A guide to linear attribution models

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What is a linear attribution model?

Linear attribution models are focused for multi-touch attribution–meaning they take into account and attribute credit to multiple touchpoints along the customer journey. In fact, this model actually takes into account every single interaction that a prospect has with your brand prior to purchasing.

Mathematically-speaking, they’re incredibly easy to understand. With a linear attribution model, every single touchpoint is given the same amount of credit, so the equation is as follows:

100 (% of the overall credit) / number of touchpoints in conversion path = amount of credit per touchpoint.

So if your customer has four interactions with your brand before deciding to convert, each would therefore be given 25% of the credit.


Linear Attribution in real life example

Say you manufacture sneakers and sell them online to a global audience.

It’s highly likely that you’ve set up a series of Instagram ads–after all, the social media platform boasts a whopping reported reach of 849.3 million users.

One of your prospects might initially see an Instagram ad before liking what they see, clicking through, and spending half an hour browsing your website. They really like your products but aren’t going to buy there and then, so they decide to sign up to receive promotional newsletters from your company.

Over the course of the next few months they receive multiple promotional emails from you. However, for whatever reason, they don’t click through (or even open) any of them. But then one day they’re retargeted by a Youtube ad.

This ad prompts them to revisit your website, where they finally decide to purchase a pair of your shoes.

With a linear attribution model, the credit for this sale would look as follows:

  • Instagram ad – 20%
  • Browsing through your website – 20%
  • Signing up to your newsletter – 20%
  • Youtube ad – 20%
  • Visiting your website and purchasing – 20%

So if the shoes cost $40, each touchpoint would be worth $8.


The benefits of using linear attribution models

  • Takes a multi-touch approach
  • Easy to understand
  • Doesn’t require in-depth data science capabilities

The true benefit of using linear attribution models is that they take all touchpoints into account. Adopting a linear attribution model is a great way to get started if you’re looking to gain a macro-level grasp of your overall marketing strategy.

By accrediting every interaction that crops up in any one buyer’s journey–all the way from the initial brand awareness stage through to the point of purchase–you can accordingly fine-tune your marketing efforts going forward (according to what campaigns work and what do not).

This makes it considerably better than any single-touch models, for model comparison. Single-touch models can be pretty misleading–after all, prospects rarely make a purchase based off one single touchpoint.

It might happen very rarely for B2C companies with low-value goods and short sales cycles, but it’s generally the exception and not the norm.

So by taking all touchpoints into account, linear attribution models mean that you’ll always have complete oversight of your consumers’ entire buying journeys.

In particular, linear attribution modeling is perfect for companies who are just starting out, or are adopting a completely new digital marketing strategy.

You don’t need to work out a precise weighing system (i.e. how much each touchpoint is worth), you get an overall view of the consumer journey, and it’s easy to understand.

This is especially useful for companies with long buying cycles involving many touches–you don’t want any touchpoint to be ignored, so a linear attribution model will take all of them into account.


Downsides of the linear attribution model

  • Too simplistic
  • Treats every touchpoint the same way
  • Over time, this could have a significant negative impact on your results

Despite the positives, there are actually quite a few negatives to take into account when considering whether or not to adopt a linear attribution model.

Its simplicity, unfortunately, is also its downfall. Linear attribution models offer little to no nuance; all touchpoints are worth the same amount of credit, whether or not they actually had that much influence over a prospect’s decision to convert.

When compared with a W-shaped model (which gives the most credit to the critical first, lead-creation, and opportunity-creation touches), this method of attribution seems to be lacking in many respects.

As a result, adopting a linear attribution model could significantly skew how effectively you deem each effort in your marketing strategy. But how exactly would this happen?

Let’s imagine for a minute that you’re a B2B company selling a novel project management tool.

A prospect of yours (let’s say they’re a marketing manager) goes to a conference and hears your CEO give a keynote address on the amount of time wasted due to poor project management.

The prospect is highly impressed with the way your CEO spoke, so they decide to follow your company’s LinkedIn page.

A few months passes and the prospect doesn’t appear to interact with your brand in any way, shape, or form. Sure, they might’ve seen a couple of posts crop up on their LinkedIn feed, but they haven’t engaged with anything, nor have they decided to visit your website.

However, when May comes, the company has their end-of-year budget review. The marketing team has had a strong year and so are allocated more budget, with the directive of continuing their good work but simultaneously increasing productivity at the same time.

