Development of a scoring algorithm for databyte® GmbH

Learn how an innovative scoring algorithm helps databyte® GmbH identify new customer potential more precisely, increase efficiency, and reduce costs. Discover the exciting details of this data-driven success story and be inspired by how your company can also benefit from customized AI solutions.

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Advantage

Individual score values ​​with high purchase probability

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Data

Customer profiles by turnover, number of employees and previous industry affinity

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Methods

Matching database entries with customer profiles

Challenge

databyte® GmbH provides its clients with comprehensive business information, including for new customer acquisition. A total of more than 5 million business owners are available, along with more than 100 million pieces of additional information. The accuracy of the target group potentials identified for new customer acquisition is the decisive quality criterion.

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Goal

In order to further improve the already good fit, a machine learning scoring algorithm is to be developed that will identify potential new customers with the highest possible probability of closing based on existing customer lists.

Data Science Implementation:

Three Steps to Your Successful Data Product

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Solution

Existing customer lists were first segmented by industry. eoda developed customer profiles for the resulting industry segments, incorporating a variety of different information such as revenue, number of employees, and previous industry affinity. To draw conclusions for new customer acquisition, eoda then analyzed the database entries for matches with the customer profiles.

The greater the match between a company in the database and the developed customer profiles, the higher its rating is. This allows the most interesting potential new customers to be identified for clients. During the development process, the algorithm's performance was validated using various customer lists. This demonstrated that the algorithm delivers reliable and even better results than before for various customer profiles.

Result

The result is an overview of all companies with customer-specific score values ​​that best fit the client's customer profile and have a high purchase probability. This increases the efficiency of new customer acquisition and simultaneously reduces costs thanks to reduced scatter loss.

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Your expert on Data-Science-Projects:

Lutz Mastmayer
projects@eoda.de
Tel. +49 561 87948-370







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