Customer Segmentation: Importance, Models & Approach

1. What is behind the term customer segmentation?
2. Why customer segmentation is so important
3. These models and methods of customer segmentation are available
4. How does customer segmentation work in practice?
5. Targeted measures for customer segments in B2B and B2C marketing
6. How to recognize successful customer segmentation
7. Conclusion on customer segmentation

Only when we understand our customers can we communicate optimally with them. Good customer contact has always been important. Regardless of the industry or company in which one is active, developing a comprehensive understanding of customers and their motives, needs, and desires is crucial. However, as direct customer contact is not always possible, it is important to take targeted measures to get to know your customers better. Customer segmentation is a good tool for this. In this article, our guest author Lili Oberdörfer explains the basics of customer segmentation, specific advantages, how to proceed, and what potentials for your marketing measures exist.

What is behind the term customer segmentation?

The term customer segmentation describes the classification of customers according to purchasing-relevant characteristics. The basis for this is customer data from various touchpoints or sources. When these data are analysed, differentiating characteristics can be defined. If several customers share certain characteristics, buyer groups can be formed that are self-contained and can be clearly demarcated from other groups. Such a grouping is referred to as a customer segment.

The size of individual customer segments can vary greatly and is usually in proportion to a company's total customer base. However, even with a very selective customer base, it is possible to define customer segments that only include a single-digit number of customers.

Why customer segmentation is so important

Why is such a classification useful for you? In general, it is always beneficial to know your own customers. If the customer base consists of a very large number of individuals, one can easily lose track of the entire customer base, even if you already use an effective CRM tool. After all, a company with hundreds of thousands of customers no longer has personal contact with each individual, and even if it does, it is difficult to maintain each customer relationship sensibly according to their individual needs.

1. Understanding and getting to know customers

It is worthwhile to know and understand these individual wishes and motives. Because a customer base certainly does not consist of just one type of customer. The definition of customer segments is therefore important in order to find the optimal approach for the various target groups. Only in this way can effective marketing measures be designed that pick up the contacts where they are and promote a good customer relationship. A client feels recognized as an individual, understood, and with a bit of luck, enters into a long-term business relationship with you.

2. Taking individual measures

Customer segmentation therefore helps you to get to know your customers better and enables you to recognize, analyze and subsequently use motives and need clusters for marketing strategies and decisions. But also the estimation of results of planned projects is improved by good customer segmentation. For a company, therefore, a very high potential is hidden behind this term.

If you use segmentation correctly, you can contribute to increasing sales and at the same time achieve considerable savings in costs and resources.

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These models and methods of customer segmentation are available

Different models and approaches can be used to implement customer segmentation. This approach should not be confused with market segmentation, which is usually the precursor to meaningful customer segmentation. This not only looks at the current customer base, but also includes potential and past customers in the analysis.

Once a suitable market has been identified, customer segmentation can begin. There are two variants to choose from: differentiation is made here between one- and multidimensional customer segmentation. Depending on how much data and time are available and what goal has been defined, both options may be a sensible solution. So let's look a bit closer to gain an understanding of the procedures and benefits of the different models.

The one-dimensional customer segmentation considers only one characteristic (Illustration/Lili Oberdörfer).

1. One-dimensional customer segmentation

In one-dimensional segmentation, only one customer feature is considered. Accordingly, it is rarely possible to define a very detailed and homogeneous segment. The one-dimensional approach is therefore usually used to analyse existing customer structures. Examples of segmentation criteria are frequency of purchase or turnover in order to get a quick overview of the composition of the database. Different approaches that can be applied in one-dimensional customer segmentation are:

• The analysis of purchase volume per purchase: In this method, the focus is on the quantity a buyer orders. So you can identify the part of your customers who invest a particularly large amount of money at once in your company's goods or services.
• The analysis of the frequency of purchase: This method is often combined with the previous analysis and is similar in its goal to the ABC analysis. This approach helps you to divide your customer base into three groups, namely one-time, occasional, and regular customers.
• The analysis of usage intensity: The goal of this approach is to divide the customer base into two groups, namely the group of "heavy users" and the group of "light users".
• The ABC analysis: This approach tells you which contact is most important for your company, i.e. who buys the most. So customers who are classified in segment A are the ones with the highest potential. For this group, it is therefore particularly important to respond with appropriate marketing measures.

Multidimensional customer segmentation analyzes more than one characteristic and derives customer segments accordingly (Illustration/Lili Oberdörfer).

2. Multidimensional customer segmentation

As the name suggests, this type of segmentation considers several variables or characteristics and their interplay. The respective customers described in a segment are thus significantly more homogeneous than in one-dimensional customer segmentation, as the contacts match in several characteristics. As the demarcation of the clusters is very precise due to several criteria, individual and effective conclusions can be drawn from a multidimensional segmentation. The resulting marketing strategies are precise and specific.

