification is based on the so-called Pareto principle, which states that 80% of the results can be achieved with 20% of the total effort. Analogously, the statement can be modified in customer segmentation, i.e. that 20% of the customers are responsible for 80% of the turnover. The analysis wants to elicit exactly these most important customers and classifies them as “A-customers”. A rule of thumb is that another 30% of the customers generate 15% of the turnover (“B-customers”) and the remaining 50% of the customers are responsible for the remaining 5% turnover (“C-customers”). These figures are only guidelines and serve as an example, the classification is company-specific, for this a cluster analysis could be helpful (see point Extensions). An ABC analysis only makes sense if the customer segments are to be treated differently afterwards and there are clear differences in customer behaviour. If the sales are distributed more or less equally among the customers, the ABC analysis does not provide any informative insights.

The following example is intended to illustrate the procedure in the application of the ABC analysis. An example company has data on the following 10 customers and their turnover in the database (already sorted):

Client
Turnover
Client 1
4.600 EUR
Client 2
2.800 EUR
Client 3
900 EUR
Client 4
750 EUR
Client 5
450 EUR
Client 6
220 EUR
Client 7
120 EUR
Client 8
90 EUR
Client 9
50 EUR
Client 10
20 EUR

In the ABC analysis, the cumulative totals and their percentages of the total result are calculated, i.e. for what percentage of the turnover are the first 2, 3, 4, 5 etc. customers responsible? customers are responsible for. Based on this, the customers can be divided into A, B and C customers. In the example this means:

Client
Turnover
Cumulative turnover
Share
Segment
Client 1
4.600 EUR
4.600 EUR
46%
A
Client 2
2.800 EUR
7.400 EUR
74%
Client 3
900 EUR
8.300 EUR
83%
B
Client 4
750 EUR
9.050 EUR
91%
Client 5
450 EUR
9.500 EUR
95%
Client 6
220 EUR
9720 EUR
97%
C
Client 7
120 EUR
9.840 EUR
98%
Client 8
90 EUR
9.930 EUR
99%
Client 9
50 EUR
9.980
100%
Client 10
20 EUR
10.000
100%

As already mentioned, the final segmentation is to be carried out specifically in each use case. In this case, the separation at 75 and 95% is suitable, but it could make sense to still count customer 3 as an A-customer or customer 5 already as a C-customer. This depends on the goal of the analysis. If a group of particularly profitable premium customers is found, the A group should only include a correspondingly small number of customers.

Bar charts of the cumulative turnover, in which the segments are marked in colour, offer a visualisation possibility.

Cumulative turnover of customers

The colour-coded areas indicate the percentage of turnover for which the segments are responsible. The values were taken from the table above.

Extensions

The division into three segments is freely chosen. An extension to an ABCD analysis with four segments is possible without any problems, as well as to an even higher number of segments. It should be borne in mind that the segments must make sense from the company’s point of view and that a major advantage of the ABC analysis lies in its simplicity, which is lost to some extent for each segment added. Nevertheless, the following extensions may be of interest in the application.

Cluster analysis: In the analysis described, the segmentation of customers is carried out solely on the basis of the generated turnover and possibly other factors of interest are disregarded. These can be included in more complex clustering methods and lead to a more well-founded classification into A, B and C customers. An example of this is the “K-Means clustering”. (possibly reference to future article)
Combination with XYZ analysis: This extension is mainly used in materials management to segment products. In addition to the A, B and C segments, the goods are divided into an X, Y and Z segment depending on constant, fluctuating or irregular demand. The combination of the two analyses yields further valuable insights. For example, an AZ item is problematic because a high proportion of turnover is associated with irregular demand.

Conclusion

The greatest advantage of ABC analysis is its simplicity, which makes the procedure equally comprehensible. Even with large amounts of customer data, the analysis can be carried out without much computational effort. The procedure is transparent and data-driven and can be extended in various ways. The downside of simplicity is that many other factors are not taken into account. If one is aware of this limitation as a company, the analysis offers a significant reduction in complexity through segmentation with a wide range of possible applications.

Sources

  • https://www.weclapp.com/de/blog/abc-analyse/
  • Christian Schawel, Fabian Billing,: Top 100 Management Tools: Das wichtigste Buch eines Managers Von ABC-Analyse bis Zielvereinbarung
  • Ford Dickie: ABC Inventory Analysis Shoots for Dollars, not Pennies. In: Factory Management and Maintenance, 6(1951)109, pp. 92–94