In today’s data-driven economy, B2B marketers find themselves in a strange place.
On the one hand, they are data-starved — locked in a perpetual struggle to claw together enough of the right data on their customers, prospects and accounts to drive business to sales. Yet at the same time, they’re also drowning in data — albeit, much of it made up of unqualified leads, or including critical data errors like missing or faulty information on job positions, or out of date contact details.
This two-headed hydra is particularly challenging, because addressing one issue exacerbates the other. Buy more data, and you will inevitably bring with it no small number of faulty, unqualified or duplicate leads or accounts. Slowly and painstakingly verify each and every lead before sending off to sales, and your sales reps will soon complain about the slim pickings fielded by marketing.
The explosion in marketing data and analytics solutions, illustrated two recent reports by leading marketing research firm Forrester, offer an insight into how companies are attempting to meet these two challenges simultaneously. The Forrester B2B Marketing Data Provider Landscape and Predictive Marketing Analytics Wave showcase the leading solutions in both the data provider and predictive intelligence fields — the latter of which is a relatively more recent phenomenon, which seeks to address marketers’ inability to make sense of their masses of data. Or as Forrester puts it: “to help unearth buyer insight from mountains of B2B data.”
Here at Leadspace, we are proud to have been featured prominently in both reports. For us, this not just a pleasant coincidence, but a validation of what we believe is the only solution to effectively tackling these two opposing yet related challenges.
First, I should add that the very existence of two separate industries — data and predictive intelligence — is a sign of how slowly the B2B marketing and sales space is adapting to the world of big data. Yes, we need both; but in order for either to be truly effective they must be used symbiotically, not as separate point solutions.
Raw data, no matter how accurate, is strategically useless for marketers in particular to use, as the sheer volume they deal with is impossible to process and draw accurate insights from manually. To do that, we need analytics technologies which leverage AI to automate the process and draw actionable intelligence (e.g. predictive scoring) from our data.
However, any analytics or AI/machine learning technology is only as good as the data it is using. And even if we assume that all your data is accurate, that doesn’t mean it’s the right data. For example, maybe “job title” or “industry” aren’t actually the determining factors for a qualified lead or account at all, but rather less obvious data sets like expertise, specific job responsibilities or installed technologies. But how can you know what the right data is before you’ve understood who your ideal customers are and what marks them out from the rest?
(And all this is without considering the more general, fundamental challenge of trying to patch together multiple disparate technologies.)
To be sure, there are lots of exciting opportunities in both the data and predictive analytics spaces. But only through a single platform which brings both big data and predictive intelligence AI together can Marketing or Sales effectively execute on either one.
We’re glad to see Forrester recognize Leadspace for taking this holistic approach — one which we believe will come to define the future of B2B demand generation.
Find out how the Leadspace’s B2B Audience Management Platform combines these two vital components — Big Data and AI — to guide marketing and sales to their best customers and prospects:
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