Enter a search term such as “mobile analytics” or browse our content using the filters above.
That’s not only a poor Scrabble score but we also couldn’t find any results matching
Check your spelling or try broadening your search.
Sorry about this, there is a problem with our search at the moment.
Please try again later.
2016 was certainly not a dull year for advertising.
There are still big questions to be answered about media agency practices, transparency and effectiveness.
All the while, adtech technology and data management becomes more sophisticated. So, what does 2017 and beyond have in store?
Thanks go to our trio of experts for providing some cogent analysis:
- Chris O'Hara, Head of Global Marketing, Krux (Salesforce DMP)
- Emily Macdonald, Head of Programmatic, International, DigitasLBi
- Tom Wright, Head of Programmatic, Tomorrow TTH
The coming democratization of data science
Chris O'Hara, Head of Global Marketing, Krux:
If we’ve learned anything over the last several years in programmatic it’s that—in a world of commoditized inventory and 3rd party data—getting a programmatic edge requires diving deep into the data for insights.
That means being better than your competitors at knowing where and how much to bid, which correlates directly with an organization’s skill at data science.
The problem is that most marketers and agencies have little native competence in user scoring and propensity modeling—and even if you had the budget to hire a dozen data scientists, they are incredibly hard to find.
What we are starting to see today are platforms that embed machine learning and artificial intelligence into their user interfaces in such a way that business users can access such capabilities without writing algorithms, using separate data visualization platforms, or having statistical abilities.
This capability was more straightforward when it was just available to display marketers seeking to manage bid pricing thresholds on cookies.
[However,] today, marketers are increasingly using data management technology to map users across their device graph, and expect the ability to score users against their interactions across every addressable channel—not just “display” advertising, but also email, commerce, and website experiences.
To do this correctly, marketers need to map users to all their devices and be able to store highly granular attribute data going back longer than the life of the typical cookie.
These are “big data” problems that require highly advanced technology. Much of what is happening today is ad hoc reporting in spreadsheets that drives manual optimizations across many different buying platforms.
In 2017, we will start to see the evolution of data science applications as they become more embedded in platforms—“AI layers” that leverage machine learning within platforms, and make things like user scoring, propensity modeling, lifetime value (LTA) analysis, and next-best action recommendations less manual and more automated.
The march of martech
Emily Macdonald, Head of Programmatic, International, DigitasLBi
No-one could ignore 2016’s massive spending spree by martech companies like Oracle, IBM and Adobe, as they grew their market share via acquisitions, plugged tech stack gaps and invested in areas such as Artificial Intelligence for smart CRM.
Notably, these acquisitions included programmatic adtech companies such as TubeMogul and Krux, making programmatic a key part of the conversation.
[These martech companies offer] brand marketers a fully integrated control centre that coordinates, unifies and simplifies data across all consumer touch points to optimally inform marketing, media activation strategies and spend.
As the martech focuses on quickly integrating acquisitions and retaining experts, they are also pushing change.
We see a desire by some brands and integrated agencies to optimise both operational and performance efficiencies with data, creative, CRM, paid and earned media all under one roof.
Data science is the new measurement
An ongoing challenge in programmatic is measurement.
Marketers tend to rely on various industry-accepted currencies to validate their media investments (Comscore for viewability, Nielsen to measure reach into a certain demographic, or Datalogix for purchase data).
These are fine yardsticks, but we are starting to see marketers desire a more granular, less panel-based, approach to measurement.
Marketers have been quick to embrace enterprise data management over the last several years, and are now starting to build “consumer data platforms” (CDPs) to align their entire organizational data around a single identifier for their customers.
As they own more of their own first-party data asset, marketers can now look across the entirety of their data—not just from display advertising, but also from email, IoT, commerce, app, website, social, and search—and begin to get a universal view of cross-channel performance.
This granularity of disparate data, available to query in a single platform, and tied to cross-device user identifier, now presents the opportunity for finer-grained measurement and is the first, important step towards changing the attribution game.
This means that the ability to query and make sense from a large scale of data using machine learning and algorithmic approaches (essentially called “data science”) is the new basis for measurement moving forward.
Will we see marketers moving away from established, traditional measurement currencies in 2017?
Probably not, but we will certainly see enterprise marketers leverage their newly acquired data capabilities to challenge the status quo, and supplement the measurement they are currently doing.
Rethinking client-agency relationships
In 2016, the role of the media agency came into question.
As more brands look to take control and invest in or restructure for the omnichannel vision, we can see the dynamic of the client-agency relationship shifting.
