The Multiplication Table of Customer Analysis: Goals, Methods, Examples

1. What is a customer analysis?
2. What is the goal of a customer analysis?
3. Why is customer analysis so important?
4. Customer segmentation
5. Methods of customer analysis
6. Customer analysis: Difference between B2B and B2C
7. 7 Customer Data Platforms (CDP) for your business
8. Conclusion

Whoever does not know their customers has already lost - this marketing wisdom perfectly hits the purpose of a customer analysis. Because only if you understand the desires and needs of your target group can your business be successful in the long term. But what does a successful and comprehensive customer analysis look like? What methods exist for customer analysis and how do they differ in B2C and B2B? You will find the answers to these and other questions in this article!

What is a customer analysis?

A customer analysis deals - surprise! - with the analysis of a customer group or individual customers. The aim is to gain a deeper understanding of your customers in order to derive possible actions for your company. The basis for this are different data that you obtain, for example, through CRM software . Regardless of whether you are in the B2B or B2C sector, customer analysis is therefore a decisive factor for your long-term business success. CRM software supports this, such as and .

Various methods can be used to create an analysis. Which method is right for your business depends, among other things, on the orientation of your company, the available data and the desired insights. However, the main goal is always the same: to answer various questions about your customers and thus gain a better feel for potential and already existing buyers.

What is the goal of a customer analysis?

At first glance, the goal of customer analysis seems obvious: to find out who your customers are. But what does that mean at all? Depending on the method, you gain in-depth insights into the needs, behaviors, shopping habits, and expectations of your target audience through customer analysis. This knowledge helps you build a long-term positive customer relationship. In addition, it can help you identify the customer life cycle that your customers go through. A comprehensive evaluation of your customers offers even more: customer analysis also helps you better understand your competitive situation and the industry in which you operate.

Why is customer analysis so important?

Quite bluntly put: Customer analysis helps you to succeed with your business. Specifically, an evaluation of your customers can help with the following business aspects:

• Finding the most profitable target group: By analyzing your customers, you can find out who your most valuable buyers are. What the customer value reflects naturally also depends on what type of product you sell: depending on the business, the most profitable target group can consist of customers who either rarely shop with you but with a high value of goods, or those who make smaller purchases more often.

• Aligning Marketing: Automotive pioneer Henry Ford once said, in effect: 50 percent of the advertising budget is wasted money, you just never know which half it is. Ford brilliantly described the scattering loss that comes with marketing. A comprehensive customer analysis can help minimize this scattering loss by more specifically targeting your marketing strategy and tactics to your customers.

• Product focus: A customer analysis is useful for taking stock of your most sought-after products and services. By evaluating purchasing behavior and usage, you get an overview of what your most coveted products are.

• Identifying unused business potential: Understanding the needs and desires of your customers can help you identify untapped potential in your business. This way, customer analysis can contribute to the development of products or services that you have not yet covered with your business.

Customer segmentation

One outcome of customer analysis is the so-called customer segmentation in which you divide your buyers into individual groups. This means you'll not only learn which individual groupings your customers consist of. It also makes it easier for you to distinguish between customer segments with greater value for your business from those with less potential for your business. In this distinction, a distinction is made between the so-called one-dimensional and multidimensional segmentation:

One-dimensional customer segmentation

In one-dimensional customer segmentation, you divide your users into different segments based on a single criterion. An example of this would be a division of your customers into buyers who have spent more than €100 per purchase and those who have spent less than €100. Depending on the criterion, this gives you a quick overview of your target group. The disadvantage of this approach: It is hardly possible to create homogeneous customer segments, because the socio-economic or demographic characteristics of the users within these segments can vary greatly.

Multidimensional customer segmentation

Much more complex, but also more informative is the multidimensional customer segmentation, where the target group is segmented based on several characteristics. For example, you classify your buyers based on similarities in terms of demographic characteristics such as age, gender, profession, place of residence or income. This of course means considerably more effort compared to one-dimensional customer segmentation, but on the other hand it also provides more insight into the actual distribution of your customers.

Methods of customer analysis

To analyze your buyers, there are numerous possibilities, which differ both in methodology and in their outcome or ultimate purpose. We explain these methods below using two important components of your customer analysis: the investigation of your customer structure and the determination of the customer value.

