In this post, we’ll examine the following aspects of attribution.
Just in case there’s still some confusion about what conversion attribution and attribution modeling is, here is a brief explanation of each:
Attribution: how credit is given to marketing activities for generating conversion activities (micro-conversions, leads, acquisitions, transactions).
Attribution Modeling: The science and art of developing and comparing different schemas for attributing conversion events to contributing marketing activities. In most cases, attribution modeling is an attempt at creating a more realistic picture of the conversion value being generated by multi-channel marketing efforts when a significant share of conversion activities has two or more measurable touchpoints.
For over a decade, attribution has largely been a single-interaction attribution universe, mostly by default. The built-in conversion tracking features of Google AdWords, Bing, Google Analytics, and various other tools used variations of last-click attribution for several years before introducing other attribution models. Over the years, I have had clients using a variety of homegrown tracking systems, and even this reporting relied largely on single-touchpoint attribution models, either first- or last-click. Adobe SiteCatalyst’s default campaign reporting was historically based on last-touch or first-touch attribution reporting as well.
Over the past five years, our reporting toolsets have quickly become much more sophisticated and more powerful in how we can attribute conversion activities to our marketing efforts. It is no longer advisable to stick with default single-touchpoint attribution because the ability to see a more realistic picture of how your marketing initiatives drive value is greater than ever. Let’s start with two assumptions and then dive a little deeper into attribution.
For attribution modeling to offer interesting insights, I assume your marketing program is multi-channel, involving at least two significant kinds of initiatives (Paid Search, Local Search, Display, Video, Social Media, SEO, Email Marketing, Content Marketing, TV, Radio, Print)
I also assume you are tracking everything that can be tracked as accurately as possible (through campaign parameters and autotagging options available in AdWords, DoubleClick Campaign Manager, and Bing Ads). If you are spending advertising dollars on a promotion, it should be tracked. If you are spending your staff’s hours on an initiative, it should be tracked. If it drives traffic to your website and/or business locations, it should be tracked to the greatest extent possible.
If assumption 1 is true for your organization and you are still using single-interaction attribution models (i.e. First- or Last-Click), then you have an attribution problem by definition. Taking a fairly extreme example for a ninety-day period, this real client example helps prove the point well. With nearly 80,000 conversions and over $130 million in revenue, only 22% of that conversion value was for transactions with a single point conversion path. Almost 80% of all transaction value involves two or more marketing channel interactions, and 49% of the conversion value was for transactions with 12 or more points in the conversion path.
The Multi-Channel Funnels > Path Length report in Google Analytics demonstrates that nearly 78% of conversions did not occur on the first session.
If assumption 2 is true for your organization, you have already done a lot of the legwork necessary to employ more sophisticated attribution modeling in your reporting, optimization, and channel-mix analysis. However, never assume you are doing everything necessary to track your marketing activities. Prove it, audit your tracking, and make sure this is the case. And once you have done that, realize that you are still only seeing part of the picture. Your TV, radio, and print tracking using vanity URLs or other efforts will still be incomplete at best.
Are you uploading your offline conversion activities into your analytics or BI platforms, too? If not, you have an incomplete picture of the value your efforts are driving. Did you know you can upload your media costs into Google Analytics, too? That’s right, you can do a much better job comparing value and ROAS of your channel-mix if you have a more complete picture of both your ad spend and your conversions/transactions.
You have multi-channel marketing program, so you know first and last click attribution are wrong. So, is multi-touchpoint attribution modeling right? Unfortunately, not, but it is really a matter of degrees. As Avinash Kaushik has pointed out, there are no perfect attribution models. Some are better than others because they inherently reflect more closely what you already know to be true about your marketing program and the various ways that users and customers interact with your company. A model that inherently reflects what your data is already telling you is much better than one that inherently ignores it.
What are your options? Take one small step in the right direction. Start looking at the following attribution models in comparison to Google Analytics default last non-direct click model: Time Decay, Position Based, and Linear models. Each model represents a different perspective on what drives conversion value. Even these other models are far from a perfect description of reality. However, they are a much better approximation.
Time Decay: gives conversion credit to all interactions in the conversion path, but it gives a disproportionate amount of credit to the most recent interactions. The default half-life is 7 days, so an interaction that happened 7 days ago receives ½ the credit of one that happened today.
Position Based: gives equal credit to the first and last interactions and then divides a smaller amount of credit among the intermediate interactions. 40% of conversion credit goes to first interaction, 40% to last interaction, and 20% is evenly distributed to the intermediate interactions.
Linear: Equal credit is given to all interactions in the conversion path.
Using the same client data we looked at above, an attribution model comparison gives you some idea of why the perspective of an attribution model is so important:
Attribution Models Determine How You Allocate Conversion Value
When we display the Time Decay and Position Based models in the Model Comparison Tool, we see that the Organic Search and Display channels are undervalued in the default Last Interaction model.
Again, assuming you are tracking everything as accurately as possible, this attribution comparison points out two very obvious perspectival differences. For example, Position Based attribution offers a bookend perspective. The first and the last click are the heroes, and they get the most credit. Okay, but how does that play out? If that perspective is correct, you can see that Organic Search efforts are vastly undervalued by a last-click model. In fact, when first and last click are the heroes, Organic Search receives credit for 30% more revenue: a $7,000,000 increase. The Time Decay perspective asks, “What have you done for me lately?” This perspective says that an interaction that happened today is more valuable than one that happened yesterday. From this perspective, Display advertising is undervalued immensely from a last-click perspective. Time Decay says Display is responsible for $31 million more in revenue than you are giving this channel credit if your just go with default reporting. That’s a pretty stunning shift in value.
We know that multi-interaction attribution is better than single-touchpoint attribution. We also know that even multi-interaction models are not perfect, so how can you put attribution models to practical use for your company?
We know that multi-interaction attribution is better than single-touchpoint attribution. We also know that even multi-interaction models are not perfect, so how can you put attribution models to practical use for your company?
Based on your industry, customer profile, and product lines, and whether you are tracking conversion activities such as lead generation or sales transactions, one particular adjustment to be mindful of is the lookback window for conversion attribution. If 95% of your conversions happen within 45 days of first touch, then you probably don’t want to max out your lookback window to 90 days (or even 75). Doing so will in all likelihood attribute an unrealistic amount of conversion credit to interactions well outside your typical customer’s consideration window. At least as a starting point (unless you truly believe your current customers do not reflect the nature of your business), try to set your lookback window close to the maximum conversion time that includes 90-95% of your conversion activity.
Use the Time Lag report to find the maximum time lag that corresponds to 90-95% of your conversions. This is a good starting point for your lookback window configuration.
Step 1: Access Model Comparison under the Attribution Sub-Section of Conversions
Step 2: Choose a default model to customize and click ‘copy’ icon
Step 3: Customize rules for assigning value to conversion path interactions
Take one step at a time. Ensure your tracking is as good as possible within your tracking limitations. Start using some better models for giving conversion credit. Apply those models to your marketing investments/channel-mix, and test the results. Keep testing, and keep refining your model to better reflect your business objectives and your customer behaviors.
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