Have you ever felt in the dark when it comes to understanding the real impact of your marketing dollars across multiple channels?
Determining where and how conversions are occurring is crucial for measuring the performance of your media channels — only then can you adjust your budget to drive the most impact with your marketing dollars.
Media mix modeling is an analytics solution that provides just that: a high-level view of channel impact that considers both controllable and uncontrollable factors to determine the best distribution of your marketing dollars to maximize sales.
Here’s an overview of how media mix modeling and how businesses can use analytics to drive smarter ad spend decisions.
What Is Media Mix Modeling?
Media mix modeling is an analytics solution that enables a business to measure the impact of their marketing spend across multiple channels, showing how various elements contribute to their goal (such as conversions or revenue).
Today’s most successful businesses have become more adept at accounting for different variables and show how each marketing channel can be optimized to engage customers and generate sales.
Media mix modeling can also be used to address common pain points for brand marketing:
- What is my impact across digital channels?
- What is the right mix of spend allocation that drives the highest ROI?
- How will the channels perform in the future based on their optimized spend allocation?
“Media Mix Modeling is a top-down approach that evaluates how historical media activity, promotions, pricing, seasonality, and uncontrollable factors such as economic activity impact sales. Additionally, it provides a measured marketing ROI which accounts for external factors such as weather, unemployment, and others.”
— Annica Nesty, Senior Director of Marketing Sciences at Tinuiti
Media Mix Modeling vs. Attribution Modeling
Like media mix modeling, attribution modeling also studies the efficiency of marketing strategies — but there are important differences.
Attribution modeling works for specific customer touchpoints, focusing on how a consumer converted, which creative on which channel led to that conversion, and what expected ROI could be if more budget were shifted to that channel.
Unlike media mix modeling, attribution modeling is not as powerful when taking offline actions into consideration. There are differently-weighted modeling techniques that can lead to error, such as first- and last-touch attribution.
“Attribution modeling is based on a bottom-up approach while media mix modeling takes a top-down approach. Media mix modeling provides a long-term view of the marketing ROI of media activity, while attribution modeling evaluates individual-level activity to provide a short term view of marketing ROI,” explains Nesty.
Common Misconceptions About Media Mix Modeling
Media mix modeling, like many other analytics solutions, has also become a marketing buzzword that has generated misconceptions.
Here are a few of the most common misconceptions around media mix modeling.
Media mix models are not transparent
With large datasets and statistical analysis involved in media mix modeling, the methods behind the technique have been critiqued for their obscurity.
If there is no perceived transparency in the process, how does a brand know if it’s media mix model is really accurate?
Any organization specializing in media mix modeling should provide a transparent approach, with deliverables such as outlines, milestones, and performance reports.
Additionally, you may want to consider partnering with an agency that truly understands how media mix modeling aligns with your needs and expectations. Every business is unique and each media mix model is based on multiple factors.
Media mix models do not provide real-time data
Nowadays, results are often measured by the timeliness of their delivery.
The current digital marketplace allows for almost instantaneous real-time data. Media mix models do actually provide compelling real-time marketing insights, perfect for evaluating new campaigns, new competitors, and assess pricing actions or changes in promotional strategies.
A powerful partner in media mix modeling will provide sophisticated tools and real-time approaches in order to satisfy your business performance assessments. Your partner should also be able to provide forecasting, simulation, or AI- and machine-learning-integrated models in order to suggest future movements.
Media mix modeling is biased to offline channels
Though media mix strategies do integrate and consider offline channels in their approaches, media mix modeling considers all digital channels — display, email, paid search, social — as well.
In fact, as customers have become more intertwined with digital channels, media marketing models have adapted to go even deeper into the analyses provided by those channels’ respective insights to support better budgeting choices and customer segmentation reports.
How We Use Media Mix Modeling
With the right media mix model, a business can measure their past marketing performance to improve future ROI by optimizing the allocation of the media budget by channel and/or tactic, including traditional and digital media channels, promotions, pricing, competitor spend, economic conditions, weather, and more.
For example, an international ecommerce brand wanted to forecast their second-half of the year, as well as create an optimal media mix to make their marketing dollars work smarter. A combination of client data, marketing data, and machine learning were required to create a powerful, custom media mix model.
Our solution took into account the last two years of digital marketing and revenue data, analyzing it by market, tactic, and by day. The data we collected and analyzed was used to create models for future spend showing how changes in investment across channels could impact revenue and sales.
The full digital media mix model gave our client a detailed analysis of where to optimize their spend across all digital marketing channels. This included shifting dollars away from social, which historically had been at or near 30%, to search (capturing current demand). An increase in affiliate and a decrease in display were also needed to maximize revenue impact.
We also developed a detailed daily, weekly, and monthly forecast broken down by region all the while creating backtesting and holdout groups to test the validity of the forecasting models.
After optimizing the media mix, we developed ~80 different models bespoke for our client in order to achieve the most accurate forecast possible.
Because of the ideal media model and a highly tuned forecast, we were able to show the exact media spend needed (by channel) in order to achieve the 30% YoY revenue goal the client had targeted.
If a brand wanted to add additional factors to their media mix model, there are many different platforms and integrations to consider.
A few factors we use for our modeling include:
- Pathmatics competitive spend trend
- Kantar competitive spend trends
- Financial Indicators
- S&P 500 trends
- Volatility Index
- Google Query (Trends) volume
- Macroeconomic Indicators
- Consumer Confidence Index
- Consumer Sentiment Index
- Business Confidence Index
- Unemployment Rate
Especially during a time as disruptive as now, there are additional factors to consider as you create strategies against coronavirus.
Some of these additional verticals include:
- Mobility Restriction data
“At Tinuiti, we use statistical techniques such as data mining, predictive modeling, AI to understand future outcomes, create deeper insights, recommendations, and what-if scenarios. We compliment the insights team, working on tests and analyses. We expand those capabilities to build predictive analytics and advanced forecasting models,” explains Nesty.
“This helps brands understand what channels they should be investing in, how they should shift budgets (media mix), creating a high-level view of what channels are driving overall sales and ROI. It also helps create custom multi-channel attribution models to provide a more scientific view of how marketing efforts contribute to sales performance.”
Click here to learn more about how our Mobius platform helps brands build custom attribution models tailored to their business needs.
Mobius: Technology That Closes the Loop on Your Marketing Performance
Mobius is Tinuiti’s proprietary suite of marketing intelligence and media activation technology that empowers businesses and marketers to truly understand every data touchpoint—and what comes next. Don’t just look at a few facts and figures and hope for the best. Interpreting results based on poor data is bad for business—and your marketing dollars deserve better.
That’s why Mobius unites every single data touchpoint in one place, with three distinct products that help unify, optimize, and activate your performance marketing data unlike any other platform on the market:
UNIFY: MobiusOS connects, collects, and synthesizes your data in one place, with visualization, integrations, and reporting that makes your insights easy to understand and act on. Think of it as the operating system where all of your data lives.
OPTIMIZE: Mobius Apps work within MobiusOS to provide targeted analytics products that allow us to enhance and customize the way your data is analyzed to uncover growth opportunities with cutting-edge insights. We can even create bespoke apps, just for you.
ACTIVATE: MobiusX is our powerful AI-enabled eCommerce activation toolset that uses product data as the foundation on which your media, bid, and feed strategies are optimized across campaigns for maxim profitability.
All brands, large and small, face unique business challenges caused by loops in marketing data.
Want to learn more about Mobius? Check out our recent announcement.