Creating a customer structure

The analysis of the customer structure is an important part of your customer analysis, in which you examine the composition and characteristics of your target group. This analysis includes the identification of customer segments, the classification of customers by age, gender, income, level of education, place of residence and other characteristics. The goal is to get a better understanding of the needs, behaviors and preferences of the target group and thus to develop targeted marketing and sales strategies.

The benefit of such an analysis is that you can better understand your customers and thus develop personalized offers and marketing campaigns. Also, they can optimize their business strategy by focusing their resources on the most suitable customer segments. A thorough analysis of the customer structure also enables companies to increase their brand awareness and customer loyalty, which in turn can lead to increased sales and long-term success.

The prerequisite for creating customer structures is that you have enough meaningful data. As part of your customer structure analysis, the following characteristics can be investigated:

• Demographic characteristics: Here you examine your customer groups for commonalities in age, gender, income, level of education, marital status or place of residence.

• Psychographic characteristics: These characteristics include attributes such as lifestyle, personal values, interests, and the personality of your customers.

• Behavioral characteristics: This mainly concerns the behavior when your customers interact with your brand. Aspects here include purchasing habits, brand loyalty, the type of information procurement, shopping behavior or also payment behavior.

• Needs and motivations: Here you examine what drives your customers to purchase a specific product or one of your services.

• Media use: In this aspect, you examine which channels are used by your customers to obtain information about products and services.

It's important to note that not all of these characteristics are equally relevant for all companies and industries. You should therefore focus on the characteristics that are most important for your target group and industry in order to gain a comprehensive understanding of your customers.

Determining customer value

Another important part of your customer analysis is determining your customer values. Because not all customers are economically profitable. Occasionally, it can happen that the measures to keep a certain target group cost more than they bring in. Therefore, determining the customer value is important to recognize who are your most profitable buyers. To determine this value, different methods are available:

ABC analysis: An ABC analysis is a methodology that allows you to prioritize large amounts of customers. Here, buyers are divided into three categories (A, B, C), depending on their importance for the business.

• Category A: These are the customers who generate the most revenue and profit. These should be taken care of especially carefully. According to the Pareto rule, this group makes up 20% of the total customer base.

• Category B: These are buyers with a moderate share of sales and profit. These still require attention, but not as much as category A. According to the Pareto rule, they make up 60% of the buyer base.

• Category C: Customers with the smallest share of sales and profit form category C. These can be reviewed to decide whether they should continue to be served. According to the Pareto rule, the remaining 20% of buyers are represented here.

Customer Lifetime Value The Customer Lifetime Value (CLV) is an estimate of the total value of customers for a company during the entire customer relationship. It measures the forecast net profit that a company can expect from the customers over their entire lifetime, taking into account factors such as purchase frequency, purchase amount, customer loyalty, and customer retention.

The CLV is an important indicator of the profitability of a customer relationship and can help make decisions about marketing spending, customer care, and product development. A high CLV signals that customers are likely to remain loyal for a long time and generate high sales and profits, while a low CLV indicates that certain customers may not be profitable.

By estimating the CLV, you can optimize your customer strategy by investing your resources in acquiring and retaining customers with high CLV and addressing those with low CLV accordingly. A precise estimate of the CLV can also help evaluate the profitability of marketing campaigns and measure the effectiveness of customer loyalty programs.

RFM analysis RFM stands for Recency (timing), Frequency, and Monetary Value. The RFM analysis is a method for segmenting customers, focusing on three important dimensions of customer behavior: When did customers last buy (Recency), how often did they buy (Frequency), and how much did they spend (Monetary Value).

With the RFM analysis, a company can classify its customer base according to these three criteria and thus gain a better understanding of their behavior and needs. Customers with frequent buying behavior and high spending are identified as "best buyers" and receive special attention, while those with low buying behavior and low spending may need to be addressed differently to change their behavior.

The RFM analysis is simple to carry out and requires only basic customer data such as purchase history and revenue. It can be used as part of a more comprehensive customer analysis or as a standalone method to make key decisions regarding customer care, marketing, and product development.

