Out of all data products, the single largest group is what we call "opportunity finders," databases used by customers to identify sales prospects. These databases, many of which originated as print directories, have followed the normal trajectory of data publishing: moving from being a mile wide and an inch deep to adding tremendous amounts of depth. As publishers add more information to each listing (e.g., revenue, number of employees, year founded, line of business) they enable their users to engage in much more sophisticated targeting of sales prospects. In those situations where a company is looking to sell into a very specific market segment and the data exists to isolate those prospects, it's pretty much mission accomplished for the data publisher. For example, if you sell a product that is only of interest to banks with more than ten branch offices, you can probably find a database that will quickly help you to identify a manageable list of qualified prospects for your product. But there are an awful lot of situations that aren't so neat and tidy. For example, some companies have huge target markets such as "all companies with revenues under $5 million." Some companies literally target everybody. And an awful lot of companies are seeking highly defined target markets for which data doesn't exist (e.g., all private companies whose are considering starting a 401(k) plan).
Until recently, what this meant is that companies were required to slog through a huge number of semi-qualified prospects. Using expensive telesales and field sales teams, they would eventually identify some good prospects, but the work to do so was expensive, slow and not a lot of fun. Could there be a better way?
What we're seeing now are remarkable advances in lead scoring and predictive sales software. The premise is simple: by bringing to bear a lot of information and a lot of smarts about what data points might identify a good prospect, we are getting better a separating strong prospects from weak prospects. Some of the companies leading the way in this area are Lattice Engines (a DataContent 2012 presenter), Context Relevant and Infer.
The potential opportunity for data publishers is to move more aggressively into lead scoring for your customers. Imagine (possibly in combination with one of these firms) to allow your customers to enter parameters about their sales targets, then let them search your data to receive not only the raw information but a predictive score as well to indicate the quality of the prospect.
It's all part of the continued push to data publishers to surround their data with more powerful tools. And is there a tool more powerful that you can offer your customers than one that can help pinpoint where their next sales are most likely to come from?