What is customer analysis?
A customer analysis is a structured process that takes a close look at a company's customers and their buying behavior. They can be used to gain insights into buying and customer behavior. For this purpose, relevant customer data is collected, processed and analyzed. There are hardly any restrictions on which data can be used for this purpose — apart from the legal provisions on Data protection and personal rights. Anyone who carries out a customer analysis learns to understand their target group better — and creates the basis for optimizing their company's marketing and sales.
What does customer analysis provide answers to?
The customer structure
Who are my customers anyway? The analysis of typical characteristics and behaviours is used to find out. They can be used to determine the structure of a typical customer or customer base quite accurately.
- Geographical criteria: State/Country, Province/State, City/Town, Neighborhoods, Neighborhood, Street, Climate, Population Density
- Demographic criteria: age, sex, marital status, number of children, household size
- Sociographic criteria: income, purchasing power, level of education, employment, education, ownership characteristics, social stration/environment
- Psychographic criteria: motives, attitudes, benefit expectation, personality, lifestyle, preferences
- Information behavior: communication behavior, media use
- Buying behavior: brand selection, brand loyalty, price awareness, choice of shopping store, location of shopping (online/offline), packaging preferences, average purchase rate or frequency, responses to campaigns
- Usage behavior: Intensity and type of use, respective situation, environment, recommendation behavior
- Payment history: early/timely/late payment, installment payments, reliability, means of payment
customer needs
If the customer structure is known, it is clear how your own customers are to be classified in terms of their external characteristics. The next step is to take a closer look at their needs and requirements as well as their requirements and expectations. Information on customer wishes, requirements, satisfaction and customer complaints.
- Customer needs: constant, universal and effective emotions and buying motives. These include safety, wellbeing, entertainment and health.
- Customer needs: Requests that are derived from customer needs, such as personally perceived deficiencies or problems that the customer currently wants to solve.
- Expectations: Characterized by individual ideas and preferences relating to specific services or products, for example expectations regarding supermarket opening times.
Customer value
An important point in customer analysis is to determine customer value. In other words: How much an individual customer or customer group contributes to the economic success of a company. However, how valuable a customer is for the company depends not only on sales — their information and reference value also play a role.
The monetary value It is easy to determine: It is calculated by turnover or contribution margin per customer. The Information value Meanwhile, it is derived from all information that a customer provides and that the company can use for itself.
The goals of a customer analysis
- Customers understand: Demographic characteristics as well as information about purchasing behavior, interests, preferences and needs of customers reveal a lot about their own customers. Only those who know their own target group can address them in a targeted manner and respond to their wishes and demands.
- Make customers happier: Once an understanding of the needs and expectations of customers has been gained, targeted measures can be taken that customer satisfaction boost. This includes, for example, the appropriate approach by a chatbot and a coordinated product portfolio.
- Retain customers more: In addition to a positive shopping experience that only has a short-term effect, it is important to satisfy your own customers in the long term. The findings from customer analysis can be used to make targeted Customer Retention Measures push ahead. These include, for example, personalized offers, bonus programs or social media campaigns.
- Improve corporate image: When companies know what their customers like, they can tailor their offering — depending on whether their customers are more interested in service, quality or price. This has a positive effect on the corporate image.
- Optimize products and services: Customer analysis reveals which products are being bought and which are not. Offers that have not been purchased or used can thus be specifically removed from the product range.
- Increase sales: By examining which customer groups are valuable to them, organizations can target and serve them in a targeted manner. This makes acquiring new customers more profitable.
- Securing competitive advantages: Companies that know their target group in detail are ahead of their competition. They can differentiate their offerings and bring them to market in a targeted manner.
What role do chatbots play in data collection?
Chatbots interact regularly and continuously with customers. They record their questions and concerns, offer solutions and provide information. How the human counterpart reacts to this provides valuable information and insights into the wishes and needs of a target group. This knowledge can then be used for an essential more targeted and personalized approach use. On the one hand, chatbots can collect information about preferences, interests and buying behavior and pass this on to marketing teams.
On the other hand, the way customers interact with the chatbot is also interesting. Marketing and sales managers learn a lot about “the language of their customers” and know much more precisely what makes their customers “tick”. In this way, targeted marketing messages can be developed that are directly tailored to the specific interests and communication patterns of individual customer segments.
The methods: This is how a customer analysis works
ABC customer analysis
This simple method of analysis aims to find out the individual value of a customer for the company. For example, they can be assessed based on the criteria of “turnover” or “profitability” and divided into different categories: Category A comprises the top customers with the highest value and potential, category B people with medium value for the company and category C includes those with low value (e.g. inactive customers).
