Five Good Uses of Data Science in Products
There was a period, not all that long ago, where startups pitched themselves first as a machine learning or artificial intelligence company, using these technologies to solve complex problems and provide a unique user experience. Now, data science methodologies are much more ubiquitous, that for many new companies and products in specific sectors, to even think about not leveraging them would be heretical.
We all interact with data science daily in the products we use. Like any well-implemented product feature, it blends in seamlessly with the user experience. As a user, you don’t need to know what technology is running in the background of the products you use. You want them to solve your headaches, or provide you joy.
Here is our list of five good uses of machine learning and data science in products,
The thing that always turned me off to shopping for food online is that there is a flow to a supermarket or grocery store. You walk through the various aisles, and the food on the shelves speak to you, catch your attention, make you think of a recipe that you want to try. You may start with a list, but you always end up finding something new that you want to try out.
Ocado is one of the leading employers of data scientists and engineers (in fact our data scientists Jeremy and Johan hail from Ocado). AI and machine learning underpin all of Ocado, including factory layout, driver logistics, customer feedback analysis, responding to customer complaints, and the shopping experience. Ocado technology also helps users to navigate through their shopping more efficiently, having the right next product suggested to them to help them get their shopping done better and quicker. Or, more cynically, so you buy more.
Smart Compose in Gmail
I am a nervous emailer. I’ll often write something and go over it three or four times, changing tiny details, because it doesn’t sound right to me. That all changed when smart compose came around. Somehow, the machine predicting what I should say gave me more confidence to say it.
While that might not be the exact use case or problem to be solved when they started building the product, it does make it one of my favourite features of G Suite. I’d imagine for many power users, and people who live in their inbox, it presents a considerable amount of time savings.
When I first came across this feature, I thought the UI would be a bit awkward, as you have to hit tab to utilise the suggestion. However, in my experience, it fits in quite nicely with how I type. And now as I tap this blog post draft out in Google Docs I wonder when they will bring this to other parts of the G Suite.
The tech behind Smart Compose is pretty impressive. There are many challenges the Google team needed to overcome, including speed (it needs to suggest quicker than people can type after all), scale (providing the right predictions for a given user), and reducing bias in the suggestions.
It uses neural networks to take into account contexts, such as email subject and prior correspondence, and predict what the next phrase might be. They have an excellent blog writeup here on the technology.
Face Grouping in Google Photos/Other Photo Services
This post might give me away as a Google product power user. I love the facial grouping of Google photos. It makes finding the right picture of people, in a sea of the millions of photos we all have on our phones, super quick. I am always impressed by how well it groups people, particularly with my kids. The technology can connect their newborn photos with them as a toddler, even as I struggle to remember” is that Frankie or Archie in this one?” It can also distinguish my cat from the many other cat photos I have on my phone (don’t ask).
This facial recognition technology used across product and features within Google, and they allow developers to deploy the technology in their products, for instance, with the Firebase ML Kit.
Spotify Song Recommendations
I recently switched from the Google Play streaming service to Spotify (see, I can use non-Google products). One of the reasons it took me so long to do so was the headache of having to build a whole new library of music in Spotify. I didn’t want to go through it all and follow my favourite artists. What really surprised me when I made the move was how quickly, and how little data was actually required for Spotify to fairly accurately understand my musical tastes and actually start suggesting to me artists and songs that I frequently listened to on Google Play.
There are a few technologies and techniques Spotify uses to predict your musical tastes and create your tailored playlists. First is collaborative filtering, which makes recommendations to you based on crossover with other listeners with similar preferences. Spotify also uses natural language processing (NLP) and scours the internet, and tags songs based on how frequently they are mentioned alongside other artists and songs. The third method is raw audio processing and recommending similar songs based on like tempos, key and signatures. (more on these methodologies here).
Financial services is an area ripe for the application of machine learning and other data science techniques. The vast amounts of available data, along with the inefficiencies, fraud, waste and high fees, make it particularly exciting as a wave of financial technology startups turns the space on its head.
My favourite consumer application in this area thus far is Wealthfront. It automatically builds users a balanced portfolio of exchange-traded funds based on risk profile. It even rebalances your portfolio for you to maximise efficiency. They have also released new features to help with financial planning, such as helping set budgets for when you want to buy a house, start a family, make large purchases, even plan to take an extended holiday. It plugs in all financial accounts you have, your current portfolio and risk preferences, and market data to help you prepare.
Wealthfront’s model allows more consumers to have access to financial planning, advice and portfolio management for significantly lower fees. Previously you would have to pay financial advisors to help you budget, and generally, they require clients to have a minimum net worth. To manage a balanced portfolio, you’d have to either do it yourself, and pay fees to whichever account manager you had, and also have to remember to rebalance your portfolio, and change it as your risk profile changes. Instead, automation, data and machine learning helps you accomplish all this at a fraction of the cost.