Sena puts recorded demand under sales forecasting, counting what buyers in each market bought up to the moment of the ask. The number goes to the board with the demand it rests on.
Most teams already produce a number every cycle. Three things decide whether it holds, and each one shows up in the room where the number gets defended.
A weighted pipeline multiplies deal value by a stage probability. Both inputs come from inside the team, so the forecast describes the team's confidence, and the market sits in a separate read.
A trend line drawn from prior quarters projects forward on the assumption that conditions hold. The quarter a category turns is the quarter the assumption breaks.
The forecast reaches the board as a figure. When someone asks what it rests on, the answer describes a method where a measurement belongs.
Sena is the decision AI with access to real-world data. It counts what buyers in each market paid and chose up to the moment of the ask, then sets its own pipeline beside that trailing demand line.
Sena reports what the category did in each market across the window, one market at a time.
Sena connects own pipeline and sets it against the demand line for identical dates.
Every figure opens to the market, the date, and the capture, so the number gets defended in the room.
Every forecasting method answers the same question and differs on one thing: what evidence sits under the estimate.
| Method | What it rests on |
|---|---|
| Opportunity stage | A probability applied per stage, set from prior conversion rates. |
| Historical trend | Prior periods projected forward, assuming conditions repeat. |
| Length of cycle | Deal age against a typical cycle, so time in stage carries the estimate. |
| Sales forecasting models | A weighted model combining the above rebuilt each cycle from prior conversion. |
| Recorded demand | What buyers in that market bought up to the ask, carried forward as the baseline. |
The first three read internal records. The fourth reads the market the pipeline sits inside, and Sena supplies it.
Name the period and each market on its own. A regional roll-up averages the country that turned into the ones that held.
Sena counts recorded purchases in each market up to the ask, so the baseline is a measurement.
Sena connects the CRM and reports own movement beside the category line, per market.
Where one's own movement diverges from the category, Sena names the market, the segment, and the competitor position behind it.
The forecast per market, with the category demand line beneath it. The divergence between one's own movement and the market, quantified. The competitor position inside the same window, dated.
Every line open to the purchases counted behind it.
Accuracy improves when a measured input carries the weight of a judgement one carried. A stage weight describes belief, and a recorded purchase describes the market. A revision carries the demand shift that caused it, so the board follows the logic.
Two cycles compare cleanly because the window and the market stayed constant.
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.
Sena answers forecast questions from consumer data collected under explicit consent, then names the number and the reason behind it.
Sena answers a forecast question across the markets a team names and reconciles each market line before reporting it.
Sena places own pipeline beside the recorded purchase for identical dates, so movement reads against the category it happened in.
Each line opens to its market, its date, and its capture. A challenged line resolves to the buyers counted behind it.
A forecast rests on the demand behind it. Sena builds every figure here from real-world signals, captured when the question needs it. It runs from the buyers moving in each market through to the dated capture a team opens when the board questions the number.
Counts what buyers in each market bought in the forecast window, so the number rests on recorded purchases.
Reads the category price and presence of images captured in real stores, dating each capture inside the same window.
Signal arrives from the consumer network under explicit consent. The demand line under the forecast rests on people who agreed to share what they buy.
Brings your pipeline, CRM, and revenue data in through 250+ integrations, Salesforce and HubSpot among them, so the demand line reads beside your own stages.
Each figure carries the market, the date, and the capture forward, so any number in the deck opens to what it was read from.
Turns scattered forecast files, exports, and spreadsheets so a forecast draws on every cycle the team has filed.
Four teams read the same forecast, and each one needs a different cut of it before they can act.
The commit. Needs the market read under the number before it goes to the board.
The model. Needs a measured input to set beside the stage weights.
The plan. Needs the category demand line for the same window as the forecast.
Coverage. Needs to see which markets are carrying the gap.
The same forecast, read against the decision each category actually makes.
Category sell-out in store across the forecast window, market by market.
Where consumer adoption of a product class moved in the region across the period.
Whether subscriber movement in each market supports the committed number.
Stated intent set against recorded purchase for the same window.
One mechanism covers every market and every window. Each step is documented, which is what keeps the number defensible.
Real contributors share what they buy and pay under explicit consent in the markets named.
Sena counts recorded purchases up to the ask in each market and captures category price across the same dates.
Own pipeline reports against that demand line, per market, with the capture behind every figure.
Most forecasting reads the pipeline. Sena reads the market that pipeline sits inside.
| Capability area | Typical setup | Sena |
|---|---|---|
| What sits under the number | Stage weightings and rep judgement. | Recorded consumer demand in that market, counted to the ask. |
| Where the data comes from | The CRM and prior periods. | Real consumers who agreed to share what they buy, plus the CRM beside it. |
| When the market turns | The method assumes conditions repeat, so the turn surfaces at the close. | The category line moves inside the window, so a read taken mid-window shows the turn while the quarter is open. |
| Market granularity | One committed number across the region. | Each forecast market read separately, with the segment named. |
| Explaining a revision | The revision arrives as a lower number. | The revision arrives with the demand shift that caused it. |
| Auditing a figure | A model description, with the underlying records held by the provider. | Any forecast line opens to the market and the capture under it. |
Three situations where a market-level demand read changes the committed number.
Take the demand line into the board meeting beside the forecast, so the number arrives evidenced.
Read category movement inside the window, so a softening market surfaces while the quarter is still open.
Separate the category share of the result from the execution share, market by market.
Sales forecasting sets the forward number. The four decisions below are what a sales team asks around it.
Every sales decision Sena answers, in one place
The accounts whose markets moved
Orders the list by category demand recorded in each account's home market.
Explore lead intelligence 02Outcomes read against the market
Reads past outcomes against the market movement behind them.
Explore win/loss analysis 03The opening, counted
Sizes the category the forecast sits inside, per market.
Explore market sizing 04What changed in the account's market?
Gives the rep what the account's category did in the forecast window.
Explore sales intelligenceThe walkthrough counts category demand for a real forecast window in the markets named, sets its own pipeline against it, and traces every figure to the capture behind it while the team watches.
Tell us the forecast window and the markets, and we will bring the demand line.