AI for insights and analytics
A sales director wants three things explained: where revenue came from, where share moved, and which promotions paid back. AI analytics reads shipments, market data, distribution and promotion files, splits every change into price, volume, mix, new and lost, and says which retailer and SKU did it.
These are the questions CPG brands ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Explain where my revenue is coming from this quarter. | Price, volume and mix bridge | The change split into price, volume, mix, new and lost lines, by retailer and category, summing to the change exactly. |
| Where are we losing market share? | Rate of sale | Share by category and retailer from the market file against the prior period, with the SKUs behind each move. |
| Which promotions actually paid back? | Promotional net lift | Lift against baseline, the dip after, net units and margin against trade funding, per event. |
| Where are we listed but not stocked, or stocked nowhere? | Distribution voids | SKU and store combinations that similar stores carry and this one does not, valued at rate of sale. |
| Are we shipping more than the retailer is selling? | Sell-in against sell-out | Shipments against retailer sales by SKU, with weeks of cover and the inferred out-of-stocks. |
| What are chargebacks costing us, and why? | Chargebacks by retailer and reason | Deductions by retailer and reason code against invoiced value, with the repeat reasons. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
What is driving the revenue change this quarter?
Net sales are up $5.7m against the prior year, split across 3 effects that sum to the change. Price is the largest at $3.1m, 54% of the movement.
| Price | $3.1m | 54% of the total |
| Volume | $1.9m | 33% of the total |
| Mix | $700k | 12% of the total |
| The 3 lines sum to | $5.7m | 0 unexplained |
Which retailers are behind the price effect?
3 retailers account for $2.9m of the $3.1m Price effect, 94% of it. The step in list price landed in March and held in all three.
| Northway Grocers | $1.4m | price up 4.1%, volume down 1.2% |
| Cityline Convenience | $900k | price up 3.8%, volume flat |
| Meadow Markets | $600k | price up 4.4%, volume down 2.9% |
| These 3 are | $2.9m | 94% of Price |
What should the category team look at next?
Volume at Meadow Markets, where the price step cost the most units, and the two promotions there that ran below baseline after the event. The bridge points at the retailer; the promotion table says which event.
| Category manager | Review Meadow Markets' post-promotion dip by SKU | This week |
| National account manager | Take the price and volume split to the Northway review | Next review |
| Sales director | Put the bridge in the monthly pack, by retailer | Monthly |
Each measure has one formula, one source and one meaning. They are computed per retailer and account manager and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Range penetration by channel | Stores ranging the SKU ÷ stores in the channel that should, by the channel's own core range | Store list; sell-out data | Whether the agreed range is on the shelf |
| Distribution voids | Stores where similar stores sell the SKU and this one does not, valued at the median rate of sale | Sell-out data; store attributes | The list of store and SKU gaps worth closing |
| Sell-in against sell-out | Units shipped − units sold through, cumulative, per retailer | Shipment ledger; sell-out data | Inventory building or draining in the channel |
| Weeks of cover at the retailer | Estimated retailer inventory ÷ average weekly sell-out | Shipments; sell-out; opening stock | Whether the next order will be large or small |
| Inferred out-of-stocks | Store-days with zero sales where the SKU normally sells and similar stores sold | Daily sell-out by store | Lost sales at the shelf, by store and SKU |
| Promotional net lift | Promo units − baseline − post-promo dip, per account, against the account's own baseline | Sell-out; promotions calendar | What a promotion really added |
| Return on trade spend | Net lift margin ÷ trade spend on the event | Net lift; deductions and promo funding | Which events pay for themselves |
| Chargebacks by retailer and reason | Deductions by reason code ÷ sales, per retailer; disputed and recovered | Deductions file | Process failures billed as penalties |
| OTIF under the retailer's rule | Orders on time and in full by the retailer's definition ÷ orders | Order and delivery files; retailer terms | The score the retailer sees, not the one the brand reports |
| Rate of sale | Units per store per week, for stores ranging the SKU | Sell-out; store list | Performance independent of distribution |
Each measure is worked through, with the export it comes from and what to drop, in Sales KPIs for CPG and consumer brands.
From your question and the measures declared for FMCG and CPG brands, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the retailer or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Sell-in and sell-out | Opening retailer stock + sell-in − sell-out = closing stock |
| Net lift | Promo units = baseline + gross lift; net lift = gross lift − post-promo dip |
| Chargebacks | Deductions = valid + disputed open + recovered + written off |
| OTIF | Orders = OTIF + late only + short only + both |
The exports CPG brands already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Inferred out-of-stocks; distribution voids | Account managers; field sales | Weekly |
| Sell-in against sell-out; weeks of cover; OTIF | Account managers with supply planning | Weekly to monthly |
| Net lift; return on trade spend | Commercial director; revenue management | After each event; quarterly |
| Chargebacks by reason | Customer service and logistics, with finance | Monthly |
Yes, and it is checked. Price, volume, mix, new and lost are computed from the shipment file by SKU and retailer, and the tool asserts that end minus start minus the sum of the lines is zero before it shows anything. If a line cannot be attributed it appears as 'other' with its value, never as a silent gap.
From the market data you upload, whether that is a syndicated panel export or retailer EPOS. Share is your sales over the category in that file, by retailer and period. Covirage does not estimate share; it computes it from the data you give it, and says which file it read.
It computes the baseline from the weeks before, the lift during, and the dip after, and nets them. Then it puts net margin against the trade funding for the event. The judgement of whether a 30 percent net lift was worth it is yours; the arithmetic is not left to a model.
Shipments by SKU, retailer and month is enough for the bridge and concentration. Add market data for share, the distribution file for voids, the promotion calendar for lift, and deductions for chargebacks. Each adds a question; none is required to start.
Bridge · Market share · Distribution void · Net lift · Weeks of cover · Inferred out-of-stock · Chargeback split
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