The digital marketing world is in near-constant flux as advertisers and agencies adjust to markets and search algorithms aimed at understanding them. However, one thing remains constant: the need for businesses to allocate their resources wisely. This crucial task often hinges on a solid understanding of the variables of marketing mix modeling (MMM).
The marketing landscape has transformed over the years but the foundational principles of MMM remain the same. Let’s explore the variables that constitute an effective marketing mix model, the data science that goes into it, and the resource allocation insights that can be derived from the process.
But before we dive into the variables, let’s review what marketing mix modeling is all about.
At its core, MMM is a data-driven approach used by businesses to evaluate and optimize their marketing efforts. By analyzing various factors, MMMs determine the impact of marketing activities on a company’s sales and profitability.
MMM solutions enable organizations to make informed decisions about not just resource allocation, marketing spend, and marketing strategy adjustments, but also budget optimization, product launches, marketing campaigns, and more.
Marketing Mix Modeling Variables
Now, let’s break down some of the essential variables that form the foundation of an effective MMM.
Remember: When it comes to marketing mix elements, not all variables (both dependent variables and independent variables) are equally important in data collection. Through statistical analysis, the model can identify which variables have the most significant impact on sales. These are the ones that should be given priority when measuring the impact of resource allocation decisions on market share, sales revenue, brand awareness, and other KPIs.
Further, it’s crucial to validate the model’s accuracy and reliability. This is done after building a marketing mix model and giving it a test run. We can then compare its predictions with actual sales data for a specified time period. If the model’s predictions align closely with reality, it’s considered valid.
Once the MMM model is validated and scenarios have been analyzed, the next step is optimization. Businesses can use the insights from the model to allocate resources more effectively, ensuring the best return on investment (ROI).
But we’re getting ahead of ourselves. Let’s look directly at some examples of marketing mix model variables.
Sales Data
At the heart of any MMM model is sales data. This data represents the outcome of all marketing efforts — i.e., the revenue generated by the products or services. It’s crucial to have accurate and detailed sales data, including historical sales figures broken down by time periods (days, weeks, months, quarters, etc.).
The first step is to gather all relevant data. This includes historical sales data, marketing expenditure data, market research, competitive data, and any external variables that may affect sales. The raw data is rarely in a usable format, so it must be scraped clean and prepared for analysis. This involves removing outliers, filling in missing values, and transforming data into a consistent format.
Various statistical and analytical techniques are used to build out the MMM sales data. Regression analysis, time series analysis, and machine learning algorithms may all be employed, depending on the complexity of the model.
Marketing Expenditure
To understand the impact of marketing activities, you need to track the resources invested in various marketing channels. This includes expenses on advertising, promotions, public relations, and any other marketing-related costs. Accurate accounting of these expenditures is vital for MMM.
Market Research
Understanding your target market is essential. Market research data provides insights into customer behavior, preferences, and market trends. It helps MMM models account for external factors that can influence sales.
Competitive Data
Keeping an eye on the competition is a must. Competitive data provides information about what your rivals are doing in terms of marketing strategies and product offerings. This data can help you identify opportunities and threats in the market.
External Factors
External variables such as economic conditions, seasonality, and industry trends can significantly impact sales. These factors need to be integrated into your MMM model to make it more accurate and robust.
These and other variables should undergo continuous monitoring and adjustment. To remain effective, new data should be incorporated and the model refined to reflect changing market conditions.
Marketing Channels
Different marketing channels, like linear TV, OTT, audio, digital video, social media and print, have varying impacts on sales. The MMM model should categorize and analyze these channels individually to understand their contribution to overall sales.
MMM models can be wonderfully fluid, dynamic, and responsive; they don’t necessarily have to be set in stone. They can be used to simulate different marketing scenarios across different channels.
For example, what happens if you increase spend on digital advertising while reducing TV ad spend? Scenario analyses help businesses explore various resource allocation options based on particular MMM variables.
Geographic Data
Geographic variables, such as location-based sales data, can provide valuable insights. These variables help businesses identify regional disparities in performance and tailor marketing efforts accordingly.
Product Attributes
The characteristics of your products or services can also influence sales. Attributes like price, quality, and features should be considered when building an MMM model.
Promotions and Discounts
Special promotions and discounts can have a short-term impact on sales. Tracking these events and their timing is vital for understanding their influence on your marketing mix.
Customer Data
Customer data, including demographics, purchase history, and loyalty, can provide a deeper understanding of your customer base. It helps in segmenting customers and targeting them more effectively. In the evolving cookieless world, this data now comes in a variety of forms.
Data, Science, Decisions
MMM variables inform the process, providing businesses with the data they need to make informed decisions about resource allocation. This reduces guesswork and leads to more efficient spending.
With a well-tuned MMM model, businesses can allocate their marketing budgets to channels and activities that generate the highest ROI. This prevents overspending on less effective strategies.
MMM models can forecast future sales based on different scenarios. This enables businesses to plan ahead and adapt to changing market conditions. Businesses that leverage MMM effectively gain a competitive advantage by staying ahead of market trends and optimizing their marketing efforts.
Finally, by identifying the most influential variables, businesses can focus their resources on what truly matters, eliminating waste and inefficiency.
Marketing mix modeling is a powerful tool that remains relevant and indispensable. By understanding and utilizing the variables that go into an effective MMM , businesses can make data-driven decisions, optimize their marketing budgets, and gain a competitive edge in the ever-evolving marketplace.
As technology and data analytics continue to advance, the importance of MMM in resource allocation and marketing strategy will only grow stronger. So, whether you’re a seasoned marketer or a curious layperson, you can grasp the variables of marketing mix models and harness their potential to drive your business forward.
The Leavened Approach
Marketers know that data-driven decision-making is paramount. Leavened has redefined the game because we deliver data with unrivaled speed, value, and transparency. We’re not just about providing insights; we transform businesses and supercharge their marketing efforts for tangible results.
We know firsthand that time is of the essence in the fast-paced world of marketing. That’s why we’ve made it our mission to deliver relevant, actionable data with unprecedented speed.
Gone are the days of waiting weeks or even months for insights. With Leavened’s accelerated timeline, businesses and agencies can adapt to market changes swiftly and make informed decisions in real time based on models Leavened can train them how to run.
In fact, our team of marketing and data experts has designed a platform that goes beyond insights to help you plan and execute more impactful campaigns utilizing the power of the variables of marketing mix models that we discussed above.
By harnessing the power of Leavened, our partners can uncover the true potential of media investments, identify growth opportunities, and maximize ROI.
Transparency that Inspires Confidence
One of the cornerstones of Leavened’s approach is transparency. We believe that trust is built on openness, and we’re committed to providing marketers with a clear view of their marketing mix.
With Leavened, you’ll have full visibility into the performance of your marketing channels, allowing you to allocate resources effectively and optimize your strategy with confidence.
Say goodbye to guesswork and hello to data-driven success with Leavened’s speedy insights, exceptional value, and unwavering transparency.
It’s time to find your own marketing mix model variables and take your marketing strategy to the next level. Let us know how we can help!
