6 Attribution Models You Should Know About

When a consumer makes a purchase online, their path to get there is rarely straightforward. In fact, it usually takes an average of eight touchpoints to make a sale. These touchpoints can include anything from advertisement, content marketing, emails, social media comments, or any other interaction with your brand.

Because customers engage with your business in so many different ways, it’s not always clear what caused them to buy. Was it your killer blog post that convinced them? Or was it a series of Facebook ads that made the difference? Maybe it was a combination of both?

Understanding which channels are responsible for revenue and which channels are underperforming is essential information. When you understand how every keyword, copy, or ad channel converts, you can optimize and adjust each one to improve its effectiveness.

Programs like Google Analytics help digital marketing teams contextualize the effect of their campaigns through a process called Attribution modeling.

 

What is Attribution Modeling?

Attribution modeling is the framework that software like Google Analytics uses to help you credit which touchpoints are directly responsible for driving revenue.

It uses cookies to track customers’ interactions with your SEM, PPC, display ads, content marketing, etc. Armed with this information, you can understand your customer’s journey, from initially searching for a product online to converting — and all the stops in between.

These analytics are a powerful part of your marketing arsenal. They can help you identify return on advertising spend (ROAS) or your search engine marketing (SEM) effectiveness. Having these figures can help you decide what you need to double down on and which channels you need to cut or improve.

Customer acquisition costs are constantly rising, so you need to ensure you are spending advertising money wisely. Being able to quantify your campaigns helps you know you are getting ROI.

Here are a few of the most popular Attribution models to help you comprehend what is driving sales and leads.

 

1. First Interaction

The First Interaction Attribution model gives 100% credit for sales or conversions to the first interaction a customer has with your brand. For example, they could find your product through Google search but decide to buy after reading a review on their favorite blog or website, but first interaction will reward the initial Google search.

Pros:

  • Simple
  • Excellent for measuring awareness

Cons:

  • Doesn’t track the entire customer journey
  • Can undervalue later touchpoints

2. Last Interaction

Last Interaction Attribution gives 100% of the credit to the last interaction before the sale. For example, over a week, a prospect:

a) finds your website through organic search and reads some blogs

b) clicks through on an Instagram ad and makes a purchase

The Last Interaction model will attribute 100% of the credit to the Instagram ad.

Pros:

  • Simple
  • Accurate

Cons:

  • Doesn’t track the entire customer journey
  • Best for short buying cycles

3. Last Non-Direct Click

The Last Non-Direct Click is a useful measure because it excludes direct clicks.

For example, a customer finds your service via a Google search. Later, they click through on an Instagram ad and browse some of your products. They shop around and compare prices with some of your competitors before going directly to your site and making a purchase.

This attribution model gives you a better idea of what factors influenced the consumer. While their last action was a direct click, Google Search and the Instagram ad were causal factors.

Pros: 

  • Provides more insights than direct clicks

Cons:

  • Doesn’t track the entire customer journey

4. Linear

A Linear Attribution model shares the credit across the variety of touchpoints a customer has with your brand before making a purchase.

For example, a customer reads a Facebook post about your business, later clicks through on an Instagram ad, and then signs up for your newsletter. A few days later, they click an email link and buy $30 worth of items.

A linear model will credit all three touchpoints and attribute them with a third of the $30, i.e., $10 per channel.

Pros:

  • Takes in the entire customer journey
  • Allows for greater optimization across the sales process

Cons:

  • Gives each touchpoint equal credit

5. Time-Decay

The Time-Decay attribution model has some similarities to the Linear model, but it factors in when the consumer touchpoint occurs.

Touchpoints that happen closer to the point of sale get more credit. For example, the first touchpoint gets less credit, while the last touchpoint gets more.

Pros:

  • Recognizes all touchpoints, but is weighted towards the most effective channels
  • Allows for a more flexible approach to channel optimization

Cons:

  • Limited recognition of important first interactions
  • Undervalues influential early interactions

6. Position-Based

The Position-Based Attribution model gives 40% to the first interaction and 40% to the last interaction. The remaining 20% is split between the other touchpoints the customer has with your brands.

For example, a customer finds your business through Google Search, signs up for your newsletter, and clicks through on a few different links. Later that week, they see an ad on Facebook and make a purchase.

Attribution is split between Google Search (40%) and Facebook (40%), with your newsletter and marketing email dividing the remaining 20%.

Pros:

  • Nicely weighted while still capturing the customer journey
  • Credits each touchpoint

Cons:

  • Can undervalue critical middle touchpoints

Alternatives to Attribution Tracking

One big problem with most attribution models is their reliance on 3rd party cookies to track customers. These processes worked well in the past. However, Apple and Google have implemented and announced plans to kill the ad cookie.

Marketing teams need to adapt or die.

Amanda AI uses an impact-driven effect model. All possible options and combinations — copy, images, audiences, keywords, etc. — are evaluated on the results they deliver. But we don’t rely on 3rd party cookies to get this data.

Instead, we measure and optimize ads depending on how they deliver. Then, we track your overall results in one place so that the data you get comes from one source. We use Google Analytics for monitoring results.

As the digital marketing space reels from changes to third-party tracking, there will be a period of adjustment. Brands will need to put a lot more time and energy into ensuring their campaigns are optimized. One solution is to use an automated AI-based service to do the work of ensuring your ad spend translates into sales and leads.

Get in touch today to find out how Amanda AI can help you survive in this cookie-free future.