How the Right Partnership Can Boost Your Company’s Paid Advertising Business

Why advertisers should work with partner platforms whose incentives are aligned with their overall business goals.

How the Right Partnership Can Boost Your Company’s Paid Advertising Business
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Whether it’s determining the right bid for an ad opportunity, deciding what message a consumer sees to compel them to buy the latest tech gadget, or creating a truly personalized online shopping experience, artificial intelligence (AI) is transforming the paid advertising business.

Now, more than ever before, it’s important for advertisers to make sure they partner with an ad platform that works for their best interests. AI works better with scale. That means the more varied, high-quality data points you have, the more sophisticated the AI infrastructure and the quality of algorithms, the better positioned you are to draw actionable insights. So consolidating on a large tech platform is essential. However, advertisers need to evaluate whether the AI platform is offering publisher or advertiser-centric incentives — ultimately, that will determine the advertiser’s long-term profitability.

In this Q&A we talk with Shri Mohan, principal product manager of Adobe Advertising Cloud, about how companies can use AI to achieve both their short-term and long-term marketing goals, and the importance of aligning with a partner platform that focuses on maximizing advertisers’ overall business goals — and not just their own.

 

Shri, you have experience building AI platforms for both publishers and the advertisers. What has been your key insight?

I have spent the last 10 years building AI platforms, both small and large, to solve advertiser and publisher challenges. Lots of smart people and algorithms power both sides. But the one question that matters most is: how do they make money?

Publisher-owned platforms such as our favorite online portals need to maximize their own long-term profits across advertisers while making a reasonable effort to meet advertiser goals. Short term, they can lose money by offering lower fees/free optimization/rebates to attract more advertisers to their platform. Long term, they need to get advertisers to bid more for ad placements, against each other.

In contrast, advertiser-focused platforms focus on maximizing short-term and long-term profits for the advertiser. Short term, they need to generate strong performance to keep the advertiser happy. Long term, they need to sustain superior performance to retain and grow the account.

This distinction between short term and long term is often missed in the typical pressure to hit a monthly or quarterly target. Yet it makes all the difference — even more so when we bring in AI.

How is AI changing paid advertising programs?

AI is sweeping through all aspects of paid advertising — planning, creative generation, campaign setup, ongoing campaign management, generating insights from data, and speeding up this iterative cycle many times over. The ultimate goal of all these steps is to achieve superior results — whether it’s performance or branding goals.

There are two axes by which you can segment AI platforms focused on advertising. One dimension is large vs. small AI platforms. The other is advertiser vs. publisher-focused.

How would you contrast large vs. smaller AI platforms?

Successful application of AI requires scale in data, infrastructure, and algorithms. To do it right, the specific solution varies with the advertiser’s goal(s), the time frame of optimization, and the structural and semantic relations unique to the advertiser. This gets quickly complicated, and requires a massive investment in technology and people.

Large AI platforms have the data, people, and systems to do this well for the advertiser’s benefit. But having the capability does not necessarily mean they have the incentive to do so.

You can find smaller AI platforms across a spectrum. I can see a niche play.

What is the benefit of using publisher vs. advertiser AI platforms?

The simplest way to explain this would be to use an auction for a house as an analogy. There’s this piece of prime real estate. You want to place a bid on this house, but there’s competition from another potential buyer.

First, let’s take a publisher AI platform. The seller of the house says they can serve as the agent for you and the other potential buyer, essentially representing both of you in the transaction. That sounds contradictory, right? Human nature would tell you it’s in the seller’s best interest to pit you and the other buyer against one another to drive up the home price.

In the advertising world, we see a similar scenario billions of times a day, one auction after another. Even if the seller of the house tries really, really hard to the keep the agent buyer-focused, ultimately, this agent is managed by the seller. By design, the higher-level system always wins. The seller has a duty to maximize their long-term profits. One way you can see this happen is by tracking auction-clearing prices across the board over time. The publisher system decides which ads (and advertisers and corresponding bids) compete in which auction, and you can do the math.

On the other hand, an advertiser platform has to only deliver advertiser goal optimization to grow long-term.

In sum, every large tech platform is using AI in wonderful ways to improve the advertiser experience, from planning, to campaign management, to insights. However, goal optimization at the core needs to remain on the advertiser side. And to do better goal optimization, we need more transparency in the marketplace.

What incentives do large publisher platforms typically offer advertisers, and how can that affect long-term goals?

The words “free” or “risk-free” are extremely attractive. Even better, throw in a “discount” for spending more. Business goals are “what” you are trying achieve, and AI is only “how” you get there.

Short term, you may get a better deal, assuming the publisher’s advertiser optimization solution can match your advertiser platform. And your competitor is doing the same.

Long term, you play this game multiple times, and are left with you, your competitor, and only the publisher’s optimization. Slowly but surely, your acquisition price will go up and so will your competitors’. AI will remain in use, but to automate and remove more and more controls.

On the other hand, a large advertiser platform can keep this behavior in check and request more transparency and controls. Your advertiser platform may use AI to simplify the user experience as well. But this time, your incentives are aligned.

It is important for the ecosystem to have strong advertiser platforms that keep the publisher platforms in check.

What do enterprises need to successfully implement AI in their marketing programs?

First, you need access to large amounts of high-quality data at a granular level. Second, you need a fast, iterative feedback loop of stimuli and response. Third, you need to apply top-quality AI technology and talent.

Beyond my points on performance, incentives come into play on data ownership and portability as well. Case in point: post-GDPR.

What’s the alternative for advertisers who want to retain ownership of their data?

There are lots of questions about what happens to a brand’s data in a closed ecosystem like a “walled garden.” Advertisers should instead collaborate with companies that offer open platforms where they can benefit from scale, yet still control their data.

We’re in the business of helping brands use, protect, and maximize the value of their data. Our solutions offer partner platforms that can scale and that work collaboratively with advertisers to achieve their short-term and long-term goals, helping them retain a competitive advantage, and deliver the best possible customer experience.

Marketers see in real time how much context matters, and how important it is to collaborate with trusted partners. For example, the bulk of video buys on Adobe Advertising Cloud today is from Adobe’s direct partners, mostly multiple-system operators (MSOs) or television networks.

Adobe Advertising Cloud, which is part of the Adobe Experience Platform, uses the AI and machine-learning capabilities of Adobe Sensei. Our solutions are built to let advertisers use whatever data or media partner delivers results, and not to white-label data, create their own data sets, or build a third-party data business. Since Adobe Advertising Cloud is natively integrated with Adobe Audience Manager and Adobe Analytics, many clients have a wealth of first-party data for targeting, attribution, and more. Advertisers have total control and visibility over how their data is used.

Another advantage is that when a customer uses our solutions, those walled gardens that seem formidable are relatively open to them, since we have product integrations with many of the large technology platforms that give customers access to more of this valuable data.

By working with a market-aligned product like ours, advertisers can benefit from AI functions that truly advance their company’s goals.

Overall, how can open platforms help advertisers better reach and connect with their customers?

It’s more important than ever that brands protect their data. Most companies have decades of built-in, hard-won trust from their customers. It’s their key to being relevant and being trusted by customers.

The real question is how advertisers want to plan, buy, and measure media — and whether it’s important to them that they have a partner who doesn’t own media or has a vested interest in where an ad runs. Adobe Advertising Cloud is an open, agnostic solution that addresses all these concerns, and truly empowers advertisers to achieve their larger business goals.

Read more stories from our Artificial Intelligence series, and more about Adobe Advertising Cloud.

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