FORECASTING
Sales forecasting: put a number on the quarter, with its reasons
Sales forecasting with TIMi estimates future revenue from your sales pipeline and sales history, to steer the budget and sales targets, and shows the factors behind the forecast.
Pipeline-based
Opportunities, stages and past conversion feed the forecast.
By region or product
Split a forecast by region, product line or sales team.
Explained forecast
You see what drives the number, not only the number.
HOW IT WORKS
How sales forecasting works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the closed sales history, the open pipeline (opportunities, stages, declared probabilities) and the seasonal factors of the business, then structures them into variables.
2. Build the model
Modeler estimates future revenue by crossing the historical conversion of the pipeline with the sales trends already observed.
3. Forecast and explain
Each forecast comes with its factors, such as the usual conversion rate at this pipeline stage, seasonality or recent trend.
Anatella
ModelerHOW IT DIFFERS
Sales forecasting is not demand forecasting.
Both can share data, but they answer different questions.
Revenue, not volume
Demand forecasting estimates a future volume of consumption or use, often to steer production or stock. Sales forecasting estimates expected revenue from the sales pipeline and the transaction history, to steer financial and commercial management.
Use real conversion by stage
The model relies on objective pipeline data and the historical conversion at each stage. It gives a second look to compare with declared forecasts, which can be too optimistic or too cautious.
Cover full sales cycles
For a long sales cycle, the history must cover enough complete cycles to identify conversion rates by stage reliably.
EXPLAINABILITY
A forecast gives a number. The factors tell if the target is realistic.
A revenue forecast alone gives an expected amount, not what justifies it. A white-box model shows the factors, so sales leaders can see whether the target is reachable given the real pipeline, or whether a conversion gap explains a more cautious forecast.
For sales teams and leadership
A forecast can be discussed with precise context, instead of being opposed to a target without justification.
For budget steering
Understanding the factors of a forecast helps to adjust the allocation of resources during the year.
For model owners
Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar. Recompute the forecast at each significant pipeline update, or on your regular reporting cycle.
DATA
What data a sales forecast uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Closed sales and cycles
Past sales and sales cycle durations.
Open pipeline
Opportunities, stages, amounts, expected dates.
Conversion rates
Historical rates by stage of the sales cycle.
Seasonality
Factors specific to the business.
PROOF
Closest references.
We do not yet have a published customer story on pipeline-based sales forecasting. These two are related: forecasting at scale, and conversion models.
300,000 stores forecast with 30 million transactions
Demand depends on weather, neighboring stores and local dynamics, so classical time series models were not enough. The models score 2.4 million records per second.
Conversion from 4% to 34%
Product-level models identified which customers were likely to convert in leasing campaigns.
FAQ
Common questions.
Does it replace the forecasts declared by the sales team?
No, it complements them. The model relies on objective pipeline data and conversion history to give a second look, useful to compare with declared forecasts that can be too optimistic or too cautious.
Does it work for a long sales cycle?
Yes, as long as the available history covers enough complete cycles to identify conversion rates by stage reliably.
Can we forecast by region or product line?
Yes. A forecast can be split by region, product or sales team, depending on the level of detail of the available data.
How often should the forecast be recomputed?
Ideally at each significant update of the pipeline, or on a regular reporting cycle (monthly, quarterly) aligned with how the organization is steered.
RELATED USE CASES
Keep exploring.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
