In the rapidly changing environment, most companies are busy determining which marketing approach is the most fruitful. With countless marketing platforms at your fingertips and customers almost everywhere, businesses need to evaluate which channels are delivering the best results and return on investment.

Marketing mix modeling (MMM) has been around for quite some time. Organizations that sell fast-moving consumer goods often use this approach, and it was one of the few tools for integrated market research with financial recommendations before the internet age. In contrast, attribution modeling emerged alongside the world of digital marketing. It is the top choice for online businesses where most or all of the consumer journey is online.

This article examines the difference between marketing mix modeling and attribution modeling to help you decide which is a better fit for your business.

Define attribution modeling

Attribution modeling is a bottom-up technique for evaluating marketing performance. It examines each phase of the conversion cycle to determine the value of each project element.

This strategy arose from the needs and challenges of online sales, where huge amounts of information are collected and processed. As such, attribution modeling is typically used to track online purchases, promotions, and other conversion initiatives. As more advertisers combine their online and offline networks, attribution models are evolving and developing a comprehensive picture to compensate for offline engagements, which are more difficult to track.

Because attribution modeling uses a detailed approach, the input is analyzed regularly and immediately.

Attribution Modeling: PROS

Optimization of the marketing budget: Attribution models inform which channels generate the most interactions, allowing companies to better allocate their marketing resources. This helps marketers make necessary adjustments to the budget.

Improved product creation: Advertisers can effectively identify their customers’ needs using person-level mapping. These results can then be used to make product improvements that prioritize the features buyers want.

Higher ROI: Good attribution helps marketers target the relevant customer with the perfect offer at the right moment, resulting in better sales and higher ROI.

Better Personalization: Businesses can use attribution intelligence to better determine the content and channels that certain consumers prefer, allowing them to focus on what they enjoy throughout the user journey.

Attribution Modeling: CONS

Attribution measures clicks only: Because attribution models often only track clicks, they don’t account for every encounter in the consumer’s journey. In other words, most models ignore significant offline interactions, dark social, or reactions. Was your employee a guest at a top-class webinar? Did you come across a Reddit post where someone praised your product? Not measurable.

False positivity: Many attribution models assume that every interaction positively influences the purchase decision. This is troubling as people may buy goods or services that they have had negative experiences with, leading to false positivity.

The deceptive assurance of complete traceability: You can’t keep track of each person’s impressions and interactions. and there’s a good chance you never will, due to legal, social, or logistical concerns. In reality, with the end of the cookie and the release of iOS 14, the list of measurable touchpoints has narrowed, making attribution much more problematic. The problem here is that you may be overstating the sources you can actually measure. Because of this, you may find yourself making some poor judgments that overlook the most important parts of your customer experience.

Define marketing mix modeling

Rather than emphasizing user interactions, marketing mix modeling takes a top-down approach to everything. It takes into account marketing and sales data, benchmarks, revenue, expenses, and external elements such as demand and market situations, profitability, competition, and anything else that can influence customer behavior.

The marketing mix model evaluates four key marketing components: pricing, product, promotion, and location. The aim is to find the best mix of these four factors to achieve a company’s goals. This is achieved by collecting data from all variables that may affect the performance of marketing platforms and performing multiple regression.

Marketing Mix Modeling: PROS

Use knowledge to increase marketing efficiency: Traditional methods of monitoring campaign success are expensive and time-consuming. Marketers can use marketing mix modeling to get data-driven suggestions and generate lucrative results. This sophisticated analysis method helps companies to quickly gain insights while accelerating tactical decision-making.

Analyzes each channel: Marketing mix modeling uses statistical approaches to examine inputs (like funding and points of sale) relative to outputs (like income or brand awareness), rather than relying on a shaky path of measurable consumer activity. So it’s not about creating a detailed model of the user journey, but about capturing the function of the individual channels and activities.

Provides a complete picture: Businesses can use MMMs to get a complete picture of their marketing performance and estimate marketing ROI. The technique examines both external and internal elements affecting traditional (and now digital) channels. This method evaluates the marketing mix elements more precisely because it combines current and future attribution of the variables.

Marketing Mix Modeling: CONS

It shows correlation rather than causation: Your model may show that revenue increased by 25% over the life of your TV campaign, but you cannot determine that the revenue growth is due to the TV ad.

You need a significant budget to make MMM work: The biggest problem with MMM is that it only makes sense if your organization has reached a certain size and has a significant media budget. Because to truly measure the impact of each platform and campaign, you need to ensure diversity in your marketing mix.Slow shifting: MMM has historically been widely used to refine marketing initiatives, but it simply cannot provide the detailed, person-level information that today’s marketers need. Marketing is constantly evolving, and because it’s happening so quickly, long-term data collection isn’t as reliable as it used to be. This was felt during the COVID-19 outbreak, which forced advertisers to immediately adapt to changes in customer behavior.

What is the main difference?

1. Different goals and priorities

Attribution modeling can be viewed as a subcategory of MMM that focuses on understanding and determining the best mix of ‘online’ sales channels – emphasis on the word ‘online’.

1. Implementing marketing mix modeling is far more complicated than implementing attribution modeling.

Implementing attribution modeling is very easy as it rarely requires an “end user” to create the statistics directly. The attribution model does the counting by itself. Therefore, no knowledge of statistics is required.

1. Attribution modeling gives you much more control over how you optimize your online communications for ROI.

The problem with marketing mix modeling is that it came before the internet. The idea came about before online advertising and browsers existed. It just couldn’t keep up with the internet era.

Mix it up…

The two perspectives can have a downside: if we take one path, we might ignore some of the elements that contribute to our revenue. While MMM allows us to allocate resources to the most profitable channels, digital channels’ data largely lags behind. We do not obtain some essential details about how user experience evolved during online interactions, nor do we identify how online factors influenced purchasing decisions. We still lack data from attribution modeling to answer these queries.

Additionally, we cannot locate any external variables that led to the consumer’s initial interaction with our brand based on attribution. Digital attribution often doesn’t cover basic purchases. Every (favorable) interaction with our company seems to be a triumph of our web marketing efforts. However, many external factors – including purchasing power, timing and trends – contribute to their success.

…but not just for the sake of it.

Many organizations combine both techniques and work with two different data sources – a set of macro facts from MMM and a collection of micro data from digital attribution modeling – to maximize the benefits and balance the disadvantages. However, instead of hybridizing, doing so could lead to misinterpretations that could do more harm than good. A chaotic mixture of two models is not practical, especially since it requires twice as much effort.

The best option is a combination of both models into a unified set of information – a marketing-mix attribution model. This method generates comprehensive observations and an overview of the various channels as well as their connections and effects on customer behavior. Offline activities are no longer tracked independently, but in conjunction with customer data collected through the attribution method.

packaging

Marketing mix and attribution modeling are tied to a much larger and more important concept in marketing known as customer behavior modeling. While they may seem simple, each has some sophisticated features that every strategist, innovator, and business owner should understand before implementing them in their own company.

Despite their limitations, both models can be very beneficial to a company’s overall marketing strategy, and both methods can help you implement effective macro and micro improvements. Providing more granular details about user behavior can help advertisers make effective financial planning decisions and maximize revenue.