Thousands of inbound leads a month, dropped on the sales team at random. Reps burned the day screening tiny or unready prospects, while the enterprise deals worth chasing waited days for a first reply and drifted to a competitor. We taught the pipeline to sort itself.
Marketing was producing thousands of inbound leads a month, and the B2B sales team simply couldn't reach them all in time, so the wrong ones got the attention.
Every lead from the site and the campaigns was poured onto the sales team at random, with no ranking and no distinction between a small business after a basic tool and an enterprise chasing a complex, high-value deal. Reps spent most of the day on screening calls and dead-end meetings with prospects that were too small or not ready (classic junk leads) while the deals that mattered went unanswered.
The delay was fatal. Average time to respond to an inbound lead sat above 48 hours (an eternity in B2B) and conversion fell off a cliff. Senior account executives, meanwhile, lost roughly 40% of their time to upfront research, checking company registries and running “data-verification” calls instead of negotiating and closing.
Hot enterprise leads reach a rep in minutes; the rest are qualified before a human touches them.
We connected the lead forms to an AI engine that scans business data sources in real time and auto-completes the missing details about the prospect (size, revenue, industry and existing tech), writing them straight into the CRM, so no rep starts from a blank record.
A predictive model reads the customer profile and browsing behaviour and gives each lead a maturity score, so the pipeline is ranked by real potential instead of arrival order.
Hot, institutional leads get top priority and drop straight into the right rep's calendar within minutes, while they're still warm, not two days later.
Medium and low-scoring leads route to a conversational assistant over email or chat that runs a needs-and-fit qualification with them, before a human rep ever spends a minute.
With enrichment and first-pass qualification automated, senior reps stop doing registry look-ups and verification calls and spend their time in deep meetings with buyers who are already vetted.
The AI ranks, enriches and warms; the account executive judges and negotiates. The scoring points people at the right conversations. It doesn't make the sale for them.
Fewer dead-end meetings, far more closed deals.
Lead-to-paying-deal conversion jumped 47.6% within two quarters, as response time on strategic leads collapsed from over 48 hours to about five minutes, reaching the best prospects before a competitor could.
Senior account executives became 35% more productive, freed from screening and research to run deep meetings only with institutional buyers who had already passed a rigorous, automated qualification.
The lift came from spending scarce human selling time only where the odds are best, the same predictive-scoring discipline behind our churn-prediction work, pointed at the top of the funnel instead of the bottom. The reps still own every negotiation; the model just makes sure the phone rings for the right prospect, in minutes rather than days.
A large Israeli content provider was losing thousands of subscribers a month with no warning. Early churn prediction with automated, personalised retention cut monthly churn ~25% and lifted usage 40%.
Read the case → Student retention · Higher edA veteran Israeli university was losing hundreds of first-year students to silent dropout. Early-warning student profiles flagged risk weeks ahead and cut first-year dropout by 25%.
Read the case →
Reef TRH