Which is better for your business? Marketing mix modeling vs. attribution

Many marketers (and their managers) are still unsure what marketing attribution is.

It is easy to understand: you give credit and value where it is due.

In today’s multichannel, analytics-driven world, we want to know where, when, who, and how our efforts work.

Most marketers don’t know which approach to measurement is best for their business.

Do I require high-level insight around budget planning or tactical insights to optimize a channel?

What can I do with my existing tools like site-side Analytics?

When am I in need of something specific?

Which solution makes the most sense to me out of all those that claim attribution?

Marketers need to know their options to answer these questions. To begin, it is important to define two of the most popular approaches to marketing measurement. These are marketing mix modeling (MMM), which can be used interchangeably with media-mix modeling and marketing attribution.

MMM and attribution are both sophisticated models of measuring cross-channel activities. However, they operate in different ways for different reasons.

Marketing Mix Modeling and Marketing Attribution Defining

You won’t find much difference between MMM and attribution if you search for “technical definitions.” Gartner defines Marketing Mix ModelingĀ as analytic solutions that allow marketers to simulate and understand the effects of advertising and optimize tactics and delivery media.

Gartner doesn’t define attribution in its glossary, but Forrester does. They say: “The practice that uses advanced statistical approaches to assign proportional credit for marketing communications and media activities across all channels which ultimately lead to the desired action by customers.”

There are some subtleties there, but they don’t help differentiate.

Let’s take a look at it. What is the difference between a marketing model and attribution?

Comparing Marketing Mix Modeling with Marketing Attribution

MMM and attribution differ in several critical dimensions, including approach, media type, timeframe, and data inputs.


MMM: Top-down, macroeconomic models. MMM analyses historical data from independent events to provide organization-level planning, budgeting, and performance metrics. It measures the value of marketing as a whole.

Attribution: Bottom-up, micro-level, data-science models. Attribution is a data-science model that uses user-level data to build and analyze the conversion path, from vendors to channels to individual creative. It measures the value of marketing tactics independently.

Media Type

MMM Primarily offline. MMM was born from CPG companies’ interest in traditional marketing methods, including TV, radio, and print. This focus has changed as digital marketing grows, but MMM remains strong offline.

Attribution Mostly online. The need to move beyond the last click/view and take advantage of digital data prompted the development of attribution. It is primarily focused on online sales and other digital conversions. This focus is also evolving as marketers try to understand the impact of their digital campaigns on offline channels and vice versa.

Time frame

MMM Uses historical data aggregated based on weekly intervals. This is usually done bi-annually or annually.

Attribution: Uses real-time or near-real-time account-specific data, measured to the second. Results are updated every day.

Data Inputs

MMM Marketing and sales data at a high level, plus external factors. MMM combines marketing and sales data – primarily offline elements like revenue data, benchmarks, and marketing costs – with macroeconomic and external variables such as market elasticity and seasonality.

Attribution Granular data on marketing and sales. Attribution uses advanced algorithms to measure each touchpoint along a user’s conversion path. It integrates with other ad tech platforms, like DSPs (demand side platforms) and DMPs(data management platforms), to provide a comprehensive view of the digital landscape. Most attribution solutions can also incorporate seasonal, geographical, and other attributes.

What is the best option for your business

Marketers can gain valuable insight into the performance of their marketing investment by using both marketing attribution modeling and marketing mix models. Your business’s objectives and key performance indicators will determine the best approach. You can use attribution, MMM, or a combination, like unified marketing impact analysis.

Key Strengths

MMM Breadth – the ability to analyze offline factors, including those outside a marketer’s control. This is done in conjunction with planning, budgeting, and performance metrics. MMM is especially effective when “push” tactics drive marketing campaigns. Events with a short period for customer reaction cause these tactics. It is also excellent at calculating the value of advertising campaigns.

Attribution Speed – the ability to understand the performance of channels, tactics, and spending daily, especially for “pull marketing,” where the customer triggers the event. Attribution allows marketers to make changes on the fly. For example, they can change budgets by tactic or gain long-term insight into scenario planning and forecasting. This granular perspective also provides impossible insights when viewed from a high-level perspective. For example, it allows marketers to identify subtle synergies among channels and to assign revenue proportionally to each channel.

Potential Drawbacks

MMM Due to MMM’s long timeframe, factors influencing the model may change after data has been collected and analyzed. This can affect the accuracy of forecasting over the long term. This way, optimizing “on the go” quickly is also challenging. MMM can be difficult to measure when marketing activities lack variation, have low reach, or strongly correlate with other plan elements. The aggregate data averages consumer behavior and does not give user-level insights that go beyond analytics to understand and optimize the customer journey.

The attribution models are skewed towards digital channels and customer acquisition, focusing less on offline strategies or external factors. This can lead to an over-attribution of digital activities in solutions that are not fully developed. In the past, attribution did not consider baseline conversions, which would have occurred without marketing efforts. However, this is changing with newer models. Even simple attribution models (such as a last-click model) or advanced ones (such as a game theory model) may not meet all business needs and use cases.

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