Scoring models are used to create a relevant basis for decision-making, especially in the B2B market. The goal of such models is to evaluate data sets using various criteria in order to use them specifically for new customer acquisition.
Target Group Predict: Buzzword-based address scoring
Dastani Consulting has developed the powerful predictive analytics tool Target Group Predict. Several million companies in the market are evaluated, for example, with a scoring model with regard to their affinity to the company, sales potential and suitability for selected product areas. The software calculates and assigns different score values depending on the degree of the criterion. The interactions between the basic data are also taken into account.
The use of scoring models serves to evaluate targeted new addresses and increase the probability of purchase. In this way, scattering losses can be significantly minimized.
Scoring models in the customer base
Such models can still be used to effectively manage advertising campaigns in order to make the most of the existing customer base.
Here, criteria such as the time of the last purchase, purchase frequency and purchase value are taken into account. They reflect the purchasing behavior of existing customers. A high score value results if the customer has only recently bought a product. However, if the purchase is already well in the past, the purchase probability decreases and the score drops.
The time of the last online visit, page views and commitment are still important criteria. A combination of all criteria can indicate a high score from a potential customer with a large shopping cart, which must be targeted with appropriate marketing measures.
Scoring models are so useful for new customer acquisition
A lot of information such as order frequencies or sales figures are not available for new addresses. However, this is not problematic, since the profile of existing customers forms the basis of the foreign addresses. It is therefore appropriate to define a somewhat broader target group from the customer base.
For example, the buyers of the last 12 months are determined. The resulting profile of this buyer group is projected based on the information in the new address database. Thus, the potential new customer addresses can be filtered out top-down. Graphically, this scenario can be imagined using a “statistical” funnel: Exactly those potential new addresses are selected which, based on the external information, show a high degree of similarity to the target group from the customer base. The probability of winning new customers is highest here.
Efficient new customer acquisition
Do not take any risks when acquiring new customers. We at Dastani Consulting are happy to support you with our powerful predictive analytics tool to ensure efficient acquisition in the B2B market.