{"id":5504,"date":"2018-08-17T19:02:06","date_gmt":"2018-08-17T18:02:06","guid":{"rendered":"https:\/\/www.visualagency.com\/?p=5504"},"modified":"2018-08-24T17:35:38","modified_gmt":"2018-08-24T16:35:38","slug":"setting-the-record-straight-optimise-before-you-attribute","status":"publish","type":"post","link":"https:\/\/visualagency.com\/?p=5504","title":{"rendered":"Test before you optimise. Before you attribute. Setting the record straight."},"content":{"rendered":"<h2><span style=\"font-weight: 400;\">The rise of attribution<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As marketing increasingly seek to demonstrate their ROI, one piece of the marketing technology stack that often claims to be central to having an efficient marketing program, is attribution modelling. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">An attribution model aims to distribute the sale value to any number of touch points that came before a sale, based on the contribution the touch points had, therefore each touch point ends up having a cost as well as a revenue value, making it theoretically easy to work out the amount that should be invested in each channel. Some years ago, marketing attribution became a popular subject that every advertiser wanted to understand. The argument from agencies and technology providers alike to convince advertisers that attribution is an important subject, is very reasonable:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Most consumers convert after multiple touchpoints<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Since Ecommerce became popular, a lot of touchpoints can be tracked by user level<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">We therefore have the data to build a picture of the value of each touchpoint, since the ultimate goal of advertisers is to sell something. This value is best expressed as a proportion of the sale value.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Each touchpoint will then have a cost as well as a revenue associated with it, so an ROI can be calculated. Given that there is an associated ROI for each channel, type of campaign, even keyword, decisions on where to invest becomes easier and most importantly, accurate.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">What is also very convenient, is that most attribution model providers offer sophisticated machine learning algorithms, where all an advertiser needs to do is to pass on the data, the machine will come up with the best model that &#8216;we can all trust&#8217;.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">The state of marketing attribution<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">In a recent article from Econsultancy \u2013 <\/span><a href=\"https:\/\/econsultancy.com\/blog\/69487-the-state-of-marketing-attribution-research\"><span style=\"font-weight: 400;\">the state of marketing attribution<\/span><\/a><span style=\"font-weight: 400;\"> \u2013 the survey suggests that the top three goals of having an attribution model are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Optimising the media mix<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Justifying media spend<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Understanding user journeys<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Two questions arise: <\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400;\">Does attribution help achieve these goals?<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Is attribution necessary for achieving these goals?<\/span><\/li>\n<\/ol>\n<h3><span style=\"font-weight: 400;\">User journeys<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The third goal \u2013 understanding user journeys \u2013 certainly does not depend on attribution, this point is well-demonstrated by the existence of attribution vendors who provide attribution technology without any user journeys visualisation tool. We will therefore focus on the first two goals, which effectively can be grouped under the same heading \u2013 optimisation.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Optimisation with attributed data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Optimising the media mix and justifying media spend \u2013 tend to be the center of the argument for having an attribution model, they are both related to understanding how much revenue, and cost, can be assigned to each channel, hence the channels can be optimised.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this article, we will show that firstly, the \u201cinsight\u201d from attribution modelling, if any, is not in fact that reliable and secondly, the effort in constructing an attribution model &#8211; whether this effort is in time, skills or money in building the model &#8211; can be diverted to an approach that will lead to better-optimised campaigns, much more in-depth insights about the marketing program the advertiser is running, and perhaps most importantly knowledge that can be shared and built upon.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><span style=\"font-weight: 400;\">Attribution modelling in brief<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">We can all agree that knowing the true value of each channel, is essential for optimisation. The question is therefore whether attribution modelling is the right process to get at the true value, and what does \u201ctrue value\u201d mean in the first place.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Generally speaking, attribution modelling uses data after the event to derive the magnitude of contribution for every combination of attributes of touch points. For example there may be a coefficient for an affiliate touchpoint, which is a click event, which is at last position of user journey. This coefficient will likely to be different to a coefficient for the combination of affiliate touchpoint, which is a click, but appears at first position of user journey. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The coefficients are derived from statistically comparing user journeys that have a certain attribute vs journeys that do not have the attribute, evaluated usually based on conversion rate, average order value difference. Let\u2019s look at a simple example to illustrate this.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Imagine that the only advertising activity being conducted is display retargeting, the modelling process will compare user journeys that involve retargeting, with those that do not involve retargeting. The difference in conversion rate will be used to construct the attribution model. By this way of evaluating retargeting, retargeting usually shows to have good value &#8211; people who have been shown a retargeting ad have higher conversion rate, hence it will have a good positive coefficient in the attribution model. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">This concept of comparison is similar to that of medicinal drug trials, where typically a group is separated into control and trial, without the participants knowing which group they belong to, then the trial is conducted where the drug is applied to the trial but not the control group, the difference in cure rate is the difference between applying the drug or not.