Partners · Clari

The forecast gap isn't a Clari problem.

Last week of the quarter, Clari said $14.2M committed. You knew two of those deals weren't closing. You ended around $10.8M and spent the first week of Q1 explaining the miss to the board. Clari showed you the pipeline accurately. It can't tell you whether the data underneath it was real.

Book a Discovery Call
Start Here
Get a GTM Diagnosis for your Clari environment.

We assess whether you have a Clari configuration problem, a data quality problem, or a process problem. We blueprint what to fix first.

We respond within one business day.
What the forecast gap actually reflects

The commit-to-close gap is rarely a Clari problem.

When forecast accuracy stalls in a Clari environment, the platform is almost never the problem. Stage criteria that let deals advance without a buyer action, activity data full of gaps, commit categories calibrated against team convention instead of real close patterns — Clari scores all of it at face value. RevShoppe fixes what goes into it.

$80M $100M $120M $140M $160M COMMITTED CLOSED Q1 Q2 Q3 Q4 Q5 Q6 THE LARGER THE BUSINESS GETS, THE MORE EXPENSIVE THE GAP BECOMES.
The typical starting point
Clari deployed, forecast unchanged
Platform running, AI Opportunity Scores visible, CRO has a new dashboard for pipeline reviews. The commit category is used differently across the team. Late-stage deals slip at end of quarter in ways the forecast didn't indicate. Pipeline reviews surface numbers but not the pattern behind them.
What RevShoppe builds toward
Clari reading an accurate foundation
Stage criteria tied to observable buyer progression so Clari's scores reflect deal health. Activity capture that closes the gaps manual logging leaves open. Forecast categories calibrated against your actual close patterns. A weekly review motion where risk surfaces early enough to act on it.
What RevShoppe does with Clari

Clari's forecast is only as good as what's feeding it.

RevShoppe works on what Clari reads, not on Clari itself. Stage criteria, activity capture, forecast configuration, deal inspection: the operating layer that determines whether the commit number reflects what's actually in the pipeline.

01
Stage Design
Stage criteria rebuilt against observable buyer behavior — what the buyer has done, not what the rep believes about where the deal stands. When stage advancement requires a buyer action, Clari's opportunity scores start reflecting deal health instead of deal position.
02
Activity Capture Design
Automated logging so email sends, calls, and meeting outcomes are captured systematically. Manual activity entry introduces gaps that affect Clari's AI scoring — automated capture closes them and gives the deal health model a complete view of engagement in every account.
03
Forecast Configuration
Clari's forecast categories, commit thresholds, and risk signal definitions calibrated against your actual pipeline patterns — what a genuine commit looks like in your motion, what signals correlate with slippage in the last 30 days of a quarter. Built from your history, not a platform template.
04
Deal Inspection Framework
The deal scoring model reconfigured so Clari's risk flags surface deals that are genuinely stalled — with a CRO review protocol that defines which questions to ask, what signals to look for, and how to separate a data gap from a deal problem in the pipeline review itself.
05
CRO Review Motion
The Rhythm of Revenue operating cadence built around Clari's data — weekly pipeline reviews, monthly forecast calls, and quarterly readouts structured so every leader is looking at the same numbers with the same definitions. Forecast confidence is a process outcome, not a platform feature.
06
GTM Infrastructure Integration
Connecting Clari's deal intelligence to what happens earlier in the funnel. When the same deal characteristic causes slippage three quarters in a row, that pattern belongs in qualification criteria and ICP filters — not only in the post-mortem after the quarter closes.
The intelligence loop

Clari captures deal loss patterns that most teams read once and file away.

Clari captures which stage every lost deal stalled in, what the activity curve looked like before it slipped, and which characteristics correlated with a push. Most teams read that data in the quarter-end review and don't touch it again. RevShoppe builds the governance process that routes it forward into qualification criteria, ICP signals, and rep coaching, so the next pipeline is built differently.

See GTM Infrastructure
01
Clari surfaces deal risk
AI Opportunity Scores flag slippage risk. Stage stagnation, declining activity, missing contacts identified.
↓
02
CRO review motion activates
Rhythm of Revenue cadence routes at-risk deals to the right inspection. Rep coaching, deal support, or disqualification — decided in the review, not three weeks later.
↓
03
Loss patterns classified
RevShoppe governance process routes deal loss signals to the Context Layer: qualification criteria, ICP filters, outbound triggers.
↓
04
Upstream motion adjusts
Next quarter's pipeline is built with sharper ICP criteria, better qualification signals, and outbound triggers that have been calibrated against what actually closes.
Client outcomes

What changes when the data Clari reads is actually right.

These aren't Clari numbers. They're what happens when RevShoppe rebuilds what Clari reads.

+29pp
Average improvement in quarterly forecast accuracy after RevShoppe data architecture and stage redesign
64%
Reduction in deals that slipped past committed close date after governed CRO review motion
2.8×
Increase in Clari activity data completeness after automated capture design, vs. manual logging baseline
Common questions

The questions that tell you whether you have a platform problem or a foundation problem.

We've had Clari running for several quarters and forecast accuracy hasn't meaningfully improved. Where do we start?
The most reliable starting point is assessing what Clari is actually reading. In most environments where the commit-to-close gap persists, we find one or more of the same things: stage criteria that allow advancement without a buyer action, so the pipeline is structurally overoptimistic; activity capture that is incomplete, leaving Clari's AI scoring on a partial view of engagement; or forecast categories that were set up at deployment and have never been calibrated against how your deals actually close. The GTM Diagnosis identifies which of these is driving the gap — and builds the fix.
We have strong pipeline coverage but still miss the number most quarters. What are we not seeing?
Coverage ratio is a volume measure. It tells you how much pipeline exists, not how much of it reflects real buyer intent. In environments where coverage looks healthy but close rates are low, we typically find that qualification criteria aren't tight enough — deals are entering the pipeline and occupying coverage without the buyer signals that distinguish a real opportunity from an early conversation. Clari shows you the coverage number accurately. It can't tell you which of those deals should never have been entered. RevShoppe builds the qualification framework and the inspection motion that surfaces that distinction before end of quarter.
We already have a Clari CSM. What does RevShoppe add?
Your Clari CSM knows Clari — how to configure it, how to use its features, how to run the platform effectively. RevShoppe works on the operating model that determines what Clari reads: stage design, activity capture architecture, the process governance that keeps data accurate quarter over quarter, and the way forecast intelligence connects back to earlier in the funnel. These aren't platform questions. They're organizational questions that show up in Clari's numbers.
How long before the forecast is actually reliable?
The first meaningful improvement usually shows in a single quarter — because the pipeline your team is reviewing at the start of that quarter reflects buyer progression rather than rep optimism. The forecast doesn't become fully reliable in one quarter. What changes in one quarter is the pattern: for the first time, the number your team committed in week one looks like the number that closes in week thirteen. That compounds as the Rhythm of Revenue cadence builds consistency in how data is entered and reviewed. Most organizations notice the change in the board conversation — not because the number went up, but because explaining the variance stopped being the whole agenda.
Start here

Get a clear diagnosis before the next quarter starts.

Most organizations don't know which problem they have until the quarter ends. The GTM Diagnosis runs before then.

Start with a GTM Diagnosis