Strategic Advantage Through Marketing Mix Modeling and Incrementality Testing
Fashion brands can significantly enhance their strategic decision-making by leveraging advanced measurement techniques like Marketing Mix Modeling (MMM) and Incrementality Testing. These methods provide a more comprehensive and accurate understanding of marketing performance than traditional attribution alone, especially in the context of challenges like cookieless tracking and the difficulty of measuring cross-channel and offline impacts.
How Fashion Brands Can Leverage These Techniques
- Holistic Budget Allocation with Marketing Mix Modeling (MMM)
- MMM provides an aggregated view of how digital and traditional marketing activities impact key outcomes like online and offline sales.
- By analyzing historical data, MMM helps brands reassess past budget allocations and better plan future investments, including hard-to-measure channels like print and out-of-home advertising.
- MMM also supports balancing investments between brand awareness and performance marketing by highlighting their combined impact on results.
- Validating Channel Effectiveness with Incrementality Testing
- Incrementality testing techniques like conversion lift and geo lift studies isolate the true incremental impact of specific channels or campaigns.
- This kind of testing clarifies the real value of upper-funnel initiatives and branded search efforts by distinguishing between pre-existing brand demand and campaign-driven results.
- Brands can use marketing mix modeling to identify promising channels for incrementality testing, creating a feedback loop to refine channel strategy.
- Addressing New Measurement Challenges
- Modern marketers face significant challenges—cookieless tracking, inconsistent attribution across platforms, and privacy-driven data fragmentation. Marketing mix modeling, which relies on aggregated data rather than user-level tracking, is naturally resilient to these limitations.
- By incorporating both digital and non-digital data, MMM offers a fuller picture of marketing effectiveness in a fragmented landscape.
- Strategic Insights for Long-Term Growth
- MMM enables brands to measure the long-term impact of brand-building efforts, such as fashion shows and collaborations, that are difficult to capture with traditional attribution.
- These insights can guide decisions around brand investments, customer acquisition, and market expansion for sustainable growth. For a practical look at how data-driven decision-making is applied in fashion, check out this fashion analytics lesson by Fillipo Chiari.
- Data Infrastructure and Continuous Iteration
- A unified data system integrating digital and offline sources is essential for effective MMM and incrementality testing.
- These techniques thrive on continuous iteration: modeling, testing, refining, and adapting. Together, they enable brands to move beyond basic attribution and embrace a more comprehensive, strategic approach to marketing measurement.
By embracing MMM and incrementality testing, fashion brands can overcome the limits of traditional attribution, leading to smarter budget allocation, optimized channel strategies, and stronger business growth.
Key Differences Between Marketing Mix Modeling (MMM) and Attribution
Marketing Mix Modeling (MMM) takes an aggregated and holistic statistical approach to model past data, understanding the impact of all marketing activities (digital and traditional) on overall business results like sales (online and offline):
- It helps brands look back at how they’ve spent their budgets and make smarter decisions for future campaigns and product launches.
- MMM also makes it easier to strike the right balance between building awareness (demand generation) and driving conversions (demand collection).
- Since it pulls together digital and traditional marketing activities, MMM is great for measuring things like print ads or in-store promotions that are usually harder to track.
- It even helps brands understand the impact of brand-building activities, like fashion shows or collaborations.
- To get Marketing mix modeling working properly, you’ll need a well-organized, unified data setup.
- Keep in mind, MMM isn’t about real-time campaign tweaks — it’s more about giving you the bigger strategic picture.
Incrementality testing, using methods like conversion lift studies and geo lift testing, measures the true incremental impact of a specific channel or campaign
- It shows the actual cause-and-effect relationship between your marketing and your sales or conversions.
- It’s especially useful for seeing how top-of-funnel brand campaigns impact sales across both online and offline channels.
- MMM can point you to channels that look promising, and incrementality tests can then confirm whether they’re really making a difference.
- This type of testing can also answer “what if” questions, like what would happen if you stopped bidding on branded keywords.
- While MMM gives you the broad overview, incrementality testing delivers more precise, channel-specific insights.
When you combine MMM and incrementality testing, you get the best of both worlds — a full-picture view plus detailed measurements. This helps fashion brands make smarter choices about where to spend their budgets and which channels to prioritize. Both techniques require a strong data infrastructure and an ongoing, iterative process …. As Yako Laganga from Pinko highlighted, MMM is a starting point that needs to be validated and refined with continuous incrementality tests to gain real insights. Tim from Funnel emphasized that this combined approach helps triangulate the “truth” of marketing effectiveness, acknowledging the limitations of each method individually.
