How machine learning frees up creativity and strategy for marketers

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Artificial Intelligence (AI) and Machine Learning (ML) have been widely published over the years. These days it seems like every company is an AI / ML company – and the reality is that, as American researcher, scientist and futurist Roy Amara puts it, “we overestimate the impact of technology in the short term and underestimate the impact. In the long run. . “

When new technology is developed or deployed, people often talk about how it will suddenly change everything in the next two years. However, we also tend to underestimate its impact, especially if it is the type of technology that can fundamentally change the way marketers solve problems and communicate with customers. If we are to take full advantage of the benefits of AI and ML, it is important to first understand the technology and understand between the facts and the fantasy of how it works today. Only then will we understand what is real, how this technology can be transformative and how it can liberate creativity and strategic thinking for machine learning and AI marketers.

Machine learning starts with data

Without the ability to analyze data, identify patterns, and use them, data is effectively useless. Machines are ruthless optimizers that can organize data to a level that is impossible for humans to replicate. However, this also works in the opposite way, because machines today cannot mimic the creative thinking and strategies that humans can generate and operate on. Machine-optimized data with machine learning provides marketers with the supercharged ability to make the most informed decisions and then devise creative strategies to achieve their desired results.

Machine learning for marketers: asking the right questions

Decisions and actions are important to companies and individuals. In the past, when I consulted with large companies that spent millions or tens of millions on “data strategies” or similarly poorly defined areas, I often advised them to start worrying about the data they needed to collect. What decisions do they need to make? And they need to be taken as a business. Starting from that perspective, businesses may ask themselves: What decisions do you want to make smarter and faster? Are you structured as an organization to make those decisions? Once it is 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 from? What range of problems can he help us with? To answer these questions, it is important to first understand the limitations of this technology. ML does not mimic the wonderful generality and adaptability of human intelligence – instead (and constantly with other technologies) it enhances human intelligence and solves a more specific set of problems with superhuman ability. To find out if ML can be applied to a problem, the following set of questions is useful:

  • Can a man solve a specific task in less than 2 seconds? (This is a rough estimate; we have not yet reached the point of solving more complex problems than this.)
  • Is it worthwhile to solve this problem repeatedly on a scale (e.g., billions of times too fast)?
  • Is it worth doing this task frequently, vigorously and consistently?
  • Can we measure “success” statistically?

If you can answer “yes” to these questions, then you have a problem that is suitable for applying machine learning. (Interestingly, these are also the kind of tasks that humans are terrified of because we get bored, distracted and tired!) This may seem very limited, but many of the problems are “yes.” “Fits in the bucket, such as identifying spam emails, detecting fraud. , By optimizing values, and making sense of the language.

Solve marketers’ problems with machine learning

When it comes to marketing and advertising, there is a whole range of issues that fit squarely into that “yes” bucket. Marketing is all about detecting changes in audience composition and behavior over time, predicting whether an ad will likely get a customer to visit my site based on the content of the article they are reading, and tuning in thousands of parameters to ensure budgets are spent efficiently and effectively. . Problems

There are also issues that do not fit into this classification, such as: How can I express my complex message in a way that cuts off the noise? How can I effectively connect with an audience I don’t currently resonate with? How do I balance long-term and short-term objectives?

Machine learning is not magic: it enables marketers to find patterns in the data to deepen our understanding, optimize delivery against well-defined targets, respond quickly and rationally to changes, and predictably implement our ideas with less friction and more feedback. Can give.

Interact with customers in real time

For marketing, a lot of information and examples that are useful are related to the behavior of the customer. Digital campaigns are significantly less effective when they are unable to respond to changing situations at the moment. For example, if you are selling gourmet coffee makers, you want to reach out to people who are still interested in buying coffee, who have been searching online since last week, and who were buying one yesterday. Everyone has experienced online shopping for a product, after it arrives, and then with every device and platform they use it they spam frequently with the same product for weeks to come. While this may be useful for consumers who continue to buy products (detergents, toiletries, etc.), most people only need a gourmet coffee maker.

Not only does real-time data ensure that the campaign is reaching the right people, but it also allows 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 that brands can find and capitalize on things like popular, recently released Netflix shows or what’s trending on Twitter, or even address the rapidly changing dynamics in the supply chain. If there’s one thing Brands has learned over the years, it’s that world events can instantly influence buying behaviors and patterns.

While machines can take care of demographics, web browsing behaviors, and analysis of data surrounding past purchases, having the right creative marketer – who can link current trends to campaign goals and make sure the right questions are asked about machines – is one. Separates the good. Campaign from a great. To borrow another great quote, this time from Alan Kay, “Simple things should be easy, complex things should be possible”. In addition to helping us gain a deeper insight and understanding of audience behavior, great technology should make it easier for marketers to respond to this information by bringing new creative ideas to life, not in months but in minutes.

Can ML predict the future?

It is not possible to predict the future. But machine learning technology combined with real-time data can enable marketers to understand emerging trends and behavioral changes and respond to these changes by automatically getting the optimized campaign alive in minutes and seeing if it is working in hours and days. . . True progress is about learning and testing strategies and ideas.

ML’s underestimated impact on the ad tech industry over the next decade will not be due to AI-generated ideas or a reduction in the dollar spent on implementation; By shortening the gap between marketing strategy, insight, thought and implementation and understanding us more deeply and quickly, becoming more creative, and testing ideas more confidently and easily and measuring impact more effectively. This technology – like all other technologies – does not change human beings, but frees us from repetitive and boring and empowers us to become superhumans.

Is the CTO of Peter Day Quantcast

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