Marketing mix modeling is a data-driven method that combines crucial sales and marketing metrics such as advertising spend, media channels, campaigns, and more to decode how these activities directly affect sales performance.
With this detailed analysis, you can make strategic decisions based on an ROI-focused approach! The learnings that are obtained from this are then adopted for the upcoming marketing strategies to make the marketing plan more efficient and also to forecast the sales in a better way.
Marketing mix models require a data-driven and resource-intensive process. Setting up a strategic marketing plan is vital for leveraging the advantages of the marketing mix model. This model must factor in various advertising channels, their performance measurements, and their interconnectedness to maximize your results.
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What is Marketing Mix Modeling?
Marketing mix modeling is a statistical analysis method for understanding how successful various marketing tactics and channels are about one another. By utilizing this approach, brand professionals can gain a better insight into the marketing performance of their strategies.
Some of the common examples of marketing mix modeling that can help you understand the MMM concept are-
1. Most marketing mix models integrate essential marketing metrics – ROI, conversion rates, cost per acquisition, and customer lifetime value – to create an all-encompassing evaluation of impactful strategies.
2. Not only do their marketing analytics analyze marketing strategies such as advertising, marketing investment, pricing, and promotions; but also take into consideration their effects on customer behavior.
3. Moreover, marketing mix models take into account outside influences like weather conditions, seasonal fluctuations, opposing companies’ promotional campaigns, and overall economic trends.
Key Concepts Associated with MMM
1) Multi-Linear Regression
Multi-linear regression in MMM refers to a statistical method that examines the relationship between two or more features. It assesses the strength, direction, and significance of correlations, determining how each independent variable impacts the dependent variable (sales or ROI in the case of marketing).
2) Linear and Non-Linear Impact of Predictors
In marketing mix modeling, predictors or independent variables can have either a linear or non-linear impact on the outcome. A linear impact implies a consistent effect irrespective of the amount of marketing spend. In contrast, a non-linear impact indicates that the effect varies with the level of marketing spend.
3) Base Sales and Incremental Sales
Base sales represent the volume of sales that would be achieved without any marketing activity. Incremental sales, however, are those driven specifically by marketing campaigns. The goal of MMM is to maximize incremental sales while being efficient with the marketing budget.
4) Contribution Charts
Contribution charts are visual representations that showcase the impact of different marketing mix elements on sales. They help in understanding which marketing tactics are working and which ones need revisiting.
5) Deep Dives
Deep dives in MMM involve a comprehensive, detailed analysis of each marketing element, aiming to understand its individual impact and effectiveness. This could include investigating specific channels, campaigns, or tactics.
6) Budget Optimization
Budget optimization refers to the process of allocating marketing budgets across different channels to maximize ROI. With the help of MMM, marketers can identify the optimal distribution of marketing spending across various marketing mix elements to drive the highest possible returns.
How does a Marketing Mix Model work?
Marketing Mix modeling analyzes business metrics to distinguish the impact of marketing and promotional activities (incremental drivers) from other factors (base drivers). It helps identify and understand the various factors that influence the marketing mix. Some of those factors are-
1. Incremental drivers: The results achieved by marketing efforts, such as television and print advertisements, digital investments, price reductions, promotions, and social engagement, contribute to business outcomes.
2. Base drivers: The desired result is attained without relying on advertisements. This can be attributed to the established reputation and customer trust that has been cultivated over time. Typically, the expected outcomes remain consistent unless there are alterations in the economic or environmental circumstances.
3. Other drivers: They are a subset of foundational factors and are evaluated as the accumulated brand value resulting from marketing activities’ long-term influence within a specific timeframe.
How to conduct Marketing Mix Modeling?
Nielsen outlines four essential stages of the Marketing Mix Modelling process to optimize marketing tactics in a result-driven manner:
Stage 1: Collect
1. Incremental drivers
- Above-the-line activity
- Below-the-line factors
2. Base drivers
In the absence of any additional marketing efforts, businesses rely on their base outcomes achieved from sales. These results are typically a result of established brand equity and customer loyalty that has accumulated over time. The following aspects form these fundamental drivers:
- Macroeconomic variables
Stage 2: Model
P.M. Cain considers Time Series analysis (or regression model) to be the best option for media mix modeling projects.
