How a Fitness Apparel Brand Made Enterprise-Level Media Modeling Affordable
Measuring true advertising impact across acquisition channels to build a long-term media optimization capability.
Vertical
CPG & Consumer Brands
Solution
Data Management & Infrastructre | Advanced Analytics | Measurment
Capability
Data Engineering | Advanced Analytics | Measurement
Benefits
Targeting the highest priority first while planning for the long term provided the brand with immediate clarity and an expandable analytical asset:
Immediate Spend Realignment: The day-one model proved that while athlete sponsorships had longer conversion windows, they were driving a massive portion of baseline demand that paid social was falsely taking credit for, allowing the team to immediately optimize their spend allocation.
An Expandable Media Suite: The initial deployment successfully launched the first phase of a broader analytical toolkit, giving the brand a permanent framework to run look-ahead simulations and test new acquisition channels as they scale.
Accessible Data Science: By breaking the project into iterative phases, the brand unlocked advanced modeling capabilities without the enterprise price tag or timeline, allowing the leadership team to independently run future investment scenarios before committing capital.
Solution
Once we catalogued the marketing activity in terms of channels and campaigns, we created a dedicated data platform with the data needed. In this way we managed to collect and unify yearsof records for ad spend, athlete posting schedules, promo codes, and daily Shopify transactions.
We built and deployed a modular Marketing Mix Model (MMM) using state-of-the-art, tested and proven open-source modules. We focused day-one execution strictly on the highest priority: isolating the true baseline sales volume driven by paid social versus athlete sponsorships. This delivered immediate budget clarity while building the underlying data structure as a scalable asset, allowing the initial setup to transition into a broader suite of forward-looking simulation reports over time.
Challenge(s)
The brand was considering to change the its marketing spend across channels to support new product drops. However the commercial team faced three core operational roadblocks:
Untrackable spend: The brand relied heavily on fitness influencers, YouTube sponsorships, and podcast placements. Because consumers viewed these ads but purchased days later without using specific discount codes, standard digital tracking couldn't quantify the real sales volume these channels generated.
The enterprise budget myth: Leadership knew their digital platform attribution was inaccurate due to modern web privacy limitations. However, they avoided looking into Marketing Mix Modeling because traditional agencies quoted prohibitive development costs and six-month implementation timelines, making it seem inaccessible.
Short-term planning limits: The team was caught in a cycle of looking backward at historical sales spreadsheets. They wanted a comprehensive set of tools to justify marketing investments, but lacked the foundational data setup to quantify how shifs in budget between paid social and organic athlete sponsorships would impact total sales next season.
Summary
Synthesis partnered with a fitness apparel brand scaling its online presence needed to evaluate the actual returns on its marketing investments. With spend heavily split across paid social, athlete sponsorships, and podcast ads, traditional analysis was failing to show what was actually driving sales. The company assumed that advanced modeling was an enterprise-only tool requiring a six-figure budget and an army of data scientists. We consolidated the data based on the company's media activity and deployed a Marketing Mix Model (MMM) that delivered immediate clarity on channel performance.
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