How Botzer (AI powered enterprise chatbot) helped automate customer support for Future Generali.

By | Botzer, Case Study, Chatbot | No Comments

Written by Gopinath P, Project Manager at Powerupcloud Technologies.

When you are one of the top Health Insurance service providers in a country such as India, with a population of over 1.3 billion people, you should know that your customer care team is going to be working around the clock to resolve customer queries & issues.

Powerup engaged with Future Generali India Insurance Company Ltd., where currently the Customer care team is the single point of contact for handling servicing queries and complaints from the customers to facilitate an end to end process in a Life insurance policy. Ensuring a high level of customer satisfaction remains core to such businesses. An approx. of 30,000 queries on a monthly basis have been received on this dedicated call centre. These calls are currently catered to by manual agents, which might lead to a higher load on agents & marred by manual inefficiencies.

A separate team looks into the selling of the policies, increasing business volumes & driving revenue. Being a completely manual process, this creates a bottleneck into no of sales and a host of times, creates incorrect recommendations on policies to be sold. Resulting in a high amount of customer churn & lost business.

When we at Powerup were approached with the above problem statement, we first analyzed the call recordings from their call centre. Most of the queries could be classified under a limited set of service queries. In addition, while enquiring about a policy, the user is generally looking out for a set of recommendations on policy, premium & payment terms, which would suit the user requirements. Powerup solution specialists designed a solution that could not only support customer queries but also recommends the most relevant policies to the customers, helping them sign up & close deals much faster. Powerup’s Botzer was a perfect fit for the Insurance giant, with readymade modules & integration available.

Botzer is an AI-Powered enterprise chatbot, which allows customized business solutions to be deployed & hosted in the customer’s account, integrating with multiple Enterprise Systems. Powered by intelligent Natural Language Processing & Machine Learning algorithms, it is capable of understanding even the most complex customer queries. On Botzer, customer support was automated for this Life Insurance giant across channels, including their website, customer mobile apps & Social channels, such as Facebook Messenger as well.

The customer response time was reduced to within 3 minutes to complex queries, as compared to 24 hours earlier. The resolution rate also increased by more than 50%, while the agent load reduced by 60% for inbound calls.

The bot not only improved the post-sales support but also gave targeted recommendations to the customers, basis their preferences & lifestyle, allowing them to buy policies within minutes.

The bot also performs sentiment analysis on the queries coming in from the users, responding to the users’ basis the identified sentiment. A positive sentiment sends the customer a happy & a blushing reply, while negative sentiment is replied to with an empathetic tone to the customer. If the bot is not able to respond to a query, the query is passed to a live agent to engage with the customer.

The bot also automates workflows to accelerate & close sales. While recommendations are given to the customers’ basis their preferences, the bot then connects users to a live agent to close the deal.

Bot provides recommendations to the user basis lifestyle & preferences

With Botzer, we not only automated their customer support, consolidating the customer support experience across channels but also provided a comprehensive set of analytics. These custom-built dashboards allowed the business to view user profiles, user preferences, journeys & how they have transacted with the system. Marketing & Customer servicing teams gain customer buying behaviour & preferences insights, allowing them to design high performing campaigns, resulting in higher ROI.

Chatbots 2.0 — The new Series of bots & their influence on Automation

By | AI, Artificial Intelligence, Blogs, Chatbot | No Comments

Written by Rishabh Sood, Associate Director — Advanced Services Group at Powerupcoud Technologies

Chatbots as a concept are not new. In fact, under the domain of Artificial Intelligence, the origin of chatbots is quite early, tracing back to as early as 1955. Alan Turing published “Complete Machinery & Intelligence”, starting an unending debate, “Can machines think?”, laying the foundation of the Turing test & eventually leading to ELIZA in 1966, the 1st ever chatbot. It failed to pass the Turing test but did start a horde of chatbots to follow, each one more mature than its predecessor.

The next few years saw a host of chatbots, from PARRY to ALICE, but hardly any saw the light of the day. The actual war on the chatbots started with the larger players coming into the picture. Apple led with Siri in 2010, followed closely by Google Now, Amazon’s Alexa & Microsoft’s Cortana. These chatbots made life a tad easier for the users, as they could now speak to Siri
to book an Uber or tell Alexa to switch off the lights (another way to make our lives more cushioned). While these chatbots did create a huge value to users in terms of making their daily chores automated (& speak to a companion, for the lonely ones), business still was a long way from extracting benefits from the automated conversational channel.

Fast track to the world of today & we see chatbots part of every business. Every company has budgets allocated for automating at least 1 process on chatbots. Oracle says that 80% of the businesses are already using or have plans to start using chatbots for major business functions by 2020.
Chatbots have been implemented across companies & functions, primarily with a focus on automating support systems (internal as well as external). Most of the bots available in the market today respond to user queries basis keywords/phrases match. The more advanced bots today use the concept of intent matching & entities extraction to respond to more complex user queries. A handful of bots today even interact with the enterprise
systems to provide real-time data to the users. Most of the commercially successful bots in the market today are text-based interactions.

