Marketing to the Right Prospects? The Story Behind the Data

Consumer outreach campaigns are one of the oldest tricks in the book

But the age of big data has created new and thorny problems. With the sheer volume of data on potential customers available on the market, accessing the right information to identify the right prospects at times feels like trying to find a needle in a haystack.

A mutual insurance provider was taking the standard approach for its direct marketing and email outreach: Step 1 identify a list of target customers; Step 2: reach out to the largest marketing groups to procure a marketing list; and Step 3: have these firms append data to enrich their customer prospect list with external data.

The match rate was less than stellar. Actually, it was less than 10%.

Read a case study on enabling a leading US bank to test and onboard more data.

You would think the bigger the pool, the better the results, right? Not quite. The gap here is actually understandable. Marketing lists that cover 70% of your target customers are only good if your prospects aren’t in that missing 30%. The explosion of consumer data has also generated hundreds of companies that all fill a specific niche, and your target customers are scattered across dozens of lists.

As our ability to micro target individual consumers gets better and better, financial institutions – small and large – are going to increasingly find that the people they need to reach are spread across an increasing number of data providers.

That’s hugely cumbersome if you’re a big bank faced with wiring up and testing 30 different sources. And that’s cost prohibitive if you’re a small lender that can’t afford to integrate dozens of providers without any guarantees of success.

Working with this insurance provider, DemystLabs was able to test 15 different types of data and help configure a solution that starts with a name and a place of work and returns a plethora of data about each lead on the list that is useful for marketing campaigns. The Labs team figured out the right types of data, as well the right sequence of data checks in a matter of days.

Within a week, the match rate had increased to 20%, A week and several tweaks later, it had risen to 70%. DemystLabs was able to help design, build, and implement a solution that was 340% better within the same amount of time it takes database companies to turn around a single file. This 40x return on data investment meant the insurance company was putting its marketing budget in the right information to reach the right customers.

Find out how you could achieve the same results.



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