In our complex marketing environment where each channel can get lost in its own silo, media mix modeling (MMM) provides a holistic view that reveals where and how your investment can work smarter, whether in brand, broadcast, digital, or beyond.
In this webinar, Amsive and DispatchHealth dive into the power of MMM as a cornerstone of effective measurement. Learn how it integrates with incrementality techniques and can help create a conscious measurement culture to maximize the impact of every dollar spent across all channels.
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Introduction
[0:02]
The webinar, Clarity Over Chaos: Using Media Mix Modeling to Power Your Measurement, was moderated by James Connell, who introduced panelists Joseph Sharp and Lauren-Nicole Martin. The session aimed to explore how Media Mix Modeling (MMM) provides actionable insights to improve marketing measurement and drive better results.
Key components of media mix modeling
The Challenge of Measurement
[01:36]
Marketing measurement has become increasingly complex due to the proliferation of tools, vendors, and data sources. James Connell illustrated this complexity with an example of a client report that expanded from 7-8 data sources to 23 over several years, highlighting the need for robust strategies to handle fragmented data.
Industry Trends in Measurement
[03:30]
The discussion underscored the shift in focus from simple reporting to understanding media’s actual contributions. Joseph Sharp emphasized the challenges posed by attribution fragmentation and over-counting while advocating for a structured data journey. Lauren-Nicole Martin shared how DispatchHealth’s rapid growth necessitated unifying measurement efforts to ensure informed decision-making and scalable investment strategies.
The role of media mix modeling
Comparing MMM with Other Models
[09:02]
Media Mix Modeling (MMM) offers a holistic approach to measurement, offering insights that go beyond the capabilities of multi-touch attribution (MTA) or incrementality tests. Unlike MTAs, which often fail to account for offline channels, MMMs are channel-agnostic, integrating digital, offline, and experiential media data to provide a complete picture of marketing performance.
Overcoming barriers to MMM adoption
Challenges and Solutions
[17:36]
The panelists addressed common barriers to MMM adoption, including leadership buy-in, financial investment, and internal collaboration. Lauren-Nicole Martin highlighted the importance of aligning leadership across teams to recognize the ROI potential of MMMs. Joseph Sharp noted that advancements in technology have reduced data requirements and accelerated insights, making MMMs more accessible to businesses with limited resources.
Advancements in media mix modeling
Evolving Processes in MMM
[19:09]
Joseph Sharp highlighted how advancements in technology, such as machine learning and AI, have significantly transformed the process of implementing Media Mix Modeling (MMM). Previously requiring two years of data for accurate insights, MMMs now need only a few months of data to begin generating actionable models. The technology enables live models that adapt and improve as they ingest data over time, achieving higher accuracy within months.
Lauren-Nicole Martin shared how DispatchHealth initially faced challenges with older processes that required engaging past vendors for historical data. The introduction of a live MMM process proved to be a game-changer, streamlining data collection and allowing faster results.
Essential Data Types for MMM
[23:49]
The discussion outlined three critical data types for successful MMM implementation:
- Marketing Data: Metrics such as media spend and business success indicators (e.g., sales or leads).
- Brand Studies: Historical data from awareness studies or previous MMMs.
- Marketing Elasticity Data: Benchmarks derived from industry-wide MMMs to fill gaps for companies with limited historical data.
James Connell emphasized that while historical data improves model precision, live MMMs allow organizations with less data to still gain actionable insights. Over time, these insights grow more specific as additional data is incorporated.
Common Data Challenges and Solutions
[27:54]
The panelists discuss challenges companies face when collecting and preparing data for MMMs, including inconsistent naming conventions and fragmented datasets. Lauren-Nicole Martin stressed the importance of engaging internal data scientists early in the process to ensure proper data aggregation and cleaning. DispatchHealth utilized a data journey map to track data sources, platforms, and ownership, which facilitated seamless handoff to Amsive for model building.
James Connell suggested companies assess their data state proactively, recommending the creation of a centralized document that standardizes naming conventions and tracks onsite actions across platforms. This practice ensures consistency and accuracy in reporting and measurement.
Translating MMM Results Into Strategy
[39:28]
Joseph Sharp explained how MMMs provide insights into the optimal budget allocation for each channel, identify points of diminishing returns, and project returns under various budget scenarios. The flexibility of live MMMs allows marketers to adapt to real-time changes, such as budget cuts or market disruptions, by reallocating spend dynamically
Lauren-Nicole Martin noted that DispatchHealth plans to use MMM insights to develop marketing playbooks and best practices across their multiple business lines.
Case study highlights
Real Results
[43:41]
A case study demonstrated how a manufacturer used MMM insights to reallocate budgets from underperforming linear TV channels to higher-performing digital and CTV channels. This adjustment led to a $1.7 million revenue increase year-over-year, even with a reduced budget, showcasing the effectiveness of MMMs in optimizing marketing spend.
Q&A
Corporate Buy-In
[46:02]
The panelists suggested positioning last-click attribution as a complementary component of MMMs to help organizations transition from traditional measurement models. This approach can demonstrate the broader value of understanding the entire customer journey.
Regulated Industries
[48:02]
For heavily regulated sectors like healthcare and insurance, MMMs can analyze aggregated trends without relying on sensitive or identifying data. This ensures compliance with regulations while delivering actionable insights.
Adapting to Rapid Change
[50:03]
Live MMMs were identified as essential tools for adapting to market disruptions or shifts in consumer behavior. By ingesting new data in real-time, MMMs provide marketers with the flexibility to adjust strategies dynamically.
Starting Small
[52:27]
The speakers advised smaller organizations to start their MMM journey by mapping existing data and creating a structured naming convention. This groundwork simplifies future modeling efforts and improves the accuracy of measurement initiatives.
Closing Remarks
[55:30]
The session concluded with the panelists emphasizing the transformative potential of MMMs for businesses. They encouraged attendees to begin building data frameworks and considering MMMs as part of their marketing strategies.
Joseph Sharp and Lauren-Nicole Martin expressed their gratitude to the audience, noting the value of such discussions in advancing marketing measurement practices.
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