In the complex world of media mix analysis, it’s important to have a trustworthy guide. Just as a last touch attribution model can lead to mis-allocation of resources, over-crediting first touches can mislead as well.

All marketing/advertising impressions are not equally valuable. A thirty-second TV spot, a quality visit to your website, a walk through your brick and mortar business are significantly more valuable ‘impressions’ than exposure to a print ad, a display ad or a text ad.

A link (paid or organic) on a SERP for a competitive non-brand search is much more likely drive incremental business than traffic from someone searching for “YourTradeMark Coupons” and coming through an affiliate. These touches shouldn’t all be treated the same, and good attribution systems need to ‘understand’ and/or ‘sniff out’ those distinctions.

Let’s take a look at an example from basketball:

The Bulls led the series 3-2. Game 6 came down to the wire.

Before the action in the video started, Pippen in-bounded the ball to Kerr.

YouTube Preview Image

So the path to conversion looked like this:

Pippen => Kerr => Pippen => Jordan => Kerr => Conversion

Let’s take a look at how 5 different approaches to attribution would handle that conversion:

  1. No Attribution System: The silo view would spread credit for the conversion as follows:
    • Kerr: 100%
    • Jordan: 100%
    • Pippen: 100%

    Grade: F

  2. Last Touch Attribution:
    • Kerr: 100%
    • Jordan: 0%
    • Pippen: 0%

    Grade: B-

  3. First Touch Attribution:
    • Kerr: 0%
    • Jordan: 0%
    • Pippen: 100%

    Grade: D-

  4. Proportional Attribution: Crediting each touch equally we’d split the credit this way
    • Kerr: 40%
    • Jordan: 20%
    • Pippen: 40%

    Grade: D+

  5. Assist Tracking Attribution: Crediting the order to the last touch and assists to each preceding touch we’d view this transaction as follows:
    • Kerr: 100% + 1 Assist
    • Jordan: 1 Assist
    • Pippen: 2 Assists

    Grade: C+

Pretty clearly, none of these attribution models provides a very good understanding of that conversion, but any of the attribution models is preferable to none.

‘Assist’ counting can be particularly misleading over time. Consider our basketball metaphor, extended. Pippen always in-bounded the ball. Blind assist counting will lead one to conclude that he was the greatest play-maker in history, averaging 50, 60, maybe 70 assists a game since he often touched the ball on the offensive end of the court as well!

Consider another case. Suppose someone develops the ultimate blanket advertisement. An ad for Acme pops up on every computer, every mobile device, every TV screen in the country on boot-up. Was every conversion on Acme’s site that day impacted by those ads? Would the site have had no conversions absent the ads? Of course not.

What we’re really interested in learning is not what ads consumers were exposed to, but what lift can be credited to those ads.

A better metaphor might be the plus/minus ratio in hockey. When this player was on the ice, did our team perform better or worse and by how much?

Pure A/B split tests for email, display ads, and direct mail provide the cleanest answers to those critical questions.

Unfortunately, pure testing isn’t possible in paid search, natural search and other, less track-able, forms of offline marketing.

It is possible to hack at the incremental value of paid search through testing, but those tests can be challenging to design and execute, and incur material opportunity costs.

Attribution systems help defray the cost of ongoing testing. We believe firmly that periodic A/B testing remains crucial to calibrate coefficients for attribution systems particularly for display advertising, but attribution allows advertisers to reduce the need for ongoing tests significantly.

As we pointed out last year, mathematicians without guidance from marketing experience will build the wrong types of models. Bayesian models tend to over-credit affiliates, email and brand ads because visits to these just before a purchase strongly correlate with conversion success.

As marketers, we recognize that the cause of this correlation relates to the unique manner in which consumers use coupon sites, email offers, and navigational search.

The Need For A Better Statistical Model

Building a smarter statistical model is a heck of a challenge. We want a model that more closely matches our intuition as marketers without unfairly biasing the outcome towards one channel or another.

We want a model that recognizes cannibalistic patterns, recognizes the difference between display impressions and display click-throughs,* and handles behaviors associated with some channels more than others.

As an example of the latter, we see consumers sometimes bang through 5 or 10 affiliate ads in the space of a few minutes looking for the best offer, and would argue that channels demonstrating that type of sequence shouldn’t get 5 or 10 bites out of the apple. This is not simple, but can improve our perceptions of how well our marketing efforts work for us and lead to better resource allocation.

