How Machine Learning Frees Up Creativity and Strategy for Marketers

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Artificial intelligence (AI) and machine learning (ML) have been hugely hyped over the years. Today, every company seems to be an AI/ML company – and the reality is, as American researcher, scientist and futurist Roy Amara said, “We tend to overestimate the short-term effect of a technology and the effect in the long-term.”

When a new technology is developed or implemented, people often talk about how everything will suddenly change in the coming years. However, 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 want to take full advantage of AI and ML, it is important to first understand the technology and differentiate between the fact and fiction of how it works today. Only then can we understand what is real, how this technology can be transformative, and how machine learning and AI can unlock creativity and strategic thinking for marketers.

Machine learning starts with data

Without the ability to analyze data, identify patterns and use it, data is basically useless. Machines are relentless optimizers that can organize data at a level impossible for humans to replicate. However, this also works in reverse, as machines today cannot replicate the creative thinking and strategies that humans can generate and execute. The data optimized by machine with machine learning gives marketers a supercharged ability to make the most informed decisions and then execute a creative strategy to achieve the desired outcome.

Machine learning for marketers: asking the right questions

The things that matter to companies and to individuals are decisions and actions. When I consulted large companies that spend millions or tens of millions on “data strategy” or equally ill-defined areas, I often advised that before worrying about the data they need to collect, they should start with what decisions and actions they should take. to undertake as a company. From that perspective, companies can ask themselves: which decisions would you like to make smarter and faster? Is your organization structured 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 it 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 abilities. To find out if ML can be applied to a problem, the following set of questions is helpful:

Can a human solve the required specific task in less than 2 seconds? (This is a rough estimate; we’re not quite there yet to solve more complex problems.) Is it worth solving this problem repeatedly on a large scale (eg billions of times incredibly fast)? Is it valuable to perform this task repeatedly, vigorously and consistently? Can we measure ‘success’ numerically?

If you can answer “yes” to these questions, you have a problem that is excellent for applying machine learning. (Interestingly, these are also the kinds of tasks that humans are really bad at because we get bored, distracted, and tired!) This may seem very limiting, but many problems fit into the “yes” bucket, such as identifying spam emails, detecting fraud, optimizing prices and understanding language.

Solving marketers’ problems with machine learning

When it comes to marketing and advertising, there’s a whole category of issues that also fit perfectly into that “yes” bucket. Detecting audience composition and behavioral changes over time, predicting whether an ad will cause a potential customer to visit my site based on the content of the article they are reading, and tuning thousands of parameters to Ensuring that budgets are spent efficiently and effectively are all such marketing issues.

There are also issues that don’t fit into this format, 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 I don’t currently resonate with? How do I balance long-term and short-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 targets, react quickly and rationally to change, and execute 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 clearly less effective if they cannot respond to today’s changing circumstances. To illustrate, if you’re selling gourmet coffee makers, you want to reach the people who still want to buy one, not those who searched online for the past week and bought one yesterday. Everyone has had the experience of shopping for a product online, having it arrive, and then spamming every device and platform they use with the same product repeatedly for the next week. While this can be useful for products that customers generally continue to buy (detergent, toiletries, etc.), most people only need one gourmet coffee maker.

Real-time data not only ensures that 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, instead of waiting for results at the end of a campaign. This means brands can detect and act on things like a popular recently released Netflix show or what’s trending on Twitter, or even address the rapidly changing dynamics within the supply chain. If there’s one thing brands have learned over the years, it’s that world events can instantly affect shopping behavior and patterns.

While machines can take care of analyzing data on demographics, internet behavior and past purchases, having the right creative marketer – who can match current trends with campaign goals and ensure that the right questions are asked of the machines – is what a good campaign from a great one. To borrow another great quote, this time from Alan Kay: “Simple things should be simple, complex things should be possible”. Great technology not only helps us gain a deeper understanding and understanding of audience behavior, but should also make it easy for marketers to act on 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 coupled with real-time data can empower marketers to understand emerging trends and behavioral changes as they happen and make it easy to respond to these changes by getting automatically optimized campaigns live in minutes and seeing if they’re working. work within hours and days. Real progress is about learning and about testing strategies and ideas.

The underestimated impact ML will have on the ad technology industry over the next decade will not be due to AI-generated ideas or reduced dollars spent on operations that will arise; the big impact will come from closing the gap between marketing strategy, insight, idea and execution and by enabling us to understand deeper and faster, be more creative, test ideas more confidently and easier and measure impact more effectively. This technology – like all other technologies – is not intended to replace humans, but to free us from the repetitive and annoying and allow us to be superhuman.

Peter Day is CTO of Quantcast

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