However, to evaluate the complex and extensive data sets as efficiently as possible, multidimensional segmentation requires support in its evaluation by machine learning or cluster analyses. Examples of analyses with different objectives are:

• The factor analysis: This helps you to find out which features are particularly meaningful. These are called supervariates.
• Creation of neural networks: Here you create a large, visually viewable network into which your customers are integrated. This reveals the respective connections and processes.
• The discriminant analysis: This analysis focuses on the sharp demarcation of customer segments. What aspects fundamentally distinguish the clusters and what conclusions can you draw from them?
• The multidimensional scaling: The MDS analysis presents your customer segments in such a way that you can read similarities or differences based on distances in a matrix.
• The contrast group analysis: Also called AID analysis, provides detailed information about the relationships between the individual customer groups. How many differences can be found within the segments until only homogeneous groupings exist? As a result, a dendrogram is created, which represents these.

How does customer segmentation work in practice?

Whether you want to carry out one-dimensional or multidimensional customer segmentation, the process is the same, even if the individual steps differ individually depending on the model. The common procedure includes the following five steps:

1. Define the target group of your customer segmentation: Who do you want to appeal to as a customer at all? Consider which customers are of interest to you and for what occasion you want to enthuse them. Is it about launching specific products, promoting an event, or do you want to get to know your customer base better and develop more individual marketing strategies in general?
2. Establish the characteristics: Based on which feature or features would you like to cluster your customers? Determine which criteria differentiate your target groups sensibly. This depends heavily on the goal you are pursuing with customer segmentation. Examples of interesting criteria would be demographic characteristics, geographical data, purchasing behavior and history, but also data that can be read from the web usage of your customers. If you want to know whether the selection of these features is meaningful and meaningful, you can check this using A/B tests.
3. Collect data and information about relevant customers: For this, you can use existing data from a CRM tool or also collect new data, for example by creating an online survey or initiating targeted surveys. To recognize clusters in the collected information, you can use different tools. Cluster analyses and machine learning are helpful options, more on that later.
4. Define the customer segments: Classify the results of your evaluation and try to describe the different customer clusters. Can buyer personas be created that accurately reflect a large group and their needs? Or describe the typical customer journey of a customer segment, which provides you with important information about the behavior of relevant customers.
5. Initiate the next steps: Now it's time to get active. Use the insights you gained in your customer segmentation and initiate the corresponding measures. These may be new marketing campaigns that are optimally adapted to the created personas. Or adjust your existing marketing activities to the new insights of the customer segmentation. Create individual approaches and concepts that respond precisely to the needs of the individual target groups.

Looking back, it is also useful to take another look at the success and execution of the customer segmentation in order to draw important conclusions for new clusterings.

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Targeted measures for customer segments in B2B and B2C marketing

Depending on whether the customer segmentation takes place in the B2B or B2C sector, different characteristics can be used in practice for clustering. The basic reason for this is that private customers and business customers differ fundamentally in their intrinsic motivation. In addition, the private circumstances of employees play a lesser role in decision-making, rather they take on a kind of representative position for the needs and goals of the company. In the B2C and B2B sectors, the following features have proven to be relevant:

Features for B2C customer segmentation:

• Sociodemography: such as age, relationship status or gender, but also the financial resources of the customers.
• Geography: Where is the contact located: federal state, connection structure, rather rural or urban area?
• Psychography and Psychology: What values or lifestyle are lived, what imprints exist?
• Behavior: Do customers buy online or on site, how often, in what volume, uninformed or considered, where are motivations etc.?

Features for a B2B customer segmentation:

• Environment-related criteria: Industry, technical status, global economic situation, potential influence and conditions through state regulations, and relevant trade unions.
• Organization-related criteria: such as company size, type of company, relevant industry, legal principles, technical infrastructure.
• Individual criteria: Aspects of risk propensity, beliefs, order volumes, or individual information behavior that an employee displays.

At this point, it becomes clear once again how important it is to deal intensively with the framework conditions and your own goals before customer segmentation. Because only with the right selection of characteristics for the differentiation of customer segments can meaningful results be achieved, which can improve marketing in the long term.

How to recognize successful customer segmentation

Whether your customer segmentation was successful can be read from the quality of your results or conclusions, which you could draw. Firstly, you should have achieved a better understanding of customers through the clustering. You know the diversity of your buyers and how they are composed. You know which arguments they consider in their decisions and can empathize with them.

From this understanding, you have many options to respond better to your customers. You can design your marketing and sales measures so that the formed target groups feel recognized and motivated to interact by your approach. It is easier for you to highlight the value of your product or service individually. In the long term, these marketing strategies lead to stronger customer loyalty, which has a positive effect on profitability and sales.

Conclusion on customer segmentation

Customer segmentation is an effective method to better understand your customers. Market segmentation is usually carried out beforehand, which fundamentally contributes to defining the target group. A distinction is made between one- and multidimensional customer segmentation. Within these two approaches, there are many ways to make the clustering as meaningful as possible. It is crucial that you select the right features and have enough data for your analysis. In the long term, the insights from customer segmentation can lead to a better customer relationship by more closely tailored marketing measures.

However, it is also important to be aware that your customer base is always in motion. So the results of your customer segmentation are only an image of your customers at a selected point in time. Therefore, to benefit from this in the long term, it is worthwhile to invest in a CRM tool that dynamically maps the development of your customer base and can thus always make statements based on real-time data.