Advertising, Digital Marketing, ECOMMERCE

Marketing Mix Modeling

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What is Marketing Mix Modeling?

Marketing Mix Modeling, or MMM, is a sophisticated statistical methodology that, by examining the interaction between a business’s various levers, resolves questions about the impact of various marketing levers, both online and offline, on a company’s sales curve.

An MMM model can answer issues such as:

What is the ideal level of spend for each marketing channel?
What was the ROI on each marketing channel?
What impact will a certain modification in my marketing budget have on sales?

The number of channels that exist today directly impacts business results – this is one of the key challenges marketers face. Knowing how to quantify that impact across strategies (including online and offline channels) is a complex task but essential to knowing what works for a business.

When working exclusively with digital media, this task is relatively easy. Through conventional attribution models, we can simply quantify. However, we will have a very limited view of advertising activities if we use these models. They only analyze the impact of one channel and do not give us a comprehensive view, nor do they allow tracking of all offline media-related activities.

In the meantime, the Marketing Mix model will develop new tools that will allow us to gain more information about the impact of marketing activities. At the same time, the Marketing Mix model also allows us to understand the contribution of each channel to the company KPI by applying these models, we can know all the changes in budget, seasonality, or even the influence of the optimal spending level in each channel.

The importance of the Marketing Mix model

Through statistical analysis models, the Marketing Mix model is very important; It helps measure the effectiveness of marketing campaigns and determine the impact of different marketing actions on sales.

One of the key advantages of measuring the effectiveness of a marketing campaign is that it allows companies to determine which actions are appropriate and have the best results for the business.

Outstanding benefits of the Marketing Mix model in marketing plans:

Improved decision making

As we’ve seen, Marketing Mix Modelling offers businesses comprehensive data on the performance of various marketing efforts, allowing them to make better-educated decisions.

Increased ROI

Marketing Mix Modelling can help businesses boost their ROI by determining the most successful marketing actions and channels, for example, by focusing resources on the most effective channels while eliminating others that have a lower influence on objectives.

Actionable results

The MMM model will generate a cost curve, allowing us to determine where each investment channel is and whether it still has an opportunity to offer good benefits. Companies can forecast the influence of different strategies on future performance by analyzing historical data and simulating various marketing campaigns.

Better targeting

Marketing Mix Modelling enables us to combine all investment channels, including offline or conventional ones, as well as any external factors that may influence results (economic conditions, weather, special dates, and so on).

MMM models are a valuable resource for businesses looking to increase the performance of their marketing initiatives and optimize their budgets. These models can assist businesses in making more educated decisions about how to use their marketing resources for optimal impact by giving data on the influence of various marketing actions and campaigns on business outcomes.

Data collection and preparation for Marketing Mix Modeling

Collecting high-quality data ensures that the information we extract from the model, as well as the judgments we make as a result, are dependable and accurate. Poor quality data increases the likelihood of inaccuracy and leads to inaccurate conclusions, limiting the usefulness of MMM models.

Proper data preparation streamlines the data analysis process, shortens the time and effort necessary to analyze the data, and increases data comprehension, resulting in more informed decisions and better outcomes. Choosing and collecting data is critical, but don’t forget to normalize the data to fit the model as well.

Data normalization is the process of translating raw data into a standard format that eliminates duplicate or inconsistent data and lowers data redundancy. Normalization aims to reduce data anomalies while improving consistency, accuracy, and reliability. This allows for easy data querying and analysis.

In short, data collection and preparation are critical to ensuring the accuracy and dependability of information gained by MMM models. Now that you’ve covered all of the preceding steps, learn about the key forms of data required for Marketing Mix Modelling:

Conversion data

This includes information about the major purpose of the campaign. Examples include sales data, leads, and so on. These are necessary for accurately measuring the impact of marketing initiatives.

Ad spend data

This covers the amount of money you’ve spent on various marketing channels and initiatives, such as marquee advertising, browser ads, social media campaigns, and so on.

Relevant market data

Economic data, competition, and consumer trends are examples of external variables that might have an impact on sales. This sort of information will assist us in understanding the broader market climate and how it influences our sales.

Brand context data

It is useful to add brand context information in the data-gathering process, such as events, promotions, discount campaigns, store counts, and so on.

