{"id":10576,"date":"2021-02-22T14:45:50","date_gmt":"2021-02-22T14:45:50","guid":{"rendered":"https:\/\/www.queryclick.com\/?p=10576"},"modified":"2023-05-15T14:04:03","modified_gmt":"2023-05-15T14:04:03","slug":"how-to-choose-a-model-for-effective-travel-attribution","status":"publish","type":"post","link":"https:\/\/corvidae.ai\/blog\/how-to-choose-a-model-for-effective-travel-attribution\/","title":{"rendered":"How to Choose a Model for Effective Travel Attribution"},"content":{"rendered":"\n

Given the current challenges that travel marketers are facing right now, ensuring that you have the right attribution model in place is a top priority. <\/p>\n\n\n\n

Despite this, QueryClick’s research<\/a> shows that 90% of those responsible for optimising marketing revenue and ROI simply aren\u2019t able to access the type of model needed to accurately attribute the impact of their marketing activities<\/strong>.<\/p>\n\n\n\n

Gartner surveyed over 400 marketing leaders and analytics practitioners to better understand the current approaches to marketing analytics that support marketing strategies to enable future growth and success. <\/p>\n\n\n\n

And the impact of this is echoed in Gartner data<\/a> showing that 54% of executives think that marketing analytics hasn\u2019t had the influence they expected on their business<\/strong>.<\/p>\n\n\n

\n
\"54%
54% of executives think that marketing analytics hasn\u2019t had the influence they expected on their business<\/strong>.<\/figcaption><\/figure><\/div>\n\n\n

Problems with existing attribution models<\/strong><\/h2>\n\n\n\n

At the heart of the issue are limitations in existing attribution models<\/a> that simply aren\u2019t set up to handle the depth, and complexity, of customer buying journeys for travel services.<\/p>\n\n\n\n

To help illustrate the point, let\u2019s consider a hypothetical travel customer journey and assess how some of the more common attribution models view and attribute activity against it.<\/p>\n\n\n\n

Rules-based attribution is just too simplistic<\/h3>\n\n\n\n

Take the example below, where someone is looking to research and book holiday which has a relatively long buying journey:<\/p>\n\n\n\n

    \n
  1. A traveller decides to book themselves a holiday and does an initial Google search for destinations on their mobile<\/strong>. <\/li>\n\n\n\n
  2. Based on the search they find a great article on your blog <\/strong>about holidays in Corfu and quickly check some initial options on your website<\/strong> before leaving without booking. <\/li>\n\n\n\n
  3. They spend some time over the next week or so drifting around other sites looking at other providers on a combination of mobile and tablet \u2013 and click through a Facebook advert for the same holiday in Corfu on your website<\/strong> but again don\u2019t book. <\/li>\n\n\n\n
  4. A week later they sit down at their desktop to do some final checks and perform a Google search<\/strong> for the Corfu holiday and your brand name – clicking through on a paid search ad<\/strong> to book the holiday.<\/li>\n<\/ol>\n\n\n\n

    In reality, this is likely to be even more complex with more steps and even more nuance (for example research by Tripadvisor<\/a> indicates that travellers browse an average of 38 websites before making a more complicated travel purchase<\/strong>). But it helps guide us for now.<\/p>\n\n\n\n

    So, how do some of the simpler attribution models attribute conversion value in this scenario?<\/p>\n\n\n\n

    Last click approach<\/h3>\n\n\n\n

    In last click attribution<\/a> \u2013 which is the method still used by many travel marketers – 100% of the credit would be allocated to the paid search advert<\/strong>. Which is the final touchpoint prior to booking.<\/p>\n\n\n

    \n
    \"Last-click
    The Last-Click attribution model places 100% credit with the final touchpoint before a conversion.<\/figcaption><\/figure><\/div>\n\n\n

    At the same time all of the other touchpoints \u2013 across search, blog, Facebook ad etc \u2013 receive 0% credit. Which is clearly not reflective of the real-life, nuanced customer journey and impact points. And which skews attribution markedly going forward.<\/p>\n\n\n\n

    While Last click<\/a> may deliver limited value in niche scenarios – for example, where your focus is lower funnel conversions alone \u2013 it is not capable of effectively analysing sophisticated, multi-touch travel purchase journeys.<\/p>\n\n\n\n

    First-click approach<\/h3>\n\n\n\n

    This approach suffers from the same, over-simplified approach above but effectively in reverse.<\/p>\n\n\n

    \n
    \"First-click
    The First-Click attribution model places 100% credit with the first touchpoint the user had before converting<\/em>.
    <\/figcaption><\/figure><\/div>\n\n\n

    Just as Last-click creates an attribution bias at the end of the customer journey, First-click applies this on the front end.<\/p>\n\n\n\n

    In our scenario above, First-click applies 100% credit conversion to the blog article<\/strong>. Which it deems to be the first, and only, crucial determinant in the purchase decision. Again, a gross over-simplification from an attribution perspective.<\/p>\n\n\n\n

    In addition to these single-click models, there are also a range of multi-touch rules-based models to choose from including: <\/p>\n\n\n\n