Data-driven marketing with psychological customer insights

Classic segmentations are usually based on existing data, such as behavioral data or already enriched or purchased socio-demographic data and segmentations (e.g. age, income, job function, Sinus Millieus, Roper Consumer Styles, etc.).

Behavioral data says something about what people have bought in the past. This is a good start, but the informative value for cross-selling into other areas or products is still limited. In addition, behavioral segmentation does not tell us why people buy specific products. And this form of segmentation also provides no information about how exactly to communicate in order to effectively trigger behavior.

Socio-demographic data and segmentation provide information about potential lifestyles based on social statuses and other personal specifications (e.g. single, family, partnership). On the one hand, this data is highly variable and must be updated at a high frequency and, on the other hand, it also provides little insight into why people buy and, above all, how exactly to communicate in order to effectively trigger behavior with the communication. This form of segmentation is justified, but on its own it has virtually no informative value. And in mass data, these forms of segmentation become diluted.

Psychometric profiling does not replace these segmentations, but complements them or even builds on the other segmentations and fills the existing customer insights gaps as to why people buy. As a result, there is more potential for cross-selling. At the same time, profiling provides a precise framework for how to communicate specifically for each psychological type (colors, design, text, product) in order to arouse interest and trigger behavior. Psychology is a very stable segmentation form and needs every 5-10 years adjustment on already profiled customers.

Basically, it is exciting for companies that have understood that customer data is an asset that makes the most important contribution to customer experience and therefore to increasing the company’s turnover and value. This mindset, combined with the desire for digital transformation with new technologies such as AI or new, digital customer touchpoints, is the right basis for psychometric profiling.

Data-driven psychometric profiling is suitable for all larger SMEs and corporations in the B2B & B2C market that either already have additional data about the customer (such as socio-demographic segmentation or data) in addition to classic behavioral & transactional data or generally want to invest heavily in the topic of customer data & insights in the future.

Technologies and customer interaction processes must also be optimized or introduced so that the handling of valuable customer insights such as profiling can achieve maximum impact and can be used on a larger scale in an ethically way.

Minimum requirements would be transactional data, socio-demographic segmentation or enrichment as well as product data.

Further behavioral data, such as digital affinity or interaction with advertising content, etc., would be nice to have.

Standard data analysis or data science approaches are based on existing transaction and behavioral data and previously known forms of segmentation from socio-demographics (Sinus Millieus, Roper Consumer Styles, Limbic Types, etc.).

These forms of segmentation have not been developed scientifically, nor are they strongly based on psychography.

Psychometric profiling initially links the content of existing behavioral and socio-demographic data with pure psychographics. This means that an additional data element is not simply “bought in”, but that it is supplemented or enriched with psychography based on behavioral patterns and socio-demographic data points. A new and very valuable data point is added.

This matching requires special expertise from the field of psychology, as a psychological, data-driven rule framework must always be developed first for meaningful data matching. This forms the basis for extrapolation to the entire customer base. This rule framework cannot be developed by general data analysts or data science specialists.

Through psychometric insights, the customer journey is not only understood based on click data, but also optimized emotionally and psychologically.

This allows trigger points, decision-making behaviour and buying motives to be addressed in a targeted manner – for a personalized journey that works.

This not only makes the customer journey more transparent, but also more relevant. Psychometric profiling provides deeper insights into the motives, needs and decision-making logic of different target groups.

This means that every touchpoint – from the approach to the offer to the conversion – can be designed much more precisely. The effect: measurably higher relevance, improved user experience and a significant increase in the conversion rate along the entire journey.

An ethical customer experience focuses on people – not just performance. It is based on the transparent handling of data, clear communication and the aspiration not to manipulate customers, but to truly understand them.

At Brandmind, this means

• Data is not misused, but interpreted sensibly
• Customers are not deceived, but honestly met
• Personalization is not based on tricks, but on genuine psychological relevance

Our psychometric models analyze unconscious motives – but never to the detriment of the individual.

We believe in a new form of customer interaction: data-driven, psychologically sound and respectful.

Because a customer experience is only truly successful if it is based on trust, transparency and fairness – not algorithmic overreach.

Yes, that is indeed the case. Because we work 100% without personal data. We receive anonymized customer databases with fake customer ID numbers. Care is also taken to ensure that only data that cannot be traced publicly is exchanged.

If companies now want to exploit the full potential of psychometric profiling in the long term in a fully automated manner, it is imperative that “ethical customer interaction measures” are introduced in parallel. This means that the company must act in the direction of transparency of data use in continuous customer communication.

The aim should be to create a win-win situation in the long term. Companies voluntarily receive deeper customer insights, which they can use with the customer’s consent, and customers receive a massively improved customer experience with added value.

We support our clients in growing into an “ethical, hyper-personalized, next-generation customer experience”.