Why Vice Presidents of Digital Product Need to Start Embracing Machine Learning Today
It used to be that you had to program a computer to do certain things. Since the late 1990’s, there’s been a number of advances in digitization and cheap computing power. Because of these, scientists can now feed computers large amounts of data and enable them to learn from experience.
From Amazon’s product recommendations and Facebook’s ability to spot faces to Google’s self-driving car, more and more companies are turning to machine learning to improve and streamline their processes or create better ones.
Don’t get the wrong idea: machine learning isn’t just for billion dollars companies like Google or Amazon or fun projects like developing an algorithm that can master Go and win against the world’s champion. Vice Presidents of Product at Fortune 500 companies can (and should) implement machine learning into their digital products too.
Here’s why and how to do it.
The Difference between Machine Learning and Artificial Intelligence
One common misconception people have about machine learning and artificial intelligence is thinking that the two concepts are interchangeable. The truth of the matter is that machine learning is just one aspect of the broader notion of artificial intelligence.
Artificial intelligence refers to the simulation of human intelligence by machines. Sure, your phone can understand voice commands and a computer can beat the world chess champion, but we still have a long way to go until we create a real AI.
Machine learning, on the other hand, is a form of AI where a computer analyzes bits of data to teach itself how to perform a task. Google didn’t program its car to drive. It learned by driving millions of miles and observing the vehicles around it. Similarly, it didn’t program its algorithm to play Go; it fed it decades of publicly available Go games, and the computer learned the rules and strategies by studying the data.
Machine learning has been at the center of numerous breakthroughs, which is why savvy companies are introducing it to their business operating models.
Why Should VPs of Product Design Be Embracing Machine Learning?
If handled properly, machine learning could bring huge benefits to your department and the business as a whole. Not only that it will allow your company to keep pace with the ever-changing business environment, but it can also provide you with valuable insights and enable you to act at the right moment and take advantage of various opportunities.
● It Will Lead to Better Product Design
The design of digital products often includes developing interactive interfaces that allow users to make choices. For example, you can establish what movie recommendations you get on Netflix by expressing your preferences. Or, in Twitter’s case, the social media platform will constrain your timeline so that it shows update only from the people you follow.
Machine learning could change all that. An ML algorithm could predict what a user wants to see and eliminate the need for a complex interface. Since users will no longer have to spend a lot of time scrolling through content, the experience they have on your platform will improve significantly.
● It Provides an Insightful Understanding of Your Customers
Knowing your customers, understanding what drives them and the triggers that convince them to choose a company over another are the overarching rules of exceptional product design. Competent VPs know their customers as well as they know their own families.
But, getting there, and creating a customer-first mentality that is ingrained into your department is no easy task. It takes a lot of time and hard work to research everything you possibly can about them, from their goals and dreams to what makes them tick.
That’s where machine learning could come in handy.
● Machine Learning Excels at Collecting and Analyzing Data
Here’s an example: you’ve designed a new fitness bracelet and leaked a video of how it looks and works. A machine learning algorithm can monitor social media chatter, examine your target audience’s response to the product, and provide possible solutions. That way, you can tweak the product to resonate with what your customers want.
Here’s another example: You’re about to update your gaming platform and the new version won’t include a feature you deemed as unimportant. But, when users learned about the change, they took to social media to voice their complaints. Using machine learning, you were able to monitor conversations and understand what your customers expect from the update. Based on the data collected, you tweaked your gaming platforms and turned complainers into brand advocates.
● Automating Processes & Boosting Efficiency
Imagine that you are standing in line at the supermarket. You have a million different things that you need to take care of, but you can’t do any of them without buying this important item. So, you’re forced to sit in line for minutes on end when you could be using your time more efficiently. To make matters worse, the cashier’s machine breaks down.
It may sound like a nightmare, but that is a scenario VPs of Product face on a regular basis. Slow and inefficient processes are making it difficult to develop outstanding products and deliver them on time.
With machine learning, you can eliminate repetitive tasks, increase the speed and efficiency of essential processes and reduce them to just a few clicks.
● Predictive Maintenance
Machine learning makes it possible to detect anomalies in the system and address them before they affect the entire design process. Instead of having to wait for hours to fix the problem, ML can anticipate breakdown and streamline maintenance.
What Does It Take to Get Started?
VPs of Digital Products can exploit the benefits of machine learning but only if they have a strategic plan in place. Without a long-term game plan, machine learning risks becoming just another tool in your department’s arsenal. Sure, it will probably help you streamline the product design process, but its capabilities will be limited to cookie cutter applications.
VPs planning to embark on the machine learning train must accomplish three crucial tasks first:
● Analyze all possible ML solutions
● Develop a long-term strategy
● Acquire the necessary expertise to ensure proper implementation
With a data scientist on your team, for example, you can develop a strategic vision that takes into account not only the need for collecting and analyzing data, but also the gaps in your inputs and the resources you’ll need to address them.
What’s the Role of VPs?
Machine learning will undoubtedly change how your department operates and will require drastic behavioral changes. Your role as VP of Product is to encourage and enable the change.
Don’t expect that to happen overnight, though. Acquiring the necessary skills and adjusting to the new requirements will take time. Make sure you provide your team with the right tools to make the change swift and smooth.
Understanding how machine learning works or why it provided you with a certain solution can be tricky. It’s like trying to understand why you choose to see that tearjerker movie or an action flick, or choose a Thai restaurant over a Chinese one. Sure, you may provide a rationale for your actions (a friend recommended the movie, or you wanted to try something new,) but the reality is that in most cases we make the decision first and then justify it with logic and reasons.
That’s part of the beauty of machine learning. By collecting and analyzing tens of millions of data bits, it can give us a glance into why we’re behaving the way we do and why do we make certain decisions. For Vice Presidents of Digital Products that could make the difference between creating something that falls flat and a product that customers will love using.
Here’s the thing: in this day and age, knowledge means power. And, machine learning could help you achieve just that. However, you need to be extremely careful about how you implement it. Simply integrating it with your existing processes without a clear vision and long-term plan in place won’t allow you to make the most out of it.
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