In this special guest feature, John Steinert, CMO of TechTarget, discusses how marketers must correctly complement CRM and marketing automation data with actionable purchase intent insights that come from external behavioral data. Purchase intent insights help marketers see the actions prospects take that are otherwise hidden. John helps bring the power of purchase intent-driven marketing and sales services to technology companies. Having spent most of his career in B2B and tech, he has earned a notable reputation by helping build business for global leaders like Dell, IBM, Pitney Bowes and SAP – as well as for fast-growth, emerging players. John is passionate about quality content, continuously improving processes and driving meaningful business results.
The use of predictive analytics in B2B marketing isn’t new, and it isn’t going anywhere. In fact, Gartner reports that the predictive analytics space will experience double-digit growth over the next few years.
Although predictive analytics can provide some directional value, B2B marketers that rely too heavily on the technique risk misleading sales teams and hindering overall results. B2B marketers need precise, data-supported tools that guide sales teams in the right direction to ultimately close more deals.
The state of predictive analytics in B2B marketing
Most marketers use predictive analytics primarily to determine fit and likelihood to buy in order to narrow their total addressable market of accounts. It makes sense—it’s better to aim for 500 targets more likely to buy than 1,000 that aren’t.
To narrow this list of targets, marketers often hire outside analytics providers who look at historical success and pinpoint companies within their own systems that most closely match accounts they have had success with.
For example, for an enterprise software company, variables like number of clients, currently installed technology, target industry and more would likely influence the predictive model. Predictive models can also incorporate your own data about inbound prospects, like their website visits, which resources they have accessed, which webinars they’ve downloaded and more. But predictive models aren’t infallible.
Where predictive modeling fails marketers
Predictive models are only as powerful as the data fed into them. And even if they use external firms to purchase data and build models, nothing is foolproof.
That’s because there’s no magic formula when it comes to predictive analytics. Marketers can use models to increase the likelihood that they are targeting the right businesses. But these models don’t reveal the competitors prospects are engaged with, relevant external content prospects have visited, related topical interests and more.
Additionally, the volume of transactions in B2B tends to be far smaller than in other industries. This limits the statistical power of the predictive models, which are better suited to much larger volumes of data (for example, polling data). And since predictive models require significant development time (and don’t often produce results right away), marketers risk misallocating resources on tools that may initially be ineffective.
Finally—and perhaps most notably for B2B marketers—predictive models do not reveal active demand in the market. You might be able to pinpoint the perfect potential client for your enterprise software firm, but modeling alone doesn’t provide sufficient insight. For instance, if a prospect was satisfied by their current solution but is now looking, a historic look at their behavior wouldn’t reveal this shift. By relying on predictive analytics alone, your sales staff aren’t adequately informed about the accounts and prospects they should go after.
Effective predictive models require strong intent data
Some analytics providers work around this weakness by offering broad-based intent data to complement predictive models. An analytics provider might build a predictive model that includes look-alikes from your own systems, supplemented with intent data on account surge activity primarily based on IP lookup and keyword search.
This is an improvement, but still not reliable enough to build pipeline. Broad-based intent data signals may suggest intent to purchase, but crucially lack the details that would empower sales teams. For example, you might learn that a prospect searched for “marketing automation technology” but you don’t know who specifically searched the term or how to contact them.
Additionally, you won’t see the recency and frequency of these searches or what content they’re consuming. Broad-based intent data provides little more than a directional hunch. To fully harness predictive modeling, you need strong intent signals that tell you exactly who is browsing content related to your solutions area (and how to contact them), when and how often they are browsing, and the exact content they are researching.
Moreover, strong intent data provides better results in less time than a predictive model alone, ensuring a higher ROI and greater buy-in from your sales teams.
In the end, effective B2B marketers do more than generate leads or implement tools. They empower sales teams with the right actionable information to actually close deals. Intent-based marketing allows you to make the most pragmatic, data-backed recommendations that your sales teams can’t afford to ignore.
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Great Article! Predictive Analytics allow organization to gain clarity on future performance of company and also receive recommendation on how to improve it.
Data analytic techniques enable you to take raw data and uncover patterns to extract valuable insights from it. So true!