Making The Most Out Of Marketing Budgets With The Power Of Robots

  • Written by Radoslaw Dobrolecki, RTB House
  • Published in Demanding Views

0radoslaw Programmatic’s hold on the marketing sector is strong; it represents one of the most efficient ways to both granularize your retargeting while gauging where every dollar is going.

But in a sea of automated solutions, all allegedly AI-powered, how do you decide which gives you a competitive advantage? This is a critical question in a marketplace where consumers hold brands to ever-higher standards. People are demanding more proactive, intelligent exchanges that predict their needs.

Before going over the technology, though, let’s consider what those needs are:

Personalization: The same way Spotify “knows” what music a customer likes, customers expect smart programs that build accurate, personal experiences for them, based on their history and preferences.

Immediacy: Mobile’s dominance has put everything at our fingertips, compelling people to demand rapid responsiveness. Companies must take advantage of CRM technology to monitor experiences in real time, and be helpful at any moment, on any channel.

Intelligence: You’re just human; you can’t respond to these rising demands on your own. Artificial intelligence is the definitive technology of the 21st century, and we’re only beginning to understand what it can do. Without a strong AI solution, brands simply can’t achieve mass personalization at scale.

These three needs — which are not going away — explain why you need AI-driven technology. The next question is, what does AI mean, and whose technology is superior?

Like any other category, AI includes great, innovative technology as well as old, less powerful technology. Typically, when a programmatic provider says it’s AI-powered, it’s telling you it uses “machine learning” — an AI subset where algorithms are taught via data input. Machine learning-driven algorithms absorb massive amounts of data and are trained, using defined categories, to improve predictions and make choices.

While machine learning is hot right now, it still has challenges with evolution. Customer preferences evolve too quickly for even machine learning to keep up with; a human will always have to update the algorithms to keep up, making this technology highly engineer-dependent.

The most advanced AI solution is “deep learning”-driven. This evolved take on machine learning is based on how the human brain works. Deep learning can learn to optimize on its own, using entirely new assumptions, making it more efficient and rapid than machine learning — its ancestor in the technological tree of life.

Say you’re running a retargeting campaign and want to show someone the most relevant ad possible. Machine learning will decide what is relevant based on pre-set categories that humans previously decided were important—like price and time of day. If any new information can be used to help your algorithm, that information must be manually added to the category-set if you want your AI to take it into account.

Deep learning, on the other hand, looks at each ad, and each impression, almost individually. It will take the dataset into account, but will also account for relevant changes in people’s responses. Imagine that Christmas is coming, and the user starts to look for presents for family and friends— they browse first for headphones, then scarves, then toys, then a kettle, then socks etc. For machine learning, which needs human input, such user behavior can appear chaotic and could be evaluated as less than promising. Deep learning will learn that this is probably a special occasion and will make a proper evaluation (the user may just want to buy multiple, varied products.) Based on the user’s search, deep learning will try to recommend the most suitable offers from each category.

Like any other new tool, deep learning should be tested alongside machine learning-driven AI to ensure whether, and in what context, it performs best for you. In addition to this hypothetical scenario, consider these marketing benefits:

  • Better ROI from your marketing spend.
  • More valuable traffic.
  • A guarantee that you know exactly where your money is going. Transparency is critical to great programmatic.
  • Visible results, and a full view of your customer. This is more than categorization by demographic or psychographic; strong AI solutions can truly treat people individually, based on past behavior and those of others like them.

Fifty-one percent of marketers say their campaign messages are identical broadcasts from one channel to the next, and 61% of moderate-performing marketers believe AI will have a transformational — or at least substantial — impact on their ability to execute predictive customer journeys in the next five years.

There are good reasons for that, but be sure you know what you’re buying, the better to both test your technology and understand the results. If you don’t, your competitor certainly will.