Customer contribution margin calculation: A customer contribution margin calculation (in English: Customer Contribution Analysis) is a method of determining the contribution of customers to the total costs and total profit of a company. This analysis looks at both direct costs such as material and labor costs and indirect costs such as marketing and administrative costs to get a comprehensive overview of the costs and contribution of all customers.

The goal of a customer contribution margin calculation is to determine the ratio between customer costs and their contribution to company profit. Here, a distinction can be made between profitable and unprofitable customers. Such an analysis enables companies to optimize their customer portfolios by focusing their resources on the most valuable target group and either improving or dismissing less valuable buyers. Customer contribution margin calculations can also be used to identify new customer potentials and to adapt products and services that lead to higher contribution margins.

Scoring model: The scoring model is a method for evaluating and classifying customers, where the transaction is rated with positive and negative points.

Each criterion is given a certain weighting and rated on a scale of 1 to 100, resulting in all customers receiving a total point value. The higher this point value, the more likely it is that certain customers will have future potential or show a higher buying behavior.

The scoring model enables companies to segment and identify their customers more efficiently, and to identify which customers are most important and which need special attention. It is an important part of marketing and sales strategies as it helps companies better utilize their resources and align their activities with the target group that promises the most.

Customer analysis: Difference between B2B and B2C

Although the purpose of customer analysis is identical for both business models, the investigations differ depending on whether you are in B2B or B2C. Important factors here are:

Size of customers: A first difference lies in the number of customers. In B2B, you generally have fewer buyers than is the case in B2C. For this, these individual customers have a greater significance for your business.

Purchase value: In B2B, higher value goods are often traded than is the case in B2C.

Amount of data: With customers in the B2B sector, the number of available data is generally large. This is because the customer relationship is usually built up (and ideally maintained) over a much longer period of time than is the case in B2C. For end customers, this data volume is comparatively the same, because the buyers usually remain anonymous.

Decision paths: The paths of decision also differ fundamentally in B2B from those in B2C. In the latter, the individual consumers are also the only decision-makers. In B2B, decisions are mostly made at different points and over a longer period of time.

Consumer behavior: The assessment of consumer behavior is easier in B2B than in B2C. The reason for this lies among other things in the larger amount of data and the longer decision-making paths that can be retraced. In the case of end customers, on the other hand, the purchase decision is often subjective and is correspondingly difficult to assess.

Contribution to economic success: In B2C, individual customers generally have a small share in the overall economic success of your business. It's different in B2B: Here, the difference between top customers and those with less purchasing volume can have a big impact on your success.

Examples: Customer analysis in B2B and B2C

To illustrate, here are two examples of what a customer analysis in B2C and B2B might look like:

• B2B: A B2B company that sells machines and equipment could focus on factors such as company size, industry, purchasing history, and budget in its customer analysis. This information helps the company better understand the needs and desires of its business customers and develop more targeted marketing and sales strategies.

• B2C: A B2C company that sells fashion items, on the other hand, could focus on factors such as age, gender, income, lifestyle, and buying behavior. This information helps the company better understand its target audience and align its products and marketing efforts with their needs and preferences.

Although the guiding questions and the purpose of the customer analysis are thus similar in both cases, it is mainly the quality and quantity of data that distinguish between B2B and B2C.

7 Customer Data Platforms (CDP) for your business

Now that you know the steps of a comprehensive customer analysis, we will show you some tools that help you with this investigation. Of course, it always depends on which (sub)area you want to analyze and optimize. So if you want to structure and optimize the relationship with your customers, tools for Customer Relationship Management (CRM) are particularly suitable. If, on the other hand, you first want to collect and merge customer data, a Customer Data Platform is the right solution. We present 7 tools for this in the following:

• BSI Customer Suite

• Segment

• Bloomreach

• Tealium

• Crossengage

• Piwik PRO Analytics Suite

• SAP Customer Data Platform

Conclusion

A comprehensive customer analysis is important to understand the needs of your customers and build a positive customer relationship in the long term. Among other things, it helps you to determine your most profitable buyers and to draw conclusions about the sensibleness of your marketing activities. For this you have different methods available, for example the ABC method or the customer contribution margin calculation. Which approach is right for you depends, among other things, on your business model, but also on the type of your product and your service.