{"id":991659,"date":"2026-04-16T13:23:22","date_gmt":"2026-04-16T13:23:22","guid":{"rendered":"https:\/\/customersx.ch\/the-3-data-levels-of-customer-centricity\/"},"modified":"2026-06-14T22:54:49","modified_gmt":"2026-06-14T22:54:49","slug":"the-3-data-levels-of-customer-centricity","status":"publish","type":"post","link":"https:\/\/customersx.ch\/en\/the-3-data-levels-of-customer-centricity\/","title":{"rendered":"The 3 data levels of customer centricity"},"content":{"rendered":"\n

How often have we heard from those responsible in companies: ‘We have so much data, we just need to evaluate it properly.’ Improving customer centricity begins with a company’s customer data. Here, creating a powerful customer-value model is an important element. But beyond that, it is also important to develop segmentations, to understand the Customer Purchase Process<\/a>, and to enable cross-selling via CRM<\/a>. If you want to follow the statements of numerous managers, this blog post is therefore only about the topic of evaluating. NO! This post refers to the 3 data levels of customer centricity and numerous misunderstandings in practice about this important topic.<\/p>\n\n

Fundamentals of the 3 data levels of customer centricity:<\/strong><\/h2>\n\n

The 3 data levels of customer centricity comprise base data, behavioural data and attitudinal data. The base data<\/strong> refers, in B2C industries, to customers’ sociodemographics: age, gender, education, income, etc. In B2B industries, the base data refers to company characteristics: industry, number of employees, region, etc. The behavioural data<\/strong> comprises purchase history, number of visits\/enquiries, website journey, cross-selling activities, etc. Attitudinal data<\/strong> is: needs (there was something about them being important, right?), preferences, loyalty (emotional bond), etc. Now, it is not just about collecting customer data like a hamster and then probably not evaluating it. To improve customer centricity, customer insights need to be determined. Customer insights<\/strong> are information about customers that enables differentiation from the competition in order to increase customer value.<\/p>\n\n

\"\"<\/figure>\n\n

Acquiring the 3 data levels of customer centricity:<\/h2>\n\n

Base data<\/strong> is available in most companies. Age, gender or number of employees and region are easily acquired data, which can in part even be bought in. The statement at the beginning \u2014 that there is so much great data available in the company \u2014 also refers to this data. Although many are surprised at how poor the data quality usually really is, that is another topic. In summary: base data is easy to acquire and usually available in the company. Problem: it hardly helps with differentiation, because the competition usually also has this data, and this data usually no longer suffices to address customers more specifically.<\/p>\n\n

Behavioural data<\/strong> is already available considerably less often in companies. Due to the tracking possibilities of a website, e-commerce providers are at a clear advantage in terms of data quality and scope. But many other companies can also analyse which customers have a high cross-selling rate, how often customers were visited by a field-sales rep, who made how many complaints. This data also has a considerably higher connection to customer value. Gender says less and less about customer value in many industries, but the number of cross-selling activities usually has a high connection to customer value. So behavioural data is already considerably better suited to acquiring customer insights. End of this post? Again NO. Problem: behavioural data refers to the past. Who doesn’t know the newsletter of a travel portal that, after buying a trip, recommends further trips to the same destination?<\/p>\n\n

There is also the possibility of determining attitudes on the basis of behavioural data. Whoever always books an expensive trip in order to stay in this world will probably not be so price-sensitive. But this approach requires many data points (the number of people and the purchase frequency of the offering must be large enough) as well as more complicated algorithms and IT power. Not impossible, but also not really quite as simple as many dream under the keywords big data, smart data or AI. In implementation, it comes down to: customers who bought this offering also looked at this offering. Completely fine, but again very product-focused.<\/p>\n\n

Attitudinal data<\/strong> is usually not available in companies at all. A small aside: attitudes are often confused with product attributes in practice. So most managers, when they speak of customer needs, mean how important a sunroof is to the customer in a car. But the term need fundamentally means how important comfort is to the customer in an offering. Looks the same at first glance, but (unfortunately) is not. Knowing customers’ attitudes has the charm that they refer to the future, can be used for numerous decisions in companies, and usually yield valuable customer insights. That sounds very good. Problem: attitudinal data is considerably more difficult to acquire. Some companies (<5%) conduct an annual customer survey and thereby measure customers’ attitudes towards the brand, company and offering. Often these are, as mentioned, more product attributes than fundamental attitudes. Other companies have implemented a voice-of-the-customer system (VoC) and measure the NPS or a comparable metric. In a customer survey, perhaps 10% of customers participate, and it should actually also be anonymous. In a VoC survey, usually not all customers are motivated to participate either. So attitudinal data is, if at all, only available for a portion of customers in companies.<\/p>\n\n

Conclusion on the 3 data levels of customer centricity:<\/h2>\n\n

In most companies, customer data is available that is not sufficient for acquiring customer insights. This is usually easy to acquire, but holds only low value potential. Attitudinal data is the ‘e-mobility of customer-data use’. Collecting it and using it in everyday business is the real game-changer of digitalisation. You think that’s not even possible in our industry? When we write game-changer, we mean it. Of course it is a huge challenge, like e-mobility. But it is about being better than the competition in a changing time. For example, salespeople could measure customers’ attitudes at every customer appointment via tablet, computer or smartphone. In retail, customers could, before each visit, briefly state their attitudinal preferences for the upcoming consultation, and the digital touchpoints could repeatedly intersperse individual attitude questions. Although this reduces convenience for customers, the value for the company increases enormously.<\/p>\n\n

It is important that, with such an approach, only 50%\u201380% of customers are reached. So bridges to behavioural and base data are always needed, so that the customer insights based on attitudinal data can be transferred to the entire customer base. ‘That sounds super difficult and complicated,’ we do hear quite often at this point. OK, but then please don’t use the words customer centricity, digitalisation, Amazon, Google, etc. in the respective workshops. Technology today offers the possibility of collecting and evaluating attitudes live in everyday business. Now people just have to seize this great opportunity.<\/p>\n","protected":false},"excerpt":{"rendered":"

How often have we heard from those responsible in companies: ‘We have so much data, we just need to evaluate it properly.’ Improving customer centricity begins with a company’s customer data. 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