Success Stories
Segment-Specific Sales Approach Through the Enrichment of the Customer Database with External Information

Customer
Our customer, a leading automotive financial services company, is represented in more than 50 countries. With nearly 50 years of history, the company employs more than 1.200 people and offers a variety of financial services.Challenge
Nowadays, many companies are pressured to utilize their advertising budget efficiently. Marketing actions are ideally designed so that as many (potential) customers as possible are reached with a minimal allocation of resources.Solution
By means of combining data from the customer database with milieu data, thaltegos helps companies to address the right target group at the right place with maximal likelihood. The customers of a specific product – for example the car model A – often exhibit great similarities with regards to age, lifestyle and family status. Therefore, it is reasonable for companies to additionally consider milieu data (e.g. SIGMA Milieus® and DELTA-Milieus®) in the context of customer segmentation and the respective communication. Once the available customer information is enriched, current customers can be divided into segments based on product and brand affinity. Moreover, customer profiles can be derived and conclusions about potential customers can be drawn. For instance, it is possible to determine which characteristics (age, income, etc.) a typical customer of the car model A has. As a result, a scoring model is derived which assigns potential customers to segments even if only little or poor information is available.Benefit
This enables companies to adjust their sales and communication activities to be more client-specific. Potential customers of a certain product can be identified and targeted by marketing actions more precisely. Customized scoring, based on product and brand affinity segmentation, makes it easier to target potential customers. Consequently, companies can learn from existing customers about potential customers to then adjust the sales approach to be segment-specific. For an automobile manufacturer this sustainably increased the efficiency of customer communication and sales activities. The conversion rate was increased by 100 % compared to a similar campaign. Furthermore, this information can be used for a customer-oriented product design.Creation of Configuration Suggestions for New Vehicles Through a Data Mining Application

Customer
Our customer, a globally operating German car manufacturer, employs nearly 130.000 people and is one of the largest commercial enterprises in Germany. The traditional company, which looks back on more than 100 years of history, is one of the premium brands in its sector.Challenge
In the automotive industry, vehicles are usually individually configured by the customer and then specifically produced. However, many markets and the respective customers are located far from the automotive manufacturer. For this reason, pre-configured vehicles are planned and produced. To ensure optimal sales, these vehicles should be aligned as closely to desires of potential customers as possible.Solution
thaltegos supports a car manufacturer with this optimization through a data mining application. To reflect the wishes of future customers as well as possible through configuration suggestions, historical order data are analyzed. On the one hand, it is determined how often certain options are ordered by different customer segments. On the other hand, correlations between these options are calculated. For this, the data mining application groups the multitude of features into overarching factors by means of a factor analysis. For instance, this results in a factor for sporty options and a factor for comfort-oriented options. Based on these findings, similar orders are aggregated to determine statistically significant configurations for the individual customer clusters. This enables the creation of configuration suggestions which enhance the ordering of new vehicles in the sales process.Benefit
The utilization of these configuration suggestions makes it possible to verifiably align vehicles with customer and to implement pre-configurations that lead to fast sales. On top of that, the insights gained from the order data analysis can be used for further optimizations. This encompasses the planning of special options as well as the forward-looking allocation to production sites. Moreover, these results enable the deduction of market trends (based on the historical order data) as well as measuring the effects of campaigns and activities in product management.Prediction of Service Intervals in After-Sales for the Optimization of the Individual Customer Approach

Customer
Our customer, a globally operating German car manufacturer, employs nearly 130.000 people and is one of the largest commercial enterprises in Germany. The traditional company, which looks back on more than 100 years of history, is one of the premium brands in its sector.Challenge
The after sales business is highly important in the automotive industry. Apart from the sale of new vehicles, the service, parts, and maintenance business constitutes a significant share of the profits. With increasing competition and decreasing customer loyalty it has become ever more important to retain customers and to submit the right offer at the right time.Solution
thaltegos supported an automobile manufacturer in the prediction of service intervals to enable a targeted sales approach. For the prediction of the next service event in after-sales it is crucial to define the relevant customer segments as well as influencing factors on the service intervals. For example, the intervals for an oil filter change can differ considerably depending on the vehicle model and age. To include these factors in the prognosis, service history data have been used as a starting point. Based on this information, the “Interpurchase Time (IPT)” – the time between two consecutive service events – as well as factors influencing the IPT have been extracted. Consequently, it was possible to determine the effect of specific factors and to derive a regression model for the prediction of service intervals.Benefit
By means of this model, future service events can be predicted more precisely than before. Compared to the simple sequence continuation, the share of correctly predicted service intervals is 20 % higher with the regression model. Hence, for instance the next oil filter change of a vehicle can be determined more accurately and the customer can be approached in time. The prediction of service intervals enables the automobile manufacturer to carry out targeted marketing actions in after sales. Depending on the service demand and service time it is possible to individually approach the customer with the right offer at the correct moment. Furthermore, segments at risk of customer churn can be identified to launch measures for customer retention. As a result, dealer and workshop capacities can be planned better and customer loyalty and profitability are positively influenced.Implementation of Business Intelligence Solution for measuring Digital Performance
