Marketing Mix Modeling (MMM) technique is a statistical as well as an analytical approach that enables marketers to quantify each factor that drives their sales, evaluate the impact of the present marketing strategies on sales, and predict the future marketing strategies that will be used.

 This model helps different firms and companies to understand the relative contribution of different  marketing strategies factors such as advertising, and promotions in bringing certain business outcomes (impressions, clicks, or conversions) and helps in making an informed decision about allocating spending. MMM uses historical data( weekly data of 2 years or more) and statistical data in estimating the relationship between marketing inputs and outputs and can provide a framework for  understanding the effectiveness of different marketing strategies. The ultimate goal of Marketing Mix Modeling is to optimize the marketing mix to maximize the business outcomes to improve the return on investment.

Marketing Mix Modeling Techniques:

        MMM largely uses regression techniques. In order to build the right marketing mix model one must understand the basic techniques used in MMM. In the regression technique, there are two types of variables. One is the dependent variable and the other is the independent variable. Here, marketers have to analyze how the independent variable affects the outcome of the dependent variable. Two of the most common techniques that are used in the MMM are

1. Linear Regression Model
2. Multilinear (multiplicative) Regression Model

1.  Linear Regression Model: The linear regression model is simply given as

           X = Independent variable

           Y = Dependent variable

 Where B0 and B1 are intercept and slope respectively. Where E Is the error term.

 In this model, if you have the knowledge of x, the independent variable, you can predict the changes or outcome of y by forecasting how the “x” will impact “y”

Similarly, using this model marketers can predict sales by specifying the marketing budget.

        Sales = Base sales+ Advertising Impact+Random Error

 If the marketers managed to estimate the slope and the intercept from the collected data, they can estimate the level of sales they could achieve in the available advertising budget.

The linear regression model can be implied to

Impact Forecasting: It is used to predict how the changes, impact.

Trend Forecasting: Here the marketers study the present trends and coming scenarios.

Casual Analysis: used to analyze the causes.

2.  Multiplicative Regression Model: The multiplicative regression model is given as:

 In the multiplicative regression model, there is more than one independent variable. Marketers use a linear regression model when the business is stable and there are no such crises that affect sales. But a multiplicative regression model keeps in view a no. of factors and provides more realistic results than the linear regression model. Therefore, many marketers choose the Multiplicative regression model over the linear model to overcome the disadvantages of the Linear model.

 The multiplicative regression model is further of two types:

•  Semi-logarithmic Model
• Logarithmic Model

●    Semi-Logarithmic Model: The semi-log model is given as:

                           Y=exp(β1+β2X2+β3X3+ϵ)

 Here the exponents of the independent variables are  multiplied. And is it is linearized then it is given by the following equation

                               logY=β1+β2X2+β3X3+ϵ

 Here the coefficient B can be decoded as the %age change in sales (business outcomes/ROI) to a unit change in the independent variables

●    Logarithmic Model: The logarithmic model is given by the formula:

                                   y = a + b*ln(x)

 X = Independent variable

 Y = Dependent Variable

 A,b = Regression coefficients.

Here in this model, the independent variables are changed to their logarithmic forms. In this model, the coefficients are interpreted as the %age change in the sales or business outcome per unit change in the independent variable.

Top Challenges in Marketing Mix Modeling:

There are multiple challenges that are faced by marketers in MMM. Some of the major challenges are as follows:

●    Obtaining Standard Data:

 Obtaining Standard data is the most challenging thing in MMM. Data limitation has generally three aspects: Availability, sparsity, and limited range or amount.

•  Availability:

 MMM generally requires a large amount of accurate data which may not always be available. As MMM uses media spend data as one of the key inputs to understand its impact on sales and other performance matrices. It analyzes the relationship between the media spend and the result it generates e.g raises brand awareness or generates leads. It helps a business or a company to allocate the right amount of investment for advertisement.

 Here the Media Mix Modeling model faces challenges like data inaccuracy, lack of complete data, data management, and lack of complete up-to-date data which are the main limiting factors.

• Messiness or Sparsity:

 Sometimes the data for the MMM model cal also be sparse or messy which means there is no proper relation between the media spending and the obtained results. It causes difficulty in estimating the right amount of media spending for the best outcomes. It can also cause a no. of other problems such as Model stability, Model overfitting (MMM models are prone to overfitting when data is sparse), Model Selection (it becomes difficult to select an appropriate model when data is sparse), and insufficient trends as MMM model relies on a large amount of accurate data so it becomes difficult to understand the trends when the data is sparse.

