Clari is the dominant revenue intelligence and forecasting platform for B2B sales teams. The deal-level risk scoring, forecast accuracy work, pipeline visibility, and exec-level reporting are mature and broadly adopted across enterprise revenue orgs.
What Clari does, and what Clari does not do, is worth being explicit about. Clari surfaces problems in your pipeline with sophisticated pattern recognition. Deals that look healthy in the CRM but show signs of late-stage risk. Forecasts that look healthy but are actually being held together by a small number of deals that may not close. Reps who report optimism that is not matched by the data underneath.
Clari sees these things. What Clari does not see, because it is not its job, is how to fix them. Risk visibility is upstream of risk intervention. Most teams run Clari well for the first half (visibility) and have no operational layer for the second half (intervention). The result is dashboards that accurately predict the miss rather than help prevent it.
What Clari solves
Clari solves the revenue visibility problem at scale. Three jobs in particular:
Forecast accuracy. Clari's models are meaningfully better than the spreadsheet-based forecasting most teams used a decade ago. The pattern recognition identifies deals that historically close at the rate the rep is claiming versus deals that historically don't, regardless of what the rep believes.
Deal-level risk scoring. When a deal has been in the same stage for too long, when the EB hasn't been engaged in 14+ days, when the champion's enthusiasm pattern has flattened, when competing vendors are getting more airtime — Clari sees these patterns earlier than humans do.
Pipeline composition analysis. What percent of pipeline is sourced from each channel, what conversion rates each is producing, where the leakage is happening. Clari makes the pipeline math visible.
For these jobs Clari is in good hands and the value is real.
What Clari does not solve
What Clari does not solve, and was never designed to solve, is the intervention layer. When Clari flags a deal as at-risk, the question the rep faces is "what do I do about it." Clari does not answer that question. Clari shows that the EB hasn't responded in 14 days. It does not show who in your network can reach the EB through a warm path that the rep does not currently have.
The intervention possibilities, in rough order of effectiveness for an at-risk deal:
Re-engage the existing champion with a stronger value reframe (this works sometimes, often does not when the deal has structural risk).
Identify a different person on the buying committee who has stronger internal influence and engage them (this requires knowing who that person is, which Clari does not surface).
Route a warm intro to a senior decision maker at the account through your network (this is the highest-impact intervention and requires relational data that lives outside Clari).
Most teams default to the first option because the other two require data they do not have surfaced. The first option produces the same result more often than not, which is why Clari's at-risk flag is too frequently a "the deal is going to die" prediction rather than a "the deal can be saved" trigger.
From the trenches
One specific customer story worth flagging. A cybersecurity company we work with had a Clari-flagged at-risk deal that had been sitting at "proposal review" for 6 weeks. The named champion had gone quiet. The EB had not responded to the rep's last 3 follow-up emails. Clari's risk score was high.
The rep's instinct was to escalate with another sequence to the EB. We ran a relational coverage check on the account instead. It turned out the cybersecurity company's CEO had a prior technical relationship with the CISO at the prospect account. They had not realized this because the relationship lived in the CEO's head, not the CRM.
The CEO sent a personal note. The meeting booked the same week. CEO to CISO at a Big Three medical device company. The deal closed within 90 days at roughly $10 million. The Clari at-risk flag had been accurate (the deal was structurally at risk under the existing motion) but the intervention available was not visible to the rep without the relational coverage layer.
This is the architecture pattern. Clari sees the risk. The relational data tells you what to do about it. Both layers are necessary; neither alone is sufficient.
What works alongside Clari
The right architecture for B2B teams running Clari is to use Clari for the visibility layer and add a relational coverage layer for the intervention work.
For each Clari-flagged at-risk deal, the question shifts from "how do we re-engage the existing thread" to "who else in our network has a relationship to someone at the account whose engagement could save this." The answer might be a different person at the same account (re-threading), or a senior endorsement from outside the deal (board member, investor, former champion now at the account).
The relational graph that surfaces these answers spans four super-connector groups: your team, your customers, your investors and board, and your advisors. For most B2B companies, 60 to 80 percent of at-risk deals have at least one warm path that could be activated for recovery. Most teams just have not surfaced it.
Where Boomerang fits
Boomerang does not replace Clari. We sit on top of the relational layer that Clari does not cover.
We map the warm paths across your four super-connector groups. When Clari flags an at-risk deal, we surface every existing warm path from your network into the account. We draft the intro request in the connector's voice. We close the loop when the meeting books.
The architecture that produces the best at-risk recovery rates: Clari for visibility and forecasting, Boomerang for intervention and recovery. Both running together. Clari tells the team where to focus. Boomerang tells the team what specifically to do.
What to do this quarter
Two operational moves:
Pull every Clari-flagged at-risk deal from the last 6 months that closed lost. For each, ask: did we have a warm path through our network to someone at the account whose involvement could have changed the outcome? Most teams discover at least half had recoverable paths nobody surfaced.
For the next 10 Clari-flagged at-risk deals, run a relational coverage check before the rep's next intervention. Surface the warm paths available. Route through them when they exist. Track the recovery rate against your historical baseline. The data after one quarter is usually clear.
For the structural reasons deals die at no-decision (which Clari accurately predicts but doesn't fully diagnose), see our losing to no decision post. For the dynamic of the hidden blocker that Clari sees indirectly through patterns, see The Hidden Blocker That Kills Your Deal. For the role of the mobilizer in at-risk deal recovery, see The Mobilizer Playbook.
Shankar Ganapathy is the co-founder of Boomerang, the operational layer for relationship-led pipeline. Before founding Boomerang, he led product in the account planning signals space.
