How To Scale Native Programmatic Advertising

I’m heartened to see that the notion of native programmatic advertising at scale is no longer considered an oxymoron, and that the digital advertising ecosystem has fully accepted it as a viable initiative.

However, there is often a lag between acceptance and action. My goal here is to elaborate on the various opportunities that have emerged to inspire marketers to ramp up their native ad efforts in a meaningful way.

Optimization in Native                              

Unlike the display ad marketplace, which has embraced campaign optimization for years, the more nascent native segment has yet to fully put in place the proper infrastructure for the precise, real-time decisioning that has been the norm in display. From my vantage point, the biggest hurdle is that some of the largest suppliers of native have closed systems, which impairs the entire industry.  



OpenRTB 2.3 is the optimal protocol for native programmatic. When OpenRTB 2.3 is employed, we can understand who the user is, where the advertisement will appear, and know if it’s working or not in real time. The closed systems currently can only be optimized via post-reporting (typically many hours later), which creates a ton of inefficiencies in the buying process.  

Big Data Has Come to Native               

Native is now starting to use the vast amount of data brought to bear in the programmatic display arena. Leading native players are now beginning to invest heavily in Big Data technologies such as Hadoop, Kafka and Spark.

An influx of Ph.D.s is now flooding into the native arena to author predictive models on bidding strategies to optimize decisioning.  
In my estimation, there should be no cap to the amount of big data that the sector employs.  The more we can leverage and access data, the better our models and results will be for clients.  In the short history of native advertising in the digital realm, audience data has been fundamentally weak and inaccurate, despite the success that many DMPs have had in selling this data.   

To build audiences properly without first-party data, you can use fingerprinting technology to understand who someone is in a cross-device context. This makes targeting efforts much more seamless. By tying into other large programmatic supply sources via OpenRTB 2.3 at scale, you can then start to retarget users at a much higher frequency than if you are limited in reach to your own direct supply. Thus, very similar to the display market, it is now possible to cast a wide net across all the notable native programmatic sources to find the users any brand would want.

Engagement is the New Currency                            

Engagement is the new focus in terms of native KPIs and success metrics.  How will this evolve? Post-click analysis of Time on Site, Bounce Rate, Visits and Pageviews should form the foundation for assessing the value of each variation of a creative on a specific placement.  This formula for calculating engagement should then become the optimization guide to adjust and reallocate traffic to favor higher engagement placements.   

There are no limits to this methodology, as it can also consider social interactions (likes, shares, comments) and conversion events.
I see the future of interaction to be more than just clicking and typing. More and more devices allow for swipes, verbal cues, facial recognition/live cam — actions that are all engaging and important to the measurement of success. Smiles could be the measurement of the future!

The definition of engagement should also be customizable by the advertiser.  Multiple KPI customization, where independent metrics are defined and weighted by the advertiser, should be the norm. This allows an advertiser to have complete control of how optimization is performed.

Native programmatic advertising is here to stay. The opportunity is huge, and the insights are actionable.  It is necessary to take these steps for the space to mature and flourish for brands and publishers.

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