RFM analysis
The abbreviation RFM stands for Recency (Timeliness), Frequency (Frequency) and Monetary Value (Monetary Value). The method makes it possible to classify customers according to their buying behavior and thus classify them into segments. The key questions are: When did a customer last buy something (recency)? How often did he buy (Frequency)? And how much did he spend (monetary value)?
Customer lifetime value analysis
The customer lifetime value (CLV) gives each customer a value for the entire duration of their relationship with the company. In doing so, CLV not only takes into account customer value in the present and past, but also the — to be assumed — future value. The CLV method for customer analysis looks at both the total costs and the total profit generated by the business relationship with a customer.
Customer coverage contribution calculation
The method analyses the contribution of customers to the total costs and profits of a company. For this purpose, direct costs (e.g. material and labor costs) and indirect costs (e.g. marketing or administration) are compared with customer contributions. In this way, profitable customers can be differentiated from unprofitable customers and the portfolio can be optimized. Customer coverage contributions help to identify potential and adjust one's own offer in order to generate higher contribution margins.
Customer segmentation: one-dimensional or multi-dimensional
Basically, the analysis methods are used to divide customers into different groups. This segmentation can be done in one or more dimensions:
- Die one-dimensional customer segmentation only uses one characteristic such as age, gender, purchase frequency or preferred products. This simply provides a quick overview. Chatbots can provide valuable data through targeted queries and interaction analyses and differentiate customers into “active” and “inactive” groups.
- Die multidimensional segmentation is more complex because it combines several variables to identify customer groups. Chatbots can also collect relevant data here through a combination of questions and interactions — e.g. about problems, concerns, or buying preferences that come to light in exchange with the bot.
Chatbots can transfer the collected data into a CRM or analytics system transfer and continuously update and supplement them. This makes it possible to create comprehensive personality profiles based on behavioral patterns, preferences or actual customer value.
5 tips for successful customer analysis
1. Clarify goals and key questions
Before you start working with customer analysis, one thing should be clear: What you want to achieve with it — e.g. insights into customer structure, buying behavior, or satisfaction. This is the only way to determine the relevant data.
2. Capture data cleanly
Without high-quality, comprehensive data, no meaningful results — collect customer data continuously and pay attention to data protection. Chatbots can be of great help here.
3. Choose the right analysis method
Depending on the goal and data, choose an appropriate analysis method. It makes sense to combine qualitative and quantitative methods — after all, it's not just “hard” facts and figures that make up a buyer and their needs and wishes.
4. Derive and implement measures
The results of the customer analysis will only help a company if they result in concrete measures to adapt sales and marketing strategies and develop tailor-made offers.
5. Update data regularly
Since customer needs and behavior are constantly changing, customer analysis must be carried out and updated continuously. Automated processes and chatbots make it possible to regularly collect customer data and keep it up to date.
B2B: Special features in customer analysis
B2B markets and decision-making processes are complex. That doesn't make customer analysis in this area any easier! There are differences in these points that make customer analysis difficult:
- The number of customers in the B2B sector is lower, making individual customers more important.
- Decision-making structures are more confusing: Several levels and groups of people are often involved in a purchase decision.
- B2B products and services are more complex and often individually tailored to individual customers.
All of this makes it difficult to make generally valid statements for marketing. On the other hand, there are also factors that have a positive effect on customer analysis in B2B companies:
- B2B customer relationships are long-term relationships; there are often long-term, personal contacts between business partners. This makes it possible to collect particularly valuable and meaningful data — over many years.
- B2B buyers act more “rationally”; there are fewer spontaneous purchases. Instead, decisions are based on arguments and processes that are easy to understand.
Summarized: Even though the key questions and goals of customer analyses in the B2B and B2C sectors are similar — data quality and quantity make a big difference here.
Conclusion: Customer analyses reveal great potential
Marketing without customer analysis is something of poking in the fog: Sometimes a lucky shot may come out, but the more precisely and clearly marketing experts keep an eye on your target group, the higher the chance of success of their measures. Chatbots can be extremely useful when collecting and evaluating data as part of customer analysis and provide companies with deep insights into the behavior and needs of their customers. The same goes for customer segmentation. Overall, customer analyses help to tailor your own marketing communication and offerings to the targeted target group in the best possible way. With the result of retaining customers with the company in the long term.
