• Blog
  • May 24, 2021
  • By Cam Sivesind
Marketing Mix vs. Attribution Modeling: What’s the Difference?

Nowadays, many businesses are obsessed with understanding which marketing strategy is the best. There are numerous marketing channels available, and customers can be found virtually everywhere, yet companies need to know which channels can give them the best conversions for the greatest ROI.

Companies that incorporate ad campaigns, social media, blogging, and webinars for their marketing efforts use attribution models. Meanwhile, those who use radio, print ads, and TV often rely on marketing mix modeling.

In this post, we’ll take a closer look at the differences between marketing mix modeling and attribution modeling to know which of the two is best for your company.

How Does Marketing Mix Modeling Work?

Marketing mix modeling first started within the retail sector. With this approach, companies aim to calculate the success of marketing endeavors (such as with radio, TV, promotional efforts, and print ads) at the point of sale. This model favors performing quarterly, annual, or biannual analysis using aggregated historical data over real-time analysis.

While it’s not rocket science, it can be somewhat complex. Instead of prioritizing user interactions, the marketing mix modeling approach takes a top-down view of everything. It utilizes marketing and sales information, benchmarks, revenue, costs, and external factors, including market and economic conditions, profit margins, competitors, and anything else that could affect customer behavior.

In the marketing mix model, four critical elements in marketing are assessed: price, product, promotion, and place. With this approach, the goal is to identify the best combination of these four components to achieve a company’s objectives.

It does so by obtaining information from all of the factors that could affect the marketing channels’ success and carrying out a regression analysis.

Regression analyses evaluate the effect of several independent variables on one dependent variable, such as sales figures. These variables are plotted on a chart that calculates a regression line to expand on the relationship between the two types of variables.

In theory, this line should illustrate the impact every tactic within a marketing strategy has on company sales. The marketing mix model helps businesses look at the bigger picture on how effective their marketing channels are when applied together.

Pros of Marketing Mix Modeling

  • Tailored for print ads, TV, radio, and other traditional marketing channels
  • Provides a wide range of data
  • Highlights major marketing trends

Cons of Marketing Mix Modeling

  • Cannot obtain granular insights to identify opportunities
  • Unable to keep up with trends
  • Difficult to acquire detailed inputs

How Attribution Modeling Works

If marketing mixed modeling offers a top-down approach, attribution modeling is the bottom-up method to measure marketing effectiveness. This approach assesses a customer’s journey to conversion by analyzing information at every engagement in the process. The goal is to identify the impact each engagement has on propelling the prospect to a buying decision.

Attribution modeling tends to prioritize digital advertising, online sales, and other similar engagements.

With a growing number of marketers integrating online and offline channels, attribution models must carry more weight to account for the offline interactions that can be difficult from which to obtain data.

With the granular approach offered by attribution modeling, data is assessed regularly, in real-time, or as close to real-time as possible.

Types of Attribution Models

Due to the vast amounts of data and marketing channels available, analysts can choose from several types of attribution models.

  • Last interaction: This type of attribution model gives all the credit for a conversion to the last engagement a customer has before buying. For instance, a user sees an ad on Google and clicks on it but doesn’t buy anything. A few days later, they see the same ad, but it is on Twitter. This time, they click on it and decide to purchase. The Twitter ad receives full credit for the sale.
  • First interaction: This model gives all of the credit to the source that introduced a business to a user. Taking the previous example, this model will assign all credit to Google for the conversion.
  • Last non-direct click: Like the previous two models, this type awards the credit to just one interaction. The reason for this model is that any direct interaction, say, when a user heads straight to a website by typing its URL, should not be credited. An example of this model would be a user clicking on an ad on Facebook and then decides to visit the website the following day. Under this model, the Facebook ad gets credited for a successful conversion.
  • Linear attribution: This attribution model splits the credit equally across all engagements a customer had before buying. With linear attribution, the initial example would see 33% of the credit go to Google, 33% to the website, and 33% to the Twitter ad.
  • Time decay attribution: This model goes a step further following the linear attribution approach. It takes into account when each engagement took place and assigns greater weight to engagements that occurred closest to the sale.
  • U-shaped attribution: This model divides the credit allocated for a conversion. It gives 40% to the initial interaction, 40% to the final interaction, and 20%, split evenly, to all interactions that occurred in-between.
  • Data-driven attribution: This approach incorporates facets of other attribution models, but its strength lies in the fact that the weight assigned to different engagements is uniquely determined by the company using it.

Pros of Attribution Modeling

  • Provides more granular insights
  • Accurately supplies data for each channel
  • Fast-paced

Cons of Attribution Modeling

  • Uses complex algorithms
  • Trial and error required
  • Multiple options to choose from


When comparing inaccurate marketing methodology of the past, businesses can now perform rigorous data analysis that provides a picture of what works and what does not. The attribution models available should point businesses toward the direction they need to take and the marketing avenues they should explore. This helpful guide to multi-touch attribution can provide additional insight when searching for more detail about implementing the right model for your business.

Picking a model that can accurately attribute credit for sales to specific marketing actions may be challenging, but there are reasons why some organizations use particular models. For example, auto dealerships or automotive marketing agencies could benefit from insight into dealership attribution by leveraging the right software for their marketing measurement initiatives.

It’s hard to deny the value attribution brings to quantifying a company’s return on marketing dollars invested.