Market Mix Modeling — Basics

The answer to cookie restrictions for Marketing Analysts

“Marketing without data is like driving with your eyes closed.” — Dan Zarrella

Dan nailed it. Over the past decade, data has been the game-changer. Nearly every marketing channel can gather user information to track their path to purchasing a product. Yet, with growing cookie restrictions and heightened awareness of data privacy, it’s becoming challenging for agencies to gather user information and conduct analysis. That’s where Market Mix Modeling steps in as a relief.

Market Mix Modeling (MMM)

Marketing mix modeling is a data-driven tool that assists marketers in enhancing how well their advertisements work and measuring the influence of their marketing and brand spending.

Marketing leaders employ MMM as a predictive model to comprehensively measure, validate, and enhance the effectiveness of marketing investments. MMM also offers insights on optimizing future marketing plans and making adjustments to ongoing campaigns. In the face of growing uncertainty, leaders can use MMM to test various future scenarios and investment strategies based on geography, product, channel mix, channel execution, or marketing spend levels.

What makes implementing MMM a compelling choice?

In the realm of modern marketing, we have a growing variety of tools and media at our disposal for promoting products or services. While some of these tools are easily traceable, others pose challenges in terms of tracking. Additionally, the effectiveness of attribution tracking may diminish when combining aggregated data with other tools.

This implies that marketers require a method to incorporate a wide range of tactics, while also delving into determining the most valuable ones for their marketing campaigns. MMM is useful to:

• Determine the ideal marketing channel allocation to support major marketing operations KPIs and maximize ROI
• Forecast future sales based on marketing spend analysis
• Identify essential revenue drivers such as marketing channel, competition, price, weather and macro-economic factors
• Calculate hypotheticals such as the media investment needed for a year-on-year revenue increase

Market Mix Modeling vs Attribution Modeling

While Marketing Mix Modeling is frequently likened to Attribution Modeling, it’s important to note that they differ significantly. Though related, each employs distinct analytical approaches when assessing the influence of marketing on business performance.

The Attribution Model uses a family of models to assess the contribution of each touchpoint, attributing credit to interactions that exerted the greatest influence on the ultimate conversion or sale.

Pros of Attribution Models:

• Implementing attribution is a swift and straightforward process, especially suitable for organizations seeking rapid and efficient results.
• Delivers instant insights into the effectiveness of individual marketing channels, serving as a valuable tool for organizations aiming to promptly assess and decide on marketing spend.

Cons of Attribution Models:

• It monitors click path data but falls short in acknowledging ‘invisible’ contributions such as ad views, offline marketing, and word of mouth.
• Its effectiveness is diminished by privacy changes and regulations.
• Attribution models may be prone to bias, particularly when built on incomplete or inaccurate data and tend to overvalue demand-capturing channels (e.g., direct and search) while undervaluing demand-generation channels (e.g., dark social).
• It’s crucial to note that attribution can only validate the value of your short-term marketing activities.

Contrary to attribution models, MMM adopts a more expansive approach to marketing measurement. It frequently integrates marketing attribution as one of its inputs in the analysis, making marketing attribution essentially a subset of MMM.

Pros of Market Mix Modeling:

• Provides concrete data, offering real numbers to substantiate your reports. This includes the ability to measure the impact of offline media channels such as TV, radio, and traditional print advertising.
• Its statistical reliability stems from its foundation on an extensive dataset and the application of proven statistical modeling methods, such as regression analysis.
• Serves as a privacy-safe measurement solution, particularly valuable in a post-cookie and privacy-legislated world.
• Grants insights into the long-term effects of marketing activities, proving valuable for strategic planning and forecasting future sales and marketing endeavors.

Cons of Market Mix Modeling:

• It offers a high-level perspective and lacks the ability to present a precise portrayal of individual campaign performance.
• The complexity of MMM often necessitates specialized software or the expertise of a skilled statistician for execution
• Successful implementation of marketing mix modeling demands the collection of a substantial volume of data.
• Traditional MMM can be both costly and time-consuming to construct.

Maximizing impact through MMM

Maximizing MMM Impact

To simplify the difficulties and uncertainties arising from the increasing complexity of marketing, creating a robust and all-encompassing MMM that empowers smarter decision-making is the go-to solution.

Prioritizing the outcomes that yields the greatest value from the marketing mix model.

MMM can be used in ways like:

• Demonstrating the overall additional influence of marketing expenditures on business outcomes.
• Forecasting the effects of increasing or decreasing marketing budgets on the overall contribution of marketing to the budget.
• Recommending adjustments to existing marketing investments aimed at enhancing performance.

As we try to dig deeper, the scope of the model expands. Identifying and prioritizing insights that your department is most likely to act upon can secure initial successes, justifying additional investment, and uncovering opportunities to enhance marketing expenditures.

Consider your marketing mix model as a reliable recommendation rather than an obligatory directive.

It’s essential to confirm the validity of models before approving their usage by:

• Examining specific model diagnostics
• Verifying model predictions through testing
• Performing in-market field tests

It is always a good practice to establish confidence in marketing mix models by implementing a targeted and well-structured testing protocol that verifies predictions.

Use scenario planning to continually enhance and optimize marketing plans.

Scenario planning, beyond adjusting marketing levers, can modify external factors using optimization engines to determine optimal future marketing spend based on marketplace dynamics and budget constraints. Utilize

• Scenario planning
• Simulation
• Optimization tools

to create marketing plans that deliver superior performance.

And that’s a wrap. Stay connected as I bring in more articles on the topics related to Marketing Analytics.

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