Marketing Mix Modeling: Benefits and Strategies

Date:

Share post:

The demise of cookies indicates that it will soon become difficult for marketers to measure the impact of their digital marketing efforts. Marketing Mix Models (MMMs) can create reliable measurements and insights based on natural variance in aggregate data without user-level data.

Marketers need to determine the impact of various channels on business outcomes. This is especially complicated in strategies that involve both online and offline channels. While measurement is relatively simple for digital media, traditional models are limited in their scope. They do not provide a comprehensive view or track offline activities.

To address this complexity, marketers use Marketing Mix Modeling (MMM). This powerful statistical analysis technique uses advanced statistics to examine how online and offline marketing strategies impact a company’s sales.

MMM has today become an indispensable tool for making sound marketing decisions.

The model’s insights enable marketers to efficiently allocate limited resources and create a tangible impact on marketing channels.

What is Marketing Mix Modeling?

Marketing mix modeling was developed and popularized in the 1980s and 1990s by some experts in the marketing fraternity.

What it does is break down the results of marketing efforts by channels. This allows the marketers to see which channels and activities are actually reaching the desired business outcomes. It also provides measurable insights into which channels and strategies impact the desired outcomes most.

The analysis uses several variables to reach these conclusions. Some of these variables are

  • Sales data.  like unit sales, revenue, and market share.
  • Marketing spend data. The marketing budget for various activities typically includes advertising, promotions, and sales support.
  • Market and consumer data. Consumer Demographics and Preferences Data
  • Product data. Data on the product- features and pricing.
  • Economic data. macroeconomic data that indicates the state of the industry
  • Competitor data. Competitor marketing activities data like budgets, channels etc.

These data markers indicate the impact of different marketing strategies and activities over different channels. they enable marketers to see where their marketing budget will be best optimized for higher RoI.

Also this data can help in crafting marketing strategies and messages as per the customer demographic-based preferences. so, in a way, it helps in the personalization of marketing strategies, for best revenue outcomes.

Marketers can use this analysis to decide where they should invest more, and where the investments should be reduced.

Let us see some of the most significant benefits and challenges of using MMM for B2B marketing planning:

Benefits and Challenges in Marketing Mix Modeling

Benefits of MMM models for B2B Marketing 

Harvard Business Review study describes marketing Mix Modeling as the “new gold standard for digital ad measurement.”

According to the findings of the HBR Ad Measurement study,  “More narrowly targeted digital ads require more calibration. Custom audience ads in the United States required the largest overall calibration adjustment of 56%. Companies that rely on a limited number of channels and smaller brands with niche market segments should conduct more frequent experiments to refine their models.”

Here are some benefits of incorporating the MMM model in your b2B marketing strategy:

Performs What-if-Analysis 

Marketing Mix modeling enables CMOs to forecast the potential revenue effects of specific marketing actions and provides insights into decisions.

What-if analysis aims to provide businesses with insights into the potential consequences of implementing specific changes or strategies before their implementation. By running these simulations, marketers can make more informed decisions and reduce the risks associated with untested initiatives.

Identifies Performance Drivers

Marketing mix modeling enables businesses to determine which factors contributed to the success of a specific campaign or channel.

It can also identify discrepancies and barriers that may impede a specific campaign or channel. By analyzing historical data, marketers can identify the marketing elements that impact sales and business outcomes most.

Identifies synergies and trade-offs

Marketing mix modeling reveals the relationships and trade-offs between various marketing variables. It enables businesses to understand how changes in one aspect of the marketing mix may affect others.

For example, increasing advertising spending may result in higher sales but at the expense of profit margins. Understanding synergies and trade-offs enables businesses to make informed decisions while balancing short-term gains and long-term sustainability.

Quantifies the impact of marketing variables

Marketing mix modeling divides total revenue into base and incremental revenue. It then identifies the factors that influence incremental revenue and quantifies their impact. It also compares the ROI of various marketing channels within the budget.

Marketing mix modeling uses statistical analysis techniques to determine the relationship between marketing variables and revenue. The model enables marketers to calculate how much each marketing element contributes to revenue growth.

Understanding these models is critical because they provide several significant benefits for marketing professionals and businesses in general:

Understanding these models is critical because they provide several significant benefits for marketing professionals and businesses in general:

Challenges of MMM for B2B Marketing:

Customers’ Over-Exposure: 

As the marketing landscape has become more fragmented, with more channels for reaching consumers, consumers are exposed to more brand messaging across all channels. As they interact with myriad channels, they have begun to tune out messages unrelated to their needs.

Producing ads that do not target a specific individual can reduce marketing ROI while also harming brand perception in the eyes of the consumer.

MMM’s aggregate insights, which do not delve into the consumer level, do not assist marketers in customizing messaging to meet consumer demands.

It is complex

Statistics are used in the analysis of media mixes. As a result, setup will necessitate collaborating with a data scientist familiar with media mix modeling tools or using resources to obtain marketing mix modeling software.

Initially, you must enter a large amount of data into your marketing mix modeling feed. While the setup of your model may be time-consuming, even if you are using automated marketing mix modeling software, the benefits of the proper setup will most likely outweigh the resource cost.

Does not offer granular insights or cross-channel impact.

It’s important to remember that an MMM framework gives marketers an overview of their marketing activities.

It is not used to drill down to see how creatives perform at the channel level or how ads on one channel affect campaigns on another.

If brands work with a mobile measurement and analytics platform, they can see how their campaigns perform at the channel or creative level.

Conclusion:

For businesses that operate both online and offline, marketing mix modeling is the best option for implementing a data-driven approach and simultaneously measuring both online and offline marketing channels.

This all-inclusive method is crucial for crafting a comprehensive data-driven marketing strategy, which can benefit greatly from incorporating valuable insights into the external non-marketing factors that influence business performance. The data may be very different, but the measurement methods should be treated as complementary.

They can be combined into a single analysis incorporating new digital measurement methods while maintaining the benefits of MMM’s well-established econometric approach.

Swapnil Mishra
Swapnil Mishrahttps://talkmartech.com/
Swapnil Mishra is a global news correspondent at OnDot media, with over six years of experience in the field. Specializing in technology journalism encompassing enterprise tech, marketig automation, and marketing technologies, Swapnil has established herself as a trusted voice in the industry. Having collaborated with various media outlets, she has honed her skills in content strategy, executive leadership, business strategy, industry insights, best practices, and thought leadership. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

spot_img

Related articles

Lumar Launches New Site Speed Metrics to Enhance Website Performance and User Experience

Lumar, a SaaS provider, has launched new site speed metrics for its website intelligence platform. It will help...

Prove Identity Solutions Now Available in AWS Marketplace

Prove Identity, a digital identity solutions provider has made its digital customer experience, identity verification, and identity authentication...

Semasio Launches ContextualPLUS for Revolutionary Contextual Targeting in Advertising

Semasio has launched ContextualPLUS, a product that generates precise contextual targeting segments from a minimal dataset of 300...

Navigating the AI Content Landscape: Challenges, Policies, and Opportunities

https://youtu.be/RmCmASj0uf4 The podcast between Kanika Goswami, Managing Editor, OnDot Media and Andrew Kirkcaldy, CEO By Gamers For Gamers, explores...