[VIDEO] How to Evaluate a Data Provider

Does this situation sound familiar?

I’m in the middle of evaluating sales coaching tools (3 to be exact), and while I see some pros and cons to each, there’s nothing really standing out to make one of them the clear winner.  One of our partners called this recently, “selling in a sea of sameness” – so many tools, it’s hard to differentiate.

The flip side is I’m sure those vendors just can’t stand that I’m unable see how each one is the best.  They all surely think I’m missing something obvious,  and the reality is, at least one of them is probably right about that.

My guess is we all feel as though many of our potential clients don’t do a good job evaluating our services – they focus on the wrong things when it comes to solutions in our space, their RFPs miss the mark, and so forth. Of course, that’s our job, the job of sales and marketing – to help educate our markets, to cast a bigger and better vision of our solutions

One of our partners called this recently, “selling in a sea of sameness” – so many tools, it’s hard to differentiate.

With that in mind – today I’m going to give you some inside baseball thoughts, on how a data provider thinks you should evaluate a data provider.

Watch the Video

 

What You May Be Doing Wrong

Not everyone does this – but it’s pretty common for people to evaluate data 1 of 2 ways:

  1. They take a demo, ask to pull up an account or two that they know well, and then judge the quality of the data provider based on whether or not the people they know show up in that company’s profile.
  2. They ask for raw volume numbers – how many companies do you have – how many contacts do you have – and data points – do you provide email, title, social links, firmographic data – etc.

The first one attempts to evaluate the quality of the data and the second method attempts to evaluate the quantity of the data.

The problem with the first method is it’s too small a sample size.  You’d end up buying the luckiest data provider, not necessarily the best.  Maybe they had your buddy Johnny in their profile, maybe they didn’t – neither fact tells you much about the data overall.

The fact is data is not a commodity, and we can’t treat it as such.

The problem with the second way (RFP method) is it assumes data is a commodity.  Specifically, that the contact record in one data provider is as good as one in another, but we all know that’s not the case.  We’ve all been burned by bad data – lists we’ve purchased where half the data is wrong, no direct phone numbers and so forth. The fact is data is not a commodity, and we can’t treat it as such.

 

The Good Way to Evaluate a Data Provider

  1. Figure out what data points you need.  Everyone needs company/firmograhpic data – industry, size, location, etc..  Everyone needs contact data – name, title, email, phone, social links…
  2. Depending on your business you made need more nuanced data points.  Do you need to find companies using a specific technology, either one you integrate with or one you compete with?  Do you need bed count data for hospitals?  Number of locations?
  3. Prioritize these data points; no one is going to have all of them in spades.
  4. Go ahead and do the RFP style analysis – get counts on companies and contacts in your TAM and meeting your ICP – but don’t stop there.
  5. After you get an overall picture of the data – you have to test it.  You have to see if the data is as good as advertised because data is not a commodity. Instead of asking for 1 or 2 examples – ask for a sample list of 100 or as many as they’ll give you – records that meet your ICP. Then call and email those records to check the validity.  Even better, have them pull the list while you’re watching so they can’t “game it”. Most companies are ethical, most sales reps are ethical – but some will subscribe to “the ends justify the means” and will pull the wool over your eyes by giving you a list with a bias by selecting data that is not representative of the overall database quality.

If you can confirm that A) they have the amount of data you need, B) they have the data points you need, and most importantly C) that data is good, clean, and accurate – then you’ve done a good evaluation and can feel confident in your choice.

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As Chief Revenue Officer, Patrick manages sales and customer success at DiscoverOrg where he is responsible for ..read more