Last updated on July 5, 2017
Introduction
Currently, there is very strong hype involved with programmatic buying. Corporations are increasing their investments on programmatic advertising and publishers are developing their own technologies to provide better targeting information for demand-side platforms.
But all is not well in the kingdom. Display advertising still faces fundamental problems which are, in my opinion, more critical to advertising performance than more granular targeting.
Problems of display advertising
In particular, there are four major problems:
- banner blindness
- ad blocking
- ad clutter
- post-click behavior
Banner blindness is a classical problem, stating that banner ads are not cognitively processed but left either consciously or unconsciously unprocessed by people exposed to them (Benway & Lane, 1998). This is a form of automatic behavior ignoring ads and focusing on primary task, i.e. processing website content. Various solutions have been proposed in the industry, including native advertising which “blends in” with the content, and moving to calculating only viewable impressions which would guarantee people actually see the banner ads they are exposed to. The problem with the former is confounding sponsored and organic content, while the problem with the latter is that seeing is not equivalent to processing (hence banner blindness).
Ad blocking has been in tremendous rise lately (Priori Data, 2016). Consumers across the world are rejecting ads, both in desktop and mobile. Partly, this is related to ad clutter referring to high ads-to-content ratio in the websites. The proliferation of ad blocking should be interpreted as an alarming signal by the media houses. Instead, many of them seem to take no notice, keeping their website layout and ads-to-content ratio high. If there are no major improvements in user satisfaction, visible in reducing ads-to-content ratio, demanding higher quality ads from advertisers and making changes to website layouts, ad blocking is likely to continue despite pleas of publishers. Less advertising, of better quality, is needed to trigger a positive sentiment towards online advertising.
Finally, post-click behavior of traffic originating from display ads tends to be unsatisfactory. Bounce rates are exceptionally high (80-90% in some cases), direct ROI is orders of magnitude lower than search, and alarmingly enough display often seems weak also when examining the entire conversion path. Consequently, using direct ROI as a measure for success in display advertising yields sub-par results. Unfortunately, direct ROI is used more and more by performance-oriented advertisers.
Brand advertisers, who seek no direct returns in their online ad spend (think Coca-Cola), may continue using reach metrics. Thus, focusing on these advertisers, which still make a large share of the advertising market, would seem like a good strategy for publishers. Moreover, combating click-fraud and other invalid click forms is essential. If shortsightedly optimizing for revenue at all means – including allowing bots to participate in RTB auctions – media houses and DSPs are shooting themselves in the foot.
Root causes
But let’s talk about why these problems have not been addressed, at least not fundamentally by the majority of media companies. There are a few reasons for that.
First, the organizational incentives are geared towards sales. The companies follow a media business model which principally means: the more ads you sell, the better. This equation does not consider user satisfaction or quality of ads you’re showing, only their number and the revenue originating from them.
At a more abstract level, the media houses face an optimization conundrum:
- MAX number of ads
- MAX price of ads
- MAX ad revenue
- (MAX ad performance)
- (MAX user satisfaction)
Maximizing number of ads (shown on the website) and price of ads also maximizes ad revenue. However, it is not maximizing user satisfaction. User satisfaction and performance are in parentheses because they are not considered in the media company’s optimization function, although they should be because there is a feedback mechanism from user satisfaction to ad performance and from ad performance to price of ads.
Seemingly, many media companies are maximizing revenue in the short-term through power-selling strategy. However, they should be maximizing revenue in the long-term and that cannot take place without considering user satisfaction from consumer perspective and ad performance from advertiser’s perspective. Power selling actually hurts their interests in the long-term through the feedback mechanism.
Finding solutions
How to dismantle this conundrum? First, the media companies should obviously consider both user satisfaction and ad performance. The former is done by actively studying the satisfaction of their users in terms of ad exposure. The latter is done by actively asking or otherwise acquiring data from advertisers on campaign performance. I, as a marketing manager, rarely found media sales people interested in my campaign performance – they just wanted a quick sell. Even better than asking would be to find a way to directly dip into campaign performance, e.g. by gaining access to advertiser’s analytics.
Second, media companies should consider the dynamics between the variables they are working with. For example,
- ad performance (as a dependent variable) and number of ads (as an independent variable)
- ad performance and user satisfaction
- user satisfaction and number of ads
- price of ads and ad performance
It can be hypothesized, for example, that a higher ad performance leads to a higher price of ads as ads become more valuable to advertisers. If in addition ad performance increases as the number of ads decreases, there is a clear signal to decrease the number of ads on the website. Some of these hypotheses can be tested through controlled experiments.
Third, media companies should re-align incentives from power-selling to value-based selling. They should not want to “fill the slots” at any means, but only fill the slots with good advertising that performs well for the advertiser. Achieving such a goal may require a stronger collaboration with advertisers, including sharing data with them and intervening in their production processes to deliver such advertising which does not annoy end users and based on prior data is likely to perform well.
Conclusion
In conclusion, there is a bottleneck at advertising-customer interface. Red herring effect takes place when we are focusing on a secondary issue – in the context of digital marketing we have to acknowledge that there is no intrinsic value in impressions or programmatic advertising technology, if the baseline results remain low. Ultimately, advertisers face a choice of abundance with channels both online and offline. And although they are momentarily pushing for large programmatic investments, if the results don’t follow they are likely to shift budget allocations into a different sort of equilibrium in the long-run, once again under-weighing display advertising.
Personally, I believe the media industry is too slow to react and display advertising will lose budget share in the coming years especially against social media advertising and search advertising, but also against some traditional channels such as television.