Conversational Analytics: Voice of Citizen Data Scientists

“One good conversation can shift the direction of change forever.”

This quote by Linda Lambert holds ample significance for modern-day business activities thriving mostly on customer interaction to run their technologies, brands, or devices. Buzz words like ‘Alexa’, ‘Siri’, and ‘Ok Google’ are the common terms used these days which have redefined modern-day business activities. Conversational analytics is the latest technology today adapted by companies to gain first-hand information about their customers and for businesses to gain reports and insights for marketing, sales, product, employee, finance, etc. in real-time.

The advent of advanced technologies like Natural Language Processing has redefined customer-technology interaction allowing the former to get an instant explanation of rendered insights with just a simple query in semantic language. Natural language query allows citizen data scientists to pose relevant business queries in simple language and get instant answers. Apart from that, voice processing has taken a step further by providing required answers to verbal questions, and in fact conversational in form of virtual AI assistants or chatbots.

Gartner in its last year report had accurately said: “The user experience will undergo a significant shift in how users perceive the digital world and how they interact with it or a ‘multi-experience’”.

What is Conversational Analytics?

Despite the claims of being self-service compliant and providing augmented analytics solutions, many BI tools still lag in providing a user friendly, convenient, and effortless climax or ‘last mile’ of the analytics journey. The ‘last mile’ is the stage where a citizen data scientist interacts with the analytics/BI tool and gets answers to his queries.

Imagine a virtual assistant which can guide you through the disparate applications, systems, and provide you with real time insights on a simple query. This is where the credibility of conversational analytics comes into picture.

As the name suggests, conversational analytics is defined by the technology to analyze human conversation. It encompasses natural language processing, artificial intelligence, and machine learning techniques to transliterate human conversation or queries into data and refine it for analysis.

Conversational analytics is an advanced cognitive computing technology that leverages artificial intelligence, machine learning, and speech recognition technology to assist humans with personalized analytics and intelligence.

What is BOT Analytics?

Chatbots have long been used by companies to facilitate user machine interaction and data collection. Potentiality of bots has often been leveraged in form of virtual assistants aiding customers to carry out specific tasks or activities, or in form of conversational agents, capturing, analyzing, and converting human response to actionable insights.

However, usage of bots is not just limited to cultivating new and emerging data sets, but also to align with customer experience and business intelligence technology. This lays the substratum of bot analytics, enabling the transition of data analytics paradigm from being sophisticated BI tools oriented to being application-independent. In fact, the effective combination of an efficacious bot architecture with the complex and unfathomable data analytics capabilities can bring immense business value in terms of improved operational performance and customer experience.

So, what exactly is bot analytics? Contrary to the traditional definition of bot analytics being the method of analyzing human-bot conversations to extract user insights, here we are referring to bot analytics as being an integral aspect of business analytics, delivering actionable insights at accelerated rate, higher accuracy, and most important, comprehensible language.

Bot analytics is integrating your analytics application with third party messaging applications through BI chatbots, eradicating the hassles of juggling across multiple applications and dashboards.

Given below is a screenshot of Chirpbot, BIRD’s AI-powered data analytics bot.

Figure 1: Chirpbot, BIRD’s bot analytics agent

The bot provides the following functionalities:

  • Accelerated access to microgranular business insights and key metrics in semantic language.
  • Real time alerts and on-time updates on critical business data to keep a tab on business operations.
  • Contextualized and personalized notifications subjected to user location, department, designation, etc.
  • Seamless integration with third party applications such as data warehouses, ERP, CRM, databases, facilitating easy extraction of data.
  • Safe and secured data analytics through multi factor authentication, user authorization, one-time authorization, etc.

While companies are contemplating the implementation of bot analytics into their business operations, it is important to realize and unleash the hallmark of AI in chatbot applications. In fact, here AI here does not refer to artificial intelligence, but rather to augmented intelligence which combines, not replaces, human intuition with machine intelligence. 

AI powered chatbot analytics works by the combination of two engines: Associative engine to enable exploration by leveraging relationship between data from various sources, and cognitive engine to suggest new insights based on data set and user given search criteria.

How does AI-powered Chatbot Analytics and Voice Analytics work?

