Win Rate from 14% to 26% with AI Account Prioritisation on Actively AI
A mid-market SaaS team had 1,200+ accounts in Salesforce and no signal on which were ready to buy, so AEs picked accounts by gut feel and win rate sat at 14%. SiyaSphere made Actively AI the account intelligence layer, surfacing daily propensity-to-buy scores in Salesforce, Slack, and Marketo. Win rate rose to 26%, and AI-flagged accounts drove 58% of Q4 closed-won revenue.
- Win rate
- 14% → 26%
- Win rate
- Incremental revenue from AI-prioritised pipeline
- $2.4M
- Incremental revenue from AI-prioritised pipeline
- Of Q4 closed-won from AI-flagged accounts
- 58%
- Of Q4 closed-won from AI-flagged accounts
The challenge
- 1,200+ accounts in Salesforce with no systematic view of buying readiness.
- AEs self-selected accounts by gut feel and missed high-intent signals.
- Marketing ran broad campaigns with no account-level prioritisation, and win rate was 14%.
What we built
- 1
One intent model across the stack
Implemented Actively AI to ingest CRM data, website visits, email engagement, and third-party intent signals into daily propensity-to-buy scores per account.
- 2
Scores where reps work
Surfaced scores in Salesforce as a custom field so prioritisation lived inside the AE's daily workflow.
- 3
Real-time intent alerts
Triggered automated Slack alerts to the owning AE whenever a tier-1 account entered a high-intent window.
- 4
Intent-driven ABM
Used Actively AI segments to trigger Marketo ABM campaigns for accounts in the 80th+ percentile of intent.
Results
- Win rate improved from 14% to 26% (an 86% increase)
- AE time spent on the right accounts up 65%
- ABM campaign click-through rate up 112%
- 58% of Q4 closed-won came from AI-flagged accounts
- $2.4M incremental revenue attributed to AI-prioritised pipeline
Key takeaway
Intent scores only change outcomes when they reach the rep at the right moment. Putting scores in Salesforce, alerts in Slack, and segments in Marketo turned one model into aligned sales and marketing action.