Marketing & Sales
Data enrichment and advanced lead scoring for B2B growth
Learn how data enrichment and a lead scoring algorithm helped a B2B company understand customer behavior, identify high-potential clients, and effectively target marketing and sales efforts.
To protect confidentiality, we may alter specific details while preserving the accuracy of our core contribution.
Context and objectives
A European B2B food and drinks distributor wished to gain a deeper understanding of their client base and to increase the potential number of leads.
They were facing two main challenges:
Poor data actionability for the client base, with only consumption behavior data and few external data manually filled in by the sales team
An overwhelming number of prospects to accurately qualify from a list provided by (expensive) B2B data services
Approach
Data enrichment
The first step of the project involved gathering internal data about the behavior of the customers, such as:
their consumption patterns,
the type of products and services they gravitate towards,
their purchase frequency.
We then collected relevant B2B open data (financial & sectorial) and created extra variables linked to company size, EBIT, and other necessary factors. This process was crucial in providing a more comprehensive view of the clients database.
Lead scoring
Next, we developed a lead scoring algorithm designed to differentiate between “high” and “low” potential clients. This was a critical phase of the process, as it allowed the company to understand their customers better and identify potential leads.
The criteria for defining a high/low client were initially restrictive, which made this stage especially challenging. However, after some refinements, the algorithm was able to clearly differentiate between the two statuses.
Results
We managed to enrich over half of the CRM entire database, with more than 80% of the clients having made a purchase in the last 3 years.
Additionally, the data enrichment project revealed which sectors and firmographic types linked to higher commercial performance; a valuable insight that helped them target their marketing and sales efforts more effectively.
The lead scoring algorithm was also a success. It not only provided the client with a deeper understanding of the type of companies they were working with, but it also identified hundreds of companies with the highest potential, enabling them to focus their resources effectively.
In terms of deliverables, the client received:
their customer base now enriched with relevant data,
a list of target companies, each scored based on the likelihood of being a profitable client and their respective financial/sectorial data,
a trained model,
the data enrichment project’s code,
comprehensive documentation,
a Power BI dashboard.
To safeguard confidentiality, we may modify certain details within our case studies.