How a footwear brand selling through stores, wholesalers, and its own website ran omnichannel measurement across all three sales channels, and built a media plan around what it found.
- At a glanceSector
- Retail, footwear
- Scope
- One footwear brand, media and sales data across stores, wholesale, and e-commerce
- Engagement
- Ongoing
Always-ON measurement, delivered on the MassTer platform.
The challenge
Selling through stores, wholesalers, and a website makes a brand harder to read. Each sales channel has its own customers, its own pace, and its own reasons to buy. Media spend reaches all of them at once. A footwear brand had grown its distribution channels in recent years with one goal: a true omnichannel presence. Its Marketing team could see revenue rise. It could not say which media drove revenue in which sales channel, or how much of the growth came from distribution rather than marketing. Without that split, every budget decision rested on an average. This case study shows how MASS Analytics ran omnichannel measurement on each sales channel separately and together, and what that changed.
Omnichannel measurement starts with each channel
Revenue arrived from three places: brick-and-mortar stores, wholesale partners, and the brand’s e-commerce website. A single sales line adds them together and hides the differences. Media that lifts online orders can also send shoppers into stores, and wider distribution adds sales with no media behind it. When these effects sit in one number, media can get credit for growth it did not cause. The team needed a model that kept the three sales channels apart, then brought them back together.
“Now we can see what our media does in each sales channel, and how much of our growth came from distribution.”
A member of the brand’s marketing team
Four things omnichannel measurement had to show
- The media effect inside each sales channel. Stores, wholesale, and the website each respond to media differently, but one total sales line shows only the blend.
- The combined effect across channels. Media in one channel can move sales in another. Without a joint view, that spillover is invisible or credited to the wrong place.
- The share of growth that distribution delivered. New points of sale add volume with no media behind them. Without a base measure, that volume can be mistaken for media impact.
- The size of natural demand. Brand awareness, loyalty, price, seasonality, and the economy all move sales. The team had no clear figure for how much of its revenue came from them.
Each gap points to a modeling choice. MASS Analytics addressed them with one model design, summarized in Table 1.
| Sales Channel | Model Treatment | What It Measures | Question |
|---|---|---|---|
| Retail stores | Own revenue line inside the pooled model | Media and non-media effects on brick-and-mortar sales | Which media drives sales in the stores? |
| Wholesale | Own revenue line inside the pooled model | Media and non-media effects on sales through wholesale partners | Which media supports the wholesale channel? |
| E-commerce | Own revenue line inside the pooled model | Media and non-media effects on sales from the brand website | Which media drives online sales? |
Table 1: Three sales channels entered one pooled model. Each kept its own revenue line, so media effects could be read separately and in total.
The solution
MASS Analytics proposed a pooled regression model to identify the separate and total contribution of media to revenue. Total revenue sits at the top, with retail, wholesale, and e-commerce beneath it. Each sales channel has its own revenue line, and the pooled design lets the three be read together. The inputs were revenue and media data for all three sales channels, plus software built to run MMM for omnichannel sales.
One omnichannel measurement model, three revenue lines
Pooled regression estimates media effects for omnichannel measurement across the three sales channels in one pass, so results can be compared and added up. The model returns the contribution of each media channel to each sales channel individually, and the total across all three. Media and other factors are measured separately. That splits sales into two parts: the effective base, and the incremental sales media generated. This one omnichannel measurement structure answered both business questions: how media drives revenue in each sales channel, alone and together, and how much distribution added. It applies the same approach MASS Analytics uses for marketing mix modeling for the retail sector.
The effective base explained the growth
The effective base is the natural demand for the product. It comes from qualitative factors such as brand awareness, loyalty, and equity, from non-media factors such as price and distribution, and from external factors such as long-term trends, seasonality, and the economy. Measuring it on its own stopped distribution growth from being read as media impact.
Results and Impact
The model showed that the brand’s growth rested on its base, not on media alone. Year over year, the effective base grew 71%, driven mainly by distribution. Base sales, distribution, organic media, promotions, and seasonality together made up 85.5% of total sales.
+14%
modeled lift in media driven revenue
+71%
effective base growth, year over year
85.5%
of sales from base and non-media drivers
21.4%
media budget increase in the optimal plan
Distribution drove a 71% rise in the base
Year over year, the effective base grew 71%, and distribution was the main driver. This supports the brand’s decision to build an omnichannel network across stores, wholesale, and online. Base sales, distribution, organic media, promotions, and seasonality together accounted for 85.5% of total sales. That points to a strong brand image and shows how much non-media activity matters. Paid media drove the rest. Branded Paid Search and Branded Shopping Paid Search were the top media contributors, at about 7.5% and 4.3% of sales.
A modeled plan lifts media revenue 14%
MASS Analytics used these results to build an optimal media allocation plan. It raises the total media budget by 21.4%, focused on Branded Paid Search (+31%) and Branded Shopping Paid Search (+34.2%). Video and Display rises 28% and Paid Social Meta 12%. The model projects a 14% increase in media driven revenue, with lifts of 15% for Branded Paid Search, 11% for Branded Shopping Paid Search, 18% for Video and Display, and 14% for Paid Social Meta. These are modeled results, not measured outcomes.
Want the same view of your channels?
MASS Analytics builds MMM models that measure media in every sales channel, alone and together, and separate the base from what media adds. Talk to our team.
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