Most of the bots in action today augment tasks, which are repeatable/predictable in nature. Such tasks, if not automated, would require considerable human effort, if not automated. These chatbots are powered by Natural Language Processing engines to identify user’s intent (verb or action), which then is passed to the bot’s brain to execute a series of steps, to generate a response for the identified intent. A handful of bots also contain Natural Language Generation engines to generate conversations, with a human touch to it. Sadly, 99.9% of today’s implementations will still fail more than 60 years old Turing test.

It’s true that the conversational Engines, as chatbots are often referred to as, have been there for a couple of years, but the usefulness of their existence will now be brought to test. The last couple of months have seen a considerable improvement in how the conversational engines add value to the businesses, that someone refers to as the chatbot 2.0 wave.

At Powerup, we continuously spend efforts on researching & making our products & offerings better, to suit the increasing market demands. So, what can one expect from this new wave of bots? For starters, the whole world is moving towards voice-based interactions, the text remains only for the traditional few. So, the bots need to be equipped with the smart & intelligent voice to text engines, which can understand different accents & word pronunciations, in addition, to be able to extract the relevant text from the noise in the user’s query, to deliver actual value. The likes of Google & Microsoft have spent billions of dollars on voice to text engines, but the above still remains a tough nut to crack, keeping the accuracy of the voice-based system limited in the business world.

With the voice-based devices, such as Amazon Echo & Google Home, bring convenience & accessibility together. Being available for cheap & in mass (the smart speakers’ market is slated to grow to $11.79 billion by 2023), makes it a regular household item, rather than a luxury. The bots will have to start interacting with users via such devices, not limited to the
traditional channels of Web & Social. This will not only require the traditional voice to text layers to be built in, but specific skills (such as Alexa Voice Services for Alexa compatible devices) to be written. A key factor here is how the user experience on a platform that is purely voice-based (although Echo Spot also has a small screen attached to it), where visual rendering is almost nil, is seamless & equally engaging for the users, as is on traditional channels.

In 2017, 45% of the people globally were reported to have preferred speaking to a chatbot, rather than a human agent. 2 years down the line, chatbots are all set to become mainstream, rather than alternative sources of communication. But this poses a greater challenge for the companies into the business. The bots will now have to start delivering business value, in terms of ROI, conversions, conversation drops & metrics that matter to the business. HnM uses a bot that quizzes the users to understand their references & then show clothing recommendations basis the above-identified preferences. This significantly increased their conversion on customer queries.

The new age of chatbots has already started moving in a more conversational direction, rather than the rule-based response generation, which the earlier bots were capable of. This means the bots now understand human speech better & are able to sustain conversations with humans for longer periods. This has been possible due to the movement of the traditional intent & entity models on NLP to advancement on Neural networks & Convolutional networks, building word clouds & deriving relations on these to understand user queries.

Traditionally, Retail has remained the biggest adopter of the chatbots. According to, Retail remained to occupy more than 50% of the chunk in the chatbots market till 2016. With the advancement being brought into the world of chatbots at lightning speed, other sectors are picking up the pace. Healthcare & Telecommunications, followed by Banking are joining the race of deriving business outputs via chatbots, reporting 27%, 25% & 20% acceptance in the area in 2018. The new wave of bots is slated to narrow this gap across sectors in terms of adoption further. A study released by Deloitte this year highlights the increase of internal chatbot use-cases growing more than customer-facing functions, reporting IT use-cases to be the highest.

Chatbots have always remained as a way of conversing with users. Businesses have always focused on how the experience on a chatbot can be improved for the end customer, while technology has focused on how chatbots can be made more intelligent. The bots, being one of the highest growing channels of communication with the customers, generates a host of data in the form of conversational logs. Business can derive a host of insights from this data,
as the adoption of bots among customers increases over the next couple of years. A challenge that most businesses will face would be the regulatory authorities, such as GDPR in the EU. How business work around these, would be interesting to see.

Mobile apps remain the widest adopted means of usage & communication in the 21 st century, but the customers are tired of installing multiple apps on their phones. An average user installs more than 50 apps on a smartphone, the trend is only going to change. With multiple players consolidating the usage of apps, users will limit the no of apps that get the coveted memory on their mobile phones. This will give an opportunity to the businesses to push chatbots as a communication channel, by integrating bots not only on their websites (mobile compatible of course) but other mobile adaptable channels, such as Google Assistant.

According to Harvard Business Review researchers, a 5-minute delay in responding to a customer query increases the chances of losing the customer by 100%, while a 10-minute delay increases this chance 4 times. This basic premise of customer service is taken care of by automated conversational engines, chatbots.

Chatbots have a bright future, especially with the technological advancement, availability & adaptability increasing. How the new age bots add value to the business, remains to be seen and monitored.

It would be great to hear what you think the future of automated user engagement would be and their degree of influence.