Introduction

Hands-down, marketing attribution is a pretty awesome thing. It reveals the touchpoint(s) that a lead interacts with before completing a desired action like buying a product or signing up for a service. In turn, this helps us find out how long a marketing cycle truly is and how conversion points look like at a very granular level. Whether you’re a B2B or B2C brand, this knowledge comes in really handy when you’re planning to scale your advertising and maximize your ROI.

That being said, attribution models can sometimes be hard to navigate. First-touch, multi-touch, time decay… there are many methods to go about finding out which channels drive most conversions. Without the right analytics setup and team, it can get pretty messy. This is one of the main reasons why many brands rely on one of the most straight-forward attribution models there is: last-touch attribution.

What is Last-Touch Attribution

In short, last-touch attribution is a model that gives the conversion credit entirely to the final touchpoint where a lead has converted from (i.e: made a purchase). This is usually the standard attribution option in Google Analytics or most analytics tools.

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Here’s an example: let’s say that you’re a B2B brand driving inbound campaigns. One of your leads has followed different steps in the customer journey before making a purchase: engaged with one of your blog articles, signed up to a webinar, and lastly - responded to an email campaign upon converting. When you’re using the last-touch attribution model, you’re giving all the credit to the last touchpoint before the sale – in this case, the email channel.

The Pros of Last-Touch Attribution

Before we delve into the limitations of this model, let’s take a look at the positive things that come with it. Here are 3 reasons why you might want to consider last-touch attribution:

1. It’s easy to implement

All in all, last-touch attribution is pretty straightforward. It’s easy to implement and it doesn’t require a complex reporting setup, which makes it a popular choice for smaller teams that are just getting started with attribution. This model is usually offered as a default on many online advertising channels or analytics tools. Adwords Analytics has Last Adwords models as a standard, Facebook uses the Last Facebook Touch model.

In reality, the interactions that your leads have with your brand before making that purchase are… numerous. With a last-touch attribution model, the customer journey is narrowed down to one single touchpoint – a landing page or an email, for instance. That isolated instance doesn’t give much room for understanding what drove your leads to your product to begin with.

2. It's good for short sales cycles

Another upside is that it does a good job for short buying cycles, where brands are confident that their potential customers are not going through many touchpoints before converting. If the last channel before the purchase is the one that a business wants to prioritize, the last touch attribution model can help with that. It shifts focus from the early discovery phase to conversion.

3. It has the smallest window for an error to occur

As we’ve established, this attribution model only looks at the last touchpoint before conversion, which means that the period between this touchpoint and the actual sale can be pretty short. In turn, having a short window to analyze means that this method is less likely to bring errors in the process.

This is very different from the first-touch model, for instance, where the conversion is 100% attributed to the first touchpoint that a contact interacts with. In the light of Apple’s latest intelligent tracking prevention measures - where the cookie expiration period has been dramatically reduced, this can have detrimental effects on campaign performance and overall marketing data quality.

Let’s phrase it differently: when a brand uses the first-touch attribution model and has a complex customer journey where the purchase doesn’t happen within the window pretty much dictated by the cookie, all that great marketing data that they’ve gathered becomes redundant.

With the last-touch attribution model, the cookie expiration window is not really a deciding factor since the period between the last touchpoint and conversion point is short. However, this does not mean that this model is not prone to attribution confusions and errors (keep reading for more information on that).

The Cons of Last-Touch Attribution

1. It fragments the customer journey

Today’s customers have complex needs and are exposed to big amounts of content every day bombarding them from all sides. This is reflected in the complexity of the customer journey. In an ideal world where marketers rejoice, the buying cycle would be pretty linear: a lead is exposed to your messaging, discovers your product or service, buys it. Bam! A sale is made.

In reality, the interactions that your leads have with your brand before making that purchase are… numerous. With a last-touch attribution model, the customer journey is narrowed down to one single touchpoint – a landing page or an email, for instance. That isolated instance doesn’t give much room for understanding what drove your leads to your product to begin with.

2. It gives mixed signals about your content’s performance

Picture this: one of your main content and marketing goals is providing valuable knowledge to your audiences. You have a talented team that produces some great content that’s getting a lot of readers. Then, you’re using a last-touch attribution model to figure out where your conversions are coming from. In 95% of the cases, your leads are converting straight from your product pages. This is reflected in your analytics, so it must be the single source of truth, right? Does this mean that your content is not influencing the buyer journey at all? Should you stop spending resources on creating content and instead invest in putting your product pages in front of your leads?

Well, the answer is not all black and white. While some of your audiences might land straight on your product page and make a purchase, many of today’s customers go through a lot of stages before they actually make a decision. Your inspiring blog posts might be what really made them trust your brand and your product, even if your analytics is telling you otherwise.

3. It feeds your analytics with misleading traffic

This goes hand in hand with the two previous points. The last-touch attribution model is normally linked to the respective advertising channel that offers it. This makes it partial to those channels, be it Facebook, Google Adwords, etc. Here's an example: let's say that you're simultaneously using last-touch attribution via Adwords and Facebook. If you're running campaigns for the same landing page on both, you might not be able to figure out where are your conversions really coming from since both channels are claiming them individually.

This renders it almost impossible to measure marketing ROI accurately and gain a 360-degree overview of touchpoints to know where to increase or decrease budgets accordingly. The result of linking a purchase to the touchpoint that immediately precedes it is that individual high-conversion channels are over-prioritized.

In a Nutshell

Used strategically, marketing attribution does wonders. Being the most simplistic model of them all, last-touch attribution often takes the role of the scapegoat as it risks giving incomplete and sometimes twisted conversion insights. However, many businesses can benefit from it, as it's easy and quick to implement. To find out what is the best attribution model for your marketing strategy, a good place to start is taking a close look at your customers' journey and your KPIs.

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About Diana Ellegaard-Daia: ‍

Diana Daia is the Head of Content Marketing at Accutics and a strategist with a no-nonsense growth approach. She is passionate about digital marketing, CRO, digital analytics, and account-based strategy. She works closely with collaborative content & influencer outreach by creating guest articles and deep-dive interviews with leading marketing and digital analytics experts.

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