For most agencies and their clients, the specifics of linear regression analysis (i.e., how to build a marketing mix model) are less relevant than the data and insights it produces.
The beauty of the Leavened platform is that it automates these internal technical processes — simplifying the method in such dramatic fashion that even the most tech-averse user can run highly sophisticated data analyses without ever needing to lift the hood to see how the machine does what it does.
However, one of the reasons we developed Leavened in the first place was that we understood and sympathized with the three main frustrations that came along with the marketing measurement and optimization process: time, cost, and lack of transparency.
That final frustration is the inspiration behind this blog post. As mentioned above, most agencies and their clients don’t need to know how the sausage is made; they just want cost-effective results, and they want them quickly.
Beyond our desire to be fully transparent, we also know that it can be helpful and informative from a general knowledge standpoint for agencies and businesses to see how we build a marketing mix model (MMM) that delivers actionable insights and informs marketing decisions.
So let’s get into it!
Building A Marketing Mix Model
We’ve already published a blog post in which we cover in broad outline how we perform marketing mix modeling. This piece will serve as a companion to that one, providing some more details regarding the steps involved in building the model itself: the aforementioned linear regression techniques (model and analysis).
In today’s fast-paced and data-science-driven business environment, understanding the impact of marketing activities is crucial for making informed decisions and optimizing marketing budgets. One effective way to achieve this is by building a marketing mix model.
This guide should help you understand a little better the process of building a marketing mix model and why it is beneficial for business.
What is a Marketing Mix Model?
First things first: Let’s define our terms and expand on our understanding of them.
What is a marketing mix model?
A marketing mix model is a statistical tool that helps businesses analyze and quantify the impact of various marketing inputs on key performance metrics, such as sales, revenue, or market share. It takes into account different marketing (target) variables, such as marketing spend (money spent in each marketing channel — TV, OTT, SEM, etc.), pricing, promotions, distribution channels, and macro-economic factors, to provide valuable insights into their individual contributions and overall effectiveness.
Why build a marketing mix model?
There are several reasons why a business should harness the power of a marketing mix model.
- Better decision-making: By understanding the effectiveness of different marketing activities, you can allocate resources more efficiently, optimize marketing budgets, and make data-driven decisions to achieve desired outcomes.
- Competitive advantage: A marketing mix model allows you to gain insights into the strengths and weaknesses of your marketing strategy, helping you identify opportunities to optimize efforts and outperform competitors in the market.
- Return on investment (ROI) optimization: With a well-built marketing mix model, you can identify the marketing activities that generate the highest return on investment, enabling you to allocate resources where they will have the most impact on sales.
- Performance evaluation: By measuring and tracking the impact of your marketing efforts, you can assess the success of campaigns and initiatives, enabling you to refine and improve your marketing strategies over time.
Steps to Building a Marketing Mix Model
Generally speaking, knowing how to build a marketing mix model means knowing how to:
- Define objectives: Clearly identify the goals and objectives of your marketing mix model. What key performance metrics do you want to measure? What insights do you hope to gain? Defining clear objectives will help produce your own guide to building a marketing mix.
- Gather data: Collect comprehensive and accurate data on your marketing activities, such as advertising spend, sales data, pricing information, and any other relevant, independent variables. Leavened can assist you in data collection and aggregation to ensure the availability of high-quality data for analysis.
- Identify key variables: Determine the key factors that impact your business, such as advertising channels, pricing strategies, promotions, and external factors (e.g., seasonality, market trends). Leavened’s expertise in marketing measurement can help identify the most influential variables for your specific industry and business model.
- Statistical modeling techniques: Leavened’s marketing measurement technology leverages advanced statistical techniques to build a robust model that quantifies the relationships between marketing inputs and desired outcomes. These models can include techniques like regression analysis, time-series analysis, and machine learning algorithms.
- Validate and refine: Once the initial model is developed, it’s important to validate its accuracy and effectiveness. Leavened can assist in validating the model against historical data and market trends, and make refinements as necessary.
- Interpret insights: Leavened’s platform will help you interpret the insights derived from the model. Data should help advertisers formulate actionable recommendations based on the findings, helping businesses optimize marketing strategies and improve business performance through the use of budget scenario planning.
- Continuous monitoring: Marketing mix models are not static. To ensure long-term effectiveness, ongoing monitoring and recalibration are necessary. Leavened can help you establish a process for continuous monitoring, keeping your model up-to-date and relevant.
A marketing mix model performs a linear regression model. Investing in this type of modeling can yield significant benefits for your business. By working with Leavened’s trusted marketing measurement technologies, agencies and businesses can learn to run the marketing mix models on their own, leverage the insights gained to help impact their marketing activities moving forward, optimize marketing budgets, and make data-driven decisions that increase ROI.
One doesn’t need to be a data scientist to learn how to run a linear regression model. (Although, as we like to say at Leavened, we’re marketers who know analytics and data scientists who know marketing.) Let us know if you have questions about this process, attribution modeling, values, marketing measurement, and more.