How Machine Learning Helps You Think Like Netflix, Act Like Facebook, and Other Useful Lessons for Retail Marketers

Over the past few years, it has not been uncommon to see the term “big data” closely followed by words such as disruptive, innovative, and powerful. And sure, big data certainly is all of these things, and it has been a great aid for marketers as they strive to learn more about customers.

However, big data is not the end-all, be-all of marketing strategies. It’s not that big data is too simplistic, rather, it’s that our standard way of using data is limiting. Typical big data analytics are incapable of painting the full picture of consumer behaviors and often fail to quickly provide complete insights. When this happens, marketers end up with broad insights that miss the mark when translated into real-world advertisements and campaigns.

So let’s repeat—there is more to big data analysis than demographics and purchase history. And what’s out there in the cooler and smarter beyond, you ask?

Machine Learning

Machine learning is exactly what it claims to be. Humans are, despite our best attempts, incapable of processing and analyzing data at the volume and rate required to drive effective marketing strategies. To support our inadequacies, machine learning can generate a truly holistic picture of customers at an individual level. Through a series of complex algorithms that make use of many data points at once, machine-learning platforms churn out comprehensive insights based on multiple perspectives.

What’s more, these insights are highly contextual, meaning that machine-learning platforms are always discovering more about customers over time. Through a combination of marketing automation strategies, customer segmentation practices, and price elasticity information, machine-learning platforms can effectively predict not just what shoppers are most interested in buying, but also how, where, and why they are making purchases. This learning happens both online and offline, giving marketers the most accurate understanding of shoppers possible.

Cooler and smarter, right?

Think Like Netflix, Act Like Facebook

An added strength of machine learning is that it’s invisible. Without ever having to enter information or respond to prompts, consumers help personalize their own online experiences simply by acting as they normally would. Machine-learning platforms collect all data points with no burden to the customer.

It’s the Netflix effect. As you watch your favorite shows, Netflix collects this information—never asking anything of its users—and is then able to make smart inferences and constantly curate suggestions based on your unique preferences. Recently watched Scandal? Netflix then knows not to recommend Scandal to you again. Instead, it may propose another Shonda Rhimes show, or a similarly themed political program.

The same algorithm is at play on Facebook. By combining what is known about a user and his or her recent behavior on the site, Facebook is able to accurately predict the kind of content an individual prefers on his or her timeline. The best part is that, thanks to machine-learning capabilities, the social media platform knows that user preferences change over time (such as friends or a significant other), and they can reflect that understanding in their interactions with consumers. As our wants and behaviors update, machine learning processes these differences and informs marketers on our most up-to-date preferences.

Let’s take a look at what this means in the context of marketing. Say, for example, you are the father of a 1-year-old and recently got a half-off coupon for an expensive brand of diapers. You make your purchase and go about your day. Thanks to machine-learning technologies, the retailer you purchased from understands that you likely bought these diapers because they were at a discounted price, and therefore sends you another coupon for them, or advertisements for a similar, but more affordable alternative. And, because machine-learning platforms understand that you are a real person with changing needs, 5 years later you are not still receiving advertisements for diapers. Your 1-year-old is now 6 years old (and hopefully out of diapers), and the advertisements you receive have evolved similarly over time.

This is what makes machine learning ideal. If marketers used traditional data analytics in the same scenario, you would be pigeonholed by the demographics at your initial purchase and forever receive advertisements for products you either 1) could not normally afford, or 2) no longer need.

Beyond providing the right content, machine learning also can help marketers ensure timing is right. Based on a consumer’s habits as well as real-world shopping needs (for example, you only need to buy allergy medications once a month and have no use for multiple coupons for Zyrtec in a single week), machine learning dictates when advertisements are most likely to make the greatest impact. Ultimately, better timing increases the ROI and effectiveness of any marketing campaign.

Finally, to top things off, machine-learning platforms can help retailers optimize pricing to better compete with similar companies. At a macro level, machine-learning technology analyzes massive amounts of online and offline data to inform future pricing structures in the market. This prevents retailers from being overshadowed by competitors’ offerings, while also keeping them one step ahead of market trends and consumers’ purchasing preferences.

Now Is the Time to Explore Machine Learning

The benefits of machine learning may be straightforward, but actually implementing a machine-learning platform can be daunting. For retailers unfamiliar with the technology, the first step is defining particular end goals of machine-learning investments. Why? Because, thanks to advanced capabilities and analytics, machine-learning platforms can accomplish many tasks. For example, retailers can use machine learning to increase revenue, push a particular product, and increase customer loyalty.

By defining a target goal early, marketers can develop the strongest machine-learning strategy possible. This upfront work can even help retailers determine if machine learning is the right solution at all. Machine-learning platforms are great at many things, but they are not able to solve every business problem a company may face. For instance, while machine learning can help with difficult pricing and marketing decisions, a platform cannot make inferences about hiring.

If machine learning really is the right choice, retailers are encouraged to reach out to a third-party machine-learning platform. As mentioned above, implementing machine learning can be overwhelming, not to mention costly and time-consuming. Third-party platforms are equipped to navigate these stresses and also provide added benefits such as the expertise of operational support teams and seasoned data analysts, both of which enhance a retailer’s ability to leverage machine-learning investments and develop stronger customer relationships.

There’s no doubt we’ll still be hearing about big data years from now. However, as more retailers begin to embrace machine-learning technologies, it will become clear that new platforms have eclipsed traditional data analytics. Big data may be disruptive, innovative, and powerful, but so is machine learning. And machine learning is a lot more than that, too.