Marketing mix modelling is a statistical technique that helps businesses understand how different marketing activities and channels contribute to their overall revenue and profits. By analyzing data on sales, marketing spend, and other factors, marketing mix modelling can provide valuable insights into the impact of various marketing initiatives on business performance.
Using marketing mix modelling, businesses can optimize their marketing spend by identifying which activities are generating the highest ROI and which ones are not. By reallocating resources to the most effective marketing channels, businesses can increase their revenue and profits while reducing waste and inefficiency in their marketing programs.
Marketing mix modelling can be especially valuable for businesses operating in competitive markets or facing budget constraints, where every dollar of marketing spend needs to be carefully allocated to maximize impact. By leveraging the power of data and analytics, businesses can make more informed decisions about how to allocate their marketing resources, and achieve better results with their marketing campaigns.
Missing values and outliers can both have an impact on the accuracy and reliability of data analysis.
Missing values can result in biased or incomplete results, and outliers can skew the analysis by representing extreme values that are not representative of the overall dataset.
Multiple imputation is a common method used to handle missing values, which involves estimating the missing values based on the available data.
Descriptive analysis and statistical techniques such as z-score or boxplot can be used to detect outliers, and it is important to investigate the reasons behind the outliers to determine whether they should be removed or retained in the analysis.
Collect-Econometrics plays a crucial role in MMM as it helps in predicting the demand for a product based on historical data and sales drivers. Econometric techniques such as regression analysis, time series analysis, and other statistical modeling approaches are used to identify the relationship between different variables, including advertising, promotions, price, and other factors, and their impact on sales. By using these methodologies, marketers can develop a better understanding of the effectiveness of different marketing strategies and make data-driven decisions to optimize their marketing mix.
Modelling-Time series regression is a statistical method used in MMM to analyze the relationship between different sales drivers and the response variable (e.g. sales, demand, or revenue) over time. It takes into account the historical patterns of the response variable and the influence of the explanatory variables such as advertising, price, promotions, seasonality, and other external factors. Adstock is a commonly used approach to model the decay effect of advertising on consumer behavior, which assumes that the impact of advertising is not immediate but lasts for some time after exposure. By incorporating the adstock concept into the regression model, it is possible to estimate the optimal level and timing of advertising spend to maximize the return on investment (ROI).
Analysis-The output of the model will depend on the variables selected and the specific model chosen. The output could range from metrics such as TRPs (target rating points), which measure the reach and frequency of advertising, to metrics such as MROI (monthly return on investment), which is a measure of the profitability of a marketing campaign. In addition, three key metrics that are commonly used to analyze the impact of marketing activities on sales include effectiveness (sales/TRP), efficiency (sales/cost), and MROI (profit/cost).
These metrics can help businesses assess the impact of their marketing efforts and make informed decisions about how to allocate their marketing resources.
1. Effectiveness: This metric measures the effectiveness of advertising by looking at the ratio of sales to advertising expenditure. The higher the sales per TRP (target rating point, a metric used in advertising to measure the size of an audience reached by a specific advertisement), the more effective the advertising is considered to be.
2. Efficiency: This metric measures the efficiency of advertising by looking at the ratio of sales to advertising cost. The higher the sales per cost, the more efficient the advertising is considered to be. This can help in evaluating the return on investment (ROI) of advertising campaigns.
3. MROI: This metric is a measure of profitability and is calculated by dividing the profit generated by advertising by the advertising cost. MROI considers the costs involved in advertising and can help in evaluating the overall profitability of the advertising campaign.
By analyzing these metrics, marketers can better understand the impact of their advertising campaigns and make data-driven decisions about how to allocate their advertising budgets for maximum impact.
optimization-Simulation is an important aspect of MMM and can be used to optimize inputs to achieve the desired output. In the example you gave, a simulation could be created to forecast the impact of changing spending on a specific activity while keeping other promotional activities constant. This would help the company to understand the impact of this change on sales and make informed decisions about their marketing strategy. The simulation can be performed using the model developed in the previous steps and varying the inputs to see how the output changes. The results of the simulation can then be used to optimize the marketing mix and achieve the desired outcome.
MMM can help decision-makers make more informed and effective decisions by providing insights into how different marketing activities and external factors affect sales and other business objectives. By simulating the impact of various factors on sales, managers can anticipate the impact of changes and optimize their marketing mix accordingly, thereby maximizing their ROI and achieving their growth objectives.