While it’s true that branded search terms are far more likely to convert, they’re in short supply compared to non-branded queries. Unless you’re already an established brand, the majority of your traffic probably comes from non-branded search terms and maximising conversions from this traffic is high on your list of search marketing priorities.
The problem is, we see many brands using only the default attribution models to measure non-brand conversions. The consumer journey for these search terms tends to be much longer, which means you need a way to measure the effectiveness of your campaigns across multiple interactions. And today we want to show you two different attribution models that’ll help you do just that.
When you set up goals or e-commerce tracking in Google Analytics, the default attribution model is Last Non-Direct Click. What this does is give all the credit for a conversion to the final channel a user comes through. So, for example, if a user searches in Google, clicks through to your site from an organic listing and then converts, all credit for that conversion will go to your Organic channel.
This is useful for understanding which channel users come from when they take action (organic, email, search ads, etc), but it doesn’t show you the full story.
Most consumer journeys involve multiple searches, multiple ads and a range of interactions with different brands. In these cases, last non-direct click attribution gives too much credit to the final channel while neglecting previous ads, emails and other marketing channels that contributed to the conversion.
The danger is you could end up thinking an ad campaign that played a role earlier in the consumer journey isn’t performing. And, if you drop your bids for this ad group, you could be killing conversions at the source without even realising it.
There are seven predefined attribution models you can select in Google Analytics for goals and e-commerce tracking:
You can find out more about each of these attribution models over at Google Support, but we’ll be focusing on two of them for the purposes of this article.
The first attribution model we want to talk about is First click, which is also known as “First Interaction” in Google Analytics. As the name suggests, this gives 100 per cent of the credit for your conversion to the first ad (and its assigned keywords) that users click before converting.
Of course, this comes with the downside of giving too much credit to the first click and not enough to later interactions. But you can use this to measure the effectiveness of campaigns designed to build awareness from non-branded queries.
Let’s say you’re expanding your services and your brand is starting with zero reputation in this new area. You’ll probably want to bid competitively with campaigns for non-branded queries (or even queries including your competitors’ brand names) and choose the first click attribution model to measure the success of these campaigns.
That’s a fairly extreme example, but it helps illustrate the benefit of the first click attribution model. A more common use might be campaigns designed for lead generation (e.g. email signups) and other low-intent conversions, rather than product purchases.
Unlike first and last non-direct click attributions, the linear model gives equal credit to every interaction along the path to converting. The obvious downside (and each attribution model has them) is this doesn’t help you measure/optimise for individual actions.
What the linear attribution model is good for, though, is measuring how effectively your campaigns steer users from non-branded searches to the finishing line. Instead of optimising for individual interactions, you’re looking at the entire consumer journey – from low intent, non-branded searches to the final conversion.
This confirms your ads and organic results at the top of the funnel aren’t just bringing in the leads, but these leads are ultimately converting.
The aim of using first click and linear attribution models is to measure the effectiveness of your ads and other lead generation strategies early on in the consumer journey. This is where the majority of your leads come from and merely the first of many interactions they’ll have with your brand. But, without this first interaction, consumers will never discover your brand to begin with – so it’s important you’re able to gauge the effectiveness of the campaigns that get everything started.
If you’d like to find out more about how we are using automation to make PPC analysis more effective and efficient, check out this interview with our Senior Data Scientist Henry: Expert Interview: Henry Carless on automating PPC analysis
Henry is a PPC and data science specialist at Vertical Leap and has worked in digital marketing since 2007. He specialises in data and analytics, enterprise PPC and design. In his spare time he likes golf, Photoshop battles, 3D printing, stencil graffiti, drone photography and brewing his own alcohol.
Categories: Data & Analytics, Data Science, Machine Learning, SEO
Categories: Content Marketing, PPC, SEO, Social Media
Categories: Data Science, Machine Learning, PPC
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