Maor Sadra: “Let’s Evolve the Entire Mobile Ecosystem by Using the Data and Technology Available to Us”

Even as media buying continues its sea change from traditional direct order technology to programmatic, mobile advertisers struggle to unlock the potential of unified media buying at scale. Programmatic offers an alternative to the labor-intensive and inefficient process of manual direct buys, yet advertisers are finding publishers hesitant to get on board.

Mobile advertisers tapping into the programmatic opportunity should expect to see this resolved in the very near future, as penetration among publishers increases. In fact, programmatic ad spend grew to over $10 billion in the U.S. in 2014, and mobile’s share of all US digital ad spend will reach 72% by 2019, predicts eMarketer.

Are you prepared and equipped to capitalize on this burgeoning opportunity in mobile programmatic?

Understanding what a modern media buy looks like today — and how it will look in the near future — is key. From RTB to direct programmatic, the future is bright for savvy mobile advertisers able to use big data analysis to create smarter media buys, track results in a granular way, and optimize toward campaign goals.

The process of human interaction and negotiation is almost completely off the table in today’s online buying and selling environment. As more and more inventories have been converted to trade programmatically, the days of email, phone and fax machine for one-to-one negotiations have become increasingly obsolete.

Several companies are already ‘converting’ their old media buying team, either through retraining/skills upgrading or via new hires. Former stock exchange day traders and computer science grads have become a hot commodity in programmatic. MediaMath is a great example of this new generation of advertising professionals, as the majority of its employees are either engineers or former traders. The same can be said for RocketFuel, AppNexus and countless others.

Media buys are increasingly done programmatically, either via an API (whereby buyers and sellers connect their ‘machines’ together, allowing those to speak directly to one another), or through the use of RTB.

Programmatic via an API still allows the seller to set restrictions and requirements on their end, including which buyer is allowed to buy their inventory (and for how much). With RTB, however, the buyer no longer sets the price; their pricing strategy defines the limitations of their campaigns, applying “what/if” formulas (for example, if CTR is x%, increase bid by y%; if country = USA, limit spending to 5% max per placement, etc.).

As sophisticated as the technology has become, it demands ever more detailed data — and skilled professionals to feed the machine the right data. Companies must be open to onboarding their first-party data, to resolve the drive for more targeted, relevant marketing activities, but also increased transparency. See, an awful lot of publishers place a premium on their inventory when they really shouldn’t. Premium is often reflected in the price across the entire inventory, when at the granular level — the single impression — demand should set the price, not the publisher.

Those publishers who’ve decided to place all of their inventory in auction (think New York Times and, for example) are finding that, contrary to the old conventional wisdom that programmatic means remnants that will cannibalize their inventory, they’re actually seeing demand and profit increase. Premium inventory is so priced based on data and logic; it better serves the needs of advertisers and proves its value.

Programmatic has also solved for the desire of some advertisers to continue dealing direct with publishers by offering private marketplaces, or programmatic guaranteed. It essentially uses the RTB environment in place of a traditional buying environment, by facilitating private auctions.

The modern media buy kicks inefficiencies to the curb, but also offers consumers a relevant, compelling advertising experience. Big data analysis fuels powerful targeting that reduces views by users outside of the target audience, which can create brand resentment. In the not far-off future, I predict we’ll evolve to almost complete automation, potentially plugging CRM data directly into marketing software. We’ll apply complete automation between mobile device-triggered events (such as time, device location, etc.) to marketing messages beyond their application.

Take an insurance company, for example, advertising a car insurance product. Upon “listening” to programmatic traffic segmented to identify car shoppers, the insurance company can offer messaging around the best offers for car insurance, monetizing based on (presumably) logical intent — the user is buying a car and therefore will probably also need insurance.

As you learn which types of users engage or monetize well, you can train the marketing software to identify and acquire similar users. This is how we evolve this entire industry; by using the data and technology available to us to ensure the message is single-handedly picked for the user’s interests.

In this way, the modern media buy is more effectively delivering transparency, relevance and efficiency at scale. It holds the promise of continued evolution in the near future, bringing us ever closer to delivering exactly what each individual seeks, regardless of device, across platforms, and taking into account every shred of data available to us.

This article was originally published on Talking New Media and can be found here.