The Machine Learning Showdown: Comparing Supervised, Unsupervised, & Reinforcement Learning

Many companies are making machine learning (ML) a cornerstone of their business strategies. You’re likely wondering how adopting an ML solution will transform your customer experience. A number of ML technologies are currently available with distinct advantages and disadvantages. Whichever solution you choose will guide your approach to customer retention and the overall lifecycle.

Adoption can be a dubious venture, however. ML is largely mysterious despite quickly nearing ubiquity. Our goal is to provide clarity and demonstrate how these solutions can automate your customer experience. 

ML frameworks depend on a foundational, technological approach that determines how they interpret contextual data. These approaches are commonly known as supervised and unsupervised. We’ll also break down a third learning method—reinforcement learning—which forms the backbone of our own automation platform.

Supervised Learning

Supervised models emphasize label-based learning. We have a plethora of data we must analyze and organize. Assigned categories help supervised algorithms pick out common relationships between items.

Example: Photo Libraries

Say you’re a photographer who owns a collection of 1,000 photos. Your device records details called metadata for every picture taken. This data is organized according to resolution, location, shutter speed, and time taken. This is extremely useful when it comes to sorting.

Apple and Google (to name a couple) devices curate groups of photos. ML algorithms sort these into collections by making metadata associations. This is how your photos app creates “memories,” or special collections based on context. These automations require no user interaction. The models learn from new photos, boosting accuracy and relevancy over time.

Advantages and Shortcomings

Supervised models make informed predictions based on the data they collect. Supervised algorithms draw more accurate connections between items over time. This is ideal for classifying things and making predictions. What will customers prefer in the future? How can we target certain demographics or usage patterns? Supervised solutions can answer these questions.

Supervised models require attention from data scientists. Growth specialists, marketing teams, and customer service departments may struggle to get the most from their frameworks. Companies must hire employees to analyze customer data, weakening the value proposition.

However, teams can trigger the nuclear option: “brute force” inspection. This allows the ML framework to inspect all available data at once. That approach is prone to inaccuracies since incoming data can have missing pieces.

Unsupervised Learning

Unsupervised models excel at learning via observation. These frameworks operate without a teacher, examining large pools of data without labels or categories to guide them. This ML solution’s job is clumping related items together, in a process called clustering. Team members don’t need to babysit their algorithms, cutting down on required experimentation.

Instead of merely finding links between items, their goal is identifying rules that describe large portions of data. The unsupervised approach looks at the big picture, making it easier to identify low-hanging fruit and quick wins.

Example: Diving into Online Retail

Unsupervised approaches are quite useful when identifying consumer trends. Consider the online retail space, where customers often buy complementary items in one session.

We can discover shopping trends by analyzing customer behaviors, which is where our associations come into play. Unsupervised ML algorithms will compile purchase histories and record customer interactions. The models determine if customers buy certain products together. In response, retailers provide suggestions to buyers as they browse. It may also be useful to group customers who purchase similar items.

Advantages and Shortcomings

Unsupervised learning is ideal when evaluating customer behavior. Associations and clustering are two effective ways of doing so. However, accurately identifying trends can rely on larger quantities of data. This is befitting of established services with large subscription bases—not those who are building from scratch. 

Unsupervised models are quite usable, since they’re more automated than other alternatives. Teams can offload more duties to the framework at all stages of the customer lifecycle. Companies must ensure their algorithms are configured appropriately, however, or problems can arise.

The Bellwethr Approach: Reinforcement Learning

Machine learning’s primary advantage rests with its ability to emulate human behavior. While supervised and unsupervised learning are beneficial, these approaches fall short in this department.

Reinforcement learning mirrors human thought processes quite closely. Through trial and error, these ML solutions are adept at finding efficiencies and inefficiencies in existing customer experiences. Reinforcement learning—and our framework in particular—focuses on actions in relation to various rewards.

Example: Subscription Service Customer Retention

Reward functions make it easy to A/B/C test offers that resonate with customers. These offers have the most impact on customers who are contemplating cancellation. Consider a service like Blue Apron, whose customers are its lifeblood. The very nature of subscription services is already fickle, yet keeping customers engaged means regularly introducing new meals into the fold. Pricing is a major factor. If the product becomes boring or the costs inflate, customers may bow out.

Reinforcement algorithms provide teams with useful tools in response to customer behaviors. You might try offering a teetering customer discounts on their service, should cost be prohibitive. Your reward system makes it easy to present customers with expanded package options. Perhaps they need to alter their subscription, or switch to a new meal plan. Customers aren’t always aware of these options, making them powerful retention tools.

ML managers can interchange these options as needed. This system is also presented graphically to users through their browsers. Such a presentation makes interaction simple and enjoyable, further reducing friction.

Advantages and Shortcomings

Reinforcement learning is extremely powerful, though that functionality doesn’t come freely. Companies must initially specify reward offerings, meaning there’s setup involved. Whoever manages the ML solution may need to experiment with options to find an optimal fit. However, our framework’s automations make it simple to identify actions with the most promise.

Thankfully, these customization options are nearly limitless; they lend themselves to customer experiences in multiple industries. Reinforcement learning harnesses both big data and context, when data sets aren’t developed. Even when customer enrollment is low, these frameworks are beneficial.

You don’t need a roster of data specialists to extract value from reinforcement learning. This ML approach is inherently approachable, making it friendly and practical for those touching the customer lifecycle. Reinforcement learning’s human elements—such as experiential learning—make it easier to work with.

The Perfect, Scalable Solution for Non-Technical Staff

Bellwethr is continually reshaping how businesses can wield the power of machine learning. Our mission is to democratize ML by making it comprehensible, accessible, and viable within multiple industries.

To learn more about our unique reinforcement framework and how it supercharges customer retention, download our latest eBook: The Dreaded Exit.