Contents
Why is attribution modelling important?
What models can I choose from?
The last-click model
The first-click model
The position-based model
The time-decay model
The linear model
The data-driven model
The right model for you - I want to...
...keep things simple
...focus on top-of-the-funnel engagement
...attract people at the moment of purchase
...convert traffic into paying customers
...run short promotional campaigns
...use the most accurate model
Final thoughts

Attribution models are used to better understand your customer journey, and the search terms and campaigns involved in conversions. According to Google, attribution modelling is:

The rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths”.

In layman’s terms, attribution modelling informs Google Analytics and Google Ads which channel or keyword is assigned the credit for a conversion.

The clicks recorded can be any form of interaction with your digital presence. This includes engagement with your social pages, Search, Display or Shopping ads, organic searches, or links to your website.

Regardless of what brought the user to make a purchase, these touchpoints can vary in their influence to nudge the shopper towards checking out.

Why is attribution modelling important?

The models can help you understand your customers’ journeys, informing you of high performing search terms, and where you should increase your CPCs to appear for the most relevant, ready-to-purchase shoppers.

Recent research shows that consumers engage at least eight times before purchasing a product. As such, we can’t assume the final (or first) interaction is entirely responsible for the conversion, whether all of your channels worked together equally or if some were more effective than others.

Attribution modelling uses real data to provide a clear understanding of how different channels help to convert given prospects.

What attribution models are available on Google Shopping?

1) Last-click

last click attribution model

A last-click model attributes the credit for the conversion to the last click made by the user in their entire journey leading to their purchase. It’s also the default model in Google Analytics, meaning this is the model you will currently be using unless you’ve already made changes.

positive bullet point The last-click model is simple to use.
negative bullet point However, the same simplicity can be a disadvantage. With little information, the last-click model fails to provide a full picture, attributing all credit to the final click which resulted in a purchase.

It’s often the case that brands are discovered by an unbranded search term or even a number of searches before they visit your website and checkout. Using this model would suggest all your conversions are the result of branded searches or direct web traffic.

In this instance, it would appear that your ads or social channels don’t perform as well, as engagements building brand awareness have been omitted in the ultimate attribution.

2) First-click

first-click-model-01

A first-click model attributes the credit for the conversion to the first click made by the user in their entire journey leading to their purchase. Here’s a scenario:

  • You’re targeting a keyword, attracting a user who clicks your text ad. The user is a top-of-funnel visitor and doesn’t make a purchase.
  • A remarketing ad reaches them, generating another click which results in a purchase.

With a first-click model, that first click would be attributed the credit for the conversion, suggesting the first keyword demonstrated high purchase intent, which isn’t accurate in this example.

positive bullet point The first-click model demonstrates how your customers came across your product or website.
negative bullet point Like the last-click model, it doesn’t give a full picture of the customer journey, highlighting the initial click as the most important which might not always be the case.

When should you use first click modelling? Jump ahead here

3) Position-based

position based attribution modelA hybrid of the previous two, this model gives 40% of the credit to both the first and last ad clicks, spreading the remainder over the other interactions in the customer path to purchase.

positive bullet point This model illustrates the full journey from initial click right through to the purchase.
negative bullet point Yet, dividing just 20% of the credit over any interaction between the first and final click means some may be undervalued. Similarly, the initial and final stages may be assigned more credit.

Use this model if you most value the touchpoints introducing customers to your brand and the final touchpoints which result in sales.

4) Time-decay

time-decay-model

The time-decay model assigns more credit to ad interactions occurring closer to the conversion.

The credit is distributed using a seven day half-life, meaning that interaction with an ad eight days before the conversion is assigned half as much credit as an interaction occurring one day before the customer makes their purchase.

positive bullet point This model puts an emphasis on interactions closest to the conversion, demonstrating search terms and keywords used when your customer is ready to purchase.
negative bullet point However, the spread of conversion credit may not represent an accurate weighting across each click, and may not demonstrate the bigger picture.

When is the time decay model useful? Skip straight to it

5) Linear

position based attribution modelThis model distributes the credit for all conversions equally across all interactions in the path-to-purchase. Linear modelling can provide an idea of which keywords and channels work and which don’t.

If your ad serves for a keyword and isn't clicked, it simply won’t receive any credit. This model is useful if the aim of your campaigns is to maintain awareness with your customers, gradually moving them through the sales funnel.


positive bullet point With the linear model, retailers can see each step in the path.
negative bullet point However, by assigning an equal value to each stage, the data may be skewed and some steps may be attributed with too low or too high a value.

6) Data-driven

data driven model attribution modelUsing a data-driven attribution model enables retailers to draw on historical ad clicks and conversion data. The credit is distributed by calculating the actual contribution of each interaction across the path.

By comparing the paths of customers who convert to the paths of customers who don’t, the model identifies patterns among those ad interactions which ultimately result in a conversion. The converting paths may have specific stages with a higher probability of leading the customer to make a purchase.

These more valuable ad interactions are then attributed with higher credit, demonstrating to retailers the purchase intent of customers using the keywords associated with that ad or touchpoint.

positive bullet point A data-driven approach is naturally the most accurate attribution model available to retailers.
negative bullet point Unfortunately, this model is only suitable for retailers with a high volume of site traffic and conversions.

To use this model, Google states you must:

  • Have at least 3,000 ad interactions in supported networks
  • A conversion action must have min. 300 conversions within 30 consecutive days.

If you don’t have enough data, you won’t see the option to use data-driven attribution., and it will revert back to the last-click model.

Similarly, if your data drops below 2,000 ad interactions or below 200 conversions for the conversion action in 30 days, you will no longer be able to continue using the model.

How do I choose the right attribution model for my business?

I want to keep things simple…

The last-click model is great for retailers looking for simplicity. The model remains useful to communicate to marketers the keywords and channels nudging shoppers to checkout, so we can better understand how popular or successful certain ads are performing.

I want to focus on top-of-the-funnel engagement…

The first-click model may be the most suitable approach. As this is the stage where your target audience engages with your brand for the first time, the first-click model helps in understanding how your audience found you, and what strategies, keywords and campaigns work for you.

I want to attract people at the moment of purchase…

If your business is primarily transactional, and your sales cycle doesn’t involve a consideration phase, then the last-click model is a suitable approach for you.

I want to convert my audience into paying customers…

Since the linear model provides an idea of the keywords and channels receiving clicks in the path to purchase, you can garner insights into the search terms used at different levels of purchase intent.

Understanding which search terms denote low/high levels of purchase intent helps to optimise your bid values on Google Shopping, to reduce ad spend and deploy more budget to high intent keywords.

I'm running short promotional campaigns...

The time-decay model is best for short 1-2 day promotion campaigns, as you can assign more credit to interactions during the short promotional period. This means that the clicks with larger values are more closely related to the campaign you’re running.

I'm looking for the most accurate attribution model...

The data-driven model uses historical data to calculate how each step is credited. The information collected by the data-driven model can be used to inform your CPCs if you’re already benefiting from an automated bidding strategy on Google Shopping.

Final thoughts

Ultimately, your attribution model should inform you of high-intent keywords to target and help to optimise your CPC bids on Google Shopping.

We hope our guide has been helpful in understanding attribution models for Google ads and in finding the right model for you and your business.

Are we a perfect match? Book a call to chat about your Google Shopping strategy and Bidnamic’s technology with one of our Google Shopping experts today.

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Karman Luu

Karman Luu

Karman is a Data Analyst at Bidnamic. She works alongside clients to optimise their Google Ads accounts. She also plays a major role in the transition phase, ensuring a smooth experience and continued success.