This article describes the key terms used in Marketing Mix Modeling. The content is also available here: The common terms and definitions used in Marketing Mix Modeling
Marketing Mix Modeling (MMM)
Marketing Mix Modeling (MMM), sometimes also referred to as Media Mix Modeling, is a statistical analysis technique that helps businesses measure and optimize the impact of their marketing efforts on sales. It involves analyzing the different components of a marketing campaign, known as the marketing mix, and their impact on sales performance. MMM considers both base sales and incremental sales when analyzing the impact of marketing efforts. Another important metric of MMM is Return on Marketing Investment (ROMI).
Base Sales refers to the level of sales that can be attributed to factors other than marketing activities. These factors may include seasonality, economic conditions, and other external factors that can influence sales. Base sales are usually estimated by analyzing historical sales data and identifying patterns or trends that are unrelated to marketing variables. By separating the effect of base sales from the impact of marketing activities, MMM models can more accurately measure the incremental effect of marketing on sales. This allows marketers to better understand the ROI of their marketing investments and make more informed decisions about future marketing strategies.
Incremental Sales refers to the change in sales that can be attributed to a specific marketing variable or activity. Incremental sales are the additional sales that are generated by a particular marketing initiative or campaign, over and above the base sales.
The math is simple: Total Sales = Base Sales + Incremental Sales
ROMI stands for "Return on Marketing Investment" and is a key performance indicator (KPI) used in Marketing Mix Modeling (MMM) to measure the effectiveness and profitability of a company's marketing campaigns.
ROMI is calculated by dividing the incremental revenue generated by the marketing campaign by the total cost of the campaign, including all marketing expenses. ROMI is a useful metric for evaluating the profitability of marketing campaigns because it measures the return on investment (ROI) generated by each dollar/euro spent on marketing. A positive ROMI indicates that the marketing campaign is profitable and generating a positive return on investment, while a negative ROMI indicates that the campaign is not generating enough incremental revenue to cover its costs.
Data refers to the information that is collected and analyzed to understand the impact of various marketing efforts on sales performance. This data can include a wide range of metrics and variables, such as:
Sales data: Information on the number of products sold, the revenue generated from those sales, and other key performance indicators (KPIs) related to sales performance.
Marketing data: Information on the different marketing efforts that were implemented during the period being analyzed, such as advertising spend, promotional activities, pricing changes, and other marketing initiatives.
External data: Data on external factors that may have impacted sales performance, such as economic indicators, weather patterns, and changes in consumer behavior or preferences.
Other relevant data: This may include data on market trends, competitor activity, and other factors that may have influenced sales performance during the period being analyzed.
A data connector is a software tool or application that is used to connect and transfer data between different systems or databases. In the context of MMM, data connectors are used to connect data sources such as sales data, marketing spend data, and external data sources to the modeling tool.
API stands for Application Programming Interface. An API is a set of protocols, tools, and standards for building software applications, allowing different software systems to communicate with each other and exchange data.
In the context of MMM, an API can be used to connect different software applications or tools used in the modeling process, such as data management tools, statistical modeling software, or visualization tools. This allows for a more efficient and streamlined modeling process.
Key effects and assumptions in Marketing Mix Modeling
The Adstock effect is the phenomenon where the impact of advertising on sales or revenue persists over time, even after the advertising has ended. You can read more about it here.
Diminishing return curve
A Diminishing return curve, or advertising response curve, is a graph that shows how the incremental impact of a marketing effort decreases as the level of that effort increases. The shape of the curve can vary depending on factors such as the industry, product, or market conditions. You can find out more about this topic here.
Saturation points represent the point at which additional investment in a particular marketing activity, such as advertising or promotions, will no longer result in a proportional increase in sales or revenue. Simply put, at a certain point all marketing investments reach a saturation stage, making them unprofitable.
Terms related to Bayesian Marketing Mix Modeling
Bayesian Marketing Mix Modeling is the gold standard within the field, as it enables the modeler to utilize hierarchical models and business priors in setting up the model.