Developed upon the non-linear concept of Adstock from 1979, time-series regression analysis involves constructing numerous periods with their respective business outcomes. By conducting this type of study, advertisers can better understand how consumers respond to different marketing strategies and campaigns over some time.
Adstock theory teaches us that advertising is not instantaneous, and has diminishing returns. This means that the impact of an advertisement on a consumer decreases after a certain period – even if more money is spent to keep it going. To make sure their efforts are maximized, marketers should utilize time regression analysis to understand how long ads will stay effective, as well as ways they can optimize marketing mix planning accordingly.
Stage 3: Analyse
During the analysis phase, you will evaluate the outputs of your model, which take on a decompositional approach to sales data by breaking it down into volume for each tactic. When it comes to understanding the breakdown of sales, there are 3 essential metrics to consider.
- Median Return on Investment (MROI)
By utilizing these metrics, you can keep track of your overall marketing efforts as well as each tactic.
Stage 4: Optimise
Completing the MMM process, you’ll be able to use your outputs as inputs and fully optimize your marketing mix for future campaigns. This is a remarkable achievement that will ensure ongoing success in all of your future promotional endeavors!
As a part of the optimization, you’ll be able to gain insight through simulations and “what if” scenarios. With equations that represent the correlation between your marketing initiatives and sales outcomes, you can forecast what would arise from altering your current marketing mix. Consequently, this enables flexible forecasting to craft effective strategies for success!
Components in Marketing Mix Modeling
1) Base Sales :
This is an uninfluenced, natural demand for the product which is driven by economic factors like long-term trends for pricing seasonality and also other factors like brand awareness and brand loyalty which are qualitative.
2) Incremental Sales :
These are the additional sales returned by promotional activities on marketing campaigns which can be further decomposed into different marketing components like radio or television, advertising, print advertising, direct mail, internet, coupons, display, promotions, etc.
The analysis of the marketing mix is usually carried out by the use of linear regression modeling.
Elements Measured in MMM
1) Base and Incremental Volume
various insights are obtained when the breakup of sales volume is done to be incremental sales. Base sales are nothing but the series of volume that would be generated without any additional effort from marketing strategies and incremental sales is the volume that is generated by the use of marketing activities.
The Base volume increases or decreases over a long range of time, on the other hand, the activities that increase the sales volume that is incremental sales in the short run impact the Base volume in the long run. When there is a variation in the Bass volume it indicates that the strength of the brand and their loyalty is on the better side.
2) Advertising and Media
Marketing mix modeling can also be used to determine the impact of media on sales achievement. Different media such as television online display ads or magazines can be used to evaluate this measurement. In a few cases, the impact of envision advertising campaigns can also be determined upon sales For example in digital advertising when an ad is displayed on a YouTube video and if the customer clicks, he is directed to the landing page of the website where the promotional offer for the event is displayed and the customer can make the purchase.
It is a measurable factor that how many customers saw the ad how many of them clicked on the ad, how many of them stayed on the website for how long, and how many of them were at the actual purchase.
3) Trade Promotions
Every marketing plan has a trade promotion activity which is considered a key activity in the marketing strategy. The primary game of trade promotions is to get incremental sales in the short term by the use of promotion schemes that increase the Awareness of the customer towards the business and its products.
There is much debate on the subject of trade promotions and the response of customers towards it says it is not very straightforward. To stimulate the response of the customers towards trade promotions, non-linear models are used.
This is where mmm comes into the picture which helps to understand the overall impact of trade promotions and getting the incremental volumes of sales. An estimate of the volume generated for promotion events is possible to obtain and different outlets can help to identify the least and most effective channels of trade.
Sales are negatively affected if the price of the product is increased. This effect is captured by modeling the pricing in mmm. The model provides the elasticity of price and brand which helps to tell the percentage change in this is or even a small change in percentage and price. This is the information that is used by the marketing manager to evaluate the effect of price on the market and if the decision of price changes is to be taken or not.
Distribution volume will determine the element of distribution and how the volume will change. The percentage change in the depth or width of the distribution can be determined. The distribution change can be identified for every channel individually and for every outlet. Moving to this insight the efforts in the distribution can be prioritized for every channel or store to get the maximum output from the same.
