Will 2018 be the year ad tech comes clean?

Melbourne Business School asst professor Nico Neumann
By Melbourne Business School asst professor Nico Neumann | 3 January 2018
Nico Neumann

The year 2017 was a tough year for ad tech because many weaknesses and structural problems were exposed. We witnessed brand safety issues (keyword ‘YouTube boycott’), industry-wide critique regarding the messy digital supply chain (recall Marc Pritchard and Mark Ritson), numerous ad fraud revelations and also possible misuse of targeting technology (Facebook’s involvement in US election).

However, to many market observers these events were not surprising. While there is no doubt that automation is the future of media buying, the current programmatic ecosystem needs a big clean up as well as changes in legacy practices. Therefore, we will probably see many trends that have indicated positive transformation towards ‘cleaner’ standards continue to affect ad land in 2018.

For example, ad fraud, brand safety and transparency concerns will certainly not disappear from the headlines, although new industry initiatives like ads.txt and ads.cert or the upcoming RTB 3.0 will help tackle some basic flaws in our open programmatic markets. The role of innovative solutions leveraging blockchain technology for transparent transactions will be interesting to watch too.

Privacy and ad-track blocking trends continue

Other important milestones to consider for 2018 will be several new data laws, such as the EU’s General Data Protection Regulation and Australia's New Breach Notification Law. Addressing data security, preventing breaches and fraud schemes will impose additional challenges for everyone working in data monetisation businesses. Moreover, more solutions than ever before will be available to avoid and block tracking technology – recall Apple’s Intelligent Tracking Prevention, which removes ‘unapproved’ third-party cookies after 24hours. If consumer sentiment against web-tracking (and also ad serving) keeps rising, many ad tech data brokers and middle men may face a serious threat to the foundations of their businesses. For instance, Criteo expects a negative impact of 22% on its 2018 revenues as result of Apple’s Safari browser updates.

Middle-men businesses struggling

After anticipating an ad tech market squeeze for years, we could slowly witness the consequences of consolidation even locally last year. AdRoll and ComScore cut its global workforce, including their APAC presence. Holding groups felt the ad tech struggle too: Omnicom, Publicis Groupe, WPP, IPG, Dentsu Aegis Network and Havas showed little to no growth in 2017, many with share prices declining in 2017. The reasons are obvious – most agencies have relied on programmatic and digital arbitrage services to make profits and still need to regain trust after the ongoing transparency, hidden fee and rebate discussions over the last years.

Generally, the long-term replacement of any middle men by technology is a natural market evolution, which applies to all industries. Think of travel agents who suffered from the emergence of booking platforms like Skyscanner or Hotelscombined.com. The key question will be who can provide extra value beyond connecting two partners and demonstrate that the benefits outweigh the costs without using fake analytics or self-graded attribution.

The big return of marketing mix modelling

From a different perspective, the growing privacy concerns and walled garden mentality have also significant consequences for marketing measurement practices. With Apple’s latest announcements around third-party cookie deletion, analysts will have less access to cookie data than ever before. Recall that Facebook, YouTube and Amazon don’t share much information with external parties either. Hence, one must wonder how useful attribution modelling still is when large chunks of the customer journey are missing. Therefore, we are likely to see a big return of marketing mix modelling. This is also driven by organisations’ desire to identify and build long-term branding effects, which are rather difficult to measure in controlled experiments or attribution models.

By Nico Neumann, assistant professor and fellow at the Centre for Business Analytics, Melbourne Business School

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