But at the heart of an attribution tool, lies its attribution model. And if that isn’t up to scratch, then achieving accurate attribution is never going to be possible for you.<\/p>\n\n\n\n
Choosing the right attribution model for your business, and its goals, is just another piece of the complex attribution puzzle. <\/p>\n\n\n\n
So, in this blog, we’ve set out to cover everything you need to know about marketing attribution<\/a> models to help you make an informed decision.<\/p>\n\n\n\n Free download: A Beginner’s Guide to Marketing Attribution<\/a><\/strong><\/p>\n\n\n\n Before we dive into the different types of marketing attribution models, let’s cover off a simple definition of what they actually are:<\/p>\n\n\n\n Attribution models are used to analyse which touchpoints, or marketing channels, should be receiving credit for a conversion.<\/em><\/p>\n\n\n\n These models are relatively simple, and work on the basis of allocating 100% of the credit for a revenue conversion or other customer behaviour to a single touchpoint with the customer<\/strong>, either at the beginning or the end of the customer journey<\/a>. <\/p>\n\n\n\n All other touchpoints in between are ignored for attribution purposes.<\/p>\n\n\n\n There are two types of single channel models: First <\/strong>and Last-touch attribution<\/strong>.<\/p>\n\n\n\n Built around the premise that the first touchpoint with the customer is the crucial determinant in a customer\u2019s decision to do business with you.<\/p>\n\n\n\n So, 100% of the credit for revenue is directly attributed to the first interaction in the customer journey<\/strong>. <\/p>\n\n\n\n For example, if a customer comes in through an Organic searchon Google, that interaction gets full credit for the conversion. Regardless of any other touchpoints that came after that on the lead up to conversion.<\/p>\n\n\n\n Similar to first-touch attribution<\/a>, last-touch is predicated on allocating 100% of the credit for conversion to a single touchpoint<\/strong>.<\/p>\n\n\n\n However, in this case it allocates credit to the last touchpoint prior to purchase or conversion<\/strong>. And similarly, it gives zero credit to all other interactions along the way.<\/p>\n\n\n\n So, taken together, single channel attribution models do have their benefits \u2013 and you may well start your journey using them in early growth stage scenarios in smaller businesses. <\/p>\n\n\n\n But it is also highly likely that even an initial spurt of growth will see you outgrowing their usefulness quickly.<\/p>\n\n\n\n As your marketing evolves and matures your need for more sophisticated attribution is likely to follow suit.<\/p>\n\n\n\n Related: Last Click vs. Corvidae – Solving the Attribution Problem<\/a><\/strong><\/p>\n\n\n\n Multi-touch marketing attribution models take a broader view of attribution and attempt to place a relative value on the impact of all touchpoints across the customer journey<\/strong>. Although, these are weighted differently depending on the model you select.<\/p>\n\n\n\n If you are operating with a more complex model, longer cycles and a multitude of channels it’s likely that this is where you find yourself gravitating to. <\/p>\n\n\n\n Let\u2019s take a quick look at each of the commonly used multi-touch models:<\/p>\n\n\n\n Very often you don\u2019t want to give single channels or campaigns all the credit for conversion and a Linear model moves the principle on by applying equal weighting and credit to all contributing touchpoints<\/strong>.<\/p>\n\n\n\n Works well \u2013<\/font> <\/strong>Where you are looking to measure a campaign holistically and get a feel for the customer journey, including seeing which channels contribute across it.<\/p>\n\n\n\n Possible limitations \u2013<\/font><\/strong> Where you already understand the customer journey and have a more detailed need to dig into the data on individual channels. To understand which ones are critical so you can focus your effort on them.<\/p>\n\n\n\n Takes the view that some touchpoints have more influence on the way to conversion than others and scores them separately<\/strong>. <\/p>\n\n\n\n Here, first touch and lead conversion matter most and are both heavily weighted with 40% of the credit being applied to each. <\/p>\n\n\n\n While the remaining touchpoints are given an equal share of the remaining 20%.<\/p>\n\n\n\n A time decay attribution model weights each touchpoint differently and is driven by the assumption that touchpoints closer to sale should get more credit<\/strong>. <\/p>\n\n\n\n The assumption being they have a heavier impact on conversion as the buyer moves down the funnel.