Last time, we talked about the pitfalls of buying data, so, now let’s look at how we resolve some of those – assuming we haven’t talked you out of it!
We can break this down into two parts: acquiring the data, and what to do with it afterwards.
Today, we are going to look at acquiring the data
- Before you do anything else you must decide who you want to target and what data you want. By that, we mean the type of company, industry sector, the decision maker/job function you will be contacting and so on, plus whether you want named contacts, postal addresses, telephone numbers, email addresses… Very often the more detail you want the more expensive the list, but equally there is no point in cutting corners only to have to go back and do the job again – negating any savings you made!
- The closer the list looks to your own customer database the better. In an ideal world, we would clone our best customers, but since that isn’t possible (yet!), we need a list that does that for us.
- Be just as clear about what you don’t want, as what you do want. You are able to request that certain market sectors, or any defining factor, can be excluded from the data. This is far more important than it might sound. Think for a moment about your own experiences, and how often you have received a mailing for something that is just completely inappropriate for you – the bath aid for the twenty something person springs to mind, as an example here.
- Choose your provider wisely – it is essential that whoever you choose complies with the Data Protection Act 2018 and GDPR (absolutely no way do you want to get on the wrong side of that!). We also recommend choosing a provider who is a member of the Direct Marketing Association and registered with the List Warranty Register – many thanks to Dee Blick for advice on this too
- Change your mindset from buying data to renting data. What’s the difference? If you buy the data then you take ownership of it, and it is your responsibility to look after it (and it costs more!), but if you rent it then you can only use it a limited number of times (which reduces the cost). And don’t think you can cheat and use the data more often, as there are “dummy” names in the list set to check you don’t break the rules.
- Regular cleansing – following on from renting your list, you want the provider to keep the list maintained and cleanse it regularly to reduce inaccurate information, this includes changes in personnel, business moves and companies that have closed. Of course, that comes at a cost.
- Consumer & Business rights – whichever provider you choose needs to check the data against the Corporate Telephone Service for business, the Telephone Preference Service (TPS) for consumer data and Mailing Preference Service (MPS). You don’t want to communicate with anyone who has expressly requested no contact, and potentially you can land in deep water.
- Success rates – any reliable provider should be happy to guarantee their success. You should be looking for rates in the high 90s, and steer clear of any company that is not prepared to provide those guarantees. Usually, companies will provide additional free data for any rejections over their guaranteed percentage.
- When evaluating the cost of the list think about the value of the customer. If your average customer spends £10,000 per year with you the value of converting one name to a paying customer is better than for a customer spending £10 once per year. Hmmm, we can see that this sounds complicated, so we will come back to this point again another time…
- If it sounds too good to be true, it probably is. Any company that appears to offer incredible value – vast quantities of data for very limited expense, the warning bells should ring. There is a cost to maintaining good quality lists that are reliable and well researched, and that is ultimately what you are paying for.
So, if you’re looking at purchasing some data for your business right now, make sure you read through this list first, and tick it off against your requirements.
Next time, we will look at using the data.
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