Navigating the Complex Landscape of Marketing Measurement
Marketers face growing complexity due to cookie restrictions, Intelligent Tracking Prevention (ITP), and the inconsistent attribution models used by different digital platforms. As a result, many brands still rely heavily on tools like Google Analytics, which do a good job tracking lower-funnel activities but often miss the true influence of upper-funnel efforts — leading to skewed budget decisions.
To navigate this landscape, marketers need a more advanced measurement approach that integrates Marketing Mix Modeling and Incrementality Testing. Two key concepts help guide the way:
- The Ad Stock Effect: As explained by Tim Camp, marketing efforts like a TV ad don’t just influence consumers at the moment of exposure — the effects persist for days or even weeks. Traditional attribution models often fail to capture this extended impact.
- Triangulation for Better Insights: No single measurement method is perfect. The most reliable results come from combining different approaches:
- Marketing Mix Modeling (MMM): Looks at historical data across all channels — online and offline — to guide strategic investment decisions.
- Incrementality Testing (e.g., Conversion Lift Studies): Isolates the true impact of specific campaigns by comparing exposed vs. control groups.
- Attribution Models (e.g., Google Analytics): Provide useful but partial views, especially for lower-funnel activity.
By triangulating insights from these different methods – including MMM, incrementality, and attribution – marketers can build a fuller, more accurate picture of their true marketing impact.
Effective Measurement: A strong data infrastructure is critical to making this work:
- Data needs to be clean, unified, and integrated across platforms.
- Tools like Funnel, which recently acquired Atria, are helping brands by streamlining the connection between marketing platforms, cleaning data, and making advanced measurement faster and more actionable.
Marketing Measuring Testing: Finally, as Yakobo Laganga highlighted, measurement should be an ongoing, iterative process:
- Start with MMM to identify high-impact channels.
- Then run incrementality tests to validate and refine your insights.
- Continuously update and adjust based on new data and findings.
This cycle of modeling, testing, and refining – grounded in both MMM and incrementality, leads to smarter budget allocation, more accurate predictions, and ultimately, stronger marketing performance.
Commonly Asked Questions
What do fashion brands typically ask about when measuring marketing investments?
Fashion brands often want to know how much revenue different marketing channels bring in, the best way to allocate budgets for new product launches, and whether they should shift budgets around. They’re also keen to understand if branded paid search drives extra revenue or if those customers would have bought anyway. These are classic incrementality questions—brands want to know if a campaign truly influenced behavior or if results would’ve happened regardless. Additionally, brands are looking for advice on how to balance awareness campaigns with performance marketing, especially when times are tough financially.
What are the limitations of traditional marketing measurement methods?
Conventional tools like Google Analytics are good for tracking lower-funnel activities using attribution models, but miss out on upper-funnel and offline efforts. They struggle to show how specific campaigns impact overall sales and don’t always provide a clear view of how to balance investments across different channels.
How are privacy rules and platform changes affecting measurement?
Privacy regulations and updates like Google Analytics are making it harder to track users and fully understand their journey. These changes can also create issues with data reliability, making it harder for brands to get a clear picture of their marketing performance.
What’s a conversion lift study, and how does it help?
A conversion lift study is a form of incrementality testing—similar to an A/B test— that helps brands measure the true impact of a campaign. By comparing a group exposed to the campaign with a control group, brands can isolate the real effect on things like revenue or conversions.
What’s Marketing Mix Modelling (MMM), and how is it different?
MMM uses historical data to understand how various marketing efforts—both online and offline—affect sales. It gives a broad view of overall effectiveness, rather than focusing on individual clicks or users like traditional attribution tools.
How can fashion brands use MMM effectively?
MMM helps brands assess their overall marketing strategy, evaluate past performance, and guide future decisions. For it to work, brands need solid historical data, and they should update the model regularly. To make the insights more actionable, MMM should be paired with more precise methods like incrementality testing, which provides channel-specific validation.
How can a data hub or unified measurement approach help?
A data hub like Funnel connects all marketing platforms, cleans the data, and gives brands one unified place to work with it. This makes the process more efficient and helps brands focus on analysis rather than sorting through data. By combining marketing mix modeling, attribution modeling, and incrementality methods, fashion brands get a clearer and more trustworthy view of their marketing performance.
How can fashion brands measure the broader impact of investments like stores or fashion shows?
For broader brand-building activities like physical stores or fashion shows, MMM can help track their impact by linking in-store sales with online ads, measuring brand search volume, and looking at social media engagement. Fashion brands can then use incrementality testing to evaluate the true lift in specific markets or regions, ensuring these high-investment efforts are driving real value.