There are signs that the economy is getting worse. While not in a recession, the United States is seeing a slowdown in the second quarter of 2023.
Although the debt-ceiling debates have been resolved and inflation seems to be coming down, there is still a risk of a recession. People are holding on to more of their money because of high interest rates and inflation, which, in turn, leads to brands seeing a decrease in customer purchases.
If you want to make your brand thrive despite troubled times, tracking the customer journey is a must. You need to know where customers are getting tripped up, which marketing channels have the most influence, and other information to make the most of your marketing campaigns. With this knowledge, you have the best chance of targeting those who can support your brand and of making sales even when inflation, a potential recession and other factors are slowing down the market.
Multi-touch attribution, a marketing effectiveness measurement, shows you the various touchpoints that customers encounter as they complete a journey through your funnel. This gives you a lot more information than just collecting data during a single touch (known as single-touch attribution). With multi-touch, multi-channel attribution, each interaction or move receives a fractional credit. With those credits, you see exactly which parts of your marketing campaign are working and which are lackluster.
To make attribution work, you'll need to use technology that:
Once you do, you have the possibility of deep insight into how customers get from point A to point B in your marketing funnel. With that knowledge, you can expand on your marketing efforts where they matter most.
Many challenges come with tracking customer behavior, though. Complexities arise when you try to identify customers as they move between devices or take breaks in their journeys. For that reason, most marketers use first- or last-touch attribution models. However, other models may be better for your needs.
What are the challenges that multi-touch attribution poses, and why should you persist until you get it right? Here are some key insights into Google Analytics attribution models that you might choose.
Collecting data through a multi-touch attribution model has advantages. To start, gathering data lets you learn more about your prospects and customers. You're also learning about your brand's marketing campaigns and what is and is not working during a customer's journey.
If you collect touchpoint data and can piece it together, you stand to gain a much stronger sense of what's working for your business. You can also gain insights into cross-channel marketing interactions and start allocating your marketing resources where they're needed most.
Essentially, multi-touch attribution gives you the detailed data you need to make data-driven decisions that boost your business.
Multi-touch attribution models do have challenges and limitations. Here are four to keep in mind:
Other challenges and limitations can also arise, but with a well-chosen attribution model and strategic support, you can make the most of your attribution data.
The model you choose will influence the data you collect and the extent of the customer journey you focus on.
Using models that only focus on first- or last-touch attribution can give you insight into what makes a customer interested in your brand or what final touchpoint led to a conversion, but those touchpoints aren't the full story.
To implement a multi-touch attribution model and make the most of it, you have to decide how much of the customer journey you need to know about. If it's all of it, then an option like linear attribution works best. For only final conversion touchpoints, last-touch attribution could be the smartest choice.
Multi-touch attribution models help you track a customer's journey through your funnel. These are more effective than single-touch options like first-touch attribution or last-touch attribution. First, here are two common types of single-touch attribution models that you should understand so you can see how they differ from multi-touch models.
First-touch attribution is a single-touch model and refers to a method of assigning credit to the first interaction or event with a customer. For example, when a person clicks on your ad, this might be the "first touch" on their path as a prospective buyer. With this model, you focus on the first touch more heavily than on other parts of the customer journey.
First-touch attribution is important for identifying what draws customers in. It gives all credit to the first touchpoint for any conversion that occurs. But it doesn't consider any later touchpoints.
Last-touch attribution is also a single-touch model similar to first-touch attribution, except that the last interaction with your brand receives the most credit. That last interaction may be when a customer submits their information to you, or it could be when they make a purchase.
Last-touch attribution is perfect for identifying the last step of a customer's journey and seeing what makes them convert, but it doesn't tell the whole story. It doesn't go into details about what led up to the conversion, which could mean you're missing the bigger picture.
The following three attribution models are multi-touch attribution models that help you see a larger picture of your customers' journeys.
Unlike other kinds of attribution, the linear model gives equal credit to each touchpoint along the customer journey. A linear attribution model recognizes that a prospect might need multiple interactions before they become a customer or take a desired action.
Linear attribution is a comprehensive data collection approach. It is more nuanced than other types of attribution, providing thorough details on the full customer journey.
The time decay model examines how a customer's journey looks from the start, but it gives more weight to the touchpoints closer to the conversion of the prospect into a paying customer.
Time decay attribution allows you to see the influence of touchpoints along the customer journey so you can optimize the touchpoints that matter most.
With time decay attribution, the full story is still there, but you see the most emphasis on the last few touchpoints. For customers with a more complex journey, this attribution method may not tell you all you need to know about what made them convert.
To see what captures your customers' attention and when they convert, one method is the U-shaped model. Also known as position-based attribution, this helps you understand what starts a prospect's journey as well as what helps convert them into a paying customer.
Here's an example.
User A clicks on one of your Google Ads to go to your landing page. That's the first thing that will receive attribution and conversion credit. As the first touchpoint, this touchpoint gets more credit than all but the final touchpoint.
In a normal funnel, the prospect will go through several steps before (potentially) converting. For instance, they might read your website and click through a few links. They might add an item to their cart. Or they might sign up for emails and start getting your newsletter.
In U-shaped data-driven attribution models, you pay less attention to the journey — until it comes time to convert. When someone makes a purchase or takes an action you wanted to see, you capture that information by giving the touchpoint more credit (or weight) compared to others.
Using popular attribution models for your business has the potential to transform the way you work. Whether you choose a popular model or a custom model, that data becomes an almost endless source of support for your company.
After reading this blog post, are you ready to track your prospects and gain insight into the customer journey? Check out our Chief Data Scientist, Chase Zieman's recent LinkedIn Post. At Cart, our team of specialists is here to help you choose a custom attribution model or perform multi-touch attribution analysis to understand where your marketing efforts are falling flat and where they're thriving. Visit us at Cart.com today to learn more.