Everybody who nurses their inner geek would be familiar with JARVIS, Iron Man aka Tony Stark’s very own AI-enabled personal assistant. One of the most fascinating aspects of Jarvis was his ability to speak to Tony, understand his needs and provide fixes and solutions.

Modern life isn’t looking too different from the magic of the Marvel Cinematic Universe, at least from a technology perspective. Conversational AI is probably one of the biggest developments in the field of AI today, quickly transcending the boundaries of entertainment and leisure, and playing a critical role in shaping business insights, enabling governance and aiding the public with information.

One of the first companies in India to revolutionise the AI landscape is Haptik.ai, also among the world’s largest conversational AI companies having reached more than 100 million devices, processing more than 2 billion conversations. Haptik’s enterprise partners include Coca Cola, KFC, Future Group, Club Mahindra, OYO, Grofers, Viacom18, among others – these companies have converted several aspects of their service pipeline into a conversational model using the tools provided by Haptik.ai – namely an enterprise tool kit, a software development kit (SDK) and an ad-tech platform for engagement.

Recently, Haptik discovered that Mobile Consumer Engagement is a challenge faced by every business in the digital era. One of the main reasons for this is the oversaturation of the market. Given that the average smartphone user receives over 49.5 push notifications per day and more than 100 apps on their phone, with access to a catalog of 3M Android apps on Google Play, standing out among this multitude is a tall order.

While acquiring customers is fairly easier and cheaper, retention is a growing challenge for new apps in the market. Retention is a key metric, which determines an app’s success in the long run. Engagement determines how connected a user to the product or service. Therefore, good engagement rates justify a high loan to value ratio for a business as they promise to build on existing engagement rates and grow them.

Haptik’s clients wanted to boost engagement on their existing mobile platforms with an all-in-one service – which did not require major development efforts and without an exponential increase in their app’s memory footprint. They wished to leverage a medium that would increase their retention rates and provide great utility to their users. It was also necessary to stand apart from other mobile utility services keeping in mind the problems the mobile industry was facing and make use of messaging as a communication tool due to its growing usage and popularity.

Messaging as a Communication Medium:

The main factor that drove Haptik’s breakthrough solution was the rise of Messaging as a Communication Medium. With the meteoric rise of Smartphone users in India with a majority comfortable with chat as a communication medium, it made sense to leverage that growth to boost engagement. High user engagement could be achieved through high-utility chatbots (to complete day-to-day tasks). This roster of pre-trained chatbots then needed to be bundled into an SDK that could be plugged into an app.

One of the major ways mobile engagement runs is through push notifications. However, as more and more brands start leveraging push notifications, their effectiveness lowers. The solution is personalisation. For instance, any user is more likely to click on a message that comes from a friend or a utility based message as opposed to an advertisement from a brand. However, the costs of building an in-app messenger are too high to justify the returns. In the same vein, all digital marketing efforts and engagement efforts (SMS, Social Media platforms) are slowly becoming ineffective as saturation rates hit an all-time high.

The only solution that can address all these issues at the same time is a chatbot. However, there are very few existing tools where a chatbot (or multiple) could easily plug in to an app and provide multiple services at once. Chatbots are also a fairly new entrant into the market purely as engagement tools.

In order to fulfill our clients requirements, Haptik had to build the following capabilities:

• An SDK that contains multiple chatbots that can be instantly embedded into any app or web client with a memory footprint under 1 MB.
• A chatbot with great utility: Our entire roster of chatbots includes over 40+ bots that offer everything from reminders to flight/cab bookings to bill payments to jokes. We will be talking about one of our most important use cases: Our ‘Reminders’ chatbot.

The chatbot converses with a user to understand their needs and then calls and reminds them about their specific task.

Haptik trained its models based on User Generated Data – where conversations that the users have is fed back to our algorithms so that they can improve with every conversation. A chat assistant can take over the chat when the bot is unable to handle it and this data is used to train the chatbot. For an idea of scale, Haptik had processed more than 0.65 billion messages till date from our network across platforms.

There’s an entire team of people dedicatedly responsible for generating new data every day. These roles can be broken down into 3 sub-areas Bot Builders: Making a bot end to end i.e. developing the character, tone, content, and responses of the bot. AI Bot Trainers: Monitoring user queries, labelling data that is unanswered and feeding it back into the data set for the bot to learn and respond to those queries over time and Bot Quality Analysts: Reviewing the bot from a qualitative perspective, understanding user requirements and relaying that feedback to the product management team to improve the experience. These Bot Quality Analysts are responsible for improving a bots capabilities by observing it’s behaviour, and improving its personality and quality over time. Their responses and suggestions are fed into the system and we have built tools for easy entry of data which is already annotated. Additionally, Wikipedia articles so that the bot could understand the basics of the English language.

A robust data pipeline helps understand what the user says and tries to find an answer for the user. To actually call a user once a reminder is set, an event-based architecture is set up – where a job is scheduled to run at the time of the reminder, so at the time of the reminder, Haptik tell the partner to call the user. When the user answers the call, TTS (Text-to-Speech) is applied to have a machine remind the user with the appropriate message.

How Does The Solution Work?

Once the user enters the channel, they can either tap a particular task or directly enter their reminder request as a message. Eg. Wake me up at 7 am. The chatbot asks for any details that are missing. Eg. On which days should I remind you? The reminder is set.

Source: NASSCOM Community

Image: Flickr