This is an excerpt from the full article titled Chatbots and The Future of Apps in India, originally published on our Medium publication.
I had just moved back from my startup stint in London and I wanted to see how the burst of smartphone adoption had impacted the lives of those traditionally used to a life without dependency on technology, here in India. There were quite a few trends that stuck out, but one in particular. The neck-breaking growth of messaging, especially WhatsApp. It was this realization that would drive my contribution to Haptik, a personal assistance service over chat.
Imagining the future of apps and sharing novel ideas on the subject of Chats & Chatbots has became a trend amongst strong opinionated (smart) minds of the human race. In these tides of strong opinions, Indian companies are not well represented. My experience at Haptik helps me contribute with insights into what I believe could be the future of our interaction with mobile phones.
People engage in 100–200 transactions everyday. Right from sending text messages, replying to emails, setting reminders and placing lunch orders the list goes on. We love transacting. Maybe because it makes us feel productive. So when Jobs put a smartphone in our hands, that was it made us feel powerful and we naturally transitioned from old/dumb phones.
Now, with over 4.1M Apps on the Google Playstore & App Store combined, our mobile phones are heavily cluttered. In fact mobile apps in 2016 are more like tasks that a user wants to get done than startup companies. As an effort to stay in the lead, startups moved with the strategy of making their services accessible from within other apps. They started assigning separate teams to build and maintain APIs (and SDKs in some cases) that other apps can consume to include their experience within new interfaces. This eventually led to the age of API’s and suddenly everybody was seen competing with each other. If they weren’t competing, they were merging and consolidating. This had to end, and one thing was clear it wasn’t a phone (hardware) memory problem. There had to be evolution in the way users interacted with apps.
Haptik’s Approach and Artificial Intelligence
When things started blowing out of proportion, Haptik had to sync up on the big bets. We used our first-movers advantage to make some data-driven decisions.
The conversation that eventually led to our decision started out with Aakrit (CEO) and I having breakfast one morning. The (food) menu was a never ending list of things and it was impossible for me to decide my order. It struck me how similar this menu was to our app/play store. The App store is a massive menu of things I can do when I’m busy or bored.
Think about when you open up your phone. You’re either busy and want to get something done (call, text, reply, post, upload, share) or you’re bored and you want to entertain/educate/enhance yourself (games, photos, discovery, reading). The apps on your phone and more importantly your notifications say so much about you. They are the choices you made when you were given the menu.
But, menus are confusing and frustrating when you’re looking for something specific. So, we came up with a simple theory:
If you control the items on a menu, you control the choices one can make. If you control the choices, you can predict decisions. Knowing how people invest their time and which choices they make when given a fixed menu defines their mental (space) investments. This is the meta-data of your so called “interests” that can be used to provide hyper-contextual experiences.
This theory led to an experiment that eventually refined the services that Haptik currently focuses on.
What works for Haptik is human-enabled AI (Artificial Intelligence). Think of it this way, when you send a message on Haptik our bot takes the first shot at trying to decipher what you mean. If unable to meet 99% accuracy, the bot will break (in the background) and ask a real human to help resolve this query. The request is then matched to a relevant human (assistant) who is chosen based on age, geography and expertise to answer. So a user asking about recommendations for good Italian food in New Delhi is more likely to be answered by the bot. But a request for pet-friendly restaurants in Mumbai which have great veggies burgers will be taken up by an assistant in Mumbai . This assistant while being hired would have been categorized as a foodie and loves speaking to users about the best places to eat.
As the assistant answers, the bot learns and saves context for future users from that demography. It also learns the meaning of what was being asked for and relates it to one of our pre-defined task categories. It’s extremely difficult for a bot to understand and respond to users from varied cultural backgrounds. This is where actual humans help our bot learn about these interactions. While this approach takes time, and we’re in the early stages, the beauty will be in scale.
Our bot will soon be able to understand you as a person based on your previous chats and respond in chat lingo that you’re used to. So when you say “Ur taking to long” or “You takin so long.” The bot is able to interpret your impatience and comeback with a status update on your request.
You set a daily wake up reminder on Haptik for 9am.
Result: We know you’re up at 9am.
When you launch the app for this reminder, you’re most likely at your home.
Result: We know the area that you live in.
You ask us about best routes/traffic between 2 points.
Result: We know the areas you usually commute between.
If you’ve ordered lunch to a specific address few times it’s most likely your workplace.
Result: We know the area in which you work.
With subtle information, we’re able to do some really cool stuff. We can arrange a phone call if you don’t snooze the wake up reminder. Notify you 30mins after you wake up with the fastest route to work or the option to book a cab. Also, allow you to pre-book your meals while en-route to work and maybe get a coffee delivered to your table before you get there.
But that’s not all.
The next step in making chatbots smarter is….well, other chatbots.
We build and deploy chatbots that have learnt how users frame sentences and run tests on the main consumer facing chatbot. This is a continuous process that runs 24 x 7 to teach the bot how similar users would (most likely) ask about different use cases.
Where is all this going?
Every company has a subjective opinion on what they think the future of their industry is. Everyone optimizes to find their spot in the grand scheme of things. In my subjective opinion, I think all our (apps) efforts will converge into a singular objective of reducing the mental space and time required to get things done. Mobile distribution is capping out, and it’s called for some urgent and important paradigm shifts.
This post is an excerpt of the entire article that talks about “Chatbots & The Future of Apps in India.” You can read the entire post here.