Marketing Mix Modeling is a time-tested analytical technique used by companies for measuring the impact of their marketing investments. First adopted by large multinational Consumer Packaged Goods companies in the early 1990s, this comprehensive analytical approach takes a close look at the historical relationship between marketing spending and business performance.

It determines the effectiveness of different marketing elements like TV advertising, print advertising, digital marketing, pricing discounts, trade promotions, etc. in terms of their contribution to sales volumes, revenue, profitability or other relevant Key Performance Indicators (KPIs) and has become a crucial tool for guiding strategic and tactical marketing budget decisions.

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) also referred to as Media Mix Modeling, helps brands determine the actual impact of each marketing input or activity on KPIs such as volumes, revenue or profits. With Marketing Mix Modeling, companies get data-driven insights that allow them to adjust their marketing mix to achieve their sales forecast targets. It also provides inputs for optimal budget allocations for each particular marketing campaign or channel, based on how different channels are contributing to sales.

MMM platforms helps companies to streamline all the factors of a marketing mix such as product, pricing and promotions, and also determines the effect of each of the factors on sales. An important feature of Marketing Mix Modeling is that it takes into account not just the impact of marketing and media drivers on sales but also non-media drivers such as competitor activity, seasonality, weather, and holidays as well as macro drivers such as GDP, unemployment, inflation, purchasing power etc. This makes it an extremely comprehensive technique to understand and measure how different business drivers impact sales and other KPIs.

Marketing Mix Modeling can help companies make better decisions by comparing the trade-offs between marketing mix elements. The ultimate goal is to estimate the contribution of each marketing element to the company’s overall performance. This technique is used to help identify which of these elements are most important for a company’s success.

Why Use Marketing Mix Modeling?

While Marketing Mix Modeling has been used by consumer goods companies for many years, the technique of Marketing Attribution has gained popularity in recent times with the rise of digital marketing and digital-first businesses where most or all of the customer journey takes place online. MMM and attribution have co-existed, though the choice of which tool to use for marketing measurement was primarily a question of the industry you were in.

However, recent changes in privacy laws, corporate policies, and other factors are forcing companies to look towards adopting Market Mix Modeling, since it is privacy friendly and is not affected by changes such as GDPR and privacy regulations, phasing out of cookies on various browsers and Apple’s restrictions on IFDA on iOS devices. Using automated Marketing Mix Modeling is quite effective because it takes into consideration various external and internal factors for its aggregate data collection when assisting in marketing budget planning. This is especially beneficial for CMOs in companies, as Marketing Mix Modeling takes a holistic approach to marketing trends or rather provides a 360º view into the impact of various marketing and non-marketing drivers on sales.

The MMM SaaS tool by Analytic Edge enables effective ROI measurement without the limitations of traditional MMM such as being completely dependent on external consultants, being time and resource intensive, expensive, not easily scalable, and lack of transparency into the modeling process (black box). Analytic Edge’s MMM software allows brands to run predictive marketing analytics in-house and on-demand, with the cost, scale, and speed advantages they need.

Methodology Of Marketing Mix Modeling: Art or Science?

In a nutshell, Marketing Mix Modeling is about accurately defining the simultaneous relationship of various marketing activities with sales, using the statistical technique of regression. Getting the ‘model’ right for each brand and business and market is at the heart of the process. This is done by considering sales as the ‘dependent’ variable and various marketing efforts and external factors as ‘independent’ variables and running regression analyses and iterations to arrive at a model that explains the sales trends satisfactorily. The process is as much an art as it is a science. While technology does enable crunching large sets of data and running multiple iterations rapidly, identifying and selecting the right variables from amongst dozens that could impact sales, and teasing out the impact of each individual variable requires not just an intricate appreciation of econometrics, but also a deep understanding of the industry, the brand, and the market.

When we talk about the Marketing Mix Modeling methodology, the principle of multi-linear regression is the most important aspect. Sales or market share is usually considered the dependent variable whereas independent variables could be the factors like distribution, price, TV spends, digital spends, website visitors, outdoor campaign spends, newspaper, and magazine spends, below-the-line promotional spends, consumer promotions information, etc. A linear or non-linear equation forms depending on the relationship between the dependent variable and various marketing inputs.

