SALES PIPELINE
Lead scoring: focus on the prospects most likely to convert
Lead scoring with TIMi estimates each prospect’s probability of becoming a customer and shows the factors behind the score, so sales effort goes where it pays.
Conversion propensity
Modeler compares prospects who converted with those still being qualified.
All channels
Declared data, acquisition source and marketing interactions in one table.
Explained score
Each score comes with the factors behind it.
HOW IT WORKS
How lead scoring works.
Three steps, in one engine, on a standard server with no cluster.
1. Prepare the data
Anatella gathers the prospect’s declared data, the interactions with marketing and sales channels and the conversion history of previous prospects, then structures them into variables.
2. Build the model
Modeler compares prospects who became customers with those still in qualification, and estimates a probability of conversion.
3. Score and explain
Each prospect gets a conversion score with the factors behind it, such as acquisition source, engagement with content or a profile similar to prospects already converted.
GETTING IT RIGHT
What a lead scoring model needs.
A score is only as good as the history it learns from and the way sales teams use it.
Start from the conversion history
The model learns from prospects whose outcome is known, so some conversion history is needed. Without it, a collection phase or a provisional score based on business rules can come first.
Keep the score fresh
A score reflects engagement at a point in time. Recomputing it frequently, ideally after each interaction, keeps the ranking aligned with the prospect’s current behavior.
Keep people in the loop
The score prioritizes effort, it does not replace qualification. The final decision stays with the sales team, who can read the factors behind each score.
EXPLAINABILITY
A score says who. The factors say how to approach.
A score alone tells you that a prospect looks promising, not why. A white-box model shows what drives it, so the approach can match the real reason for interest instead of following a number without context.
For sales and marketing teams
Qualification and follow-up concentrate on high-score prospects, with an approach built on the identified factors.
For transparency requirements
A model that justifies each result variable by variable gives you the documentation to answer transparency expectations such as GDPR and the AI Act. The compliance assessment itself stays with your legal team.
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.
DATA
What data a lead scoring model uses.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Declared data
Sector, company size and role in B2B, or profile data in B2C.
Acquisition source
The channel and campaign that brought the prospect in.
Marketing interactions
Pages visited, emails opened, content downloaded.
Conversion history
Past prospects and their outcome, as a learning base.
PROOF
Seen in production.
Results from our published customer stories on prospect and client targeting.
Sales performance multiplied by 4
Cofidis profiled the prospects most likely to convert and contacted them proactively: 6.2% conversion against 1.5%, and €3.5 million of additional gross revenue.
26% of the top 15% contacted bought a credit
ING targeted the 15% of business clients most likely to need funding and raised conversion by 20%, with limited commercial resources.
Conversion up from 4% to 34%
Targeting accuracy let BNP Paribas launch smaller, more accurate campaigns instead of broad ones.
FAQ
Common questions.
Does lead scoring work without a conversion history?
Some conversion history, even limited, is needed to train the model. Without it, a collection phase or a provisional score based on business rules can come before the predictive model.
Does the score need to be recomputed at each interaction?
Ideally yes, or at short intervals, because the score reflects how the prospect’s engagement evolves over time.
Does lead scoring replace human qualification?
No, it complements it by prioritizing effort. The final qualification decision stays with the sales teams.
Does it work in B2B and B2C?
Yes. The logic is the same: compare the profile and behavior of a prospect with those of prospects who already converted, whether the sales cycle is B2B or B2C.
RELATED USE CASES
Keep exploring.
Cross-Sell Prediction
Find the customers most likely to buy a complementary product, and why.
Upsell Prediction
Find the customers most likely to move to a higher offer, and why.
Sales Forecasting
Forecast revenue from the sales pipeline and past conversion, with the factors explained.
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.
