Why Your First or Last Click Attribution Model is Failing You

by Andrew Reed, on Jun 15, 2021 9:00:00 AM

Russell McAthey is the founder and CEO of ringside.ai, a cutting edge marketing analytics solution that uses in-depth data analytics and machine learning to expand your understanding of your customer behavior and your revenue margins. He is a leading expert in ecommerce and attribution specialist.

First touch and last touch have long been the most commonly used methods of attribution reporting in ecommerce, but their shortcomings may be getting in the way of your ability to truly understand your customers and keeping you from making the best use of your marketing strategy and budget. 

Leaving all the credit for a conversion with one touchpoint vastly overstates its ability to drive sales, and you may find yourself spending extra dollars on tactics that appear to be converting customers but in reality, played a very small role in the customer’s overall journey.

We sat down with Ringside Data’s Russell McAthy to learn more about the problems with these standard attribution models and what online retailers can and should be doing to more fully understand the customer journey and make informed decisions on marketing spend.

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The Problem With Single Touch Attribution Models 

The most typical kinds of attribution that brands use, be it last click or first click, are convenient, simple to set up, and easy to understand. But what you gain in convenience, you lose in in-depth analytics and detailed customer tracking.

If you aren’t tracking the entire customer journey, from awareness to conversion, not only are there key behavioral indicators you are missing out on, but you could be being misled on exactly which touchpoint your conversions are being most influenced by.

This can lead to poor decision-making when it comes to spending your marketing dollars and can ultimately prevent you from achieving your customer acquisition and revenue goals by steering you towards investments in unprofitable channels.

More often than not, these kinds of models are skewed by channels either failing to properly alert you to what inbound creative actually drove your sale, or inflated by multiple channels scrambling to take credit for the same conversion. 

If you are tracking a Facebook ad through Facebook and a Google ad through Google, then plugging it all back into a platform like Google or Adobe Analytics, each platform will be telling you something different in their attribution report. 

If you were to actually split out each interaction to understand what value is provided in the final conversion, you’d find the sum of the parts is significantly greater than the individual channels, because every channel is competing for full credit.

Problems may arise from the fact that the platforms that provide the results about how effective their marketing is benefit from you spending more on that platform.

These kinds of metrics can also trap you in a cycle of short-term thinking when it comes to your marketing efforts. You will constantly be chasing that one or two percent of customers converting on your site, without tracking the activity of those coming to your site and leaving without making a purchase.

Although 98% of customers aren’t spending any money, they still hold tremendous value. If you take a step back and examine how a customer engages with your inbound marketing, website, and product throughout their buying experience, you can gain granular insights into what tactics are working and which aren’t. 

You can also learn more about what behaviors indicate a customer is likely to buy, and what you can do to push them to convert.


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How You Can Gather Data That Will Better Inform Your Attribution

If you want to truly understand your data, you’re going to have to think outside the traditional attribution models. 

Think about what you are doing on the marketing side to drive customers to your website, and if you are actually catering to their needs once they are there. 

The 98% of customers who are coming to your site but not converting are there because they are interested in your products and services. Each one is a potential customer that you can try to engage or re-engage with. Just because they are not ready to convert right now, does not mean they won’t convert in the future.

Contrary to the ethos around first and last-click attribution, in most cases, you won’t be able to pick which steps in the customer journey deliver the most value to a consumer. Instead, you’ll have to let the customer’s data and behavior on your website inform what the best attribution model is, then create output that is meaningful to you as a marketer.

For example, if someone views five products on their visit, does that make them more likely to purchase than someone who viewed four or three? Or does adding to a basket make someone more or less likely to convert?

Find out what makes a customer leave, and what makes a customer come back. How long does it take for a customer to re-engage and return to your site?

For each brand the data around what drives conversions will be different, so you’ll need to take the time to learn exactly what is driving your customer behavior before you can effectively market towards them. 

Simply put, attribution and behavioral models overlap. If you want to gain a greater understanding of the 98% of customers that are not currently buying from you, you’re going to need to collect data on them beyond first and last-click attribution. 

Take a Look At Your First and Last Touch Attribution Models

The first step in learning how your customers behave is to run both first click and last-click attribution and record the difference between them. If a customer’s first touch in their conversion journey is a different channel than their last touch, those are the types of activities that you can influence to drive stronger conversions. 

This is top-of-the-funnel activity or activity that will drive a higher volume of sales but will have a lower conversion rate because customers will require another or several visits to ultimately convert down the line.

Understanding which channels bring people to your website and which lead to conversions will help you know where to focus your efforts, showing you what tactics can truly be attributed to which specific activity. For instance, email marketing may be more effective in re-engaging customers than PCC campaigns, which in turn may be best for bringing in first-time shoppers. 

Knowing what channels and tools are performing well will give you more granular control over your customer conversion journey, and you’ll be able to target customers with information and nuances they need in order to convert.


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Machine Learning Multi-Touch Attribution

In a multi-touch attribution model, no one model is going to work across all brands and retailers. A customer’s shopping experience differs from business to business, so you need to understand your customers' habits on your specific site before you’ll be able to build a truly effective strategy.

There are some very basic algorithms to help you see where the value is in your marketing, but staying on top of those can be time-consuming. At Ringside Data, they’ve realized that because multi-touch attribution generates insights that are constantly evolving and changing, sticking to static equations will most likely leave out important information that could be used to fine-tune your marketing strategy.

The Ringside platform uses artificial intelligence to provide an “always-on” solution that is constantly analyzing your customers’ behaviors in order to inform you, in real-time, about what is working and what is not.

Click here to learn more about Ringside Data’s multi-touch attribution solution.


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