Marketing Mix Modeling: Find Out Which Channels Really Drive Sales

How Does Marketing Mix Modeling Work?

By using old data, people can get clear understandings about the marketing techniques. Marketing mix modelling uses a simple process that helps to improve company’s budget decisions.

Step-by-Step Process of Marketing Mix Modelling:

• Collect Historical Sales & Marketing Data: People need to use old data. This can be from anywhere between the last 2-3 years which include sales figures, advertising spend, pricing and promotions.
• Identify Marketing Channels & External Factors: Identify all of the marketing channels. This could be TV, radio, social media and email. Also identify any external factors that can affect sales like holidays, weather and competitors.
• Create Statistical Models: Analysts will create a model that can link the marketing activities to sales. Many teams create a marketing mix model in Python during this process.
• Evaluate Impact of Each Channel on Sales: The marketing mix model will show how much has been added to overall sales. The marketing mix model also use adstock in market mix modelling to show how long an ad has been effective for.
• Analyse Results & Optimize Marketing Budgets: Finally, people will have their results. These will show which channels provide the best ROI. Investment will be put more into the most effective digital marketing strategies rather than weaker marketing channels. 

Also Read: Skills You Need Before Starting Digital Marketing

What are the key Components of Marketing Mix Modeling?

Marketing mix modeling is built from some main parts. Each part has its role in showing what increases sales. Understanding these parts makes it easier for people to understand how market mix modelling works.

Main Components of Marketing Mix Model:

• Sales: Sales is the key dependent variable. It could be either daily, weekly or monthly sales that shows performance changes.
• Channels: Marketing channels include various media through which the brand communicates with their target customers. These channels include TV, radio, social media, search advertising, and e-mail. This component calculates contribution of each channel to the overall sales.
• Spend: It is the money invested in each of the channels. Together with sales data it is used to estimate efficiency of each marketing channel (in terms of sales per dollar invested).
• Price and promotions: Changes in pricing strategy, special offers, discounts, and promotions influence purchases decisions. Including these into the marketing mix modeling structure is very important.
• Adstock: Adstock is a feature of market mix modeling which shows that advertising works for a certain period after being viewed by customers. So, this component ensures the proper valuation of ads having a long-term effect on sales.
• Saturation: After some point additional expenses bring less profit than they do before it. Saturation effect estimation allows avoiding unnecessary investments.
• External variables: These are the factors that affect sales which are not controlled by the brand. They include holidays, weather, competitor's actions etc. 
• Baseline Sales: This is how many sales brand would get if they didn’t do any marketing. It shows the natural demand of the brand.

Dive Deeper: Components of Digital Marketing 

What are the Benefits of Marketing Mix Modeling?

Marketing mix modeling gives companies a clear idea of what works and what does not. It turns data into useful understandings, so people can make decision as per proper knowledge not on guesses. 

Here is how market mix modelling helps a business grow:

Whar are the Marketing Mix Modeling Examples?

Many businesses use marketing mix modeling to get smarter idea on where to invest money. Below are some practical examples that show how MMM works in real life. Each example is a short market mix modelling case study.

Practical Examples of Marketing Mix Modeling

Example 1: A clothing brand. 

• A clothing brand spent money on TV ads, social media, and email.
• After using marketing mix modeling, it found that social media brought more sales than TV for every money they spent.
• The brand now makes more investments in social media than TV which increases sales without any rise in total spend.

Example 2: A Grocery Store Chain. 

• A grocery store chain offer various discounts and also ran radio ads.
• The marketing mix model showed that discounts increases the overall sales and radio ads kept working for weeks because of adstock in market mix modeling.
• The chain kept both, but ran radio ads more often and cut extra discounts to save money.

Example 3: An Online Shoe Store.

• An online shoe store was not sure if search ads and video ads were both worth the cost.
• Its team built a marketing mix model in Python and found that video ads helped them to showcase the brand, while search ads helped close the sale.
• The store kept both channels and set a better budget for each channel.

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Common Marketing Mix Modeling Techniques

There are lot of different ways through which brands can create their marketing mix model, depending on data and business goals.

The most common techniques are:

1. Regression Analysis

• What it is: Regression analysis is the most commonly used technique for market mix modelling to understand how changes to advertising spend, pricing or promotion will influence sales.
• Why it helps: Each channel gets calculates on how much they contribute to sales.
• Did you know? This is an easy and simple technique to understand from where to begin.

2. Bayesian Modelling

• What it is: This technique uses information that brands already have such as past performance or expert opinions,and updates it as new data comes in.
• Why it helps: This can work good even with less and improper data. It also show how accurate the model in answering the questions.
• Did you know? Many teams create their marketing mix model using Python and bayesian techniques.

3. Time Series Analysis

• What it is: This helps brands tp understand how  sales have changed over time (weekly or monthly).
• Why it helps: This can identify trends like seasonality, peaks during holidays and overall trends.
• Did you know? This can also be used for adstock within market mix modelling as the ad is being tracked over time.

4. Incrementality Testing

• What it is: Run a live test where one group will see ad and one won’t. This will help to understand the true impact of running an ad had on sales.
• Why it helps: This helps to understand whether the outputs from the model are accurate.
• Did you know? Many experts recommend using this with market mix modelling to provide more stronger proof.

5. Machine Learning Models

• What it is: Machine Learning Models use smart computer techniques to uncover difficult trends within large amounts of data.
• How it helps: They can handle many channels and many factors at once, and they can spot effects that simple methods may miss.
• Good to know: They can be harder to explain, so teams often pair them with simpler methods to keep results clear.

What are the Best Marketing Mix Modeling Tools?

There are numerous software tools that can help in creating a marketing mix model without creating everything from scratch. Some tools are available for free and some offer paid service. It all depends on what works best for a company’s needs.

Some examples of marketing mix modeling software tools include:

What is the Future of Marketing Mix Modeling?

Marketing mix modeling is growing very fast. As privacy get stronger and tracking gets harder, many brands are turning to market mix modelling to measure results. 

This is what the future will hold:

• AI-driven measurement: AI speeds up the process of developing marketing mix models through faster data processing and modeling. Now, more and more people can develop marketing mix models in Python without being an expert.
• Measurement driven by privacy: The practice of marketing mix modelling does not track individual users, which aligns with the privacy laws.
• Real-time data collection: Models can be connected to real-time data and therefore changes in sales and adstock can be detected in market mix modeling fastly.
• Prediction and budget optimization: Future models will predict upcoming trends and offer the most efficient allocation of budget.

Also Read: Future of Digital Marketing

Conclusion

Marketing mix modeling helps businesses to see which channels increases sales. It uses past data to show what works, what does not, and where to invest more money. 

From adstock in market mix modeling to building a marketing mix model in Python, the right approach leads to smarter budget choices. As privacy rules grow, market mix modelling will matter even more.

Want to get started? Schedule a free counseling session with our experts today and take the first step toward better marketing results.