How Empire Today Used Layered Measurement to Unlock $25M+ in Incremental Contribution Margin

When Empire Today, a national direct-to-consumer flooring retailer that had built decades of brand equity, faced a structurally weakening demand environment, the question was simple: What could marketing still drive, and what was outside its control? Not just what the dashboards showed, but what was actually driving the business.

Who Is Empire Today?

Empire Today operates across a broad U.S. footprint and has built a highly recognizable brand through decades of advertising-led demand generation. Its model is built around in-home consultations, with flooring samples brought directly to the customer and installation managed end to end. It is a high-consideration purchase category with long decision cycles and meaningful local operational complexity.

But the macro environment had shifted beneath them.

When External Forces Reshape Demand

Flooring demand was shifting in ways that had little to do with marketing. Consumers had become more cautious about large-ticket purchases. Housing activity and renovation triggers softened. Price sensitivity increased, and purchase decisions became harder to make.

The challenge was not simply explaining weaker performance. It was clarifying marketing's true contribution while underlying demand was deteriorating, and building enough confidence to keep investing through a constrained period.

That required separating two things most measurement approaches blur together: what marketing could influence, and what it could not.

Three Layers of Insight

When baseline demand is under pressure, growth requires a more precise understanding of what is actually driving performance.

Macro forces had materially reduced underlying demand, independent of anything marketing did. That created a critical need to separate what was structural from what was media-driven. A single measurement approach could not do that on its own.

The solution was three complementary layers of insight:

Method — Marketing Mix Modeling

Separating Structural Demand Pressure from Marketing Impact.

We built two MMMs using Source of Truth (SoT) data: one for Organic Leads and one for Non-Organic Leads. External macro factors were explicitly modeled into both, allowing us to isolate baseline demand effects rather than absorb them as noise.

This created a clearer separation between structural demand pressure and incremental demand driven by media. Channels could then be evaluated by role, not just by topline performance metrics. Some were helping sustain existing demand, while others were driving incremental volume. Without that distinction, very different channel roles could appear similar in standard performance reporting.

To understand what marketing was being asked to overcome, we modeled five external variables:

1. Consumer Price Index (CPI).
2. Consumer Sentiment.
3. Housing Market Activity.
4. Interest Rates and Affordability.
5. Labor Market Signals.

Method — Geo-Cluster Model

Where Marketing Could Most Effectively Offset Baseline Pressure.

We ran a DMA-level Meridian MMM to evaluate performance at the local market level. It revealed that baseline erosion and media effectiveness were not consistent across markets. National averages were masking both risk and opportunity.

Markets were clustered into three segments: Emerging, Growth, and Mature.

The differences were significant. In Baltimore, 87.91% of contribution came from media, with only 12.09% coming from baseline demand. In Springfield, the pattern was almost reversed, with 62.94% coming from baseline and only 37.06% from media. Without this view, the same national budget would have been applied to fundamentally different markets in the same way.

Emerging markets showed the strongest media responsiveness, the lowest CPL ($375 vs. $566 in Mature), and the highest ROI (2.65 vs. 1.76). They offered the greatest opportunity for marketing to offset weaker baseline demand. Growth and Mature markets required a different investment posture, with greater focus on efficiency and demand capture than on incremental expansion.

This enabled market-specific budget allocation, channel prioritization by cluster, and smarter testing and scaling decisions.

Method — Qual Study

Why Markets Responded Differently to the Same Conditions.

The qualitative work helped explain something the models alone could not: consumer psychology was shaping how demand responded under pressure.

In flooring, decisions are driven as much by perceived risk as by price. As macro pressure increased, so did consumers' hesitation, uncertainty, and need for reassurance. In a constrained demand environment, confidence-building communication became as important as media investment.

That dynamic did not play out the same way across markets. Different market clusters responded differently based on where consumers were in their decision process, how much trust they needed, and what kind of proof helped them move forward.

When Measurement, Markets, and Messaging Align

Together, these insights created a more actionable understanding of how marketing could offset pressure on baseline demand.

Collectively, these insights enabled confident reallocation, stronger efficiency, and a more resilient growth engine.