Experimental data

In this situation, the information will help us calibrate the model and increase its accuracy. We’re talking about Geolifts or Conversion Lifts, which help us train and develop our models.

The more extensive and precise the data you add to the model, the better you’ll be able to assess the efficiency of your marketing efforts.

Data modeling techniques in MMMs

MMMs employ a statistical technique to assess and quantify the impact of various marketing efforts on company outcomes such as sales, revenues, and profits.

An MMM employs a variety of modeling methodologies, the most prevalent of which include:

Machine Learning

Machine learning algorithms can analyze complicated interactions between marketing inputs and sales to uncover patterns of behavior, which may help with process automation and decision-making.

Regression

Regression analysis is a popular approach for MMM models. It involves examining the link between a dependent variable, such as sales, and one or more independent factors. In this scenario, ridge regression is utilized to reduce model overfitting.

Time series analysis

A statistical approach is used to analyze data points accumulated over time. It may be used to assess the long-term influence of various marketing actions on business success, as well as to forecast future sales using previous data. Although we have daily data for MMMs, it is often aggregated to reduce noise and allow us to focus on the frequency of the signals.

Bayesian analysis

Bayesian analysis is a statistical approach that uses Bayes’ theorem to determine the likelihood of an event occurring given prior knowledge and new information. It may be used in an MMM to calculate the impact of various marketing efforts on business performance and generate projections based on historical data.

Multichannel attribution model

This strategy includes allocating sales credit to the many marketing channels that contributed to the sale. It can assist in identifying the most productive sales channels and optimize the advertising budget accordingly.

Econometric modeling

This entails applying economic theory and statistical models to assess the influence of marketing inputs on sales. It can assist in determining the long-term impact of marketing efforts and optimizing marketing tactics accordingly.

The modeling approach used for MMM will be determined by the company’s unique goals and objectives, as well as the data available for analysis.

Interpreting Marketing Mix Modeling results

MMM models show how different marketing efforts, like as ad expenditure, promotions, and price, affect sales. However, depending on the data modeling approach employed and the analysis aims, MMM results can be interpreted in a variety of ways. Here are some popular approaches to interpreting the results:

Coefficient estimates

In linear regression and other statistical approaches, coefficients indicate the estimated effect of each marketing input on sales. A positive coefficient suggests that the input has a positive effect on sales, whilst a negative coefficient indicates a negative influence. The coefficient’s magnitude indicates the strength of the influence. The higher the coefficient, the greater the influence of the input on sales.

Elasticity

Elasticity refers to the change in sales of a product or service caused by a change in a marketing input. It may assist in discovering the most efficient marketing actions or channels and optimizing the advertising budget appropriately.

Sales contribution

Marketing Mix Modelling may calculate the contribution of each marketing action or channel to overall sales. This can assist in identifying the most significant contributions and prioritizing future actions.

Return on Investment (ROI)

Represents the amount of revenue generated for every dollar spent on marketing. MMM models can help calculate the ROI of different marketing actions and identify those channels that are most effective in terms of ROI.

Scenario analysis

They may also be used to analyze various marketing situations and forecast the effect of changes in inputs on sales. This can assist in finding the most effective marketing plan for achieving certain goals.

In general, the interpretation of MMM results will be determined by the analysis’s unique aims and modeling approach. Before making judgments based on the analysis, it is critical to understand the modeling technique’s limits and assumptions, as well as check the conclusions with real-world data.

Conclusion

Identifying which marketing channels generate the most revenue is an important step in optimizing a marketing plan. Data gathering, the building of a Marketing Mix Modelling model, and the following analysis of the results will allow you to discover the most effective marketing channels. When working with MMM models, it is best to propose them regularly so that they may be fed data and learn from it, allowing them to become increasingly accurate.

This study, which allows you to measure any sort of marketing action, will provide you with information to help you optimize your marketing approach. Allocating greater money to the most productive channels while lowering or eliminating advertising spending on the least effective. You’ll also be able to experiment with various marketing situations to determine the best method for achieving your goals.

Marketing Mix Modelling may appear to be a hard environment to manage, which is why Adsmurai has a team specializing in Marketing Science that has developed a proprietary model using our data and benchmarking infrastructure. This entirely customized, transparent MMM is always accompanied by a Marketing Scientist and a data modeling analyst.

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