Recently I was asked to source some data for a client, and after engaging the main data suppliers, I chose a vendor I trusted.
I’ve purchased data many times, so I wasn’t expecting this result.
Well you can imagine I wasn’t too happy to find over 60% of the contacts supplied where no longer at their stated organisations.
Now 60% is very high. With B2B data degrading at 10-22% per annum depending on which industry, I was expecting 1 in 10 or at worst case 1 in 5 contacts to be out of date (the latter case is what I consider really bad data!)
But 60% blew-my-mind. How could someone sell that.
I went back to the vendor and questioned them, only to be told that the supplier they got the data from doesn’t guarantee the contact but only the organisation.
But, I am buying contacts!
So, I couldn’t get my money back!
The number of contacts wasn’t too big, so we fixed the data. Found the right contacts and their contact details and supplied it to my client who was happy, totally unaware of our efforts to supply good data.
I just couldn’t pass on poor data to a client, but clearly others can.
I guess you can always challenge the vendor but the monetary amount was small and not worth the effort going down a legal route. I’m just focused on getting good data to my client.
Now the vendor should have told me that the contacts were not guaranteed, in which case I would have been suspicious and very likely used another vendor (I guess, that’s why I wasn’t told.)
When buying data, in many cases (as was in this case), you are asked for money up-front. You’re paying for something you have not seen!
To me this is un-natural, because if I don’t like what I see, I can’t get my money back.
I can hear the data suppliers shouting, ‘what about those who use the data and then ask for the money back, that’s so unfair to us’.
And indeed, that is unfair. There will always be a contingent that looks to take unfair advantage, but hopefully that is the minority rather than the majority.
It doesn’t make me feel any better that this process seems un-natural. Un-natural in the sense I can’t see what I’m buying before payment and have no opportunity for a refund.
Ah…the legal minds echoing “caveat emptor” – Buyer Beware. Certainly, in this case the seller knew more about what they were selling than I knew about what I was buying. I’ll continue to learn to ask more questions.
Buying something you can’t see without a good way of getting your money back is dangerous.
So, here are some tips for would-be data buyers:
- Check what is guaranteed and what’s not – Are contacts guaranteed, are contact details (phone numbers, emails) guaranteed, are organisations guaranteed, etc.
- What’s the compensation – If something is wrong, what can be refunded. Sometimes the refund is per record sometimes it’s when some percentage is wrong, say 10%, etc.
- Find the error rate - Don’t assume because you’re buying data from a reputable vendor that it’s correct, there are error rates and it’s important to understand them.
- Define your data precisely – be specific about the parameters you give for the data you want.
a. For B2B, be precise with geography, industries, organisation size (employee counts and revenue), contact job titles, contact job levels, contact job functions, contact details you want, etc.
b. For B2C, be precise with geography, demographics, propensities, contact details you want, etc. - Ask for Samples – Good data vendors will send you a sample of their data. This gives you a little insight into the data, but remember it’s a sample and it may be a good part of the data and not representative of the whole data set.
- Ask for Statistics – Ask for how many contact details they have (how many phone numbers, how many emails), how many contacts with a specific job title or how many contacts within a demographic, etc. You should have a detailed insight into the data before any purchase.
I see so many clients who are put off by buying data, because their expectation is that it’s all good, in fact you’re buying a percentage of good data and it’s important to know what that percentage is.
I share this post simply to help improve the data buying experience.