A B2B OEM manufacturer was growing its sales capabilities and launching an internal direct-sales team to complement its reseller partner network.
To achieve this customer-centric shift, they needed a 360-degree view of all the existing customers who have, or would in the future, purchase their product. The initial data about this originated from their 1,500+ reseller partners.
The existing data suffered from typical data quality issues: it was frequently incomplete, inconsistent, or otherwise dirty.
There was also very little data in the records. Only company names and addresses, phone numbers, and sometimes a website were available.
Within our client's organization, the team working with us was small, and faced significant bureaucracy in interacting with the wider client organization.
This had significant effects on their ability to execute the project: for example, they would have to repeatedly request a dataset, or encountered long delays in having systems turned on.
We built a solution for our client that, after only one month of active development, had resolved the customer data from all 1,500+ partners.
With the initial cleansing and matching complete, the client’s Salesforce connects to our solution via a custom API to request data processing.
As the client's new sales team makes updates, or additional data is received from a reseller partner, our solution checks the new data against the existing data and determines whether the customer is already represented in the database.
Because there is very little data available for matching, we use our nuanced fuzzy match and merge rules to identify and resolve high-confidence automatically, while low-confidence matches are diverted for manual verification by data entry specialists.
With a 360-degree customer view, our client is actively growing its internal direct sales team. Our solution's real-time integration with the Salesforce database makes sure that the client always has the best and most up-to-date customer data.
Our client wanted to be able to operate the solution independently to some degree. We trained the selected specialist to handle many of the most important needs for the project, including extending the API to accept new file formats and changing the match rules to better reflect evolving business understanding.
Our personalized, boutique-style service was particularly useful in this project, since the team we worked with was facing external bureaucracy.
We were able to respond to our client very quickly as project barriers were resolved, minimizing delays. We were also able to work with the data in whatever form it was delivered to us with, which the team we worked with had little control over.
Finally, the client had been considering purchasing Dun & Bradstreet data to enrich the customer data. As a byproduct of our solution, the client discovered that the D&B data did not improve the quality of the results, saving the client the cost of the unnecessary purchase.