Companies across the globe are capitalizing on AI powered chatbot and voice analytics to enhance their business intelligence and analytics adoption. Through effective collaboration with third party messaging applications and by leveraging combination of natural language processing and artificial intelligence, business users can now get a feasible solution to ask questions and generate insights on key metrics, key influencers, business calculations, etc. 

Unlike the one-dimensional rule-based chatbots, the AI-powered chatbots use methods like deep learning, machine learning to draw context out of raw data and provide users with relevant responses. Before you plan to ease your analytics woes by incorporating an AI-powered chatbot, let me acquaint you with the backend process involved in AI-powered chatbot analytics: 

How does NLP & Conversational Analytics work? 

Conversational analytics along with NLP has dramatically ameliorated the adoption of analytics by every employee in the organization. Queries can now be asked in text or verbally, imitating Google type search or digital assistants like Alexa. NLP and conversational analytics along with augmented analytics are not only providing easiest way to ask complex questions in simplest language but also facilitate automated insight generation and explanation.

This technology involves the following: 

As illustrated in above picture, conversational analytics involves concoction of several technologies to turn a conversation into meaningful insights.  

The first stage consists of a voice recognition system or natural language processing software to capture user query in text or voice format and transcribes the speech into data. It involves an effective acoustic engine processing which considers several factors like tone, ambient noise, language, and accent of the speaker before converting voice commands into data. 

The second stage or the most important stage involves technologies like natural language understanding (a part of natural language processing) to decipher human intent behind the text or voice queries. NLU is an integral part of techniques like sentiment analysis (to extract human emotions and sentiments), language detection (to comprehend written text), and topic classification to sort user texts to groups or topics. Another important technique involved is phrase clustering which groups similar queries and interactions. 

The third stage consists of an indexing layer which sorts and organizes the unstructured data in ordered format and makes it suitable for searching. The queried data is searched across multiple applications and systems before concluding upon a feasible solution. 

The fourth stage is the formation of response using dialog management technique. This consists of intent and entity mapping engine to categorize user queries and make relevant responses. Another important technique involved is natural language generation which converts the responses to human comprehensible format.  

Final stage is delivering the search results in human understandable text format or graphical formats such as charts, maps, graphs, etc. 

Even though the above-mentioned stages sound to be labor intensive and time consuming, in reality; these processes function in the backend while you get instant real time insights on a single type of a query.  

How Conversational AI Analytics will transform the business? 

Analysing conversational data would allow companies to get individual user interactions in real time. You can not only analyse ‘what’ content of the user interaction but also the ‘how’ or vocal cues of the conversation. Emphasis can be laid more on identifying customer interests and ways to optimize it for the company’s benefits. In simple words, conversational analytics allows you to provide a personalized experience to your customers. 

Conversation analytics not only helps in analysing human emotion embedded in a caller’s tone of voice, but also collaborate with artificial intelligence to collate this analysed data into your day to day metrics leading to behavioural prediction. 

Gartner predicts: “By 2022, most of the companies will be assessed based on their information portfolios rather than on physical assets and revenues.” 

Conversational analytics is proven to be the most indicative solution in leveraging this information to gain actionable insights in real time. 

Given below are different applications of conversational analytics in businesses 

  1. Ease of Accessibility: Traditional way of accessing and leveraging the massive amount of data was attributed mainly to specialized coders, statisticians, and analysts. AI powered conversational analytics has now opened the doors of imperative data to every business stakeholder starting from a salesperson to a CEO.
  1. Data Reports and Insights Consistency: AI enabled conversational interface or apps enables accurate and consistent data reports and insights to be available to the entire enterprise.
  1. Enhancing Customer Engagement: Conversational analytics would help companies bring sales to a personalized level for their customers. The real time metrics gained from user conversations can help decode individual interests, target required segment of audience, achieve higher conversion rates, and eventually increase in sales.
  1. Developing Better Business Strategies: Benefits of conversational analysis include sentiment analysis, root cause analysis which can unearth a lot of queries for many business departments, leading to new market opportunities, and eventually better business strategies.