●    Lack of Standard of Measurement:

 In the MMM model, the most common challenge faced is the lack of standard methods of measurement that can measure the effectiveness of a marketing strategy.  A marketing strategy depends on a no. of factors including the product as well as the company. It is difficult to analyze how much a campaign adds to it. Marketing has multiple effects such as short-term effects (increase in sales), long-term effects (remarketing e.g repeat purchases), and very long-term effects e.g. brand awareness.

 Here we can measure the short-term effects but it is quite impossible to measure the long-term or very long-term effects. As the MMM model is used to make informed decisions regarding media spending so when there will be incomplete data it will become difficult to do proper planning about media budget allocation due to a lack of standard of measurement.

●    Lack of Transparency:

 As there is no standard of measurement, so there is a lack of transparency as the other major challenge in the MMM model. As there is no standard method that can be used to measure the effectiveness of a particular marketing strategy, different companies or firms develop their own internal standards for measuring the effectiveness of marketing strategies. Predictions based on such data are often not trustworthy because they have no solid foundation.

 The lack of transparency may cause difficulty in decision-making due to the lack of transparency in the analysis of results and other findings. Lack of transparency may also cause data bias which may cause hurdles in making accurate predictions because the MMM model uses unbiased data for prediction and forecasting.

●    Multicollinearity:

 In some cases, one or more variables are strongly related to each other which causes a combined effect in the marketing strategy which is termed multicollinearity. In this case, it becomes difficult to separate the effect of one variable from the other variable and consequently we get a joint result of the two variables that are strongly correlated which can’t be used to make a separate estimation for a certain single parameter. Sometimes the correlation may also arise from seasonal peaks, where it becomes difficult to separate the seasonality peak from the marketing.

 In order to overcome this problem, delete some of the variables/predictors that are supposed to be correlated to obtain accurate results or use the other estimation methods that are developed particularly for the strongly correlated variables.

●    Dynamic Effects:

 The dynamic effect is one of the challenges in the MMM model. It refers to the relationship between media spending and the business outcome over time. Sometimes, the effect of a campaign does not appear immediately e.g a customer sees an ad but made a purchase based on the ad after a month or makes a repurchase after a long time. This is termed the wear-in phase. It may be due to the Viewing delay if a customer views an ad after a long time or Customer Inertia i.e. a customer takes an action after a long time of viewing the ad.

 This lagged time period effect must be included in the MMM model. So determining the lag period in different marketing strategies is a real challenge. Advertising wear-outs due to overexposure to an advertisement is also a real challenge. Hence the advertisement wear-in and wear-out must be considered in an MMM model for accurate predictions.

●    Synergistic Effect between ads:

 If the different marketing strategies are combined with one another, they can have a synergetic effect that brings excellent results. For example, if a TV ad is similar to a display ad on a billboard, it will boost the usefulness of both ads. Different methods e.g. classification, CHAID, or decision tree methods can be used to find the synergies between the ads. After finding these variables, add an interaction variable.

●    Non-Linear relationship:

 In a non-linear relationship, there is no direct relation between investment and conversion goals(impressions, clicks, or lead generation). As in the case of Google Adwords, if we increase our investment in a campaign by about 3% there will be a 3% increase in the no. of conversions of that campaign. This is termed a linear relationship. But in the case of digital advertisements like T.V or print media e.g in newspapers, the relationship is not followed. In some of the TV channels, you need to have reached a certain threshold to have a minimum effect. This is given by an S-curve and is a non-linear relationship.

●    Instability of Coefficients:

 The above-mentioned issues can cause instability of coefficients which can be great trouble for the modeling team. In the finalized model with unstable coefficients, you are appointing a team for a disastrous future.

●   Best Practices for MMM MODEL:

• Conduct proper market research.
• Use accurate and reliable data
• Dont allocate too much time and budget
• Carry out Mix Marketing Model twice a year

●   Conclusion:

Marketing Mix Modeling is one of the reliable methods to optimize media spending to obtain the maximum return on investment. However, there are certain challenges in selecting the right model for your campaign by using the right and accurate data. By avoiding the multi-collinearity and omitting other factors and using the best practices of the MMM model, one can obtain the best possible results.