One of the most important SalesTech trends, besides the emergence of ChatGPT, is the rapid incorporation of engagement datasets alongside intent datasets for prioritization and messaging.
A few years ago, we saw the emergence of intent data sets such as first-party web visitor tracking, second-party product review site research, and third-party B2B media research. Initially, this content was integrated into MAPs, ABX platforms, and CDPs, but it was not well integrated into SalesTech. We are now seeing intent data being integrated into SalesTech platforms in a simplified fashion (e.g., High Intent Topics in CRM profiles and Slack alerts) that is digestible for sales reps.
However, intent data only indicates whether a company is in-market, not whether the buying committee is considering your offering or seriously engaged with your sales team. This intelligence comes from a new category of engagement data captured from digital interactions between the revenue team (sales, marketing, and customer success) and the buying committee. Engagement intelligence consists of both traditional digital interactions (e.g., clickthroughs, downloads) and Natural Language Processing (NLP) analytics derived from sales and buying team activities.
NLP helps RevTech platforms determine who is interacting with your firm. It also analyzes buyer sentiment, buyer concerns, deal health, and risk flags. The primary sources of engagement data are emails, recorded phone calls, and recorded meetings. However, any digital interaction between buyers and sellers can be captured such as activity in digital sales rooms, webinar attendance, chat messaging, and scheduled meetings. I anticipate that customer support platforms will also be tapped for engagement data to help gauge churn risk and friction during product trials.
Engagement data indicates whether a deal is on track and what issues could result in lost deals or pushed out pipeline. For example, engagement data assesses whether:
- Discussions are single or multi-threaded
- Key decisionmakers are involved (e.g., has a security review been performed or has legal been included?)
- Competitors have been mentioned
- Pricing concerns were raised
- Follow on meetings have been scheduled
- Meetings had a positive flow or were dominated by the sales rep
In short, engagement data provides sales reps and managers deal health and risk analytics that improve forecasting and ensure that deal risks are quickly mitigated. And as interactions are digital, managers can discuss these issues during one-on-ones or offer quick tips on next steps. They can even review the discussion associated with the risk and identify skills and knowledge gaps for coaching.
The interesting thing about intent and engagement data is they are highly complementary with each other. Operations teams should be looking at integrating intent data alongside engagement data. Intent data is valuable for identifying who and when to reach out to ideal customers. However, once a relationship is established, the focus shifts to engagement data for monitoring deal health. After a deal is signed, both engagement and intent data are in play. Intent data identifies cross-sell opportunities and churn risk through second and third-party intent topic monitoring while Engagement and Product Usage data evaluate adoption rates and potential implementation issues.
Engagement data and deal health analytics can be found in Revenue Intelligence services (e.g., Clari, Revenue Grid), Sales Engagement (e.g., Salesloft, Outreach, Groove), Conversational Sales (e.g., Gong, Chorus), Revenue Operations (Nektar), and Sales Enablement (e.g., Seismic, Bigtincan) platforms.