While it’s every marketer’s dream, it’s rare for a consumer to purchase something at first click. This is why multi-touch attribution is an important marketing measurement tool — it evaluates the impact of each touchpoint leading up to a conversion. Using this method, marketers can get an in-depth look into consumers’ experiences and devote spend to the channels that provide the highest ROI.
The first step is to determine which attribution model is right for your business. Let’s dive into six common attribution models and evaluate their pros and cons.
Full Path Model
The full path model is the most extensive and technical multi-touch attribution model. In its purest form, the full path model tracks every marketing effort that a person experiences, including the customer close touchpoint. This lets marketers see precisely what works and what doesn’t for any particular consumer.
Pro: It’s effective.
Con: It’s costly in terms of resources and time.
Linear attribution methods assign equal value to all marketing touchpoints. For instance, a customer finds you on Facebook, signs up for your email list, and then clicks an email link. The next week, they go to your site to make a purchase. There are three touch points in total, and the credit is split evenly (33%).
Pro: It demonstrates how each point has value.
Con: It doesn’t offer the amount of insight that other approaches do.
Time Decay Based
The time decay model is similar to the linear attribution model — credit is spread among all of the touchpoints, but time is also a factor. Less credit is given to the first interaction, while the last interaction receives more credit. For example, the first touchpoint in the form of a flyer may not get much credit, but the click on the call to action button on the website gets much more.
Pro: It’s good for relationship-building.
Con: It’s less suitable for shorter sales cycles, which isn’t helpful for most D2C brands.
The W-shaped model follows a very set pattern: 90% of the credit is evenly split between the first, third, and last marketing touchpoints. The last 10% is divided between the second and fourth touchpoints, hence the “W” shape. This model attributes the most value to the three main customer journey stages: Visit, lead, and sale.
Pro: This model gives credit to all touchpoints, but specific key interactions are more weighted.
Con: It doesn’t touch on points outside of the first, middle, and last touchpoints.
The U-shaped model is similar to the W-shaped insofar as it distributes credit unevenly across the sum of the marketing efforts. In this model, the most credit (80%) goes to the first, and the last touchpoints and everything else (20%) is doled out to the middle.
Pro: Similar to the W-shaped model, it highlights the major touchpoints.
Con: It doesn’t consider marketing efforts beyond lead conversion.
Finally, custom multi-touch attribution models are highly specialized for the individual company’s needs. Generally, they combine features from the more standard models instead of creating something else entirely. You can get started with creating your own custom attribution model by using Google Analytics.
Pro: Custom models give you precise control over how you distribute credit for conversions.
Con: Developing custom models is resource-intensive. For most, it’s easier to use the readily available pre-built attribution models.
Here are the six multi-touch attribution models for your reference:
Measuring the Data and Results
How do companies measure data and track results? The short answer is, it’s very tricky. The slightly longer answer is by using algorithms, software trends, and intuition. These standard tools in marketing also apply here. Some ways of measuring the data can include:
- Customer reward programs
- Website analytics
- Tracking software
- Central dashboards, such as Google’s AdWords
These tools collect data from different marketing efforts into a central place. However, there’s a lot to take into account, including consumers’ varying experiences that lead to conversion. The downside to multi-touch attribution is the fact that people are unpredictable. It’s best to factor this in before relying heavily on data, patterns, and trends.
The Benefits and Challenges of Multi-Touch Attribution
While the advancement of attribution is an exciting development, there are still many limitations to consider. So, what are the benefits and challenges of multi-touch attribution?
- Offers much better insights into your marketing efforts and campaigns
- Enables a higher return on marketing investment
- Much better than previous or typical single-touch attribution methods
- Provides better insights into your customers’ journey to buying your products
- Can be very costly
- Does not take into account offline marketing touchpoints that consumers are experiencing
- Cannot exist in a vacuum or void — many factors must be taken into account
- Doesn’t take into account external factors, such as pricing, season, etc.
When Should I Use Multi-Touch Attribution?
You’re probably already using multi-touch attribution because marketing doesn’t exist in a vacuum. However, you know you’re ready to begin using multi-touch attribution if:
- You use multiple digital ads and ad networks.
- You need to show how your marketing is producing results.
- You use or are considering using offline channels, such as print media, trade shows, etc.
- You’re considering or implementing SEO best practices.
- You’re frustrated with your current methods of tracking your marketing.
- You need a new way of marketing that reaches your consumers in the competitive marketplace of today.
Keep in mind that while multi-touch attribution can be overwhelming at first, you can focus on smaller customer journeys and work your way up.
For example, if you’re consistently seeing a trend where people first like your Facebook Page, then click on an ad, followed by a visit to your website, that’s a great place to start. It’s a simple multi-touch strategy that you can study to discover where you should attribute appropriate credit. The most important part is what you do with the resulting data that your customers are providing you.
Want to learn how to build your own attribution model? Read more here.
Last updated on September 16th, 2022.