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-5707\" src=\"https:\/\/www.visualagency.com\/wp-content\/uploads\/2018\/07\/1920px-Pharmacie_in_Paulista_Avenue.jpg\" alt=\"attribution modelling visualagency\" width=\"1919\" height=\"1250\" srcset=\"https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/1920px-Pharmacie_in_Paulista_Avenue.jpg 1919w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/1920px-Pharmacie_in_Paulista_Avenue-300x195.jpg 300w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/1920px-Pharmacie_in_Paulista_Avenue-768x500.jpg 768w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/1920px-Pharmacie_in_Paulista_Avenue-1024x667.jpg 1024w\" sizes=\"(max-width: 1919px) 100vw, 1919px\" \/>There is a very important difference between the retargeting evaluation and the drug trial however: Drug trials are carefully conducted with a random allocation of patients in the trial and control group, to the point that neither the participants nor the health workers know who is in which group. This is done to ensure that there is no bias in the result. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the retargeting evaluation on the other hand, as marketing practitioners we know that retargeting ads are only served to people who are likely to convert. Comparing users who have been exposed to a retargeting ad and those who have not been exposed, is therefore simply comparing the users who are more likely to convert, with the users who are less likely to convert. It is not rocket science that the people who have been served a retargeting ad would have higher conversion rate, it is self-fulfilling. There is nothing clever statistics can do to \u201crectify\u201d the problem because the data is inherently biased. This is akin to running drug trials where drugs are only applied to people who are more likely to be cured \u2013 this will make pharmaceutical companies very happy as their drugs will surely be shown to work, but will not be very good for science.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Is this problem restricted to retargeting where vendors select the audience before serving them ads? <\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Unfortunately not, the data fed into the attribution modelling process is \u201coptimised data\u201d, meaning that in many channels, optimisation has already been done to select the audience most likely to convert before serving them any ad. In cases where no ad is served, for example cashback affiliate, users normally only click on a cashback link if they want to claim cashback (i.e. converting) hence cashback tends to have high coefficient in attribution model too. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">We have seen that while the attribution modelling process may be robust, if the data used to build the model is biased, the output will also be biased (garbage in, garbage out). The solution requires a change of process and mindset.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Scenario<\/span><\/h2>\n<p><span style=\"font-weight: 400;\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone size-full wp-image-5705\" src=\"https:\/\/www.visualagency.com\/wp-content\/uploads\/2018\/07\/Hertz_car_rental_office_Livonia_Michigan.jpg\" alt=\"attribution modelling visualagency\" width=\"2048\" height=\"943\" srcset=\"https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/Hertz_car_rental_office_Livonia_Michigan.jpg 2048w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/Hertz_car_rental_office_Livonia_Michigan-300x138.jpg 300w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/Hertz_car_rental_office_Livonia_Michigan-768x354.jpg 768w, https:\/\/visualagency.com\/wp-content\/uploads\/2018\/07\/Hertz_car_rental_office_Livonia_Michigan-1024x472.jpg 1024w\" sizes=\"(max-width: 2048px) 100vw, 2048px\" \/> Let\u2019s look at a real life scenario before we explore a solution. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">A car rental company has a large paid search program, they also have an established custom attribution model that tells them how much revenue they are generating through paid search \u2013 for every \u00a31 they spend on paid search brand keywords, they are getting \u00a312 back.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Just like any other company, the car rental company is constantly looking at ways to maximise the return from their marketing budget, this includes cutting inefficient spend as well as reallocating budget to new and profitable opportunities. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">While paid search brand spend isn\u2019t the largest proportion of their monthly marketing outgoing, very few people believe that it is delivering a true incremental return of 12:1 \u2013 in other words, if they switch off PPC brand, few people would expect that revenue would be reduced by the amount reported by the attribution model. No one however knows to what extent PPC brand spend is incremental.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The only way to figure it out is by testing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The details of how such a test is conducted is not covered in this article, <em>but the\u00a0 result from the test shows that the average cpc can be dropped by half without affecting revenue, but if CPC is reduced further then revenue will begin to suffer. <\/em><\/span><\/p>\n<p><span style=\"font-weight: 400;\">The action is clear.\u00a0 There are immediate PPC brand efficiency savings to be realised, but the learning from the test has a wider implication. By doing a control test, we are able to gather data and information that no attribution modelling can give us, and we have the confidence that the data collected from the test is sound because we have taken care to design the test so that it is unbiased. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The <strong>\u201ctest, optimise, model\u201d<\/strong> approach is a change of process and mindset,\u00a0 emphasising the importance of testing, which in turn drives optimisation.\u00a0 It diminishes the role of attribution modelling because the insight from tests always trumps the insight from attribution modelling.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">How to implement this change of mindset in practice?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Let\u2019s see how attribution-led optimisation compares with a test-driven approach first.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><\/td>\n<td><span style=\"font-weight: 400;\">Attribution-led optimisation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Test-driven optimisation<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Statistical skills<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Likely to outsource the modelling skills to a 3<\/span><span style=\"font-weight: 400;\">rd<\/span><span style=\"font-weight: 400;\"> party vendor.