A major German insurance company increased commercial sales efficiency with tetrel's intelligent B2B sales data.
A large network of offices and agencies ensures that the insurance company's B2B products are offered to a large number of companies. Until now, the key factors for sales success were primarily the insurer's strong brand and the personal network of insurance agents.
However, if sales are based primarily on network effects, relevant groups of target customers are often completely overlooked. The insurance company therefore wanted to systematically analyze and develop the market potential in relevant regions.
This could only be done on the basis of current data on the companies in the relevant regions. Target customers had to meet essential sales criteria such as industry or company size. In addition, existing customers had to be recognized and filtered out despite different spellings.
In addition, since more than half of the companies listed in the commercial register do not have their own business operations, for example shelf companies, asset or real estate management companies, or service companies, these had to be reliably removed from the sales campaign.
The insurance company uses the daily updated data from the tetrel company database as a starting point. These are filtered by employee numbers and relevant industries.
An AI-based matching of existing customers reliably identifies which companies already purchase products from the insurance company
For automatic prequalification, a Deep Learning model analyzes companies' web activities. By scoring different web sources, it recognizes which companies are actually active and thus relevant for the sales campaign. In addition, current company information is extracted from the website in this step, ensuring that the sales department is well prepared for customer meetings.