How our team thinks about M&A execution and closing KPIs

If your dashboard has five KPIs on it, you already have more visibility than most teams. Last week we held part two of our webinar series on the KPIs buy-side teams should be tracking in 2026, and when we polled attendees, only 22% said they were tracking most of the metrics we covered.

This recap covers the session on execution and closing KPIs, where Midaxo's Vil (chief product officer) and AP (project manager, analytics and reporting) walked through the headline metrics, the diagnostic layer underneath them, and how to calibrate both for your own deal mix. If you missed it live, here's what was covered,. plus a few things that came up that aren't in the slides.

The headline KPIs: where part one left off

The five headline KPIs mentioned in part one — deal cycle time by stage, cost per deal closed, LOI-to-close conversion, diligence accuracy score, and price discipline — are built to answer one main question: are we being efficient and disciplined? They're good for spotting a trend across your whole pipeline. As Vil put it, the key takeaway from that session was to track both efficiency and judgment, so you don't end up optimizing for the wrong thing. This session focused on what to look at once one of those five flashes red.

The ten diagnostic KPIs, category by category

AP walked through ten diagnostic metrics across five categories, each with a formula, a benchmark, and a three-state read (red, amber, green). A few things worth calling out beyond what's in the deck:

• Diligence workstream completion. The target is around 95%, not 100%. The team was clear that chasing full completion usually means you're either padding low-priority workstreams or not being disciplined about what actually needs deeper attention. Anything under 80% is where you should expect post-close surprises in whatever got deferred.
• Material issues per deal. The slide benchmark is 8–12 as a healthy range, but Vil flagged in the session that for non-healthcare, mid-sized deals, his own conversations with clients put the practical average closer to 6–8. Healthcare and other regulated verticals should expect a structurally higher count.
• Reps & warranties claim rate. This one got a live question in the Q&A—someone asked how common R&W claims actually are. AP's honest answer: she's personally seen it happen once. But depending on industry, insurer data puts it as high as one in five deals. It's a lagging signal, since claims surface well after close, so this is one to track as a pattern across your closed deals over time, not deal by deal.
• Deals per FTE and advisor spend ratio. The ceiling of roughly three active deals per person applies once a deal is in active diligence, closing, or integration. Earlier-stage sourcing and screening can support more per person since there's less depth required. On advisor spend, the team's rule of thumb: if 70%+ of a deal's diligence spend is going to external advisors, you're running a procurement function, not a corp dev function. That's fine early on, as long as you're building toward less dependency over time.
• Drop-off reason codes. Several Midaxo customers make this a required field before a deal can move into a terminated stage—worth borrowing if you don't already structure your pipeline that way. Categorizing every loss (valuation gap, diligence finding, competitive loss, and so on) is what turns a graveyard of dead deals into a sourcing feedback loop.
• Stage progression rate. The team stressed looking at each transition separately rather than one blended conversion number. A bottleneck between LOI and signing, specifically, usually points to a terms or expectations problem rather than a sourcing or diligence issue. This is worth a gut check with your deal closing process if that's where your deals are stalling.
• Decision velocity. Vil called this "a sneaky eleventh" KPI, since it's really two things bundled together: IC decision-to-action lag (best-in-class is under three days) and IC deferral rate (healthy is under 20%). A high deferral rate usually means your pipeline of "active" deals is smaller than it looks; if six of your ten deals keep getting punted for more information, you effectively have four.
• Banker engagement and regulatory risk. On banker relationships, the advice was depth over breadth—a handful of well-tracked relationships beats a long shallow list. On regulatory cycle time, the team noted that FDI screening isn't the rubber stamp it was five or ten years ago, and rising scrutiny is a real factor in how deals get structured and timed today.

If you only add a few, add these four

Given time constraints, the team's standing recommendation is to start with four:

1. Stage progression rate
2. Drop-off reason codes
3. Material issues per deal
4. Advisor spend ratio.

If one of your headline five flags a problem, these four are the first place to look for the root cause.

Calibration: same framework, different numbers

The back half of the session covered how to adjust these benchmarks for your own context. The framework itself—i.e. the three-bucket lifecycle, the split between trend-monitoring and diagnostic metrics, the idea that every KPI needs an owner—doesn't change. What changes is the numbers, and the team walked through a few examples:

• Regulatory review timelines vary widely by jurisdiction. Phase 1 review in the US, UK, and EU is roughly 30–40 days, but a Phase 2 trigger can push break rates toward 20%, and jurisdictions like China should be planned for as a longer process from the start rather than treated as a delay.
• Advisor spend ratio and banker engagement norms shift by region. What's typical in Japan looks different from the UK, largely due to differences in banking culture and deal process.
• Material issues per deal should be recalibrated by vertical. Software and SaaS deals structurally surface more findings than industrial or environmental-heavy deals, so the same raw number means something different depending on what you're buying.

The team's four-step process for getting there: inventory your last 24–36 months of deals by geography and vertical, adjust your workstream list to match, set internal benchmarks before treating any number as diagnostic, and revisit at least once a year, ideally after every deal.

What's next

This was part two of the series; next we'll cover sourcing and pipeline KPIs, and a future session in the fall will dig into value realization—the third bucket, and the one most teams have the hardest time with once a deal moves into integration. Keep an eye on our webinars and events page for the recordings and the next date in the series.

Want to talk through how any of this maps to your own deal workflow? Book time with our team.