Along with the need for greater transparency, expertise and sharing of knowledge, it’s also essential to have a clear partnership focused on putting the personalisation and synchronisation of consumer messaging at the centre of everything.
Brands need to rethink consumer engagement and storytelling with the optimised blend of data, technology, media and creative, rather than operating in brand marketing or creative and media agency silos.
This applies not just to big brand marketers wishing to take programmatic in-house and hiring programmatic expertise, but also to small to medium-sized brands looking to navigate this complex and often confusing new world.
With IBMs Watson, Salesforce's Einstein and Amazon's Alexa, I'm left wondering if the soothing voice of AOL's Connie will return for the programmatic industry. "You've got a new customer", perhaps?
Rise of peer-to-peer data sharing (programmatic 3.0)
The story of programmatic can be summed up as battle for control over the user, and the gateways for audience access.
In its first iteration, programmatic meant finding users on exchanges using real-time bidding.
It was a difficult and manual process to “whitelist” preferred sites, and impossible to control reach into specific audiences, due to the inherent nature of bidding (you might not win enough bids to get scale).
Then, we saw the first green shoots of “programmatic direct” in which premium marketplaces tied to media planning platforms sprung up (iSocket and ShinyAds), where publishers could set their own price for premium inventory and make direct deals with buyers.
Those platforms never found scale, mostly due to the lack of dynamic inventory management, and the fact that buyers did not want to embrace another buying technology.
The real programmatic 2.0 model started with the introduction of private marketplaces and Deal ID. This was a great way to leverage an efficient RTB buying methodology with some restrictions, and limit access to preferred inventory.
We have seen this model further evolve into header bidding technology, which is basically a smarter waterfall approach for publishers.
These innovations helped publishers get more for their premium inventory, and marketers can leverage programmatic tech to get more precision reach, with more granular controls over inventory.
However, these approaches were built on top of an existing ecosystem that was built to shrink the number of working media dollars and distribute them to technology providers, before money ended up in the publisher’s pocket.
Marketers still find a $10 spend reduced to $2 in effective media, after the “ad tech tax” is extracted by trading desks, DSPs, 3rd party data costs, SSPs, and private marketplace fees. Unsustainable, to say the least.
What we are seeing now, however, is the rise of a dramatic new approach to data driven marketing that gives the data buyer and owner more control.
Marketers have increasingly turned to DMPs to manage their inventory, and publishers are leveraging their DMP’s trust infrastructure to manage exactly which data they can make available to customers—and for very specific use cases.
A marketer and publisher on the same DMP infrastructure can choose to “open the pipes” between their instances and share user data for specific campaigns, and start to leverage their audience targeting capabilities on more premium inventory, where people are more engaged.
This type of peer-to-peer data sharing is happening today, and we will see not only marketers buying data from premium publishers within DMPs—but also the beginning of peer-to-peer data sharing among marketers.
Imagine if a group of CPG marketers pooled their shopping data for non-competitive products. Or, we might see a car rental company start to share business traveler data with its preferred airline.
The programmatic halo effect
Tom Wright, Head of Programmatic, Tomorrow TTH:
In an ever increasing programmatic landscape, fluid and responsive media trading has become a necessity not an option.
With the acceptance that programmatic media buying is having an incredible impact on multi-channel conversions, now is the time for brands to implement fluid media budget strategies.
Programmatic display has grown from being a channel auctioning off unsold display impressions, to being a sophisticated and integral part of the overall marketing mix.
As it exists now, buying media programmatically, creates a framework allowing the delivery of data driven, multi media content to audiences via all connected devices, at scale.
I believe 2017 will see programmatic media buying cement itself as an infrastructure that paves the way for traditional channels, such as TV, to move into a programmatic format capable of learning, optimising and reacting, considerate of data made available from other programmatic enabled formats.
It is this potential for symbiosis which requires a commitment to real fluidity of advertising budget, at scale, in real time between channel, format and device.
It will be the responsibility of the 2017 marketer to be brave enough to move away from treating programmatic display in isolation, and start considering the halo effect that the scale and impact of this channel has upon the overall performance of all other paid media channels in the same way TV, Outdoor or print does.
This blended approach is something we're championing with our clients and the uplift in performance has been dramatic.
Combined with enhancements in attribution technology, it will come down to the orchestration of quantitative evidence and qualitative reasoning to unlock the true power of programmatic media buying, but it will always be about a balance between media trader and the technology at their disposal.