Category analytics measures how a category moved, and the measurement runs on the accounts that report their scan data. Sena adds the rest of the market: what shoppers bought in every outlet type, dated to the week.
Category analytics measures the category cycle after cycle: how large it is, how fast it moves, who holds what share, and where volume travels inside it. Three properties of the reporting base bend the trend line.
Scan data covers the retailers who license it. A category trend built on that base describes the reporting half and reports it as the market.
Share falls, and the report shows the fall. Whether the category shrank, a rival grew, or shoppers left for a category filed elsewhere are three different events behind one number.
Reports land weeks after the period closes. A category trend that turned in week three surfaces in the review that follows the quarter.
Sena is the decision AI with access to real-world data. It records what shoppers bought across every item and every outlet type, including the channels that license zero data, and asks those shoppers what moved them, so a trend carries its cause.
Purchase is recorded at the till wherever it happens, so the category reads at market size.
Basket and switching evidence show which of the three causes moved a share figure.
Signal arrives when the question is asked, so a turn surfaces in the week it happens.
Category analytics is the standing measurement of a category: its size, its rate of change, the share each participant holds, and the movement of volume between items inside it. It runs on connected systems each cycle and produces the figures behind every range, space, and price decision in the category.
Category analytics is the continuous measurement of a category, run off connected systems every cycle. Product category analysis is the discrete exercise of answering one structural question about that category. Category performance analysis is the verdict on a single closed period.
A search for the term returns two subjects. One is the measurement of a retail category. The other is the configuration of an analytics product: affinity categories, event categories, industry categories, and product category fields.
| Measure | What it returns | Where it goes wrong |
|---|---|---|
| Category size | Total volume and value, per market. | Scaled up from the reporting base. |
| Rate of change | How fast the category moves. | Read against a base that shifted underneath. |
| Share | What each participant holds. | Computed inside partial coverage. |
| Switching | Where volume travels between items. | Inferred from correlation, absent the basket. |
The four are commonly produced by two systems and reconciled in a deck. That reconciliation is where a coverage change gets reported as a category movement, and it is the most frequent error in category reporting.
Retail category analytics adds the outlet. A category performs differently in a large format, a convenience store, and an independent trader, and the three carry different ranges at different prices. Three reads separate them.
Volume, share, and price resolved to the channel that sold it.
The same four measures held separately, since a category rarely moves in step across borders.
What each outlet carried the week it sold, which decides what share was even reachable.
Four collected inputs produce the measures above, held per market.
| Input | What it answers |
|---|---|
| Receipts | Every category purchase a shopper made, dated to the day. |
| Store captures | What each outlet carried and listed that week. |
| Geo-verified photos | Price and facing at the moment of capture, dated. |
| Stated preference | What moved a shopper, so a trend arrives with a cause. |
The team's own numbers join separately. Scan data, shipment history, price files, and promotional calendars connect through 250+ integrations, so licensed measurement and recorded purchase read together.
Three questions live beyond a licensed scan extract, and each changes the trend.
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 set of shoppers supplies both the purchase and the reason, and the market and week travel with each figure.
A share fall opens into whether the category grew or the range shrank.
Outlets outside the licensed base carry their own volume and share.
Stated reasons arrive from the same shoppers whose purchases produced the trend.
Category analytics built on licensed scan data can measure the reporting base precisely and struggles to state the market. Sena captures real-world signals from the category photographed in an independent outlet through to the receipt showing what the shopper chose.
Purchase is recorded wherever it happens, so the unreported half of a market arrives with its own volume.
Reads what each outlet carried and charged, off images captured in real stores, so reachable share separates from lost share.
Signal arrives from the consumer network under explicit consent. What moved a shopper comes from the shopper who moved.
Scan data, shipment history, price files, and promotional calendars connect over 250+ integrations, so licensed measurement meets recorded purchase.
Each figure carries its market and its capture week, so a share movement opens onto the baskets underneath it.
Past scan extracts, category reviews, and price files load in, spanning every cycle the category has been scanned in.
Four teams quote the same measurement, and each one needs a different cut of it before they can act.
The standing read. Needs market size ahead of reporting-base size.
The numbers everyone quotes. Needs one figure that survives a challenge.
The retailer review. Needs share the buyer accepts as neutral.
The category forecast. Needs the base movement separated from the share.
The same four measures, held against whatever each industry counts as a category purchase.
Category size and share per outlet type, with the unreported channel included.
Counter volume against own label, resolved per market.
Category movement by occasion and format, week by week.
Tier-level share, read against the trade-up path.
One mechanism, applied per market and per category. Each step is documented, which is what carries a category figure through a finance review.
Receipts across the category return what shoppers bought, per outlet type, dated to the day.
Sena computes size, rate of change, share, and switching on that base, so the market figure covers the market.
Basket movement and stated cause attach a reason to each change, so a trend is explained.
Most category analytics runs on licensed scan data, which measures the accounts that license it and holds zero rows for the outlets outside. Sena covers the market around them.
| Capability area | Typical setup | Sena |
|---|---|---|
| The dataset | Licensed scan data from reporting accounts. | Purchase recorded at the till, every outlet type. |
| Category size | Scaled up from the reporting base. | Counted across the market, per outlet type. |
| A share fall | The figure, reported. | The cause named: base, rival, or exit. |
| Switching | Inferred from correlation. | Read from the basket, item against item. |
| The unreported channel | Estimated. | Recorded at the till in outlets that license zero data. |
| Timing | Weeks after the period closes. | Dated to the week, inside the cycle. |
| Evidence in a review | A licensed figure. | Open any measure onto the purchases behind it. |
Three measurement problems where the unreported half changes the number.
Split the movement into category growth, rival gain, and shoppers leaving, so the cause is named before the response.
Count volume across every outlet type, including the channels that license zero data.
Reconcile a licensed read against recorded purchase, with the coverage gap quantified.
A standing measurement sets the figures everyone argues from. The four decisions below are what a category team runs around it.
Every category decision Sena answers, in one place
The verdict on a period
Reads the standing measurement as a verdict on one period.
Explore category performance analysis 02What the category contains
Answers one structural question about the category the measurement covers.
Explore product category analysis 03The software the team buys
Names the team that owns the measurement and the software it runs on.
Explore category management 04Which items the category carries
Turns the measured demand into a listing decision, item by item.
Explore assortment optimizationThe walkthrough takes one category in one market, sizes it across every outlet type, and splits a share movement into base growth, rival gain, and shoppers leaving while the team watches.
Name the category and the markets it sells in, and we will measure it across every outlet type.