*Note: This is not to suggest that display impressions are meaningless. Far from it. Well-targeted, thoughtful display advertising with in-market data and smart Real Time Bidding can be a terrific means of driving business cost effectively. It is also imperative to understand that the lift in traffic from display advertising is mostly produced by impressions, not direct click-throughs. Those impressions really do have value. That value just needs to be measured carefully.

At the end of the day, attribution’s real value lies in providing better insights to drive by. Managing paid search based on attributed conversions rather than silo-view matters. Paying commissions to CSE vendors and affiliates based on attributed conversions can save a ton on unearned commission.

Teasing insights out of conversion path data can prove useful as well. One client of ours believed that 40% of orders ‘driven’ by affiliates were from new-to-file customers. Studying the data more carefully revealed that most of the new-to-file customers credited to affiliates had come to the site previously through search. Affiliate-only conversions were existing customers 92% of the time!

The interrelationships between marketing activities creates new and exciting challenges for marketers from every channel. The more we understand and embrace those challenges, the more we work together instead of in silos, the more successful our businesses will be going forward.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: SEM | Paid Search Column

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About The Author: is Co-Founder and Chief Marketing Scientist of RKG, a technology and service leader in paid search, SEO, performance display, social media, and the science of online marketing. He also writes for the RKG Blog. Follow him on Twitter at @georgemichie1.

Connect with the author via: Email | Twitter | Google+



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  • http://www.smsrd.com Bob

    Great article.

    How do you handle conversions that are likely time delayed in the pipeline? The basketball example is great but traffic often has a life cycle that can be 30-90 days or more. We can see this impact in the start up or shut down of a campaign.

  • http://www.rimmkaufman.com George Michie

    Thanks for the kind words. You’re absolutely right, the longer the ‘sales’ cycle the more challenging this becomes. Extending the window of consideration makes sense, but that also highlights the challenge: we never really know with certainty what the ‘first’ touch is, we’re really stuck with a notion of the first touch within some window, or the first touch since we turned on this tracking. Imperfection is the norm.

    The other challenge related to this is: whether those early early touches were actually reflective of a different shopping mission. I visited your site after searching for ‘shoes’ 4 months ago, bought shoes somewhere else. I visited the site again looking for a scarf and made the purchase. Should the shoe ad/ads get credit for this scarf sale? How much?

    The folks who claim to have the perfect solution are either delusional or deceitful. There is danger in false precision.

    We do recommend a longer window when it’s a long sales cycle/ high consideration purchase. Doesn’t make sense when you’re selling flowers or running shoes, but for real estate… Often, these businesses don’t have to worry too much about the department store effect either.

    Hope that helps.

  • RobK

    Hi George,

    Interesting read. What is your view on measuring and reporting on banner conversions ‘post view’.

    Thanks,

    RobK

  • http://www.rimmkaufman.com George Michie

    Hi Rob,

    As I mentioned in the italicized note: view through conversions are real, and carefully measured one finds that most of the incremental traffic lift created by display ads comes from folks who don’t click on the ad. Unquestionably the degree of view through effect will depend on the business, as well as on the Display creative. “Check out the great deals at Foobar.com” will probably have a bigger view-through effect than other messaging might.

    Attribution is useful here, particularly when calibrated by hold out tests.

  • http://www.brianfosse.com brianfosse

    George – Based on your experience at RKG where do you see value in affiliate marketing? This example taken in isolation would indicate that they bring little value. However, are their certain business types that you’ve found to be a good fit for affiliates? What best practices have you established to measure their value?

  • http://www.rimmkaufman.com George Michie

    Brian, great question. The value of affiliate traffic depends entirely on how they generate their traffic. There are affiliates who do their work through social networking, through organic optimization and other legitimate techniques that require hard work and know-how. There are others that focus all of their efforts on ranking well for their client’s trademarks and trademark + coupon searches. Whether the balance of affiliates, affiliate traffic, and affiliate commissions are produced by the type that drives incremental value or by the type that produces significantly less incremental lift will depend on how well regulated the program is.

    We think it’s helpful to classify coupon affiliates differently from other types of affiliates so the model can identify those distinct behavior patterns correctly, and not tar all affiliates with the same brush.

    We have found that for most of our clients moving from last touch attribution to a sophisticated attribution approach drops the perceived sales generated by affiliates by 50 – 70%.

    That’s actually less of a drop than we expected to see. It seems a pretty good chunk of customers start their shopping at coupon sites and search for deals on products and services.

    Hope that’s useful

 

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