360° Customer View with AI: Data-Driven Sales for 1.2 Million Customers

A 360° customer view means bringing all available information about a customer together in one place: master data, product usage, contact history, preferences. Instead of fragmented data in different systems, a complete picture emerges.

For sales, this is decisive: those who know the customer advise better. Those who recognize patterns can leverage cross-selling potential. And those who spot churn risks early can take countermeasures. At the same time, a consolidated data foundation creates the prerequisite for deploying AI: only when data from different sources comes together do correlations become visible from which models can learn. Without a holistic customer view, these potentials remain invisible.

In the project with ADAC Hansa, PLAN D consolidated data from more than ten different sources and made it accessible in a central interface. The result: a doubling of sales conversion probability.

Churn prediction refers to the data-based prediction of whether a customer will leave the company. A machine learning model analyzes historical data — such as membership duration, contact behavior, tariff type, and past product usage — and identifies patterns that are typical for churning customers.

The model outputs a churn probability for each member. Sales representatives can proactively approach customers with elevated risk before they cancel. The decisive advantage: instead of reacting to cancellations, action is taken proactively.

In the ADAC Hansa project, the churn model complements the recommendation models for travel and trainings. Together, they provide a comprehensive picture of the customer situation.

Data-driven sales means making decisions in the sales process based on data and analytics — not solely on experience and gut feeling. This ranges from the question of which customer should be recommended which product to the prioritization of contacts by conversion probability.

The prerequisites: a consolidated data foundation that brings together all relevant customer data, and analytical methods that derive actionable insights from this data. AI models can generate individual recommendations that go beyond simple segmentation.

In the project with ADAC Hansa, this approach was implemented end-to-end: from data consolidation through three AI models to a sales interface that delivers recommendations directly during consultations.

Data protection and AI are not mutually exclusive — if the process is designed from the start. In the project with ADAC Hansa, PLAN D implemented several measures: personal data is anonymized before analysis. Names, dates of birth, phone numbers, and email addresses are replaced with random values, membership numbers are cryptographically processed. Data processing takes place in a secure infrastructure within Germany.

Equally important is close collaboration with the data protection officer from the very beginning — not as an afterthought, but as an integral part of the project. This produces solutions that are both privacy-compliant and performant.

A similarity factor is a machine learning approach used when no negative training data is available. Classical classification methods require both positive examples (customer has purchased) and negative examples (customer has not purchased). When only positive data exists, standard methods cannot be applied.

The similarity factor solves this problem by comparing each customer with the characteristics of known buyers. The more similar a customer is to the profile of satisfied customers in a given segment, the higher the predicted purchase probability. This approach is particularly useful in sales contexts where only purchase data but no rejection data exists.

In the ADAC Hansa project, the similarity factor was used for all three AI models: travel recommendations, training recommendations, and churn prediction.

Cross-selling potential becomes visible when customer data from different business units is brought together and analyzed jointly for the first time. This frequently reveals: the majority of customers use only a fraction of the product portfolio. In the case of ADAC Hansa, fewer than one percent of members had booked products from more than one business unit.

Exploratory data analysis identifies customer journeys, seasonal patterns, and customer segments. AI models go one step further: they calculate for each individual member which products from other business units are most likely to be of interest. This transforms an invisible potential into a concrete recommendation.

That depends on the starting point: How many data sources are there? What is the data quality? What infrastructure exists? Typically, such a project is structured in phases: data expedition and consolidation, exploratory analysis, AI model development, interface development, and production operations.

What matters is that each phase delivers standalone value. The exploratory analysis already yields insights before the first AI model is built. The consolidated data foundation is a gain even independent of the sales interface. Pragmatism beats perfection: better to start with available data and improve iteratively than to wait for the perfect data situation.