For example, in the case of Apple, the offers that are presented in the offline store are more or less common with the online stores which again makes the customer move towards the offline store rather than the online purchase specifically for the Apple products.
On the other hand, a few companies like Asus and Motorola have opted to be present only in online stores and not offline stores in most places. This makes them put increased efforts into creating an online presence since that particular distribution channel gets more return on investment and more sales.
Whenever a new product is launched by a company the promotions and publicity that is associated with the products result and higher volume generation than expected. This happens in the case of most of the products most of the time. The additional volume is captured by using existing variables in the model.
Special variables may be used more often than not to capture the incremental effect of the launches. The total contribution will be given where the marketing effort is associated along with the combined contribution of these variables. To calculate the effectiveness of the launches, their effectiveness and ROI are compared with other launches.
Competition variables are created to capture the impact of competition on the sales of the brand. These variables are based on the marketing activities of the competition like advertising and television trade promotions and product launches all of the factors of a competitor.
These results are used to identify the threat from the competition to the sales of own brand. Cross-promotional elasticity and cross-price elasticity are used to get an appropriate response from the competitive strategies.
Application of MMM marketing tactics can be used in the broadcast media to determine the success of the competitive campaign and this can be used as a valuable lesson for the upgradation in the marketing strategies of own brand. Many factors are taken into consideration like the time of the program adding the presenter attributes and the content of the program which is why these will be considered independent variables.
Here is a video by Marketing91 on Marketing Mix.
Types of Analysis in Marketing Mix Modeling
1) Univariate analysis
Univariate analysis is used to gain deeper insight into data sets containing a single variable. This analytical technique can be utilized for uncovering patterns existing in marketing mix variables, and it plays an important role when making decisions about the effectiveness of different strategies. Explaining the patterns uncovered during a univariate analysis of an individual variable can be achieved through –
- Standard deviation
- Central tendency (mean, median & mode)
- Dispersion (range & variance)
- Quartiles, etc
2) Bivariate analysis
By utilizing bivariate analysis, you can identify the relationship between two distinct variables within your marketing mix and gain valuable insights. In MMM, Bivariate analysis is useful to
- Pinpoint the core factors that demonstrate a positive correlation with the dependent variable.
- Determine the correlation between the variable and the dependent variable.
Limitations of Marketing Mix Modeling
Marketing mix models provide extremely crucial information but there are a few areas in which limitations have to be taken into consideration. They are:
- Long-term brand-building activities and equity-building activities can be affected if the company focuses on short-term sales.
- Time-specific media such as TV commercials have a bias when this is used with media mix Optimization. When broadly based media vs. original your demographic easily targeted media is used even when the bias is bound to happen.
Questions to ask while Choosing a Marketing Mix Modeling Vendor
Are you wanting to boost your marketing campaigns? Then utilize the power of marketing mix modeling and observe an upgrade in sales performance. Through MMM, measure accurately how much influence each campaign has on ROI so that it will be easier for you to maximize your spending with greater effectiveness.
Nevertheless, finding the correct media mix modeling vendor for your requirements can be a difficult task. To begin your search, assess the various media mix modeling techniques available and pick one that best meets your business objectives. Research vendors in this domain thoroughly; some of the critical questions to ask include:
- What marketing and promotional activities does this company offer to help you succeed?
- Are they equipped to run campaigns across multiple marketing channels?
- What level of experience do the company’s media mix modeling analysts possess?
- What successful marketing mix models can they provide?
- Does the vendor provide supplemental marketing services to maximize returns on investment?
Benefits of Marketing Mix Modelling
- With proof, marketers can easily demonstrate the tangible returns on their investments in marketing campaigns.
- Maximizes your marketing budgets by accessing insightful return data.
- Enhances predictive sales capacity for future success.
- Checks marketing impact to optimize marketing decisions.
Marketing Mix Modeling (MMM) is an essential resource for marketing teams who want to maximize their return on investment.
By analyzing the impact of various marketing investments, MMM helps organizations understand how they can best optimize spending and increase sales. Leveraging its analysis capabilities, it equips businesses with valuable insights that lead to greater ROI.
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