Attribution models can be a contentious topic amongst marketers and analysts.<\/p>\n\n\n\n
It makes sense because beneath all the debate and math, attribution ultimately determines how much we give “credit” to each touchpoint in the customer journey.<\/p>\n\n\n\n
The \u201ccredit\u201d determines how much impact a marketer has on the business and ultimately justifies their role. So of course<\/em> marketers will argue about how much credit they should get.<\/p>\n\n\n\n Of all the available attribution models, there\u2019s a common belief that last-touch attribution is good for direct response marketing channels like paid marketing.<\/p>\n\n\n\n Others say it\u2019s unfair because it gives all credit to the last engagement without considering all the prior touchpoints. These conversations end up discussing the merits of multi-touch attribution models<\/a> (linear, U-shaped, time decay, W-shaped, custom models, oh my\u2026).<\/p>\n\n\n\n Then there are folks who say trying to do any attribution modeling is a waste of time. To be fair, there\u2019s some merit to this (data is never perfect), but it isn\u2019t helpful. The conclusion may be to rely on self-reported attribution which, based on decades of qualitative research, has its own flaws<\/a>.<\/p>\n\n\n\n My take? There\u2019s no perfect solution. Just pick one that works for you and stick with it. I know, not the most satisfying answer.<\/p>\n\n\n\n (Okay the more nuanced answer is that models like incrementality<\/a> and Markov chain<\/a> models can be more reliable, but they require highly sophisticated tooling, data infrastructure, and data scientists to make sure they\u2019re done correctly. 1) This is overkill and 2) a data-driven approach still won\u2019t end the debates. \ud83d\ude05)<\/p>\n\n\n\n For our work with clients<\/a>, we use last-touch attribution. Here\u2019s why.<\/p>\n\n\n\n Let\u2019s say you\u2019re at a small company and have stakeholders across multiple departments. They aren\u2019t as privy to the various types of attribution models. Trying to explain a W-shaped attribution model to your head of sales or CEO isn\u2019t going to be a great use of time, because they don\u2019t care to know and it\u2019s not the most important thing to debate for a small company. <\/p>\n\n\n\n What the head of sales and CEO care about is whether you\u2019re generating leads for the business. It doesn\u2019t matter if a lead visited the marketing site seven times before responding to a sales rep\u2019s cold email. That sales rep converted that contact into an opportunity, and that\u2019s that. They get the credit.<\/p>\n\n\n\n Now, if you can say to your CEO, \u201c100 people visited this blog post and of those people, 10 filled out a form to contact sales,\u201d that\u2019s easy to understand and hard to argue with. You don\u2019t need to understand W-shaped attribution models to understand that.<\/p>\n\n\n\n Once you\u2019ve built trust and demonstrated last-touch attributable revenue to content, you can get fancier with the attribution models.<\/p>\n\n\n\n Our take, say f@%# the fancy attribution models. Keep using the simple model and spend that time on more creative brand experiments.<\/p>\n\n\n\n You\u2019ll have customer journey data across the board from website sessions (Google Analytics), paid social<\/a> engagements (Facebook, Instagram, LinkedIn), paid search engagements (Google Ads), webinar registrations (Zoom), various content downloads (HubSpot), website chats (Qualified), and more.<\/p>\n\n\n\n You need to unify all that data needs to get a view of the customer journey. That\u2019s a lot of time either spent with data engineering (the scope of which can expand really quickly) or setting up a platform to do this (Census, Customer.io, Segment).<\/p>\n\n\n\n Instead, just use the default last-touch attribution with Google Analytics and call it a day. Save yourself the thousands of dollars that you would\u2019ve spent on data engineering or new software.<\/p>\n\n\n\n If you\u2019re a small team trying to move fast, building these data pipelines shouldn\u2019t be a priority.<\/p>\n\n\n\n Blog content isn\u2019t the first medium of content that comes to mind when you think of \u201cdirect response conversion.\u201d You\u2019d normally think about paid marketing, which is what people tend to say is better suited for last-touch attribution.<\/p>\n\n\n\n Content tends to be somewhere along the customer journey, but it\u2019s not often believed to be the \u201clast touch.\u201d <\/p>\n\n\n\n By choosing last-touch attribution to determine the impact of the content program, we stack the deck against ourselves and make it more difficult<\/em> to give credit to content. So any outcome (leads, pipeline, revenue) that is attributed to content is harder to dispute.<\/p>\n\n\n\n When I say we\u2019re \u201cstacking the deck against ourselves\u201d I\u2019m referring to the fact that these companies are still continuing all their other marketing efforts (paid, social, webinars, outbound sales, etc.).<\/p>\n\n\n\n Despite that \u201cstacking of the deck,\u201d we\u2019ve proven that content programs can compete with all those other marketing programs for credit, and still demonstrate meaningful attributable results<\/a>:<\/p>\n\n\n\n If you use last-touch attribution, you know what page the person visited before they converted. <\/p>\n\n\n\n If you’re lucky, your ops team already has this reporting set up in their CRM<\/a> or BI tool, and you can find out how many people who became a lead from content turned into paying customers.<\/p>\n\n\n\n This is still easier said than done, but it\u2019s significantly easier than setting up a time decay model and then writing formulas to attribute a certain percentage of revenue to each touchpoint. (Bleh.)<\/p>\n\n\n\n If the reporting doesn’t exist, it will require some manual work to understand the impact of content on revenue.<\/p>\n\n\n\n That manual work is worth it to prove the impact. That means you should build the reporting yourself if your ops team won’t build it for you.<\/p>\n\n\n\n This type of reporting that ties activities to revenue will demonstrate the value of the content program and build trust and confidence with leadership and stakeholders.<\/p>\n\n\n\n There are plenty of flaws, and I\u2019m not here to argue that last-touch attribution is the best<\/em> attribution model. Merely that it\u2019s a reasonable attribution model to build trust and show impact.<\/p>\n\n\n\n Here are some of the trade-offs of using last-touch attribution:<\/p>\n\n\n\n One solution is to use self-reported attribution. In the form used for conversions, have an open text field asking how they found out about your product or business. It may be flawed (maybe they don\u2019t remember or misremembered), but it at least gives you an idea of what part of your marketing had the biggest impression on them.<\/p>\n\n\n\n All of this is to say, all models are flawed. All models are wrong, but some are useful. <\/p>\n\n\n\n The goal isn\u2019t 100% accuracy, but to have a model that teams agree on and work within the flaws, instead of constantly debating who gets credit.<\/p>\n\n\n\n Using a simple, understandable attribution model that allows you to report on the revenue impact of the content program is how you communicate impact and get more budget.<\/strong><\/p>\n\n\n\n Want more insights like this? Connect with me on LinkedIn<\/a>.<\/p>\n\n\n\n Attribution models can be a contentious topic amongst marketers and…<\/p>\n","protected":false},"author":5,"featured_media":3236,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[184],"tags":[],"wf_post_folders":[],"class_list":{"0":"post-3233","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-field-notes"},"yoast_head":"\nIt\u2019s simple to understand when communicating with stakeholders.<\/strong><\/h3>\n\n\n\n
Easier and cheaper to implement.<\/strong><\/h3>\n\n\n\n
It\u2019s the most challenging model for content so results are harder to argue against.<\/strong><\/h3>\n\n\n\n
Easier to tie to revenue.<\/strong><\/h3>\n\n\n\n
How about the flaws of last-touch attribution?<\/strong><\/h3>\n\n\n\n
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Recommended Reads<\/h2>\n\n\n\n
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