A guide to last non-direct click attribution models

This post was originally published on this site

Last non-direct click attribution models give 100% of the credit to your customers’ last non-direct touchpoint.

For example, if a customer receives one of your email newsletters, clicks onto your website and browses around, then a week later goes directly to your website and purchases, then the email campaign would get all the credit. Let’s explore last non-direct click models in further detail: how to differentiate between non-direct and direct traffic, the pros of this particular attribution model, the cons, and when it should be used.

  • Direct versus non-direct traffic
  • The positives of last non-direct click attribution
  • The negatives of last non-direct click attribution
  • When last non-direct click attribution should be used


Direct versus non-direct traffic

Direct traffic essentially refers to any time a visitor contacts you directly–for example, if they enter your company’s URL into their browser or if they click on a bookmark that goes straight to your site. When Google Analytics (or any other attribution provider) can’t find anything else that might’ve caused this visitor to come to your site, they mark it as direct traffic.

On the other hand, non-direct traffic is all traffic that’s been guided to your website from another source. Your email campaigns, Twitter posts, influencer marketing campaigns, etc. should all be accompanied by a corresponding UTM. UTMs are little tags that let you know where the traffic originated from–which helps your attribution efforts.

For example:

Direct traffic URL – www.callrail.com

Non-direct traffic – www.callrail.com?utm_source=twitter&utm_medium=social&utm_campaign=spring-sale

As shown, this UTM shows that a customer specifically visited your company’s website after clicking through on a Twitter post about your spring sale.

Let’s imagine this customer then went on to purchase from you. A last non-direct click attribution model would give the Twitter post the credit for this sale–even if the customer clicked off before returning directly to your website a few days later to make the purchase.

Pros of last non-direct click attribution models

  • It makes an important distinction between direct and non-direct traffic
  • It’s easy to set up

If you think about it, last non-direct click attribution models make quite a lot of sense.

Customer journey example

Maybe they were introduced to your organization via Google AdWords–they searched for a relevant keyword online, and your company’s website was the first result to appear. After browsing around your website, they decide to leave their details and receive further marketing materials. For the next month or so you send them regular emails (all of which remain unopened), but they’ve left their address–so they also receive your direct mail campaigns through the post.

One day, they call up one of your direct mail tracking numbers and speak to a customer service representative to find out more about your products. They don’t buy there and then but a few days later, after careful consideration, they go directly to your website and decide to make a purchase.

So which touchpoint deserves the credit in this scenario?

A case could perhaps be made for the Google Ad– without this, they might never have even known about your company in the first place. That said, it didn’t really have that much of an effect on their end decision to purchase. The emails weren’t even opened and so can’t really be given much (if any) of the credit. However, when they did get in touch with you, they did so using the direct mail tracking number.

This means that they decided to ring up your company (and ultimately purchase) as a result of having seen one of your direct mail campaigns. With a last non-direct click model, the direct mail campaign would therefore receive 100% of the credit. Despite not technically being the last touchpoint (which in this case was them directly visiting your website), the letter that they received through the mail was what made them call you up in the first place.

So how does this compare to using a last-touch model, for example?

In this case, a last-touch model would say that the customer directly visiting your website was the most important touchpoint–and it would attribute 100% of the credit to this action. Over time, you’ll begin to see that most of your customers who end up purchasing from you do so having directly visited your website. But this only reveals half of the truth; after all, what made them go there in the first place? Without knowing how customers go from not knowing your brand at all to directly entering your URL into their browser, you won’t have any idea of how you can increase the number of visitors.

By adopting a last non-direct click attribution model, you can ensure that you always give credit to the correct touchpoint–what actually attracted customers to purchase from you, rather than the last direct touchpoint in their purchasing journey.

Plus, it’s incredibly easy to set up a last non-direct click attribution model if you use Google Analytics (it’s the standard attribution model that they offer). If you don’t use Google Analytics, it should still be a fairly simple process to get started. You just need to make sure your attribution model ignores all direct traffic and instead looks at the last non-direct touchpoint.

Cons of last non-direct touch attribution

  • Overly simplistic
  • Ignores any potential impact that direct traffic could have

That being said, last non-direct touch attribution isn’t necessarily the best option out there. Its single-touch approach is particularly problematic. We all know that the customer journey is increasingly long and fragmented–especially for B2B businesses that have long average buying journeys.

Giving 100% of the credit to one touchpoint alone (whether direct or non-direct) is never a very accurate portrayal of how your customers interacted with your brand prior to purchasing. It’s not as simple as deciding that all direct traffic is worthless.
Let’s continue with the same example as above–one of your customers calls up a direct mail tracking number, speaks to a customer service representative, and then later on directly visits your website to purchase from you.

What if they browsed around your website prior to purchasing? What if, instead of simply clicking onto the purchasing page, they first read your blog posts, trawled through the testimonials section, and browsed through your product pages?

It’s clear that they still needed to do some further research before deciding to buy. While the chat with one of your team went some way to convincing them, they might not have ended up purchasing if they didn’t like what they saw on your website.

A last non-direct click attribution model would ignore this step entirely. Sure, there might be occasions when customers visit your site directly knowing they want to buy, but there might be other times when they need to find out more.

When should you use a last non-direct click attribution model?

  • When customers from direct traffic quickly convert
  • If you want actionable insights
  • If you lack in-house data scientists

The key to working out if a last non-direct click attribution model is right for you is to look at your previous customer journeys in detail.

For example, do customers who visit your website directly tend to purchase there and then? In other words, do they seem to visit your site knowing that they want to buy? If they do, then it’s clear that another touchpoint made them decide to convert. However, if they browse around for a while on your site, or even visit your site directly multiple times before converting, then this model might be misleading.

You need to work out whether your average direct site visitor is at all close to purchasing. If they’re usually pretty close, this is a decent model to use (as it ignores the influence of direct traffic)–if they seem like they still need a bit of convincing, then this model’s probably not right for you.

This model is also pretty useful if you’re looking for quick, highly-actionable insights. It can be hard to work out where to optimize direct traffic–does your blog need updating? Do you need to add new client testimonials? What about the website design?

However, if you focus on the marketing campaigns that brought those direct customers to your site, then you can begin to invest more heavily in those. Sure, your website still needs to be optimized, but at least this model shows the best campaigns for getting visitors to the site in the first place.

Lastly, a last non-direct click model is great if you lack in-house data scientists. It’s an easy, off-the-shelf option that anyone with a Google Analytics account can use–it doesn’t require ongoing tweaking like customizable multi-touch options, and it gives you some actionable insights right off the bat.

Last non-direct click attribution models are great if you want to shine a light on your most influential marketing campaigns–but they’re not necessarily the most accurate portrayal of your customers’ journeys. There’s the usual problem that all single-touch models have–namely, that conversions are rarely due to one single touchpoint alone. And then there’s the fact that while direct traffic sometimes isn’t that important, there are other occasions when it’s very important. Always choosing to ignore it won’t be very helpful to your marketing strategy in the long run.

The post A guide to last non-direct click attribution models appeared first on CallRail.