A straightforward breakdown of attribution modelling
Given that 67% of consumers regularly use more than one channel to purchase goods, analysing the consumer’s journey seems to be a largely important area within online marketing.
Attribution modelling is one of the most useful analytical tools that marketers can utilise to better understand their customers' experience and interaction with the brand.
With attribution modelling, you can gain deeper insight into which sources are working. This gives you the opportunity to optimise your efficiency; by 15-30% to be exact!
Discover all you need to know on attribution modelling and boost your business and efficiency!
So, attribution modelling…what is it?
Attribution modelling is used for analysing which sources or channels obtain the credit for a conversion.
Utilising an attribution modelling is an effective way to understand your return on investment (ROI) for each individual marketing channel.
This allows you to see the larger overview of all of your marketing activities and to identify the most effective source in terms of conversion.
There are several models that are commonly used, including last-click, first-click, linear and position-based etc, but we will cover that more in-depth further down.
Attribution modelling can assist you by analysing your customers' journey, providing you information on their voyage from first contact to final purchase/enquiry.
Under each model, you can obtain different types of information, supplying you with the most insight and knowledge through various perspectives.
How attribution modelling can benefit your business
So, why should you be using attribution modelling? Well, analysing your customers' journey gives you a better insight into their experience with your business.
Did they convert from direct search only? Have they checked out your Facebook page before converting? Which ad did they click on before they made their purchase? These are the questions that attribution modelling answers.
For instance, your customer may have clicked onto your page after responding to a paid ad, then visited your social media pages and then purchased a few days later.
This creates a conversion in the end but there is so much information in between their first visit and their final enquiry/purchase.
Optimise to be better
Attribution modelling can give you in-depth data on which sources are the strongest at converting and which are the weaker links in the chain.
By displaying these stronger sources and weaker sources, you can identify whether your investment is worthwhile. Maybe your email marketing is not as effective as you first thought and does not yield particularly great conversion rates.
This information enables you to remove your investment from the weaker channels and invest your resources into the higher-performing channels.
Attribution modelling presents marketers with a more useful overview of which channels are working best, enabling them to optimise their marketing activities and save money on investing in unworthy sources.
7 attribution models you must know about
When it comes to the different attribution models, there are a few that are commonly used and can benefit your company. Check out the most common models and their practicalities below.
Last-click attribution model
The last-click model is also known as the “last touch” model, and it is probably the most common model type.
This type of model gives 100% of the credit to the very last interaction that the lead had before they converted.
For example, say a visitor clicks onto your webpage via organic search, and then a few days later they see a Facebook Ad which they click on. Then a week later, they visit the website directly this time and make a purchase.
For this particular example, the direct search would be the channel which gains all of the recognition. This is typical, as the average shopper visits a retailer’s website 9.5 times before deciding to purchase.
The customer purchased after visiting the site via direct search which is the last interaction they had.
All other touching/interaction points - the organic search and the Facebook Ad click - are ignored as they are not the final interaction.
This is the standard attribution modelling used in most analytical tools like Google Analytics.
This particular model is useful for firms with short sales cycles, where the purchase process is fast and conversion is almost immediate.
First-click attribution model
Just as the last-click model assigns the credit to the last interaction, first-click attributes the credit to the first interaction between the customer and business.
For instance, let's say a customer first found your webpage via Instagram, then a day later they visit your site directly and make a purchase.
Then Instagram would be the interaction point that gains all of the credit here. As this is the first point of interaction that the customer had, the direct visit interaction is not counted in this case and gains no recognition.
When analysing your data, you may find that these sources don't provide the greatest conversion rates. However, keep in mind that they may be most effective at increasing awareness or engagement.
This is the ideal attribution modelling if your goal is to create loyal customers rather than one-time purchasers.
Time-decay attribution model
This specific model places a heavy focus on the points that the customer interacted with nearest to the time of conversion. The most recent interaction points are given most of the credit. It's worth noting that time decay attribution modelling has a default half-life of 7 days.
