Prospect Pools - a 'how to' guide

Jed Mooney, Managing Director of Datahold, explains why prospect pools are rapidly becoming the most sought-after tool in direct marketing

As recently as one year ago, if you had typed the words 'data pool' or 'prospect pool' into Google you'd have come up with very little.

Now, however, prospect pools are becoming the most sought-after tool in direct marketing.

A prospect pool is basically a unification of different data sources into a single database structure.

It's ingenious because by merging different sources of data together, the information held on any one particular customer or prospect is considerably expanded.

Imagine list A holding information on a person's credit rating, list B holding information on what types of discretionary purchases they make, list C what income bracket they fall into and list D their age, gender and geographical location.

Merge these sources together and you have a powerful prospect pool that would interest any marketer.

Obviously, the above is a simplistic version.

Modern prospect pools are phenomenally powerful and contain dozens of selections for as many as 30 million individuals.

Additionally, once the prospect pool has been built, it can then be overlaid with a company's own customers and prospects.

From this, analytical models can be built which in theory mean that really good prospects can be identified and scored/ranked accordingly.

Is a prospect pool easy to build? No, it takes specialists who have the proper expertise in sourcing relevant data and consolidating it together.

However, there are a few golden rules that will point you in the right direction.

1 Choose your source data carefully: a prospect pool is underpinned by the quality of the source data.

This doesn't just mean data freshness, uniqueness and accuracy.

It's also about identifying the various data sources relevant to your product or service.

The more holistic the data universe is, the more powerful the subsequent profiling and analysis.

2 Arrange a license agreement with the data owners: once you have identified the sources of data most relevant to your product or service, you will need to approach the data provider and strike a usage deal with them.

Most of the big data providers will go on a per-usage basis along with some guarantees.

The cost will vary but it makes sense to be judicious about exactly what type of data you need as irrelevant selections will drive up the price unnecessarily.

3 Suppress the data: make sure that you run all the data against relevant suppression lists, thereby ensuring that people who have moved house, died, who have asked not to receive mailings (MPS files) etc are removed from the prospect pool.

While not legally binding, suppression is 'best practice' and all competent computer bureaux will offer suppression solutions.

4 Consolidate the data: once you have procured the data and suppressed it, you have to consolidate it and merge it together to create a single unified prospect pool.

The data needs to be configured and presented in a way that facilitates ease of use, selection and analysis.

An acknowledged data expert will need to steer and oversee this.

5 Overlay your own customer data: once the prospect pool has been built, you can overlay it with your own customers and prospects.

From that you can build your analytical models which in theory mean that you can identify the really good prospects and score/rank them accordingly.

Given that the data you select has been overlaid by your own customer profile, the prospects that emerge will be very finely targeted.

6 Assessing the results: once the first few campaigns have been implemented, it's necessary to analyse the results and assess the success of the campaign(s).

The advantage of a prospect pool is that you can overlay the respondents and find more prospects of a similar profile.

It's a superb way of quickly rolling out a campaign using exactly the same criteria that was successful in the first place.

The genius behind prospect pools is that they harness the individual power of each data list and maximise them by carefully merging them together.

The end result can often mean an unparalleled source of rich data.

It's no wonder the Americans have been creating them for years and, if current trends are anything to go by, it looks like the UK is following suit.

However, a word of warning: find an expert.

Author Jed Mooney is Managing Director of Datahold.

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