Marketing Mix Modeling helps to effectively quantify the impact of each of the marketing inputs from the betas that are generated from the regression analysis performed. The beta shows that one unit increase in the input value would increase the sales by beta units while keeping the other marketing inputs constant. The variables can have either a linear or non-linear relationship with the sales. Deep Dives Analysis and Budget Optimization are the other key things that MMM software assists marketers with.

Components of marketing mix modeling

Here’s a look at the basic Marketing Mix Modeling components:

  • Base Sales – The base sales of a product are often driven by economic factors like trends, pricing, seasonality, or qualitative factors such as brand awareness or brand loyalty.
  • Incremental Sales – This is the additional sales that come about as a result of promotional activities or marketing campaigns across different mediums.

The different elements that can be measured with the help of a Marketing Mix Modeling optimization are the following:

  • Base and Incremental Volume – This is the volume that is generated as a result of the various marketing activities.
  • Advertising and Media – Measuring the actual impact of media on sales achievement. This is a measurable factor that can determine the number of people that saw or clicked on an ad or visited the web page with promotional offers and how many ended up making an actual purchase. It gives a detailed and elaborate idea about the effectiveness of the ad.
  • Trade Promotions – Trade promotions are an important part of every marketing strategy and the main purpose is to generate incremental sales during a short period with promotion schemes that increase product awareness.
  • Pricing – An increase in product pricing can negatively affect sales. Marketing Mix Modeling and optimization offers an idea of the price elasticity which helps to determine the percentage change in this. Pricing decisions can be taken based on this information.
  • Distribution – Marketing Mix Modeling can help determine the percentage change in the depth or width of the distribution for every channel individually and every outlet.
  • Launches – Existing variables in the model help to capture the additional volume generated by promotions during product launches. Special variables are used to determine the incremental effect of the launches.
  • Competition – Competition variables are created for capturing the impact of competition on brand sales. Automated Marketing Mix Modeling uses cross-promotional elasticity and cross-price elasticity to get an appropriate response from the competitive strategies.

Building a Marketing Mix Model

Implementing Marketing Mix Modeling SaaS in your organization can bring numerous benefits to your brand. Correctly establishing your goals is the first step to effectively building an in-house or DIY MMM. Have a clear idea of the specific goals that you intend to attain through your strategy whether it has got to do with budgeting, planning campaigns, product pricing or assessing brand competition.

Next, ensure that a clear internal alignment is created across your organization with regard to the Marketing Mix Modeling as you will require to pull data from different departments or systems for this. Once compliance across teams is ensured, determine the volume of data that is relevant to your goals. You don’t want to be caught up amidst irregular data, repetitive data, or data that has errors and missing pieces of information.

The last step to building Marketing Mix Modeling is to have a clear idea of the access and limitations you have for obtaining the data required for analysis. If gathering certain data from various platforms requires extra permissions, then factor in the time delays for getting the permissions into your plan as well.

The Working Of A Marketing Mix Model

With the right DIY Marketing Mix Modeling, businesses can maximize their advertising mix and promotional tactics to generate higher sales or revenues. The key aim of every MMM is for mixing four key business elements or the 4Ps — product, place, price, and promotion. Marketing Mix Modeling helps by analyzing the gathered and processed data that is obtained from all the channels in the marketing mix.

In some cases, factors such as traditional channels, promotions, seasonality, or other variables are also factored into the MMM by marketers. Once the data is collected from various sources, advanced statistical analysis and Artificial Intelligence (AI) are applied to the data. The insights can help to determine the effectiveness of the current marketing campaigns.

Marketing Mix Modeling can use metrics and variables like sales, ratings, and online analytics to find out the actual campaign impact in a measurable way. Both the linear and non-linear variables are analyzed to find the quantifiable impact of marketing activities be it advertising, pricing, PR, or sponsorships.

Benefits Of Marketing Mix Modeling

Marketing Mix Modeling vendors often use advanced methods such as linear or multivariate regression to forecast the impact of marketing tactics on sales. Using these insights, marketers can refine their cross-channel campaigns to drive optimal overall engagement and sales. With the help of data-driven analysis, automated Marketing Mix Modeling helps marketers remove the guesswork from the equation.

