How machine learning frees up creativity and strategy for marketers

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Artificial intelligence (AI) and machine learning (ML) have been heavily promoted over the years. These days, it seems that all companies are AI/ML companies, and the reality is that, as the American researcher, scientist and futurist Roy Amara said: “We tend to overestimate the effect of a technology in the short term and underestimate the effect long-term. long term.”

When a new technology is developed or implemented, people often talk about how it will suddenly transform everything in the next two years. Yet we also tend to completely underestimate its effect, especially if it’s the kind of technology that could fundamentally change the way we solve marketers’ problems and interact with customers. If we are going to reap the full benefits of AI and ML, it is important to first understand the technology and discern between the fact and fiction of how it works today. Only then will we be able to understand what is real, how this technology can be transformative, and how machine learning and AI can unleash creativity and strategic thinking for marketers.

Machine learning starts with data

Without the ability to analyze data, identify patterns, and put it to use, data is useless. Machines are ruthless optimizers that can organize data on a level that is impossible for humans to replicate. However, this also works in reverse, as today’s machines cannot replicate the creative thinking and strategies that humans can generate and act on. Data optimized through machine learning with machine learning gives marketers a supercharged ability to make the most informed decisions and then enact a creative strategy to achieve the desired result.

Machine Learning for Marketers: Asking the Right Questions

What matters to companies and people are decisions and actions. Back when I used to consult with large companies that were spending millions or tens of millions on “data strategy” or similarly ill-defined areas, I would often advise them that before they start worrying about what data they need to collect, they need to start with what decisions and the actions they should take as a company. From that perspective, companies can ask themselves: What decisions would you like to be able to make smarter and faster? Are you structurally set up as an organization to make those decisions? Once these are defined, you can ask questions like, what information do I need to make these decisions faster and smarter? And which of these decisions can be automated?

So where does machine learning come in? What category of problems can you help us with? To answer these questions, it is first helpful to understand the limitations of this technology. ML doesn’t replicate the amazing generality and adaptability of human intelligence; instead (and consistently with other technologies) it augments human intelligence and solves a more specific set of problems with superhuman ability. To determine if ML can be applied to a problem, the following set of questions is helpful:

  • Can a human solve the specific task required in less than 2 seconds? (This is a rough estimate; we haven’t gotten to the point of solving problems more complex than this yet.)
  • Is there value in solving this problem repeatedly at scale (say billions of times incredibly fast)?
  • Is there value in doing this task repeatedly, robustly, and consistently?
  • Can we measure “success” numerically?

If you can answer “yes” to these questions, then you have a problem that is ideal for applying machine learning. (Interestingly, these are also the kinds of tasks we humans are terrible at because we get bored, distracted, and tired.) , optimize prices and make sense of language.

Solving marketers’ problems with machine learning

When it comes to marketing and advertising, there’s a whole category of problems that also fit neatly into that “yes” bucket. Detect audience composition and behavior changes over time, predict whether an ad will lead a potential customer to visit my site based on the content of the article they’re reading, and adjust thousands of parameters to ensure budgets are spend efficiently and effectively is all that marketing. problems.

There are also issues that don’t fit into this categorization, such as: how do I get my complex message across in a way that cuts through the noise? How do I effectively connect with an audience that I’m not currently resonating with? How do I balance short-term and long-term goals?

Machine learning isn’t magic: it can give marketers superhuman abilities to find patterns in data to deepen our understanding, optimize delivery against well-defined goals, react to change quickly and rationally, and execute on our ideas. predictably, with less friction and more feedback.

Interact with customers in real time

For marketing, much of the information and patterns that are useful relate to customer behavior. Digital campaigns are noticeably less effective when they cannot respond to the changing conditions of the moment. To illustrate, if you’re selling gourmet coffee makers, you want to reach people who are still interested in buying one, not those who had been searching online for the last week and bought one yesterday. Everyone has experienced buying a product online, arriving, and then being spammed with the same product repeatedly for the next week by all the devices and platforms they use. While this can be useful for products that customers typically continue to buy (detergent, toiletries, etc.), most people just need a gourmet coffee maker.

Real-time data not only ensures campaigns reach the right people, but also enables marketers to respond to changing market conditions. By combining machine learning with real-time data, marketers can see results live, rather than waiting for results at the end of a campaign. This means brands can spot and capitalize on things like a recently released Netflix show or what’s trending on Twitter, or even address rapidly changing dynamics within the supply chain. If there’s one thing brands have learned in recent years, it’s that global events can affect buying behaviors and patterns in an instant.

While the machines may take care of analyzing demographics, web browsing behaviors and past purchases, having the right creative marketer in place, who can connect current trends with campaign goals and ensure the right questions are being asked machines, is what distinguishes a good campaign from a great one. Borrowing another great quote, this time from Alan Kay, “Simple things should be simple, complex things should be possible.” In addition to helping us gain deeper insight and understanding of audience behavior, great technology should also make it easier for marketers to react to this information by getting new creative ideas live in minutes, not months.

Can ML predict the future?

Predicting the future is not possible. But machine learning technology combined with real-time data can enable marketers to understand emerging trends and behavioral changes as they happen and make it easier to react to these changes by getting automatically optimized campaigns live in minutes and seeing if they are working in hours and days. . Real progress is about learning and testing strategies and ideas.

The underappreciated impact that ML will have on the ad tech industry over the next decade will not be due to AI-generated ideas or declining dollars spent on operations coming to fruition; the big impact will come from bridging the gaps between marketing strategy, knowledge, idea and execution and enabling us to understand more deeply and quickly, be more creative and test ideas with greater confidence and ease, and measure impact more effectively. . This technology, like all other technologies, does not replace humans, but frees us from repetition and tediousness and empowers us to be superhuman.

Peter Day is CTO of Quantcast

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