by Trivas.aiBuyers don't just diligence your P&L anymore. They diligence your data.
Private equity firms and strategic acquirers now treat your analytics infrastructure as its own line item in the deal, right alongside inventory and contracts. If your reporting is a patchwork of Shopify exports, ad platform screenshots, and a spreadsheet someone updates every Friday, that's a problem you'll have to answer for.
Messy, stitched-together reporting doesn't just look bad. It slows deals down and gives buyers leverage to knock down your multiple. Every unanswered question about how a number was calculated is a reason to discount the offer, or walk.
This is a checklist for founders sitting six to eighteen months out from a potential exit. Whether you've got an offer on the table already or you're just starting to think about it, the analytics work needs to start now, not once the term sheet lands.
Diligence requests follow a pattern. If you've talked to an M&A advisor or been through a process before, none of this will surprise you. If you haven't, here's what lands in your inbox.
Revenue by channel, monthly, for 24 to 36 months. Amazon, Shopify, wholesale, broken out separately, not blended into one top-line number.
CAC and LTV by acquisition channel. Not a blended CAC that hides the fact that your Meta spend is propping up a weak organic channel. Buyers want to see which channels actually pay for themselves.
Cohort retention curves and repeat purchase rate. This is how buyers model what happens after they own the brand. A flat repeat rate is a red flag no growth story can talk around.
SKU-level and channel-level contribution margin. Revenue growth means nothing if your best-selling SKU is barely profitable once you account for channel fees and returns.
Ad spend efficiency history. ROAS and MER trends across Meta, Google, and Amazon Ads, tracked over time, to prove growth was earned and not manufactured by a temporary spend spike.
If your current reporting can't produce all five of these on demand, that's your starting point.
Most of the friction in diligence comes from a small set of recurring issues.
Attribution inconsistency. Shopify's dashboard says one CAC. Meta says another. Neither reconciles with what actually landed in the bank account. Buyers notice immediately, and it makes every other number in the deck suspect.
Historical data loss. Brands switch ad platforms, swap agencies, or migrate tools, and the trailing 12 to 24 months of history goes with the old login. Suddenly there's no clean baseline to show growth against.
Manual spreadsheet reporting. If a diligence team can't independently verify a number, they'll assume the worst version of it. Spreadsheets built by hand, with formulas nobody else can audit, don't survive scrutiny.
No audit trail. When a buyer asks "how was this calculated," the answer needs to be more than "I think Dave set that up in 2022." Founders end up rebuilding reports from scratch, mid-negotiation, because nobody documented the methodology.
Tribal knowledge risk. One analyst or one agency holds the definitions in their head. If that person isn't in the room during diligence, the numbers stop making sense to everyone else.
Every one of these is fixable. None of them are fixable in the two weeks after a term sheet shows up.
The fix is centralization, not more spreadsheets.
Trivas pulls Amazon, Shopify, Meta and Google Ads, and GA4 into one Redshift-backed warehouse. Instead of five sources telling five slightly different stories, buyers get one consistent number set they can trace back to the source data. That alone removes most of the friction in the "why doesn't this number match that number" conversations that stall deals.
Historical retention matters just as much. Trivas keeps 24-plus months of clean, structured trend data, so switching ad platforms or firing an agency doesn't wipe out the history a buyer needs to model growth. The data lives in the warehouse, not in whatever tool happened to be connected at the time.
The BI reporting dashboards generate channel-level and SKU-level margin views that map directly onto what diligence teams ask for, revenue by channel, contribution margin by SKU, cohort retention, all pulled from the same source. You're not building custom reports for every request. You're exporting what's already there.
That matters for a reason beyond tidiness. It cuts down the founder's time spent fielding repeat data requests during a deal, which is exactly when your attention needs to be on negotiating terms, not rebuilding a pivot table for the third time this week.
A growth story backed by real projections lands differently than one built on a hopeful hockey-stick slide in a pitch deck.
AI-driven forecasting takes your actual historical performance and turns it into a projection a buyer can stress-test, not just believe on faith. That's a meaningfully different conversation than "we think we can grow 40% next year."
Scenario modeling adds another layer. You can show a buyer what happens to revenue and margin if ad spend increases 20%, or if you launch on a new marketplace, or if a channel gets cut entirely. That's the kind of modeling PE firms build their own investment thesis around, so handing it to them pre-built, grounded in your actual data, speeds up their internal approval process.
Forecasts built on live Redshift data carry more weight in negotiations than a static spreadsheet projection somebody built in Excel last quarter. One is defensible. The other is a guess with a chart on top.
6 to 12 months out. Connect every channel, Amazon, Shopify, ad platforms, GA4, into a single Trivas dashboard. This is also when you start building the historical baseline you'll need later, so don't wait.
3 to 6 months out. Run a self-audit against the standard diligence checklist: channel-level CAC and LTV, cohort retention curves, margin by SKU. Find the gaps now, while you still have time to fix them, not during a data room review.
Deal in progress. Use exportable, timestamped reports as the backbone of your data room. Ad hoc spreadsheet exports, manually assembled the night before a call, are exactly what makes buyers nervous. A report with a timestamp and a source you can point to is what makes them comfortable.
Founders and CEOs running lean teams often don't have a data analyst dedicated to this kind of prep. That's the gap Trivas is built to close for the founders and CEOs who need clean numbers without hiring for it.
If you're actively preparing for a sale, or you've had an early conversation with a buyer and know one is coming, don't wait for the data room to force the issue.
Getting your analytics for brand preparing for acquisition sorted now, while you still control the timeline, is a lot cheaper than scrambling to reconcile numbers mid-negotiation. Talk to us about which channels and metrics your specific buyer is going to want to see, and we'll help you get there before the first diligence request lands.
Talk to a founder about what your data room actually needs to look like.
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