Attempting to home in on a B2B sale can feel like a courtroom drama: You point to intent data as evidence that the prospect is ready to buy, only to realize intent data alone is insufficient proof. Precision is everything: Targeting the wrong prospects due to misleading (or incomplete) intent data is one of the biggest challenges in B2B sales and marketing, especially as teams are asked to be more efficient.
To focus time and resources more efficiently — and actually close your case with more customers — intent data needs to be corroborated by other key signals indicative of ready-to-buy behavior.
Why Intent Data Doesn’t Prove Intent To Buy
B2B marketing and sales teams that go all in on customer targets based on intent data alone are taking a significant risk, as intent data only proves that someone at a business is poking around for information on a certain topic. Tempting as it may be to jump to a conclusion, that signal alone doesn’t necessarily map to the purchasing fast track… or even to a purchase track at all.
Intent to explore an offering doesn’t mean there’s budget allocated or a desire/need to make a purchase anytime soon.
Perceived intent may not be real.
If your company is entering a new market or offers solutions in a particularly innovative space, interest may not yet exist and probably won’t until you create it. In this case, intent data will primarily be red herrings until marketing efforts generate true intent, which takes time and effort.
“Intent” might have come from someone who isn’t a valid customer or decisionmaker.
While targeting relevant personas is essential to B2B sales success, intent data alone can’t discern the precise source of that intent within an organization. Interest in a topic might originate with an intern learning about an industry or marketers/consultants without much purchasing power or knowledge of pain points and needs.
Target Sales Opportunities With More Insightful & Accurate Readiness Metrics
While intent data is a valuable component in the B2B sales and marketing tool chest, it can’t be the only tool. By adding specific, additional readiness signals to the repertoire, teams can leverage a more dimensional and accurate portrait of a target organization’s specific needs and preparedness to buy.
Signals that go beyond intent data to indicate a better potential for more imminent purchases include:
- Signs of specific pain points that your offering addresses. The sales process can become a downhill sprint if sales reps can position their solution as a seamless fit to a pressing issue. For instance, if a potential customer is hiring to bolster its customer support team, that business may be coping with pain points in that area. Sales reps that can reach out with an on-point offering can maximize their chances of converting that ready-to-move opportunity.
- Resources and budget are actually in place for relevant projects. Businesses often aren’t shy about new internal initiatives and publicize them through press releases, blogs and job postings. Staffing up in a relevant area is often one of the clearest indicators that a business is likely ready to make purchasing decisions.
- The business has key decisionmakers in positions that match your familiar customer profiles. A purchase usually comes down to a leader who wants to pull the trigger. If a potential customer has empowered leaders in the types of positions your company commonly works with, that’s a strong indicator.
- The right technologies are present. Insights into the technologies that a business relies on — often available through public information — can significantly determine its potential as a customer. For example, identifying customers with on-prem infrastructure is key if you offer cloud migration assistance. Businesses using highly complementary technologies are often ideal targets, as well.
Team Up Intent Data With A Breadth of More Precise Buying Indicators
For practitioners, prioritizing accounts on intent data alone leads to deep and disheartening inefficiencies. Instead, teams should prioritize targeting accounts with a deeper strategy that harnesses multi-faceted buying signals to transform the quality of their pipeline and conversion rates.
Leena Joshi is the CEO and co-founder of CloseFactor, which uses machine learning to help curate unstructured information about companies and extract intelligence for go-to-market (GTM) teams. Joshi has spent much of her career in B2B GTM roles, including at Petuum, Redis Labs, Splunk and VMware.