Here are some of the top benefits of Marketing Mix Modeling:

  • To justify the ROI of marketing initiatives Correlating the various data insights back to the factors in each successful marketing campaign can help brands understand the full impact of their efforts.
  • To collate insights – In-house or DIY MMM helps to gather important insights from the various business initiatives and this can aid in allocating budgets within marketing or sales departments.
  • To accurately forecast sales – Marketing Mix Modeling can effectively predict the future revenue that could be generated based on the past impact of sales or marketing efforts.
  • To understand historical data & trends Traditional models often ignore this valuable data from past campaigns.
  • To understand the negative impacts – It is important that brands take corrective actions and learn from the negative impacts of their marketing efforts as well to stay relevant.
  • To better plan marketing budgets & campaigns – Businesses can get a better understanding of which marketing channels are best suited for their brands and spend accordingly to get maximum returns. Knowing the relevant markets for the campaigns is also important to avoid saturation.

Examples Of Market Mix Modeling

Here are some Marketing Mix Modeling examples from the business world:

  • A leading bank wanted to move away from print and increase digital spending. Before doing that, they used a customized Marketing Mix Modeling SaaS to understand the effectiveness of different campaigns in specific regions, across different service channels and age cohorts, and optimize budget allocations. They were also able to identify the best channels to target certain unique offerings as well.
  • A large supermarket chain wanted to devise an integrated marketing strategy based on customer purchase behavior in stores. They wanted to change the merchandising based on the items that were purchased by the customers. They used automated Marketing Mix Modeling for better customer segmentation analysis.
  • A global nutrition company dedicated to delivering better nutrition, wanted to understand price elasticities across its core channels and accounts. They also wanted insights to determine the most appropriate pricing strategy. A customized MMM software helped determine the volume and value impact of price increases for different Promoted Price Groups (PPGs).
  • A multinational CPG company’s automated Marketing Mix Modeling offered them a continuous and ‘always-on’ MMM framework to aid frequent business planning decisions.
  • A beverage manufacturer employed a Marketing Mix Modeling platform to evaluate the impact of a new sampling activity on sales of its core brand. The sampling activity was planned in a particular region. The Matched Market Test had to answer the following questions – Identify a ‘Control’ market for the Matched Market Test, quantify the impact of sampling activity on sales, and make a “Go/No-Go” recommendation on the expansion of the sampling activity to other key markets.
  • A leading telecom provider wanted to know the potential value of their customers to design a strategy around available headroom. While they knew the current revenue generated from a customer, they wanted to calculate additional revenues that can be generated from the same customer using wallet size, in order to cross-sell and up-sell value-added services optimally. They used Marketing Mix Modeling for an accurate Customer Potential Value Analysis.

Getting Started With Marketing Mix Modeling

With Marketing Mix Modeling businesses can better understand how changes in their marketing efforts affect their sales and profits. Establishing business objectives is the first step to getting started with Marketing Mix Modeling optimization. Following this, the required data is gathered and analyzed to determine the optimal combination of marketing efforts to maximize the company’s profits.

Marketing Mix Modeling was traditionally seen as a technique for more traditional brands, with a higher proportion of offline spend. But even digital advertisers can significantly benefit from the wider view that MMM delivers. Modern MMM software offers marketers a holistic, telescope view, allowing them to optimize marketing allocations across various digital and offline channels and maximize marketing ROI.

Marketing Mix Modeling uses predictive modeling to quantify the impact of various marketing activities on any Key Performance Indicator (KPI) such as sales, revenues, number of customers, number of installs, etc. With the help of such insights, your company can allocate marketing budgets optimally for maximum ROI.

Until recently, the choice of which solution to use for measurement, whether Marketing Mix Modeling or attribution was largely driven by the industry companies belong to. Digital-native advertisers typically opted for the speed and granularity of attribution, while more traditional or omnichannel advertisers opted for the accuracy and wider view that Marketing Mix Modeling platforms delivered.

With the improvements that next-gen Marketing Mix Modeling platforms now offer, digital advertisers can have the best of both worlds. They can combine and leverage the benefits of both attribution and Marketing Mix Modeling for a much more comprehensive measurement and insights through a future-proof measurement stack.

How Do Organizations Use Marketing Mix Modeling?

