Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now.

A true genius, Alan Turing was played brilliantly by Benedict Cumberbatch inThe Imitation Game — the movie about his life and role in ending WWII — which introduced him to a whole new generation of admirers. It was Turing who predicted machine learning would play a big role in modern computing in his article the “Turing Test,” way back in 1950.

Indeed, Turing was way ahead of his time, which was a major theme in the movie, but now the world has caught up. The major advancements in readily accessible computing power, the quantity of data available, and algorithms that truly make machine learning possible are driving our ability to process data, analyze it, and act on it in ways that would make Mr. Turing proud.

These advances have completely changed the machine learning game: The fundamental concept remains the same, but now it’s far more sophisticated, efficient, and easily deployable.

Beyond the big headline-grabbing examples of how machine learning will impact our lives — such as through driverless cars — it has exciting potential to put an end to the bland and sometimes ineffective customer experiences that many retailers are delivering to their customers.


Intelligent Security Summit

Learn the critical role of AI & ML in cybersecurity and industry specific case studies on December 8. Register for your free pass today.

Register Now

By harnessing machine-learning, businesses can revolutionize the way we engage with their store or use their service. And forget product recommendations as we know them today, this takes us into the realms of hyper-personal, sophisticated shopping experiences.

The rise of programmatic customer experiences

To get a sense of the potential impact of machine learning on the customer experience, you only need to look as far back as the arrival of programmatic advertising a few years ago.

This completely revolutionized how ads are bought and targeted online, harnessing data to not only automate a lot of the “grunt” work but also to make much smarter, more strategic decisions about where the opportunities for brands lie. The use of programmatic techniques enables campaign performance improvements of between 30 percent and 50 percent, according to some studies.

In the same way, harnessing machine learning for programmatic customer experience has enabled marketers to identify clear customer segments and target them in ways that brands know will resonate. They now start from a position of knowing who their customers are and what will excite them, which empowers them to focus their efforts on meeting customers’ needs and exceeding their expectations with every interaction. This will change the face of digital commerce in the next decade.

See, think, act

It’s not just about the ability of machine learning to automatically process vast quantities of data in order to understand customer behavior and identify where the opportunities lie. Today, we can act on those opportunities, as well.

To give an example, machine learning might identify that a U.S. retailer has high traffic from Spain, but lower conversion than expected. It can segment these visitors into two groups — native Spanish speakers and English speakers in Spain. The retailer can also survey visitors to find out whether the English speakers are expats or vacationers. It can then ensure that the most appropriate programmatic abandonment recovery and messaging is sent to each of these groups to encourage them to make a purchase.

Or imagine a customer buying shoes on your site, where the only information you have is on previous purchases and how much they’ve spent. In this case, personalization would be focused on the customer’s spending activity, and perhaps their brand preferences. However, leveraging the right data, you would also learn much more about the customer’s lifestyle — such as whether they run marathons, 5Ks, or triathlons — because they’ve answered survey questions that helped build a more comprehensive customer profile. This more complete customer profile now allows the retailer to suggest a higher-priced shoe, informing the customer that these shoes have won more marathons than any other.

Machine learning and better customer data allow you to build a far better and more personal experience for each customer.

Holding us back

So if we have truly arrived at the era of machine learning, what’s stopping us from seeing the optimal customer experience everywhere we turn? The answer is simple: Machine learning is not a magic bullet. Applied to meaningless data and put to work on meaningless tasks, data will be meaningless. The key to making an impact that can take us to the next level with artificial intelligence lies in quantitative and qualitative data. Think of training a coworker — if you feed them misinformation and provide the wrong tools, they won’t succeed.

We live in a world where delivering customers the content they are looking for in the very first couple of seconds is critical. If you don’t, the chances are they are going to get bored, distracted, and leave. Tinder’s “swipe-right, swipe-left” mentality is driving change across all sectors.

To sum it up, customers have a pull-push relationship with brands. Currently, they provide a huge amount of information and yet often receive inaccurate results, which means they have to spend even more time giving information and searching until they reach a desirable result.

As customers become more demanding, expect more, and become less willing to forgive experiences that miss the mark, they’re trusting brands that provide a good experience and cutting out the ones that don’t. Gaining trust takes time and is currently an imperfect science, as marketers apply insights to provide as relevant and tailored an experience as possible in order to win new customers and keep old ones.

It’s easy to blame indecisiveness on millennials. It is true that this generation is driving the demand for hyper-personal sophisticated experiences. This younger generation values experiences over commodities and is driving a change in the way brands generally interact with consumers — something known as the “experience economy.”

Before jumping into machine learning, you must first make sure you have data that shows the full picture, or it will be an expensive and ineffective experiment. With the right approach to data, however, machine learning can completely change the customer experience game.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.