<\/p>\n\n\n\n Works well \u2013<\/font> <\/strong>With a short sales cycle less than 90 days and if you are looking to identify what drives conversion in the lower funnel. <\/p>\n\n\n\n Possible limitations \u2013<\/font><\/strong> Not so appropriate for longer B2B customer journeys where it ignores the fact that customers can make important decisions earlier in the process.<\/p>\n\n\n\n W-shaped attribution models are an extension of the U-shaped model with the addition of weighting for the opportunity stage<\/strong>. <\/p>\n\n\n\n Here, first-touch, lead conversion and opportunity creation share 30% credit each, with 10% being shared out between the other touchpoints.<\/p>\n\n\n\n This is where you take the view that you need a very personalised view for your marketing needs<\/strong>.<\/p>\n\n\n\n And the discussion here opens up into a wider consideration of more complex marketing attribution models that include Statistical <\/strong>or Data-Driven Attribution<\/strong>, Media Mix\/Econometrics Attribution<\/strong> and Probabilistic modelling<\/strong>.<\/p>\n\n\n\n Which we take a look at in turn below:<\/p>\n\n\n\n Increasingly marketers have access to \u2018data-driven\u2019 models which essentially apply channel attribution rules that are determined by the data available to them<\/strong>. Instead of rules-based assumptions about channel mix, data-driven models determine the model’s weighting based on existing data.<\/p>\n\n\n\n Even the free version of Google Analytics now offers this functionality, and it is increasingly available in digital ad tech like Google Ads.<\/p>\n\n\n\n This approach applies statistical techniques to attempt to find incrementality<\/strong> \u2013 or the measure of the lift that marketing spend provides to conversion rate, and ultimately revenue \u2013 by looking at large data sets using mathematical techniques. And often relying on integrating panel data for offline activity.<\/p>\n\n\n\n In all cases, the object of MMM or Econometrics is to isolate a test case that is to be measured when it is applied in two or more variations. And the outcome is often expressed as a measure of \u2018incrementality<\/a>\u2019 to aspects of the media mix.<\/p>\n\n\n\n For example, a business may ask what the cost per acquisition is associated with using an offline catalogue.<\/p>\n\n\n This modelling approach is then used to analyse different mixes of media channels, cohorts of customers and overall acquisition outcomes (using a mix of variations including use of catalogue only, catalogue plus some marketing channel, the same channels with no catalogue etc).<\/p>\n\n\n\n By comparing the results of these variations, an overall value can be determined for the incremental acquisitions made thanks to the catalogue itself \u2013 which can then be enriched with data about cost of delivery, operational cost, marketing costs etc. Which can be used to determine incrementality or uplift associated with various media types.<\/p>\n\n\n\n Related: A Complete Guide to Customer Acquisition in 2023<\/a><\/strong><\/p>\n\n\n\n\n
\n
What are attribution models?<\/strong><\/h2>\n\n\n\n
What attribution models are available?<\/strong><\/h2>\n\n\n\n
Single-touch attribution<\/h3>\n\n\n\n
What are single-touch attribution models?<\/h4>\n\n\n\n
\n
First-touch attribution<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/first-touch.png\")
<\/figure><\/div>\n\n\n\n
Last-touch attribution<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/last-touch.png\")
<\/figure><\/div>\n\n\n\n
Multi-touch attribution<\/h3>\n\n\n\n
What are multi-touch attribution models?<\/h4>\n\n\n\n
Linear<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/linear.png\")
<\/figure><\/div>\n\n\nU-shaped<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/U-shaped.png\")
<\/figure><\/div>\n\n\n\n
Time decay<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/time-decay.png\")
<\/figure><\/div>\n\n\nW-shaped<\/h5>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/W-shaped.png\")
<\/figure><\/div>\n\n\n\n
Custom attribution<\/h3>\n\n\n\n
What are custom attribution models?<\/h4>\n\n\n
![\"Attribution](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/custom.png\")
<\/figure><\/div>\n\n\nStatistical or data-driven attribution<\/h5>\n\n\n\n
![\"An](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/datadriven.png\")
<\/figure><\/div>\n\n\n\n
Media Mix Modelling (MMM) \/ Econometrics based attribution<\/h5>\n\n\n\n
![\"An](\"https:\/\/www.queryclick.com\/app\/uploads\/2021\/06\/media-mix-modelling.png\")
<\/figure><\/div>\n\n\n\n