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">The statistical skills are relatively easy to acquire for testing purposes.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Implementation<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Tags are usually required to be put on site to track marketing activities for revenue to be attributed to.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Tags are required to track marketing activities, for some tests, tags have to be manipulated to define audience segmentation.<\/span><\/td>\n<\/tr>\n<tr>\n<td><\/td>\n<td><span style=\"font-weight: 400;\">Once orders are attributed by the 3<\/span><span style=\"font-weight: 400;\">rd<\/span><span style=\"font-weight: 400;\"> party vendor, using the numbers is no different to other models such as last click.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">For every test, KPIs could be slightly different and have to be pre-defined, interpreting the test result and subsequently drawing conclusions from them require careful consideration and experience.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Optimisation accuracy<\/span><\/td>\n<td><span style=\"font-weight: 400;\">The soundness of the modelling methodology depends on the vendor.\u00a0 However, the model will be as biased as the historical data used to build the model.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">All tests begin by designing how unbiased data can be collected, tests are as reliable as the designers of the tests can make them.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Control<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Most attribution model vendors offer a \u201cblack box\u201d solution, advertisers often only receive the output of the process without being able to gain granular insight as to how and what elements of marketing channels work.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Advertisers have complete control over the granularity of marketing that they wish to understand, tests can be tailored to answer most questions advertisers have.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Learning and sharing<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Other than high level learnings such as how attributed revenue compares with other models, learnings are not transferable.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Learnings from each test can be applied to future campaigns or even other regional marketing teams in the company, as long as the conditions of the test and future \/ new environments are similar.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">At VISU.AL , our missions\u00a0 is to\u00a0empower advertisers by bringing rigour to the optimisation process, to increase confidence in the data and knowledge advertisers collect by creating unbiased data through control testing.\u00a0\u00a0<\/span><span style=\"font-weight: 400;\">Our objective is to\u00a0 give you, the advertisers, control over what you can learn from your data, giving you the confidence to evolve your marketing campaigns though\u00a0 rigorous testing, and avoid\u00a0 the unnecessary work and waste of resources\u00a0 that does not further the purpose of optimisation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\f<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Solution- Deliverables<\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">To reduce inefficiencies by optimising to incremental revenue<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">To give confidence in using data and knowledge collected by the advertiser, by bringing rigor to the optimisation process.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">To enable knowledge sharing by systematically recording validated test results.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">Process outline<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Discovery<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Understand client\u2019s KPIs, campaign structure, past campaign performance.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Understand client\u2019s business cycles, traffic and conversion volume, which will help with test designs.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Agree with client the priority of testing, usually based on size of channel and potential gain.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Test<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Agree with client on the test plan for the first element of marketing to be tested, how the test will be conducted and evaluated.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Advise client on how the test can be implemented using client\u2019s preferred technology, ensuring that the data collected from the test will be free of inherent bias.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Monitor the test and the data collected during the test.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span style=\"font-weight: 400;\">Evaluation<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Based on the test plan, evaluate the test <\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Provide interpretation of the test result, highlight important assumptions that might have been made and conditions under which the test result can be applied.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Record the test result in a easily accessible and sharable format, facilitating knowledge sharing, based on client\u2019s requirements.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span style=\"font-weight: 400;\">Recommendation<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Recommend best course of action based on the test result.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Suggest the next test to be conducted, repeat the test cycle so as to build up a knowledge base of the client\u2019s marketing program.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>If you would like to understand how we can improve your results through our &#8216;TOM&#8217; framework , please <a href=\"https:\/\/www.visualagency.com\/contact\">contact us<\/a> in the first instance to set up an initial consultation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The rise of attribution As marketing increasingly seek to demonstrate their ROI, one piece of the marketing technology stack that often claims to be central to having an efficient marketing program, is attribution modelling. An attribution model aims to distribute the sale value to any number of touch points that came before a sale, based &#8230; <a title=\"Test before you optimise. Before you attribute. Setting the record straight.\" class=\"read-more\" href=\"https:\/\/visualagency.com\/?p=5504\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":5369,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"generate_page_header":"","footnotes":""},"categories":[150,49,151],"tags":[153,154,152,155],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Test before you optimise. Before you attribute. 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