CPG Analytics Is Fifteen Questions. Most CEOs Answer Them from Memory.

The founder of a $22M dried-fruit company sits in her Monday operating meeting with her CPG analytics open in three tabs: a margin spreadsheet updated last Thursday, a UNFI deduction report she has not reconciled, and a Retail Link extract showing two SKUs below scan velocity thresholds at Walmart. The CFO asks whether they can afford the Sprouts launch. The VP of Sales says the Costco promotion "felt strong." The ops lead reports that OTIF was 94% last month but cannot say which late shipments triggered chargebacks versus which ones the retailer absorbed.

She makes three decisions in that meeting (fund the launch, renew the Costco slot, cut the ops budget for Q3), and every one of them rests on a number somebody remembered rather than calculated.

This is not a data literacy problem. It is a data infrastructure problem. The questions that govern a specialty food brand's operating margin are not mysterious. They are finite, recurrent, and answerable. The reason they go unanswered is that the analysis to answer each one gets treated as a custom project rather than a standing capability.

The question list is shorter than anyone admits

Ask ten specialty food CEOs what keeps them up at night and you will hear variations on the same set. Should I fire my biggest customer, the one whose deduction tail consumes half the account's margin? Can I afford this retailer launch, or will slotting and free fills burn more cash than the first year returns? Which SKUs should die? Where is my trade spend actually going? What would a recall cost me? Is my product data going to break at Walmart? Which channel actually makes money after trade, deductions, and float?

These are not open-ended analytical explorations. They are binary or ranked verdicts: yes or no, this one or that one, above threshold or below. A $25M brand does not need a data science team to model them. It needs the rules written down and the data piped in.

Gartner's 2024 survey found that 77% of data and analytics leaders report low or moderate data literacy in their organizations. But for a specialty food brand at the $10M-$50M mark, the constraint is not literacy. The constraint is that the questions never get formalized, so every answer requires a fresh analytical build, and the build never happens because nobody has the bandwidth.

In CPG analytics, rules replace exploration when the questions are known

The conventional approach to analytics in CPG is the BI project. Hire a consultant or an analyst, scope the question, pull the data, build a dashboard, present findings, shelve the dashboard, repeat in six months when the question recurs. An enterprise analytics project typically runs weeks to months before it delivers value. A $20M food brand cannot absorb that cycle for fifteen recurring questions.

The alternative is to recognize that the questions are stable and the rules that answer them are documentable. "Should I fire my biggest customer?" resolves to: pull the account's gross revenue, subtract trade spend commitment, subtract trailing-twelve-month deductions by category, subtract compliance chargebacks, apply the cash conversion cycle timing cost, and compare the resulting contribution to what the same capital would produce in the next-best account. That is not a discovery exercise. It is arithmetic with documented thresholds.

The same structure holds for every question on the list. "Can I afford this launch?" is a cash-flow model with inputs from trade spend commitments, slotting terms, projected velocity, and the brand's current working capital position. "Which SKUs should die?" is a portfolio sort on contribution margin per facing after trade: the same SKU rationalization logic that category managers at Kroger and Albertsons already apply to their own shelves.

When the question set is fixed, the analytical method does not need to be reinvented each time. It needs to be encoded once and run on current data.

What a question engine looks like in practice

A CEO picks a question from the list. The engine runs documented rules against the current dataset: not a model, not a prompt, not a black box. It returns four things: a one-sentence verdict (opinionated, not hedged), one chart that shows the shape of the answer, three numbers that anchor the verdict, and a link to the full worked analysis.

"Should I fire my biggest customer?" returns: "No, but renegotiate. The account produces $412K in contribution after a $187K deduction tail, which ranks third. The deduction rate is 2.1x the portfolio average, and 62% of it traces to shortage claims disputable with better ASN data." One chart shows contribution by account with the deduction overlay. Three numbers: $412K contribution, $187K deductions, 62% traceable to data errors.

No dashboard to interpret. No SQL to write. No analyst to brief. The verdict is readable by a CEO in thirty seconds, and the rules that produced it are readable by a CFO who wants to audit the logic.

This matters because the alternative, the BI dashboard, requires the executive to form the question, navigate the interface, and interpret the visualization. SPS Commerce's guidance for mid-market CPG describes companies running four to five disconnected data systems. The dashboard that requires a user to join those systems mentally is not a tool. It is a research assignment disguised as software.

The fifteen questions are the operating system

The questions are not arbitrary. They map to the operating decisions that move margin:

Customer portfolio: which accounts earn their capital, and which ones look profitable only because nobody has netted the deduction tail and compliance cost. Channel allocation: which channels return the most per dollar deployed, after trade and float, a question most brands cannot answer because the data sits in four systems that nobody joins at the channel level. SKU economics: which products earn shelf space and which ones subsidize the portfolio. Trade effectiveness: where the 15-20% of gross revenue committed to trade actually lands, and what it produces. Launch viability: whether a new retailer authorization generates net cash or consumes it. Forecast accuracy: whether the demand plan is tight enough to hit OTIF thresholds without building excess inventory. And the operational questions underneath (recall exposure, data readiness, cash conversion) that determine whether the brand can execute the strategy the CEO just approved.

Thirteen of these fifteen questions already run against a synthetic $25M specialty food dataset (Cinderhaven Provisions, 50 SKUs, six retailers, ten channels), with every rule documented, every threshold sourced, and every verdict auditable. Two more are in development. The full list of fifteen, each phrased as the question a CEO would ask and paired with its rule, is published in the tool itself. The point is not the tool. The point is the principle: a branded food company's margin is governed by a finite set of decisions, and those decisions are answerable with rules rather than exploration.

The operating questions your brand already asks

If your Monday meeting runs on remembered numbers (the deduction line "felt high," the Costco promo "seemed strong," the launch "should work"), the questions are already there. They are just unanswered.

Fifteen questions. Rules, not models. Verdicts, not dashboards. Thirty minutes: tell me which question your Monday meeting cannot answer, and I will show you the rule that answers it. Book the call.