Large international consumer goods organizations were the first to implement marketing mix modeling in the early 1990s. This technique examined the historical correlation between marketing expenditure and business performance.
However, the newer techniques are rapid to deploy, simple to use, and cost-effective for Tier 2 or Tier 3 organizations.
Marketing Mix Modeling (MMM) is a tricky beast to tame, let alone use effectively. It can be used for almost any type of business model and in any industry.
It is frequently used for medical, pharmaceutical, and technology businesses.
What is Marketing Mix Modeling?
Marketing Mix Modeling (MMM) is the application of mathematical and statistical techniques to marketing data to estimate the effects of marketing variables on customer behavior.
Using statistical and analytical tools, MMM analyzes the past and predicts the future impact of marketing decisions.
Before the advent of digital, traditional marketers could rely on Marketing Mix Modeling for various mixes of marketing variables.
MMM involves using statistical and analytical tools to quantify the impact of past marketing decisions and predict future impacts.
The idea is to use the models to predict future success by changing and optimizing the marketing mix.
The goal of MMM is to determine the best combination of marketing variables that will generate maximum revenue.
The variables in the model include factors such as product and price, customer targeting and timing, distribution channels, pricing and package size, advertising, and public relations.
To put it differently, a Marketing Mix Model is a technique that uses statistical methods to identify how different marketing spends in various channels impact desired outcomes.
It uses historical data and regression techniques to identify the channels that have the most impact on desired KPIs.
For the model to work, it’s important to have variation in our marketing spend so the model can determine how every little change affects the outcome.
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Regression models are used to find out how various inputs (independent variables) can affect the outcome (a dependent variable like sales).
The independent variable is a value that causes a change in the dependent variable.
Having validated the model and independent variables, data analysts can control the variables to understand the net effects on sales and other related metrics.
How can a Marketing Mix Model help the Company Make Better Decisions?
It is a quantitative approach to understanding how changes in mix elements (price, product, promotion, and place) impact sales and profit.
Profitability measurements are a key element of MMM and an essential source of data for decision-making. Thus, in many ways, Marketing Mix Models are used to measure the performance of each of the elements of marketing.
MMM can help companies make better decisions by comparing the trade-offs between marketing mix elements.
The eventual goal is to estimate the contribution of each element to the company’s performance in some economic measure, such as sales and profit, and thereby help identify which of these elements are most important for a company’s success.
The model is designed to help make decisions on which marketing mix elements need to be modified to maximize the firm’s revenue and profit.
For this, it uses economic methods to estimate the impact of marketing mix elements on corporate performance.
The method allows for the full range of mix variations, including different sales promotions, product offerings, product prices, sales mix, and distribution.
What are the Benefits and Limitations of Marketing Mix Modeling?
Marketing Mix Modeling, or Media Mix Modeling, is a technique used to measure the effectiveness of a company’s marketing campaigns.
It can help to identify which marketing tactics are working and which ones need to be changed.
It is a process that uses mathematical models to estimate how different marketing mix elements (such as price, advertising, and distribution) impact consumer demand and company profits.
It can help companies optimize their marketing mix to achieve the greatest return on investment (RoI).
It is a valuable tool because it allows businesses to quantitatively measure the effects of each marketing mix element on sales and profits.
It also helps companies to understand how changes in one marketing mix element affect sales and profits.
On the flip side, Media Mix Modeling does not offer granular insights into how to optimize marketing campaigns.
In today’s digital world, it has limitations. For example, today’s marketers want insights into a customer’s personal behavior, measurement of individual media effectiveness, the evolution of the product itself, and so on.
Since these metrics are so complex standards for today’s marketers, they require a variety of marketing optimization analytics to be measured accurately. Because of this, marketers are now moving toward a unified measurement methodology.
Also, Marketing Mix Modeling is an integral part of the planning process. It requires data collection and analysis, which can be expensive and time-consuming.
To understand the effects of advertising on company sales, it is necessary to collect data about what advertisements were shown, when, and where.
This requires a lot of field research, which is expensive and time-consuming.
What’s more, companies often require access to proprietary information to conduct Marketing Mix Modeling; however, many are not willing to share it with competitors or potential investors.
Marketing Mix Modeling vs. Attribution Modeling: What is the Difference?
Marketing Mix Modeling and Attribution Modeling are two different types of models used in marketing. While MMM offers a largely top-down view, attribution modeling uses the bottom-up approach to measure marketing efficacy.
Marketing Mix Modeling is used to measure the effectiveness of a company’s marketing campaigns by analyzing the relationship between marketing spend and sales.
In marketing, attribution refers to understanding how marketing efforts attract and convert prospects into customers.
While MMM was born in the pre-digital world largely to understand the ROI of offline channels like newspapers and TV, Attribution Modeling (AM) emerged in the digital era and focuses on digital media.
It is focused on leveraging user-level data to establish the conversion credit for different digital media channels or campaigns.
Attribution is a complex exercise best conducted through touchpoints, which are interactions with your customers. In attribution, we examine how attributes contribute to customers’ responses to your marketing message.
One of the first steps to understanding your marketing attribution is to determine who, or what, is actually responsible for the conversion.
Types of attribution include first-touch, last-touch, and time-based attribution, among others. Marketing attribution modeling is a method used by marketing specialists to assign value to each touchpoint of the customer’s journey.
One of the significant advantages of attribution modeling is that it lends itself to detailed analysis of attribution data.
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How to Build A Marketing Mix Model in 4 Steps
A Marketing Mix Model is a quantitative tool that helps businesses understand how changes in their marketing efforts (e.g., price, product, promotion, and place) impact their sales and profits. Such a model can be built in four steps:
- Establish the business objectives that the Mix Model will help to achieve.
- Collect data on past marketing efforts and sales. An option is also to collect external data, such as weather reports and other relevant information.
- After preparing the data, use it to develop a mathematical model that links marketing efforts to sales.
- Determine the optimal combination of marketing efforts to maximize the company’s profits.
Marketing Mix Modeling Process
Simply put, you need to know how much your customers will buy from you with each mix of price, promotion, and place. The marketing mix is a model of your “commercial equation” to help you make business decisions.
As we mentioned earlier in this post, in marketing, the term “mix” refers to both the process and product components of the commercial equation, where different sources of demand (i.e., buyers and sellers) are matched to generate a shared pool of demand.
In conclusion: Remember, just on their own, Marketing Mix Models will not boost your marketing campaigns. You also need a measurement partner like Express Analytics to help you determine how to implement recommendations for channel spending.