Can Machine Learning Predict the Future?

Can Machine Learning Predict the Future?

When the age of the internet first dawned, many tech enthusiasts were excited about one question: Will machines be able to tell the future? At that time, the idea was a fantasy only speculated about in sci-fi flicks and cautionary tales of machine vs. man.

In the early age of machine processing, the question of machine-enabled prediction was nothing more than speculation. But in today’s modern age, where algorithms seem to dictate every aspect of our digital lives, that speculation has evolved into a concrete theory. Instead of asking whether machine learning will predict the future, we now have access to technology that enables us to affirmatively ask ourselves, how can machine learning predict the future? 

While we’re not quite at the level of predictive technology imagined by authors like Isaac Asimov or Philip K. Dick, machine learning has come a long way, and in no industry has it had such an impact on daily life as digital advertising. 

Major platforms like Google and Facebook use machine learning to provide users with the most relevant and valuable experience possible. From tools like responsive search ads to lookalike audiences, machine learning has shaped the way we target and reach ecommerce consumers across the globe. It’s facilitated certainty in the pre-click stage of advertising.

But what about the post-click side of things? Well, thanks to new advancements, a refreshed focus on conversion advertising, and the maturity of consumer expectations, we may have more concrete insights into the impact of machine learning on conversion performance.

This blog is here to explore those insights in the hopes of answering one final question: Can machine learning identify what makes conversions happen?

What is machine learning?

Before we dive further it’s important to be clear about what machine learning is and how it works.

Machine learning is a system that uses automation to analyze a wide range of data and near-instantly build insights based on the patterns presented in the data. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Simply put, machine learning identifies patterns to intelligently improve data models and make calculated recommendations for next steps.

A clear example of this is how social media networks like Instagram and TikTok offer dynamic search pages to automatically feed users content they may be interested in. When you first register for a social media profile on a platform like Instagram, your homepage might be a collection of more generic well-shot images and popular content spanning the entire network. But after a year on the platform, you’ll log in to a much different experience. Your search page will present images more relevant to your hobbies or traits. You’ll see content such as highlights of your favorite sports team, memes about the show you binge-watched last month, or influencers in an industry of interest.

That’s the point of machine learning—the ability to improve over time with minimal human interference. The algorithm can identify relevancies with a greater deal of precision once it has developed some maturity compared with early predictions. That’s where the power of machine learning has had such an impact on digital advertising. To exemplify that, consider the progression of Google’s search algorithm.

Ten years ago, Google search results were vastly different experiences. Changes like their 2012 Venice update brought geography into the machine learning framework, giving smaller businesses a chance to compete with big retailers. The Hummingbird update of 2013 brought customer intent into the equation, even populating search results that didn’t match your keywords if the search engine deemed it relevant to the overall intent. Today, it takes such minimal effort to achieve appropriate search results that page 2 of any query is considered a land of obscurity.

So how has this translated to the post-click side of advertising? Fortunately, recent years have seen plenty of traction gained in the category.

How does machine learning impact conversions?

Machine learning has shaped the way advertisers connect with consumers in a few ways, and each has a track record of proven results. 

Copy + Machine

The value of AI copywriting has become so widespread you may not even realize it’s a part of your daily routine. Services such as Grammarly help professionals every day with formulating proper tone and syntax. These days, even Gmail can craft personalized sign-offs with the click of a button.

eBay uses machine learning to pump out thousands of personalized subject lines to their users daily—a feat previously considered humanly impossible. A personalized initiative at that level of scale would’ve taken days of manpower, but instead freed up the online marketplace’s copywriters to focus on mission-critical initiatives.

Chase similarly crafts personalized marketing content using their AI partner, Persado. Ironically enough, Persado used machine learning to make some of Chase’s copy feel more human.

In one test, a headline by Chase’s copywriters urged consumers to “Access cash from the equity in your home,” with the call to action “Take a look.” A variant created by Persado’s AI was headlined “It’s true—You can unlock cash from the equity in your home” and suggested, “Click to apply.”

It can be easy to look at a change as simple as this and wonder if the value of machine learning was worth the multimillion-dollar five-year deal Chase signed with the AI company. But the value was self-evident from the outset. In beta testing alone, Chase experienced lifts as high as 450% in click-through rates on ads rendered by Persado.

Design + Machine

While less notable—perhaps by design—machine learning in UX factors such as imagery and layout are making waves in various industries.

Netflix even went so far as to put out a blog confirming how it uses machine learning to regularly update its thumbnails. The streaming service noticed early on that different images worked better with different users. The company found that thumbnails for popular titles differed in popularity between Germany, the U.S., the U.K., and Brazil. As a result they decided to build an AI system around that data.

