Prove CI impact without attribution theatre: three defensible metrics and a worked example

Why an ROI multiple fails the first question

Several CI vendors publish headline numbers, and the pages we read do not show the method behind them. Klue's Measure page shows "35% increase in win rate" and "50% increase in competitive win rate" with no sample, period or baseline on the page (read October 7, 2026). Klue's own guide to CI metrics says that "proving direct causation of any two independent variables is intensely difficult", then recommends applying predicted win-rate increases to current revenue to make the case. Crayon's Measure page defines influenced revenue as revenue "won thanks to" reps' use of compete materials, which assumes the attribution this lesson says you cannot prove. Kompyte's ROI post of May 31, 2024 uses "Reps using Battle Cards report 35% improvement in Win-Rates" as an example headline for a slide, and says that measuring impact across teams "can be tricky".

An ROI multiple needs a counterfactual. You would have to know what the same deals would have done without the programme, and you do not. Reps who open cards differ from reps who do not; pricing, product and headcount change in the same quarter; and the competitor field in the CRM gets filled late or wrong. Most teams have too few closed deals per competitor to see anything through that noise. Report the inputs leadership can check, and say what each one does and does not show. The CI Alliance's interview on measuring ROI (August 2, 2022) separates tangible wins you can measure, such as a changing win rate, from intangible ones you cannot. Keep the two apart in the deck.

Metric 1: competitive win rate on comparable deals

Competitive win rate is won deals divided by won plus lost deals, counted only where a specific competitor was recorded, within one segment, one deal-size band and one region, by quarter. The comparability rule is what makes the number mean anything. A single company-wide win rate mixes a rival that only appears in enterprise deals with one that only appears in self-serve, and the number moves for reasons that have nothing to do with the card.

Count the deals in each cell before you read the rate. Our rule of thumb, which is an editorial threshold rather than a sourced one, is to refuse to interpret a cell with fewer than about 20 closed deals. Below that, one extra win swings the rate by five points. Where the cells are thin, widen the period rather than the segment, so you are still comparing like with like.

The weakness is the CRM competitor field. If it is optional, filled at close by the rep who lost, or left blank on wins because nobody asked, the denominator is wrong before you divide. Metric 1 is only as good as that field, which is why the CRM section below comes before any chart.

Metric 2: card use in won versus lost deals

For every closed competitive deal, record whether the rival's battlecard was opened by the deal owner before the close date. Then compare the win rate of the opened group with the not-opened group. This is a usage metric with an outcome attached, and it answers a question the adoption counts in the shelfware signals lesson cannot answer, which is whether the deals where reps used the card ended differently.

Say in the same sentence that it shows correlation. Strong reps open more things. A deal that is already going well gets more preparation. Two ways to make the comparison more honest are a within-rep comparison, where you look at the same rep's opened and not-opened deals, and the control group Kompyte describes: arm "only a portion of the sales reps with Battle Cards during one sales cycle" and treat the reps without cards as the control. That is a design that could support a causal claim, and it costs one sales cycle of partial rollout. Kompyte's own line is that success "is not distribution, or consumption, but usage".

Metric 3: hours from a competitor's move to your response

Measure the time between a rival's public change, such as a new pricing page or a launch post, and the moment the battlecard or Slack alert reflects it. This is the one metric the CI team controls end to end. Log the timestamp of the change as you found it, the timestamp of the published update, and who made the update.

It claims nothing about revenue, so nobody in a review can dispute it. Report the median and the worst case rather than the average.

A worked example, with invented numbers

Take one rival, Acme, in mid-market deals. In Q1 you closed 40 competitive deals against them and won 12, a win rate of 30.0%. You shipped a new card at the start of Q2. In Q2 you closed 44 and won 18, a win rate of 40.9%.

Nearly eleven points of lift looks ready for a slide, but with 40 and 44 deals per quarter it is not. A 95% Wilson interval (our own calculation) runs from about 18% to 45% for Q1 and from about 28% to 56% for Q2, so the two quarters overlap and the change cannot be told apart from quarter-to-quarter noise. The honest line is that the rate moved in the right direction and that you need more closed deals, or a control group, before claiming the card did it.

Now metric 2 on the Q2 deals. The card was opened in 14 of the 18 wins (77.8%) and in 12 of the 26 losses (46.2%). Opened deals won more often. Say exactly that, and add that it does not show the card caused the wins, because the reps who open cards may be the reps who win anyway.

The slide that survives questioning has three rows. Win rate against Acme, mid-market, 30.0% to 40.9%, on 40 and 44 deals, not yet distinguishable from noise. Card opened in 78% of Q2 wins and 46% of Q2 losses, correlation only. Median response time to Acme's public moves, in hours, with the worst case named.

What you can and cannot claim

• You can report the win rate against a named competitor, in one segment and period, with the deal count beside it.
• You can report that deals where the card was opened won more or less often, as a correlation.
• You can report how fast the team responds to competitor moves, with the median and the worst case.
• You cannot say the programme produced a share of revenue unless you ran a control group or a within-rep comparison, and say which.
• You cannot quote a vendor's win-rate lift as a benchmark for yours; none of the pages we read gives a method, and we found no benchmark for a good lift.
• You cannot read a rate from a cell with a handful of deals, however large the swing.

Where your CRM breaks this

Both outcome metrics depend on one field, the competitor in the deal. Check three things before you build a chart. Whether the field is required at any stage, or optional and filled only when a rep remembers. Whether it is filled at close, when the rep who lost names the rival, or earlier, while the deal was open. And whether wins carry a competitor at all, because a programme that records competitors only on losses shows a competitive win rate near zero, which is an artefact of the field rather than a result. Comparing CRM loss reasons with buyer feedback on the same deal is the check for the second half of that problem.

If the field is unreliable, fix the field first and report metric 3 while you wait. A quarter of clean data beats a year of guessed data, and a reviewer who asks how the competitor was recorded deserves a one-sentence answer.

Where Steve fits, and where the same rules apply

Steve's Intelligent Deal Detection works inside HubSpot and Salesforce. It suggests the competitor on each new deal for you to approve, one at a time or in bulk, matches recorded calls to the deal and writes battle-card links onto the record; deals sync every night and on demand. On that data it shows win rate and deal size on deals where Steve was used versus not, per competitor, and how often each deal's card was opened. When a deal closes, Steve Win/Loss interviews the buyer, so the reason can sit beside the outcome.

That view is metrics 1 and 2 on your own CRM data, with the competitor field filled by a suggestion you approve rather than by the rep at close. It does not change the rules above. The site states no ROI figure, no minimum deal count and no per-seller view, and a used-versus-not comparison carries the same confounders as any opened-versus-not comparison, so the comparability rule and the deal-count floor are still yours to apply. If one or two rivals and a clean competitor field describe your programme, the spreadsheet above is enough. If the field is the part you cannot keep filled, or you want the buyer's reason beside the win rate, Steve Win/Loss is the page to read.