Finding the Right Fit for Attribution

Displaying Search“Displaying Search” is a column capturing the intersection of display advertising and search marketing.

Today’s column is written by Nick Talbert, Director of Product Marketing, Eyeblaster, a campaign management and advertising technology company.

Search and display budgets decisions made within their silos will only yield non-scalable results. Attribution decisions need to be channel agnostic and these decisions must follow consumer behavior for the category of products.

For instance, for someone with rather large feet, finding shoes is often a chore so I tend to avoid shopping for shoes until absolutely necessary. While browsing the web, I happened to see a particularly engaging ad from about sneakers. So, I ventured to (without clicking on the ad) to casually look around. I moved from page to page within the site, exploring options to adorn my feet and in the end, I simply ran out of time due to prior engagements. However, my newly attained desire for shoes still sat unquenched and it didn’t dissipate once I left the site.

It’s important to make note that when looking at attribution we should be considering browsing habits vs. consumer mindset. Thus, the next day I went searching for sneakers and typed ‘large size sneakers’ into Google to which came up as a choice. Keeping browser habits in mind, since I had previously paid a visit to the site, I found my sneakers within matter of minutes and made my purchase successfully. So, as marketers, what channel do we attribute this to? Let me remind you that it was the original ad that I saw in the first place, albeit many behaviors ago, that initially perked my interest in buying shoes, but clearly the search ultimately helped me convert. This example is not far off from how web users all over the world browse the web and go about their business daily.

The relationship of display and search has been a rough one. Each has been constantly pitted against each other by the folks who profit the specific channels. The question remains, who wins when it comes down to the game of allocating budget? Attribution comes into play at post click, post view and even post engagement, but at what stage of the consumer funnel can we begin to attribute whatever we’re tracking? Is it possible that with display finally being brought into the conversation of biddable media, search has lost its command in the conversion game or that a new attribution model must be defined?

We can prove through recent research from eMarketer and Microsoft that there’s a strong correlation that total conversions of a campaign are greater when each channel is used in parallel. Display is good at driving interest in a product, while search is great at funneling that interest into dollars and ROI factors. Both can have minor achievements in each other’s arena, however, not proven or scalable by any means.

Attribution relies heavily on how your consumers interact with the brand, within your vertical, or with your products; and these variables are too great to have any sort of prediction of the most efficient allocation of budget. The search universe represents a finite number of people that exist in a state of mind that are ready to buy or that know what they want. For the other 80% of your audience out there, they need help. They need information. They need to be inspired. This is where display comes in and for lack of better words, interrupts the clear cut attribution model for online advertising . Without display to drive search, brands are limited to the number of searches initiated which ultimately puts search in a position to plateau.

Emarketer reported that research shows that nearly one of five search conversions was preceded by at least one display impression. This suggests that the current benchmarks for defining attribution will not fit the bill when we take a multi-channel strategy into play or when folks with large feet are looking for a shoe that fits. As we move into a world of biddable media, post impression may carry a larger value than clicks as we’ll be able to know who the ad was served to and track the behavior to follow.

Without seeing that display ad, I would have never wanted to search for the sneakers I wanted, or at least at that very moment. Once I was ‘inspired’, I searched without hesitation and converted online. Without one or the other, my $82 for my new sneakers would be still in my wallet and I would be stuck with my same old selection of shoes. A lose-lose situation for myself, the consumer and the advertiser.

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1 Comment

  1. Discerning Observer

    Well articulated. This problem is age old in all of marketing. Which ad – the bill board or TV or radio or print ad actually was the most influencer? We know that they all have some, but how much is very advertiser/campaign/offer/experience dependent. In the offline world, we just couldn’t do any attribution, period. However, now that we can measure impressions by unique users across display, search, affiliate etc. the problem then comes down to a hard technology and analytical scalability issue:

    1) Each advertiser campaign defines a range of look back windows for impressions, clicks, engagements for each channel in question. And often an advertiser does not know what the right numbers are, so one wants to do what if analyses on these window sizes to arrive at the right one.

    2) We should not let any one channel or action type trump all preceding actions. In other words, you want to essentially track the history of this conversion through exposures to all channels for this user.

    3) Now we would like to assign some weights to the event + channel + conversion types.

    4) Come up with visualization of what is happening in this entire chain.

    There are only a handful of next generation ad server technologies and analytics companies that have been working on this problem from the grounds up. On the analytics front, it is Netezza and on the ad serving + analytics side it is Tumri. Tumri’s platform is more interesting since it is actually an ad server that can take actions based on observations and do explore/exploit etc. They are known for dynamic creative optimization, but their analytics platform and the underlying matching engine makes it an interesting solution in this realm.