Financial analytics reads a company's own records with precision, and every one of those records was created by a consumer decision the company holds zero rows on. Sena measures that decision in each market, so a result arrives with its cause attached.
Financial analytics measures the money: revenue by line, margin by item, cost by center, and working capital by cycle. Three properties of the data it runs on hold the measurement one step short of the cause.
The general ledger, the sales file, and the item master describe transactions the company was party to. That set is complete about the company and silent about the market it competes in, so a margin decline is measurable, and its origin sits beyond every table.
A revenue variance decomposes into price, volume, mix, and currency, and the decomposition is arithmetically sound. Underneath it a shopper picked one item over another, and the model carries zero columns for that reason.
Financial reporting rolls markets into regions because that is how the entity is structured. A regional margin figure is the average of countries doing different things, and the country that moved is invisible at the level the number is reported at.
Sena measures the market the ledger transacted in: what consumers bought, what they were choosing between, and what settled it. The internal analytics stay where they are.
One shopper supplies both the purchase and the reason, so a mix shift is explained.
Every figure names its market and its capture week, so a regional result resolves into the countries inside it.
The alternatives on offer, including items the company itself sells zero of, which gives lost share a destination.
Financial analytics is the measurement layer over a company's financial records. It states revenue by line, margin by item, cost by center, and cash by cycle, to whatever depth the business is run on.
Every financial analytics stack, whatever platform holds it, shares one boundary. Its inputs are records of transactions the company took part in. That makes it authoritative on the company and silent on the market.
The boundary shows up in three places a finance team meets weekly.
Price, mix, input cost, and currency each decompose cleanly. Which one a consumer caused stays outside the model.
Weak demand and absent stock produce the identical ledger entry, and separating them needs evidence from the shelf.
The file shows units lost and holds zero rows on the item they went to, since the company itself transacted zero of them.
The platform for half of this subject is well served. Modelling, semantic layers, reconciliation, lineage, and reporting are mature, and the published treatment of the term is largely a comparison of those platforms. A platform improves how internal records are read, and it leaves the ledger boundary exactly where it sits, since the market signal exists in zero systems the platform can connect to.
The adjacent phrase carries the same meaning with the data half emphasised: pipelines, warehouse structure, quality, and governance over financial records. The same boundary applies. Governance improves confidence in the internal number and adds zero rows about the consumer who created it.
| Output | What it settles | Where it goes wrong |
|---|---|---|
| Purchase cause | Why the mix moved. | Decomposed into price and volume. |
| Availability at sale | Whether stock was present. | Assumed from shipment records. |
| Switch destination | Where lost units went. | Absent from every internal table. |
| Market-level read | Which country moved. | Netted into a regional average. |
Explaining a result draws on four collected inputs, gathered in the market where it moved.
| Input | What it answers |
|---|---|
| Receipts | The item selected and the amount rung up for it. |
| Store captures | Which competing items stood there when the choice was made. |
| Geo-verified photos | The shelf as it stood, dated and placed. |
| Stated preference | Why one item won, and what would reverse it. |
The internal figures join from where they already sit. Ledger extracts, the sales file, price records, and the reporting model connect through 250+ integrations, so an internal result meets the consumer decision that produced it.
Three questions sit outside the ledger by construction, and each one changes the reading of a result.
Financial planning and analysis is the process this measurement supports. Revenue predictive analytics takes the same signal forward over backward. Financial benchmarking sets the measured result against the sector.
Real people share what they buy and prefer, under explicit consent. Sena captures it directly at the source, so every figure traces back to where it came from whenever a number comes under question.
One shopper supplies the purchase and the reason together, and each figure names the market and week it was captured in.
A mix shift arrives with the shopper reason attached over a component breakdown.
Photographs establish whether the item was present, so two readings of one shortfall divide.
The item that won is recorded, including items the company sells zero units of.
Financial analytics built on internal records can measure a result to the basis point and explain it only by decomposition. Sena captures real-world signals from the shelf photographed in the market through to the shopper naming what settled the choice.
Selection and amount paid enter as one record, so a margin movement rests on a real transaction.
Reads which items faced the shopper, off images captured in real outlets, so absent stock separates from weak demand.
Signal arrives from the consumer network under explicit consent. Why one item won is answered by the shopper who took it.
Ledger extracts, the sales file, price records, and the reporting model connect over 250+ integrations, so an internal result meets its external cause.
Provenance rides on the number itself, the market, and the week, so a margin explanation is auditable to the shoppers behind it.
Closed period reporting with sales history and price records, one scanned cycle at a time.
Four teams read the same result, and each one needs a different part of the cause before it can act.
The measurement layer. Needs the cause outside the ledger.
The margin argument. Needs mix movement explained per market.
The answer to why. Needs the consumer reason, over a decomposition.
The regional number. Needs the country that moved named.
The same outside layer, joined to whatever each industry reports margin on.
Mix movement per market, with availability separated out.
Pack and format shift, separated from occasion mix.
Own-label substitution, with the mix effect isolated.
Product switching, with the revenue effect attributed.
One mechanism, applied per market and per reporting period. Each step is documented, which is what carries a margin explanation into a review pack.
Purchase and stated reason come from one shopper, which is what attaches a result to its cause.
Sena joins the consumer read to the internal figure over 250+ integrations, so both sit on one line.
Shelf photographs divide absent stock from weak demand, and a ledger entry conceals exactly that split.
Most financial analytics runs on internal records alone, and every entry in them was created by somebody outside the company. Sena supplies that outside layer.
| Capability area | Typical setup | Sena |
|---|---|---|
| Scope of evidence | Transactions the company was party to. | The market those transactions happened in. |
| A margin movement | Split into price, volume, and mix. | Explained by the shopper who moved. |
| A volume shortfall | One entry, two possible causes. | Divided by shelf photographs. |
| Lost share | Units gone, destination unknown. | The winning item recorded. |
| Reporting level | Regions, as the entity is structured. | Markets, as consumers behave. |
| A product the market omits | Beyond measurement. | Sized by stated response. |
| Evidence in a review | A decomposition. | Open any figure to its consumers. |
Three financial results whose cause sits beyond the company that recorded them.
Read the mix shift back to the shoppers who made it, so the cause arrives with the number.
Establish whether the item was on the shelf, so a shortfall carries one meaning over two.
Record the item that won, including items outside the company's own range.
A result with its cause attached settles what happened and why. The four decisions below are what a finance team runs around it.
Every finance decision Sena answers, in one place
The process this supports
Uses this measurement to set and defend a committed number.
Explore financial planning and analysis 02The same signal forward
Takes the consumer read to the next period over the last one.
Explore revenue predictive analytics 03Managing to the measure
Runs the business against the figures this layer reports.
Explore financial performance management 04The result against peers
Sets the measured outcome against the sector it competes in.
Explore financial benchmarkingThe walkthrough takes one margin or volume movement from a real reporting pack, reads it back to consumer purchase in the markets that produced it, and separates availability from choice while the finance team watches.
Name the result and the markets behind it, and we will read it back to the consumer decision that produced it.