A lead score can become a self-fulfilling prediction: high-scoring leads receive more attention and ultimately close more often. Evaluation must distinguish a genuine signal from the different treatment caused by the score itself.
Define the outcome and timeframe
State whether you predict a meeting, qualified opportunity, purchase or value within a specified window. Different objectives require different models.
Use only information available at the time
Do not train using facts that became known after the decision. Leakage makes the score impressive on historical data and useless in live operation.
Compare by score band
Examine actual conversion and value within each range. A useful score should show a logical increase and enough volume for operational use.
Examine the sales team's effect
Record which leads received more contacts and how quickly. Test a small controlled sample to measure underlying quality.
Recalibrate after changes
A new market, campaign or pricing structure changes the relationships. Monitor drift and retain version history rather than treating the score as permanent.
This framework is an original editorial methodology developed by DigitalNow.