Lead scoring model lifts sales conversion by 20%

The result

A price comparison and switching service lifted its outbound sales conversion rate by 20% after QuantSpark built it a lead-scoring propensity model. The uplift was measured directly over a month of live testing, before the scoring algorithm was rolled out across the whole outbound sales funnel. The client did not need to add headcount or buy more leads to get more sales: the same sales team, working the same pool of leads, converts more of them because it now knows which ones matter most.

The problem

The client sells across several categories, including energy, insurance and telecoms, and depends on an outbound sales team with finite capacity to close deals. Every lead in the pipeline competes for the same limited hours of calling time, and not every lead is equally likely to convert, or equally valuable once it does. Without a way to rank the lead base, that capacity was necessarily spread across leads rather than concentrated where it would do the most good.

The commercial brief was simple to state and hard to solve well: increase monthly sales by improving conversion, using a finite outbound resource more intelligently rather than a larger one. That meant ranking and prioritising the lead base so the sales team's limited time was directed at the highest-value prospects first.

How QuantSpark approached it

QuantSpark's starting point was the funnel itself, not the model. The team first mapped the mechanics of the sales funnel, from lead generation through to closed sale, so that whatever predictive model came next was optimised for practical impact on the business rather than statistical elegance alone. From there, it built a modelling dataset and analysed the individual drivers of lead value, the characteristics that separated a lead likely to convert well from one that was not.

With those drivers established, QuantSpark used advanced SQL alongside machine-learning algorithms to test a series of scenarios and configurations, iterating until it converged on the strongest available predictor of valuable leads. Rather than leaving the model as an analytical exercise, the team implemented it directly within the client's own systems and sales processes, so lead scores reached the outbound team as part of its everyday workflow rather than as a separate report to consult. Alongside the model, QuantSpark designed a testing framework to monitor performance over time, so the impact of scoring, and not just its existence, could be measured and trusted before anyone committed to a full rollout.

Under the hood

The workflow ran, in sequence: historical lead and funnel data feeding the model; a propensity score generated for each lead using SQL-driven analysis and machine-learning algorithms; those scores embedded in the client's existing sales systems so agents worked leads in priority order; a live testing and reporting framework tracking conversion throughout; and, once results were proven, a full rollout across the outbound funnel together with tailored call strategies for leads with particular profiles.

Why it worked, and what it was worth

The 20% conversion uplift was not a modelled projection, it was measured in production, over a month of live testing against the client's own outbound funnel, before the scoring algorithm was rolled out fully. Because the client's sales capacity was fixed, a conversion lift of this kind converts directly into more completed sales from the same lead volume and the same headcount, without a new cost base required to capture it.

The value did not stop at the headline figure either. The model's output gave the sales team insight into which lead profiles behaved differently, which it used to tailor call strategies further and drive additional incremental gains on top of the 20% already measured. That combination, a rigorously tested headline result plus a mechanism for continuous improvement, is what turned a modelling exercise into a permanent change in how the sales team worked.

This case illustrates a pattern that recurs across QuantSpark's data science work: the biggest commercial gains often come not from a bigger sales team or a bigger lead list, but from directing existing, finite capacity more intelligently using the data an organisation already holds.