For example, if a customer interacted with a touchpoint 7 days before converting then the touchpoint gains 1/2 of the credit. If the touchpoint was used 14 days prior, the source would receive 1/4 of the credit.
If the touchpoint generated a conversion on the same day, 100% of the credit would go towards that source.
This model is helpful if you are running a 24-hour or 48-hour campaign. It makes more sense to give a larger percentage of credit to the interactions during the campaign.
This means that the interaction points that occurred one week ago will have a small significance compared to the touchpoints nearest in time to the actual conversion, giving you more accurate results.
Linear attribution model
With linear attribution modelling, the credit for the conversion is divided equally amongst each interaction point that the customer had with your business.
For example, say a customer finds you on Pinterest and then signs up to receive your email newsletters and clicks onto the site via the email link. A few days later, the customer then goes to your site directly to make a purchase worth €100.
Each point of interaction gets credit for this conversion. For this example, there are three points; Pinterest, email and then direct visit.
So, each point gets 33% of the credit of the conversion or a €33.34 conversion value to the channel when the purchase was made.
If you're looking to maintain strong contact and engagement with your customer throughout their journey and sales cycle then this attribution modelling is useful. It’s been shown that highly engaged customers make purchases 90% more often and spend 60% more per purchase than customers who are not engaged.
Each touchpoint is equally significant during the lead up to their purchase and therefore they are equally of interest.
Position-based attribution model
This type of model is also known as U-shaped attribution, and it is similar to linear attribution. This type of attribution modelling combines both first-click and last-click models.
Position modelling splits the credit for a conversion between a customer’s first interaction point and the last interaction point they engage with before they convert.
40% of the credit is allocated to each of these interaction points, and the remaining 20% is dealt up amongst any other interactions that occurred in between.
An example of this would be if a customer uses an organic search on Google to interact with your business site, then checks out your Instagram page, and then clicks on a Facebook ad where they make a purchase.
The first point, which is the organic Google search, and the third point of interaction, which is the Facebook ad, each gain 40% of the credit. Instagram (the second point of interaction) receives the remaining 20% of the credit.
This attribution modelling is most helpful if you want to know more about the effectiveness of your sources and which sources are effectively introducing your brand to your customers. But, also to identify which final sources are turning into hard sales.
Last non-direct click attribution model
This attribution modelling neglects direct traffic and gives 100% of the credit to the last channel that the customer interacted with before converting.
In non-multi-channel funnel reports, this is the type of model that is used by default, as this model gives a benchmark to compare with results from other models.
This is useful if you consider that all your direct visitors have already been gained through a different channel.
You'll want to overlook the direct traffic and concentrate on the last non-direct touchpoint before the conversion.
Last Google Ads click attribution click
This attribution modelling is used to indicate which Google ad was clicked before the customer purchased or converted. 100% of the conversion credit is given to the successful source.
This is a great option if you want to distinguish which ad performed the best in terms of converting leads.
For a full guide overview of attribution models, check out Google Analytics’ guide here.
Google Analytics uses attribution modelling but which one?
Understanding which attribution model is used by Google Analytics is useful to know. You can gain some context behind your results and have a better perception of the numbers that Google Analytics presents you.
Google Analytics uses both the last-click and last click non-direct attribution modelling as their default attribution models.
Last-click and last non-direct click are used in their multi-channel funnel reports which provide you information on the sources of your web traffic whether it may be via direct search or a blog post.
Interested to know what makes an effective customisable client report? Then find our previous blog post here.
Each model has its advantages and limitations, however, selecting the appropriate attribution modelling should be based on the company’s goals and objectives.
For instance, say a business’s goal is to increase awareness, their current strategy is to run campaigns and ads to boost awareness.
You can then see, by using the first click attribution modelling, which factor first introduced your brand to your customers.
You can capitalise on this information and perhaps place a premium on these keywords and invest more resources into these first clicked channels.
Generally, it is important to analyse the business’s goals and then afterwards use the appropriate attribution modelling to suit the needs of the business. This will ensure you’re working smarter, not harder!