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This article was contributed by Jeremy Fain, CEO and cofounder of Cognitiv.
It is easy to see the appeal of self-service programmatic ad buying. Instead of limited transparency on pricing and placement, advertisers can direct exactly where their campaign spend goes and how much they pay for each impression. Yet, as many traders have unfortunately discovered, this freedom comes with serious costs. Not only does it require a lot of time and energy to effectively optimize performance, it is also incredibly difficult to produce consistent results at the necessary scale. With deep learning at their disposal, advertisers can avoid this endless slog of tedious, unsuccessful work and instead rely on an AI algorithm delivered through integrations like dynamic Private Marketplaces (otherwise called DealIDs or PMPs), that will automatically, continuously optimize media buys to maximize performance.
At the moment, there seems to be a fairly even split between the number of brands and agencies that rely on managed services and those that prefer self-service. A recent survey by Advertiser Perceptions found that 56% use a managed service of some kind, while 46% say they utilize self-service. At the same time, 52% of buyers reported an intention to increase their self-service spend this year, while only 17% intend to raise their spending on managed services. The same survey found that The Trade Desk, Amazon Advertising and Yahoo! have each become majority self-service platforms in the past year, which speaks to the widespread desire for greater transparency into programmatic, especially with regard to fees.
Yet, despite all the optimism surrounding self-service, it is not solving many challenges in performance advertising. For instance, self-service requires a large contingent of traders in order to effectively manage day-to-day operations. As new information comes in about market conditions, consumer preferences, trends, and so on, traders have to be able to quickly synthesize that information in order to execute campaigns efficiently and accurately. However, humans are not robots — we need time to sift through information and parse the relevant patterns before we can design an effective strategy. Given the tight deadlines that many marketing teams operate under, there is no real way for marketers to produce perfectly-optimized campaigns consistently, which leads to wasted spend in the long run. It is also rare that a trading team has enough traders to cover all of their clients’ campaigns effectively. They usually have to spend most of their time on two or three of their most important clients, while the rest get less time and effort.
This system of constant, non-scalable trial-and-error also makes it incredibly difficult to operate at scale. Plenty of tactics start out strong but fade quickly, leaving marketers scrambling to cobble new ideas together as they attempt to optimize their campaigns manually. This only serves to make self-service programmatic more tedious and inefficient than it has to be — and makes it much more difficult for marketers to achieve long-term success.
According to Advertiser Perceptions, the number one reason given by most advertisers who are transitioning to self-service is “the desire for visibility into programmatic fees.” More than half (56%) of advertisers cite fee optimization as a primary rationale for making the transition — which, given the strain that the pandemic has wrought on marketing budgets, is understandable. If advertisers are unable to optimize their spend effectively, making the switch to manual self-service may not result in the cost savings they hoped for.
Self-service is here to stay, but the time crunch and difficulty finding scalable tactics is a huge limiting factor for success. Self-service advertisers should look to find solutions that address these problems. Solutions that will do the tedious work of optimization for them, freeing them up instead to be able to cover all of their campaigns equally, and focus on strategy and longer-term concerns. In particular, various forms of machine learning, such as deep learning, have been used by brands such as DoorDash to make sure they can optimize their ad spend at scale.
Deep learning is a valuable tool because of its self-learning, ever-evolving predictive abilities. For example, if you were to train a deep learning algorithm on customer data, it would be able to identify the key characteristics of your target consumer and use that information to make predictions about how new prospects will respond to your ad. This enables the algorithm to avoid advertising to people who are unlikely to convert, while honing in on those who are. Best of all, it does this automatically and in real-time, and it will adjust its predictions as it learns more about your audience and how they respond.
These algorithms are sophisticated and powerful enough to evaluate each media buying opportunity individually — which means that instead of setting up arbitrary rules about whom to target, the algorithm itself will decide who is worth the investment and whom to avoid. As a result, this enables discrete and dedicated analysis of the campaign to occur even while the campaign is live so that real-time market conditions and consumer behaviors are constantly being taken into consideration.
Manual self-service advertising is difficult, if not impossible, to master long-term and at scale. It requires taking into account many different elements that could change at a moment’s notice, while ignoring the complexities of human behavior. With deep learning algorithms, self-service advertisers can be relieved of the never-ending pressure to improve performance, and instead rely on technologies like AI-driven dynamic PMPs that will optimize campaigns continuously and finally give them the time to effectively manage all of their campaigns.
Jeremy Fain is the CEO and cofounder of Cognitiv.
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