Make Every Marketing Dollar Count with Marketing Mix Modeling

“The Hershey Company's brands saw a 40% spike in their retail ROI using marketing mix modeling” 

Like everyone else you must have faced the following question at some point in time- How do businesses measure the effectiveness of their marketing campaigns and efforts? Why do they prioritize one campaign over the other? How do they distribute their marketing cost & allocate the budget? 

To unravel these mysteries, organizations implement a technique called Marketing Mix Modelling. It’s a statistical approach that uses historical data to help businesses decipher the impact of various marketing efforts on sales, profitability, brand performance, etc. Out of them, the objectives that are selected vary depending on the unique goals of each business.  

“It helps marketers understand which channels and campaigns are most effective in driving sales and optimize their marketing mix accordingly.” 

This technique typically analyzes three main sources of data- 

Internal Marketing data- It consists of historical data related to marketing spend across various channels, promotional budget for discounts and loyalty programs, and public relations activities.  

Sales data- This is the main data that is used to measure the output. It typically compromises sales figures, sales volume, or market share. 

External data: The factors can’t be controlled by an organization, but they do have an impact on sales. Examples- Economic indicators, competitor activity, market trends, etc. 

The Math Behind Marketing Mix Modeling 

To measure the impact of marketing activities, Marketing Mix involves different analytics models like time-series model, logistic regression, Multivariate techniques, etc. The model used depends on the organization’s requirements. Out of all of them, the most used ones are regression analysis and time series modeling- 

Linear regression- A powerful statistical technique that is used to unravel the relationship between a single dependent variable with respect to an independent variable. For example, changes in sales with changes in the marketing budget. Here, sales figure is the dependent variable while the marketing budget is the independent variable. 

Multi-linear regression- It is an extension of linear regression. It helps model the effects of multiple independent variables on a dependent variable. And hence, is more accurate than linear regression models.  

Time series modeling- In this statistical model, historical data is used analyze changes over a period. It helps in determining the effect of various trends, seasonal patterns, etc on sales figures. 

What are these modeling variables? 

So, what are these dependent and independent variables that we are talking about?   

Dependent variable- These are the variables that you are trying to measure- Sales, Revenue, Customer acquisition cost, Customer lifetime value, Product satisfaction, and Market share. 

Independent variables- These are the variables that you believe are impacting the dependent variables- Advertising spent, Price, Product features, Competitor activity, Economic condition, and Seasonality.  

The variables that can be dependent, and that can be independent depends on the type of analysis you are running- 
 
Key Metrics for Measuring Marketing Impact- 

Incremental Sales: These are the bonus sales reaped as the result of different marketing activities, campaigns, promotions, etc. beyond the baseline sales. (The sales figures that a company would have garner in the absence of any marketing activity is called baseline sales) 

Return on Investment (ROI): Is budget the only deciding factor for the effectiveness of a marketing campaign? This is where RoI calculation plays a major role. It compares the effectiveness of marketing campaigns with respect to the budget allocated to it. Its analysis can helps understand the factors that increases the effectiveness of a campaign. 

Customer Lifetime Value (CLTV): A returning customer can bring further sales. But by how much? CLTV estimates the total revenue a business can expect from a customer for their entire relationship with the brand, revealing the gold mines that lie within customer loyalty. By evaluating their long-term value, brands can focus on high-value customers. 

Market Share: It is measure of how much of the market pie a company claims. The more the share, the better the control over the trends and prices. It is determined by measuring the percentage of total sales a company captures within the industry. 

Brand Awareness: This measures the customer’s familiarity with a particular brand or product. It has two main components- brand recall (consumers remember the brand name in the absence of prompts) and brand recognition (ability to identify the brand when they see it) .

Business Impact of Marketing Mix Modeling- 

Better allocation of marketing budget- Understanding the impact of marketing activities can give insights into elements that deliver the highest sales impact. This allows organizations to allocate resources more strategically.  

Better execution of ad campaigns- Marketing mix modelling can provide deeper insights into how your campaigns are performing. Businesses can understand the reason behind the better performance of one campaign compared to others considering its target audience, timing, content, etc. For example, it can give an idea of why a campaign is focused on brand awareness while it was launched to bring conversion and end sales. Using MMM, businesses can understand how different campaigns interact with each other.  

Business Scenario testing- Data related to historical sales data can delve into how different campaigns have performed in the past. Understanding the different factors that have affected sales, can empower organizations to optimize the performance of future marketing campaigns.  

Marketing Mix Modeling- step-by-step implementation journey 

Now that we understand the different models and the benefits of Marketing mix modeling. Let’s look at the step-by-step implementation process that needs to be followed- 

There are broadly six steps for MMM implementation 

Define clear objectives 

What is the business objective- marketing spending, forecasting sales, understanding customer behavior, or evaluating ROI?  

Thereafter, there should be clearly defined metrics that measure the model’s success with respect to the defined objectives. 

Data collection and preparation 

Collect the data like- historical sales data, marketing expenditures, economic indicators, competitor activity, and other relevant data, which is required. 
Data is then required to be cleaned for accuracy and consistency to run error-free analysis. 

Model selection 
 

The model that you select depends on the nature of your research and the type of data you have. More complex model has better capabilities but are harder to interpret and require more data.  

Model development and training 
 

Assess the model’s accuracy using metrics like R-squared, mean square error, or mean absolute error. According to the model’s performance, you may be required to incorporate additional variables, try different models, or optimize the current model. 

Model validation 

Split data into training, validation, and testing data. Assessing the model on unseen or new data helps in avoiding the problem of overfitting (the model works excellent on training data but suffers on unseen data) 

Model interpretation and implementation 

Implement the model and analyze the impact of various marketing variables on sales. Using this understand the ROI of each campaign and allocate the budget accordingly. With changing requirements, review the model’s performance and adjust as needed.  

Are you curious to know more, refer to this free eBook Marketing Mix Model 2.0: The Analytics and AI Powered MMM for higher marketing ROI to understand advanced MMM. 

Conclusion 

By assigning a quantifiable number to the impact of various marketing tactics, organizations can take better decisions, allocate budget strategically, and increase ROI. Furthermore, with AI coming into the mix, we can expect more sophisticated methods of marketing mix modeling techniques. This will empower organizations to make swift real-time decisions with higher accuracy. By embracing both AI and MMM, companies can gain an advantage over their competition, open new avenues of growth, and attain lasting triumph in the days to come.  

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