Predictive marketing helped a home services brand beat growth targets

A national home services provider wanted to build a direct-to-consumer marketing capability in a crowded, competitive category where customer acquisition is expensive and every media dollar has to work harder.  

With GainShare Performance Suite (GPS), our AI-powered predictive intelligence platform, we connected media, demand signals, call center activity, and sales outcomes into one cross-functional forward-looking view. That let the team forecast demand, plan investment with more certainty, and beat its growth targets.

The challenge

The client was entering direct-to-consumer marketing in a category where established competitors were spending aggressively and media costs were climbing. The opportunity was clear, but the path to purchase was not simple.

Leads came through digital and traditional channels, then moved into a consultative sales process before converting to customers. That meant media performance could not be judged by response alone. The team needed to understand how spend was influencing demand, calls, lead quality, conversion, and eventual sales.

The approach

GainShare built GPS into the client's operating workflow as a central performance intelligence layer. It connected media performance across CTV, search, online video, social, linear TV, and radio with call center, UX, CRM, competitive, and seasonal data, so the team could see how media, demand, and sales moved together instead of reading each channel in isolation.

We then built forecasting models around the indicators that mattered most: customer volume, media investment, call volume, conversion rate, and market-level performance. As more data flowed in, the model sharpened from early directional reads into reliable forecasts, helping the team respond faster and plan with greater agility. 

GPS also let stakeholders test decisions before executing. They could model “what if” investment scenarios across channels and markets, then agree on the levels most likely to improve customer acquisition and ROI.

How GPS was used in practice

Key stakeholders worked from shared GPS dashboards and forecasts. In regular planning sessions, the teams could now review forecast versus actual performance, channel and market trends, and where demand or competition was shifting.

That tightened the decision cycle. Media investment could be adjusted before underperformance became a quarter-end surprise. UX and marketing could now see how upper-funnel signals were translating into sales outcomes.

We also used causal inference modeling to measure true media lift, so the client could see not just what happened, but what likely would have happened without the campaign.

Alex Marsh, VP of AI and Analytics at GainShare, explains how GPS changed decision-making for the client: “GPS gave us a holistic view of channel impact instead of relying on last-touch reads. Bringing together more than 30 data feeds creates enormous value, but also a lot of noise. GPS cut through that complexity, helping us make intelligent decisions with greater confidence.”

The results

GPS helped the client outperform its customer growth target by ~14%, supported by predictive reallocations across media channels. Lead-to-sale conversion improved by ~16%, driven by better channel prioritization and a clearer view of how demand was moving across the funnel.

Beyond the metrics, GPS gave the client a connected way to plan, align, and respond, helping siloed teams evaluate media impact, model investment scenarios, and make faster decisions as competitive pressure and market conditions changed.

The team moved from reading performance after the fact to managing growth with a forward view.