As it stands today, the advertising and marketing tech industries are broken and have been for years. This might sound shocking, given that $8.8 billion of VC money was invested in martech in 2017 alone. But despite the investor interest in these industries, the fact is that the creation of a middle layer consisting of advertising infrastructure between brand and consumer has exacerbated the problems of fraud and money being spent in all the wrong places. To solve these problems, the industry needs players that can ensure that brands’ money is being spent efficiently and optimally.
As brands begin to move the greater slice of their advertising budgets to digital, there has been (unsurprisingly) a rise in the number of players trying to get a piece of that budget. Large companies such as Unilever have an enormous amount of money to spend: in the case of Unilever, its advertising budget in 2018 was $8.5 billion, compared to the 10.5 billion spent by the world’s second largest advertiser, Proctor and Gamble ($10.5 billion).
And as the advertising ecosystem expands, there is a corresponding increase in the potential for fraud. Fraudulent traffic, bots, click injection, the selling of mislabeled or non-existent inventory — all of these have become commonplace within the industry, providing further validation that the system is broken. Using technologies like deep learning will go a long way toward restoring trust in the industry, and those companies that make the leap first will find themselves in an ideal position.
Right now, the biggest players in the adtech and martech arenas are familiar names. Facebook, Google and Amazon lead the pack when it comes to being able to identify target audiences and reach them with precision. Their immense databases, packed with information on customers’ identities and habits — along with the use of AI algorithms — have created efficient mechanisms that are able to optimize media buys and spending. At the same time, several large brands have openly voiced their displeasure over the lack of transparency and accountability from platforms like Facebook and subsequently reduced their spend.
Meanwhile, there are other platforms out there to pick up the slack. One such adtech company is MediaMath, which recently raised a healthy $180 million to help fund acquisitions and expand its reach within the ad tech industry. A leader in the DSP (demand-side platform) space, MediaMath is constantly coming up with new ways to find unique and premium supply that their clients can tap into. For instance, the company was one of the first to market directly to brands, on the assumption that they would want to control their spend and data in-house instead of relying on agencies to carry out the work for them. MediaMath also helped craft the IAB’s Transparency and Consent Framework, which helps all parties determine whether or not they are in compliance with GDPR requirements, thus ensuring continued transparency and maintenance of consumer privacy.
One simple way for MediaMath to continue this commitment to transparency and efficiency would be through acquiring a martech company such as Cognitiv. This is a firm that has the experience and tools necessary to make digital advertising more efficient for brands – especially one that has expertise in AI and deep learning. This last point is important, because only through the use of deep learning will platforms be able to make media buying more effective and efficient, finding the real humans instead of the bots.
On top of that, a deep learning algorithm or a neural network unique to a brand or a KPI (such as the ones built and trained by Cognitiv) is capable of digesting huge amounts of data and finding patterns that humans are unable to see. In other words, it is capable of identifying trends and opportunities specific to a brand that might otherwise have gone unnoticed – a huge competitive advantage that ensures that money is being spent efficiently. It is also able to identify potentially fraudulent activity, such as traffic being generated by bots, or mis-attributed impressions. So, any company that can offer this service to its clients would find themselves better able to prove their value on two fronts. Given that marketers have proven themselves to be unyielding when they perceive any waste or inefficiency, and shown no hesitation to curb spend or remove budget from underperforming partners, having an AI ace in the back pocket allows adtech partners to demonstrate their worth more clearly.
At the moment, there are relatively few, if any, players in the advertising space who are using deep learning to make their marketing initiatives more efficient. For any brand, the goal is to make more money. Right now, it seems like the adtech industry has plateaued in that arena. Without deep learning, the gains made over the past decade will begin to falter. It’s time for big adtech players to take a step back and think about what they can do to provide better value to their partners, whether it’s to make a strategic martech acquisition or invest in deep learning. It’s time to take the industry to the next level.