Instead of having to manually identify audience segments and personalize their thumbnail selections, Netflix’s machine learning algorithm instead makes this an instant experience. The algorithm has even been able to dive deeper and discover other winning strategies to personalize thumbnail selection including:

  • Showing close-ups of emotionally expressive faces
  • Showing villains instead of heroes
  • Not showing more than three characters

Machine learning functions similarly with landing pages. Tracking user behavior and defining what does and doesn’t resonate with users helps better personalize the experience in future attempts.

That’s two significant areas of any conversion journey where machine learning is already providing a valuable impact. 

  • Machine learning can learn how to craft personalized narratives with little effort to better connect products with customers. 
  • It can also create instant variations of page layouts and designs based on consumer behavior and buying patterns. 

So can we say predicting conversions is possible with machine learning? Well, not yet, at least. There’s a piece of the puzzle we’re overlooking if we stop there. To get a concrete answer to our claim, we have to go a step further and look at how machine learning derives results.

Finding conversion influences

Machine learning aims to remove guesswork and collect enough data to make the most accurate decision possible. It’s impossible to do this through traditional testing. Machine learning deploys smarter frameworks for deriving insights, pinpointing differences in data patterns, and isolating them to examine the results. 

Unlike notable pitfalls of manually run A/B tests, this process helps the machine be precise in what it’s learning instead of looking at the whole picture and making speculation. The advantage is machine learning can perform a multitude of these precise tests at once to build insights faster. It can even go as far as to dynamically control traffic to remove low-performing pages from testing to sustain conversion rates.

Farmer’s Dog got to see the advantages of this system firsthand when participating in Postclick’s beta program. Postclick’s proprietary multi-armed bandit algorithm enabled Farmer’s Dog to dynamically run ad traffic across multiple variations of their landing page at once. This strategy allowed them to experiment for multiple conversion influences simultaneously and identify the best-performing page faster. 

As the testing proceeded, Postclick’s AI-experimentation identified the lowest-performing pages first. Removing these pages from testing helped Farmer’s Dog to continue pushing for higher conversions as testing progressed. In only 14 days of testing, the dog food supplier had already achieved a 14% lift in CVR.

Smarter testing is a major part of why machine learning is so effective at influencing conversions. Not only does it eliminate the pains of reduced conversion rates during testing, but it can also pinpoint exact data points that are impossible to find with certainty in manual efforts.

A clear answer

Can machine learning identify conversion influences? When machine learning is effective in all three categories of creative, user experience, and testing, the answer is a clear and astounding yes. Machine learning has been proven to analyze and inform marketers what makes a conversion happen.

But it can only predict conversion influence when it succeeds in all these areas.

But before you jump into adding machine learning to your MarTech stack, let’s cover some critical cautions to keep in mind. 

Machine learning needs human oversight

While machine learning reduces human interference and manual production, taking human expertise out of the equation will diminish your results exponentially. 

  • Human expertise and experiences should be at the foundation of any machine learning technology. 
  • Without human guidance and participation, your AI-created content risks losing authenticity. 
  • Consumers are not oblivious to machine-driven content and will become fatigued if they’re oversaturated with content that doesn’t feel genuine. 

Want to learn more about this? Check out our ebook, Machine + Marketer = Conversion Advertising in 2022.

Machine learning needs time to be effective

Machine learning doesn’t just start deriving results with the click of a button. That human expertise at the foundation of your system has to come from somewhere—which, in some cases, means it can take quite some time before there’s enough data for the AI to analyze. 

  • Be sure your machine learning solution has a proven track record of established data points. 
  • If not, beware of the time you’ll need to invest to get it there.

Machine learning is costly, so pick an efficient tool

Machine learning has become more accessible over the past two decades, but that doesn’t mean it won’t fill up a significant portion of your budget. So the value needs to be worth it. 

  • If you’re going to maximize ROI, you need an all-in-one system.
  • Don’t settle for one aspect of your post-click strategies and hope it compensates for lacking areas elsewhere.

Discover a new solution

Fortunately, there’s a new advancement in post-click machine learning. At Postclick, we built our Advertising Conversion Cloud on data from millions of custom landing page experiences and billions of ad clicks. It comes equipped with CRO expertise from industry-leading professionals and optimizes everything from your copy to UX and brand design. Meet the new MarTech solution that uses AI and machine learning to supercharge conversions and drive revenue. Introducing Advertising Conversion Cloud™.

Get a complimentary conversion analysis started today and let our experienced consultants identify the most effective opportunities to implement machine learning into your optimization strategy. Request a free conversion analysis here.

Steven Tindle
by Steven Tindle

Steven is a marketing copywriter at Postclick on a mission to empower brands to build connections through authentic stories. When he isn’t using his craft to impact the future of conversion marketing, Steven enjoys following the stories in the Marvel Cinematic Universe.

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