How left-field data creates a higher performing prospect list

#DataHQIDEAS

These days, there are databases built specifically for B2B marketers.

They’re not automatically offered when you buy data so make sure you ask for them. Why? Because they hold richer and broader data sets.

This fact alone gives a screamingly obvious advantage.

But it gets much more fun when you start thinking about how you can use what initially seem to be the quirkier criteria such as:

- Car park size
- Energy use
- Outdoor space square footage
- Installed IT profile
- Import/export history
- Telecoms Spend
- Fleet size

Your goal is to model your prospect database on the attributes of your best customers

If you’ve always bought data on traditional criteria such as geography, sector, and size then you know that there’s a fair amount of guess work that goes into curating your list. Intelligent guesswork of course, based on years of experience in your trade – but ultimately it creates quite a blunt instrument at the top of your sales & marketing funnel. You then throw expensive activity after that list in the hope that you can narrow it down and find the people that are interested and ready to buy.

It’s not uncommon for the wastage to be 90%+

By creating a prospect list that’s modelled on the attributes of your best customers instead – you improve success rate and reduce this huge wastage that is so widely accepted in our trade.

This is where the left-field thinking comes in.

Forget traditional criteria. Instead, model your existing customers against these much broader B2B database which examine years of trading, premises types, LinkedIn coverage, forensic level industry breakdowns and more.

We recently carried this out for a client in the financial services industry and strangely one of the factors that we found to impact the make-up of their client base was the size of the car park!

Once this profile is understood, you use these richer attributes to find prospects that are a better match.

For even better targeting, look to build propensity models based on each and every individual prospects' characteristics. If you want to know how, you can read more here

Whilst every model is unique we typically see improvement in engagement with campaigns of 200% when using modelling for target selection. In some cases we have found it improves targeting by 650%!

Adam Gould
Chief Problem Solver & Data Overlord 😉
You can email me if you need some help
Connect with me on LinkedIN for more #DataHQIDEAS