A client in the insurance industry planned to develop a prospect-targeting database to automatically create leads and distribute them among the sales reps out in the field. The client wanted to build their prospecting database from three data sources – the client’s own customer database; a Dun & Bradstreet database of business demographics; and an insurance network database which indicates who a given company’s insurance provider is and when the various policies expire.
Merging these databases together would allow the client to ask questions such as: Which companies in North Carolina are not currently a customer of ours, have more than 50 employees, and have insurance policies that are up for renewal within the next 90 days?
The information required to answer queries was spread across the three databases. The problem was that the only field that was common to all three databases was the company name field, and with the various ways a company’s name could appear, matching the relevant information from each database proved to be a difficult task considering there were over 20,000 records.
Using DataFlux data matching technology, relevant information from the three databases for each company were matched together. As a result, the new prospecting database can be queried, the knowledge workers can trust the answers, and field reps can quickly act on the information.
Yet even before a field rep made the first sales call, the client had generated an attractive ROI through a dramatic reduction in anticipated database-project expenditures.
The insurance company executive said that, had his department tackled these DQC challenges the “old fashioned” way, then the project would have taken one month to complete – plus overtime and weekends – instead of less than a week.