When considering how best to do this, the marketing manager remembers your company’s name. Deciding to check out the ins-and-outs of your project management tool, they start perusing your website: reading up on how the tool works, looking at case studies highlighting just how effective it is, and even clicking through onto client testimonials hosted on your Youtube channel.

Impressed with the accolades and ready to find out more, they arrange an in-depth demo with your sales team before deciding to go ahead and purchase.

So far so good. After all, your marketing strategy clearly worked–online and offline interactions have worked together in an effective manner to lead a prospect down the sales funnel until they became a customer.

But marketers are always looking to further refine their marketing strategy: to work out precisely what they can do to increase their conversion rate and bring in more customers. After all, consumer behavior is always changing; with it, brands need to update and refresh their marketing efforts.

However, when it comes to attributing credit to that sale, a linear attribution model will give all touchpoints exactly the same amount of credit.

As good as it is to take every touchpoint into account, this is probably not the right way to look at things. For example, despite liking your company’s LinkedIn page, the prospect never engaged with anything you posted.

So how much of an effect did it really have on their decision to make a purchase? The likelihood is that social media didn’t actually have all that much influence, yet according to a linear attribution model, it was just as important as your CEO’s keynote address at the conference and the demo that they signed up for that would list them as a direct traffic source.

It’s all well and good recognizing the many touchpoints that a prospect interacts with on the conversion path, but that doesn’t mean that they’re all of equal importance. However, linear attribution models ignore this nuance–instead, offering an easy-to-understand (but slightly inaccurate) overview of events.

Who should use a linear attribution model and when?

Despite the drawbacks, a linear attribution model can still be fairly useful–especially for a new company with limited budget who just want a decent overall picture of their marketing strategy, i.e. which efforts are involved in pushing prospects down the funnel and which don’t really seem to have too much of an impact.

They’re also a great option for companies which lack in-house data analysts to dig into the analytics. Getting an attribution model that’s 100% accurate–reflecting just how much of an impact each touchpoint had on a single customer’s decision to buy–is a difficult (if not impossible) task.

After all, it’s hard to appropriately weigh touchpoints–does a customer spending two minutes browsing on your site equate to the same level of importance as signing up to your newsletter? If so, why? If not, why not?

And this is an ongoing battle. For instance, one customer’s decision to click through on a display ad might have had more of an influence on their decision to buy than another customer also clicking through.

So if you’re looking for a fairly basic model which takes into account all relevant touchpoints, then using a linear attribution model is a pretty good place to start.

When not to use a linear attribution model

That being said, every marketer worth their salt knows that not all touchpoints have the same influence on a customer–so they shouldn’t subsequently receive the same amount of credit.

The only reason that you’d use a model which says they do is either due to budgetary constraints or because you’re not looking to refine your marketing strategy to a point of true perfection.

For instance, if you’re a large organization with a marketing budget to match, then you likely require a more accurate and far more comprehensive model to help justify marketing costs going into campaigns. Just because many of your customers like a Twitter post prior to purchasing doesn’t simply mean that they’re as important as producing thought-leadership eBooks.

At the end of the day, marketing attribution models should guide future investment: influencing the marketing channels and particular strategies where you spend most of your budget. Hopefully, these will be the ones which demonstrate the best ROI and have the highest conversion rates.

Equally attributing credit to all touchpoints might mean that you then decide to equally invest into all aspects of your overall marketing strategy. Over time, investing in ineffective channels will effectively mean pouring money down the drain and leaving potential customers on the table.

So if you’re serious about taking your marketing strategy to the next level, it’s probably best to avoid using a linear attribution model.


How to setup a linear attribution model

Given that the math is so simple, a linear attribution model really shouldn’t take all that long to set up. All your attribution software needs to know is that 100% of the credit is equally divided between all touchpoints in the customer journey.

From that point onwards, you’re ready to go. It’s worth remembering though that you’ll only start to glean useful insights after a good few months of using the model.

Simplistic attribution models might not necessarily be able to retrospectively assign credit–that’s to say, if they’ve been set up to only look at the first or the last touch, they won’t be able to suddenly look back and assign credit to all other touchpoints that were involved.

So what does this mean? Well, in reality, you’ll only really start being able to reap the benefits of a linear attribution model once you’ve had customers pass all the way from the initial brand awareness stage through to becoming paid-up customers of yours.

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