Is View-Through Attribution a Good Look for Online Marketers?

Series: How Smarter Attribution Leads to Smarter eCommerce Marketing

Six months ago, we published our white paper, Beyond the Click – Intelligent Attribution Modeling for eCommerce Marketers. We would like to thank all of you for your feedback. If we helped some eCommerce marketers to make smarter investments of budget, then we’ve done our jobs as your partners and consultants for smarter online marketing.

We had some very fruitful discussions with regards to this whitepaper, and you’ll find the first installment of some upcoming additions below. Stay tuned for more insights.

Need to catch up? Check out the previous entries in the series:

1. All Attribution Models are Not Created Equal
2. Last-Click Attribution – Last Click, Last Choice?
3. Beyond Last-Click Attribution – The Customer Journey to Conversion Model
4. Turning Your Attribution Model into Online Marketing Optimization
5. Taking Attribution to the Next Level: Customer Lifetime Value
6. Supporting Your Attribution Strategy with Real-Time Programmatic Display

Taking a Closer Look at the View-Through

If you’ve read the full white paper, you might have realized that we’re not the biggest fans of view-through attribution. Below are a few reasons why we think view-through attribution for performance marketing might be leading marketers astray.

Large online shops will often generate dozens of ad clicks in the thirty-day window before a sale is completed. (The number of course also depends on the average post-click conversion time — products or shops with longer conversion windows will usually generate more clicks before conversion.) With all these clicks, marketers are already dealing with a lot of data points! The move from last-click to customer journey (distributed) attribution based on clicks alone will give marketers a huge boost in efficiency for their marketing spend, but have no doubt that it will also be a big job for your business intelligence team.

On the other hand, the number of views of ads across all online marketing channels will be extremely high (hundreds instead of just dozens), and therefore the percentage of CPO (cost-per-order) credit that could be allocated to a single view relative to the total number of views across the customer journey is very insignificant. This means more data points to process and evaluate, which may not result in equal gains in efficiency for the amount of evaluation required.

Furthermore, when view-through attribution is used in place of click-based attribution, marketers are essentially supporting a “spray and pray” approach — showing lots of impressions (usually on low-cost inventory) with no frequency cap in an effort to get the final view before purchase. Many users feel violated or “spammed” by such a methodology, so brand-conscious marketers should be especially wary of a purely view-based attribution if they want to avoid burning bridges with their customers.  (This is why we invested a lot of time to build a global solution for user-individual frequency capping.)

Before taking the effort to evaluate the hundreds of views in the customer journey, it might be more worthwhile to evaluate the value of particular clicks. For example, how long did a user spend on your site after a click? We’ll take a look at this kind of click analysis in our next post in the series.

When View-Throughs Can Shed Some Light

While to us it’s clear that a view should never be considered as a performance indicator equal to or greater than the click, nevertheless it’s true that views can certainly support the decision to buy. Indeed, this is the very nature of branding and offline campaigns! Marketers should therefore consider how to abstract the meaningful views from the less important views. Here are three ideas to help you consider such views in your attribution model:

  1. Pre-Sale Views:

    If a view happens directly before a user is completing a purchase in your shop, the likelihood that the view had an effect is relatively high. In a last-click model, where only one touch-point gets credit for the sales, we would still not recommend that the view overrides the last click. However, in a customer journey attribution model, where multiple touch-points are given credit, this view could be taken into account, perhaps at a lower percentage than the last click..

  2. PrePre-Click Views:

    If a view happens directly before a user clicks on another channel, you could also give this view credit in the customer journey attribution model. For example, let’s say a user is sees a display ad for your shop, and within five minutes he visits a search engine, types in the product-specific keyword, and then visits your shop. The credit for this visit could be split between the display ad and the keyword. Just think: perhaps without this display view the user might have visited the shop of a competitor that also appeared in search results.

  3. PrePre-Direct-Visit Views:

    You can apply the same logic to direct site visits or navigation to your site using brand keywords through a search engine. (But remember – a brand keyword navigation should in most cases be excluded from your conversion clicks. Read the “Hot Tips” section of our previous post on customer journey attribution) if a user viewed an ad and directly thereafter visits your website (in this case without an ad click), then the likelihood that this view had influence is high.

Note: The recommended timeframe for taking a pre-sale, pre-click, or pre-direct-visit view should be five to fifteen minutes — otherwise it becomes dubious to assume that this effect is due to an ad view, and there will be too many touch-points in the customer journey.

Once again, we strongly recommend that before you start taking views into account, you first start with a customer attribution model based purely on clicks. Once the new CPO per channel has become consistent, then you should consider adding view-through or other effects to the customer journey attribution.

Screen Shot 2016-02-22 at 13.27.25Learn more about the different steps in refining attribution models in The Digital Marketer’s Attribution Handbook.