Marketing Mix Modeling was first adopted by large multinational consumer packaged goods companies around the early 90s. The technique analyzed the historical relationship between marketing spending and business performance.

Organizations use Marketing Mix Modeling SaaS to define the effectiveness of marketing elements such as TV advertising, digital advertising, print advertising, pricing discounts and trade promotions, etc. Companies can take faster and more data-driven marketing decisions with this type of information.

When it was introduced initially, Marketing Mix Modeling platforms were only used by Tier 1 brands and companies that had access to huge marketing budgets. Low awareness and wrong perceptions about Marketing Mix Modeling being too time and effort-intensive were the reasons small to medium-sized companies did not adopt it. However, this has changed in the current scenario and now companies have started to adopt Marketing Mix Modeling irrespective of their size. The DIY Marketing Mix Modeling solutions are especially beneficial in marketing-heavy industries such as retail, pharmaceuticals, financial services, telecom, automotive, travel and hospitality.

The newer Marketing Mix Modeling platforms are faster to deploy, easier to use and much more affordable for Tier 2 or Tier 3 companies. The improvements in Marketing Mix Modeling have also made it ‘Always-On’. They have the capabilities to continuously measure marketing effectiveness and deliver insights that are real-time and more actionable.

With the latest MMM DIY solutions, companies can log into the intuitive interface, evaluate recent marketing campaigns, rapidly update models based on the latest data, and generate real-time insights on demand to optimize their marketing investments on the go and maximize Return on Marketing Investment at affordable price points.

Analytics firms today are harnessing the power of technology and automation to drastically improve the way Marketing Mix Modeling is delivered. With better awareness and education about the potential of marketing analytics, more Tier 2 and Tier 3 companies will be encouraged to adopt Marketing Mix Modeling platforms.

Trends In Marketing Mix Modeling

Technologies such as Artificial intelligence (AI) and Machine Learning (ML) are leveraged by the latest Marketing Mix Modeling platforms to ensure data quality control and to identify incorrect data and outliers. Computing power is used to crunch large sets of data, run multiple iterations and build accurate models quickly and efficiently based on the latest data.

Tools such as Natural Language Processing (NLP) and Natural Language Generation (NLG) are used by automated Marketing Mix Modeling platforms for reporting and insight generation. Intuitive interfaces and easy-to-use tools are increasingly bringing Marketing Mix Modeling in-house without dependency on Marketing Mix Modeling vendors.

Apart from improvements in the process itself, analytics firms now offer newer deployment and business models including hosted and “Pay-Per-Use” models. Such models of DIY MMM are transparent without black boxes and require minimal upfront investment and nominal ongoing costs by companies.

There is a significant proliferation of data sources and marketing channels in recent times. Changes in corporate policies, privacy regulations and General Data Protection Regulations (GDPR) have impacted the data privacy practices globally. Companies that used to depend on the user-level data that was required by Multi-Touch Attribution (MTA) are forced to look beyond this.

Marketers are now turning to Market Mix Modeling for the holistic and telescopic view that it offers to better optimize marketing allocations. Here is a comprehensive article on how Marketing Mix Modeling is considered an alternative approach to filling the gaps in MMM Attribution


Automated Marketing Mix Modeling platforms have turned into a critical measurement tool for marketers because of their unique capabilities such as quantifying external impacts, shifting the focus to incremental measurement, estimating cross-channel effects and incorporating online & offline conversions.

Running Marketing Mix Modeling in-house and on-demand has the additional advantage of being privacy-friendly for brands that want to avoid sharing sensitive data with third-party analytics providers. Such models are often more cost-effective and less time-intensive. In-house Marketing Mix Modeling uses existing systems, data, and data science resources, and on the company’s planning timeline and adds strategically essential analytic agility.

Demand Drivers TM from Analytic Edge is a future-ready solution that gives brands the ability to run Marketing Mix Modeling SaaS and other measurement analytics in-house, using an automated and integrated process. It offers marketers the cost, scale and speed advantages they need, and provides an alternative to both attribution modeling and traditional Marketing Mix Modeling approaches.

Demand Drivers TM is already used by various global brands to get a holistic view of the effectiveness of their marketing investments and make faster and more responsive marketing decisions. Demand Drivers TM is offered as a full-service solution with complete transparency into the modeling process, for companies that prefer to outsource the process.