A 300-person analytics consultancy selling to enterprise insurers and banks used Decisions Lab to test how buyers might respond to its cold emails before sending them. Its positive reply rate rose from 0.2% before Decisions Lab to 4% with Decisions Lab. The first qualified conversation came in week 2.
Making each email count
The firm sent up to 1,000 cold emails per month. With a limited number of opportunities to reach enterprise buyers, the team needed to decide which message to put in front of each prospect. The firm kept its existing outreach team and cold email motion while using Decisions Lab to inform the messages.
Testing buyer response before sending
Decisions Lab used public and account evidence to simulate how a target buyer might receive an outreach message. The simulation explored which claims might attract interest, what risks could create hesitation, and what proof the buyer might need. The team used those predicted responses to revise and draft outreach.
The simulation gave the team a way to consider the message from the buyer's perspective before committing an email to the campaign. Actual replies then provided evidence to compare with those expectations.
Positive reply rate rose from 0.2% to 4%
Positive reply rate on cold email outreach
- 0.2%
- 4%
- Before Decisions Lab
- With Decisions Lab
Positive reply rate = positive replies / messages sent. The firm sent up to 1,000 emails per month. Axis scaled to 10%.
The 4% positive reply rate was reported across enterprise insurance and banking outreach. It was 20 times the firm's 0.2% baseline. The first qualified conversation came in week 2.
Learning from real responses
After outreach was sent, Decisions Lab compared positive replies and refusals with the simulation's expectations. The team used the feedback to further refine the response model used in later simulations.