As any TV property guru will tell you, you don’t make money when you sell a house you’ve developed. Your maximum profit was decided earlier, when you bought it.
Savvy developers don’t make the mistake of falling in love with a building, and paying over the odds for it. Their approach is rational: look for exactly the right property, at a price as far below the potential resale value as possible. Whether that means the worst house on the best street … or the largest house in an area with aspirational families in social group AB1.
And it’s no different for your next database-driven marketing campaign. The ROI of that campaign is hugely influenced by the decisions you made when obtaining the data. Otherwise known as your data strategy.
Your campaign collateral may look beautiful. The copy sells your benefits with aplomb. The message is perfect for your audience. But if the people on your marketing list aren’t in that audience? You’re looking at response rates one DM supremo nicknamed “the low zeroes”.
So what can today’s marketer do to boost response metrics into the digits?
The answer goes beyond just buying 5,000 names when you’re ready to roll out. It means making choices about data – and buying data – earlier.
Echoing the property pages, you make money when you buy. Not when you sell.
When should that buying process begin? The same time your marketing cycle does: the planning stage.
To find the best new prospects, look at your existing ones
At the time you’re planning your next cycle, there are countless insights to be gleaned from your existing customers. And not always in the way you’d think. The questions you can ask yourself (we do it all the time!) can send your data strategy in a whole new direction.
If your customers are all engineering companies in the automotive sector, you might think “other engineering companies” are your best prospects. And you’re probably right. But a little creative thinking can work wonders on boosting response when targeting.
Name and adddress data is not the only data. Today’s marketing databases contain a huge range of useful “metadata” (data about data) you can make use of . . . if you look at it through the right lens.
One example is the SIC codes that describe virtually every type of business in the UK. They’re not just random lists of numbers; they group together business activities at increasing levels of resolution. Starting with a few broad categories, then drilling down into exact descriptions.
When building a marketing database, the first step for many marketers is simply to search for prospects with the same SIC code. But this often means missing companies in SIC classifications a short “distance” away. Companies that are great prospects for your services, because they suffer the same business pains as your customers… but happen to work in a different sector.
Or perhaps your customers are all of a similar size: 50-80 employees. That could mean they’re ready to grow and achieve scale. What if you targeted prospects not by sector, but by size? It might unlock a whole new revenue stream for your business. (A good data strategy can sort prospects by this, too.)
It’s questions like these that can help identify fresh prospects… by looking at existing customers, and at psychographics more than demographics. The best part? You’ve got the data to answer those questions in your business already.
To target your next campaign, look back at your previous
Looking at your existing customers works. And looking at your existing campaigns works, too.
When you review your campaign data, it’s worth diving deeper into what those responses meant. Did your best customers take a while to respond? Did they receive 3-5 communications before contacting you? Did they respond themselves, or pass it to someone with another job title? Or do they read your sales letters, then respond by a different route, like coming to see you at an event?
(You wouldn’t know it from looking at some young agencies, but there’s more to life than email marketing!)
If you know the length and stages of their buying cycle, you can shape your data strategy around it. Planning your customer outreach in sync.
To find out what does work, look at what didn’t
Lastly, remember the value of a data-driven marketing strategy is – often – what it tells you not to do.
So learn from what didn’t work. There’s value in knowing your marketing promise worked on one segment but not another. That DM works for products but email for services. That what worked in Birmingham didn’t work inside the M25.
Even using a business database correctly puts you ahead of many B2B marketers. (Including some of the big boys). By buying data and making choices early, you can unlock every last bit of value in it.
In summary: to look forward, watch your six
With the right approach, a data-driven marketing strategy can be safe as houses.