Increasing Sales through Optimized Media Mix
An underperforming media strategy was transformed into a data-driven channel mix that boosted sales, cut wasted spend, and revealed the highest-performing channels.
Choosing the right retail location has always required a mix of local knowledge, business judgment, and a little bit of “we’ve seen this before.” But when budgets are tight, margins are thin, and foot traffic is shifting, intuition needs a strong data partner.
Drumline helped a national nonprofit retailer build a predictive retail location analysis framework that evaluates existing and potential thrift store locations using sales data, donation data, drive-time analysis, and third-party market data. The result was a self-service site evaluation tool that helped teams compare markets, prioritize opportunities, and make location decisions with more confidence.
A nonprofit organization operating more than 630 thrift stores across the U.S. needed a clearer way to evaluate retail location performance. The organization was always looking for ways to grow its store footprint and expand support in local communities, but location decisions were becoming harder to make with confidence.
Limited budgets, tight margins, and declining foot traffic made it difficult to understand why some stores outperformed others. The team also needed a better way to evaluate where to open new stores, where to investigate underperformance, and how to support lease negotiations with property managers.
The organization had historically relied on intuition to guide store-location decisions, and that experience still mattered. But market conditions were changing, and leadership needed a data-backed framework that could validate decisions at scale.
For thrift store performance, the model could not focus only on customer demand. It also needed to account for the supply side of the business: in-kind donations that create the inventory sold in stores. Drumline’s analysis showed that stronger retail location decisions required understanding the balance between likely buyers, likely donors, geography, and local market conditions.
Drumline used an iterative modeling framework to turn complex location data into a practical retail site evaluation tool. The work began with stakeholder interviews, alignment on what defined a successful location, and a review of the data needed to explain store performance.
After aggregating sales and donation data across territories, Drumline added third-party geographic, demographic, and psychographic data. The team then used dynamic drive-time analysis to define the market area around each store, identify the strongest predictors of performance, and develop a predictive model that could score current and potential locations.
After evaluating multiple modeling techniques, Drumline selected a random forest model because it provided the strongest predictive performance for this use case. The model generated a location performance index, which became the primary metric for expansion decisions. Drumline then paired that index with descriptive market variables in a self-service reporting portal so teams could evaluate locations in real time.
The site evaluation tool changed how the organization assessed potential thrift store locations and monitored existing store performance. Instead of relying only on intuition or one-off analysis, teams could generate consistent retail store analysis reports that combined predictive scoring with local market context.
Within the first month, more than 300 reports were generated. The tool also helped identify underperforming locations and supported lease agreement negotiations with property managers, giving the organization a clearer, more consistent way to evaluate retail decisions.
Retail location decisions get easier when the right data is doing the heavy lifting. Drumline helps organizations bring together customer, market, geographic, and performance data to create clearer paths forward.
Have a retail location, market analytics, or predictive modeling challenge you’re trying to solve? Let’s talk about how data can help you make the next decision with more confidence.
An underperforming media strategy was transformed into a data-driven channel mix that boosted sales, cut wasted spend, and revealed the highest-performing channels.
Reimagining a national nonprofit’s header around real visitor intent revealed an 86% help-seeking audience, improving service access, donor clarity, and conversion through data-driven UX.
Have a data or analytics challenge you’re wrestling with? Get in touch via our Contact Us page and let’s see how we can move your organization forward.