Bayesian is a statistical approach that involves the use of Bayesian inference to estimate the parameters of the model. Bayesian analysis is a technique that allows the incorporation of prior information into the analysis, which can be useful in cases where the sample size is small or the data is noisy. By using Bayesian analysis, MMM models can produce more accurate and reliable estimates of the impact of marketing variables on sales or other performance metrics. Additionally, Bayesian methods can help to quantify uncertainty in the estimates, which can be useful in making informed decisions about marketing strategy and budget allocation.
Bayesian inference is a statistical technique used in Marketing Mix Modeling (MMM) to estimate the posterior distribution of the parameters of the model. Essentially, prior knowledge or beliefs about the parameters are combined with the observed data to obtain a posterior distribution of the parameters, which represents the updated beliefs about the parameters after analyzing the data.
Simply put, we start with some prior knowledge or beliefs about how certain marketing activities might affect sales, like "I think that increasing advertising spending will lead to more sales."
Then we look at data and see how much sales actually increased when we increased advertising spending, as well as other marketing activities. We use this data to update our beliefs and make better predictions about how different marketing activities might affect sales in the future.
By using Bayesian inference, we can make more accurate predictions about how our marketing activities will impact sales, and make better decisions about how to allocate our marketing budget to get the best results.
Hierarchical modeling is a statistical approach used to analyze complex data sets that have a hierarchical structure, such as sales data at different levels of aggregation, such as by region, product, or channel.
Business priors are the preconceived beliefs or assumptions that a company has about the impact of different marketing activities on its sales or revenue.
In MMM, business priors are often used as a starting point for estimating the impact of marketing activities on sales or revenue. For example, a company may believe that increasing its advertising spend by 10% will result in a corresponding increase in sales by 5%. This belief can be used as a business prior in the MMM model, and the model can be refined and adjusted based on the data to arrive at more accurate estimates of the impact of advertising on sales.
The process of evaluating the accuracy and reliability of the marketing mix model by comparing its predictions with actual sales or revenue data.
Results calibration is the process of adjusting the estimates produced by the model to better match the actual results observed in the marketplace.
Lift tests are statistical tests used to measure the impact of a specific marketing activity, such as an advertising campaign or a promotion, on sales or revenue. You can read more about them here.
Other relevant terms in Marketing Mix Modeling
Short term and long term effects
Short term and long term effects are used to describe the impact of marketing efforts on sales performance over different time horizons.
Short term effects represent the immediate impact of marketing efforts on sales performance. These effects can be seen within a few days, weeks, or months of implementing a marketing campaign, and may include a temporary increase in sales, brand awareness, or customer engagement. Short term effects are measured using metrics such as sales lift, ROI, or conversion rates.
Long term effects represent the lasting impact of marketing efforts on sales performance. These effects may take longer to materialize, and may be seen over a period of months or years. Long term effects may include changes in brand perception, customer loyalty, or market share, and are typically measured using metrics such as customer lifetime value, brand equity, or market share.
Seasonality is a regular pattern of fluctuations in sales or other performance metrics that occurs as a result of predictable, recurring factors such as time of year, holidays, or weather patterns. Seasonality can have a significant impact on sales, and it is an important factor to consider when analyzing the impact of marketing activities on sales. By accounting for seasonality in the MMM model, marketers can obtain a more accurate estimate of the impact of marketing variables on sales, and identify patterns or trends that are unrelated to marketing activities.
Sales or revenue is influenced by external factors beyond the control of the company, such as weather conditions or the COVID-19 pandemic.
The Spill-over effect is the impact of one marketing activity on the performance of another, unrelated activity, either positively or negatively.
Promotion uplift is the incremental increase in sales or revenue that is generated by a promotional activity, such as a discount or a coupon, over and above the baseline level of sales or revenue that would have occurred without the promotion.
Optimization in Marketing Mix Modeling is the process of finding the best combination of marketing tactics or actions that will maximize a specific objective, such as sales, profit, or market share. It involves identifying the optimal allocation of marketing resources across various channels or campaigns. MMM usually has a budget optimization tools which will help you allocate your marketing budget more effectively or reach your established sales targets.