The Challenges of Using AI to Understand Conversational Analytics 

Artificial intelligence or should I say ‘Augmented Intelligence’ has undoubtedly raised the credibility of data analytics by gradually diminishing the barrier between humans and machine intelligence. The search capabilities of analytics engines have come a long way from simple keyword recognition to complex semantic language recognition using methods like natural language processing and machine learning. However, despite the obvious benefits of conversational AI, it is imperative to consider its status quo, especially the underlying challenges, related solutions, and the subsequent developments. 

  1. Security and Privacy ConcernsOne of the main concerns of implementing artificial intelligence in conversational analytics is related to privacy and security of confidential data. While basic queries like asking for a weather report might be trivial in terms of security, intimate business reports, personal account data, medical information, etc. needs to be processed and transmitted more securely.
  1. Diversity in human language: Even though processes like natural language understanding have been influential in easing human-machine language, there is still a long road ahead considering the fact there are about 23 languages spoken by 50% of world’s population and English speakers only count for 20 percent of that spectrum. Undoubtedly, the challenge is to build a system which can not only understand the native language but also consider the dialect and cultural differences.

  1. Comprehending Human Emotions: Apart from understanding human spoken language, an effective conversational AI engine should also consider factors like accent, dialects, slangs for emotional quotients like humour, sarcasm, irony, etc. 

  1. Multiple and Concurrent Conversations: Forbes says 63 percent of people use smart speakers in their living room, making it obvious for conversations to be superimposed over one another. Henceforth, an effective conversational AI should be able to detect data redundancy and ghost identities.

Apart from the above-mentioned challenges, a most prominent challenge which cannot be overlooked is the prospect of inherent bias in search, which can result in predilections towards a specific data set or user section. Since conversational AI devices learn from user behaviors, it is evident to incorporate human bias ideas, leading to skewed actions in a single direction. 

Why Conversational AI is the Future of First-Party Data?

While digital advertising duopolies like Google and Facebook have provided immense value for data-driven marketeers in form of valuable consumer data, a lot of scrutinies are involved in maintaining privacy and data security. This means complying with regulatory standards that impose overwhelming control over the third-party data. Also, the user data available from third party sources are more general to be considered for the specific choices and interests of consumers. The fact that first-party data gives first-hand information of user choices, inclination, and preferences makes it the most sought-after solution for companies to remain agile and certain in their data-driven marketing efforts. 

First party data is the consent driven, declarative data disclosed by the users directly to brands and conversational AI is the singular source for gathering that data. In fact, conversational AI sources like chatbots and voice assistants are the primary sources to gain first party insights into customer-machine interactions. With consumers around the globe preferring continuous conversations with brands instead of mere ‘point of click’ transactions, conversational AI is gaining traction as the pivotal means of communication; calls, text messages, or chats. 

Do you know? 70% of consumers feel irked or frustrated when not being able to connect to a human agent, despite being connected to the company. 

Conversational AI enables companies to pursue goldmine of customer data; reason for conversations, the purchasing data, and most importantly, customer feedback on the data. Of course, there is a tough and stimulating task ahead to parse through this data and gain meaningful insights. 

Latest Trends in Conversational Analytics 

Conversational analytics has indeed resulted in a revolutionary shift in the way we perceive data as well as the way we connect to brands and use devices. Rise of messaging apps over social networks along with wearable devices working on Internet of things have resulted in businesses providing a conversational experience to their users. A spate of technological developments such as robotics, machine learning, artificial intelligence, etc. have transformed global economy into the era of intelligent automation.  

Given are top 5 trends in conversational analytics which cannot be missed out in 2021: 

#1 Companies are opting for purpose driven conversational AI deployment over dozens of chatbots 

Enterprises, these days, are looking forward to deploy purpose driven conversational AI for specific, contextual, profitable use cases, instead of squandering capital on third party chatbots. An enterprise wide platform would be suitable to initiate automated conversational agents into the mainstream wherein an assortation of stakeholders would collaborate on a single administration console. 

Gartner predicts: “30 percent of major enterprises will deploy a unified enterprise wide conversational platform for both customer service and employee effectiveness. 

#2 Conversational AI agents will be trained using NLP and Reinforcement learning models 

Reinforcement learning demands machine learning model to learn from its experiences rather than a preconceived training dataset. While NLP based conversational models will be trained using supervised models, they will be fine-tuned with reinforcement learning. 

#3 Intelligent Semantic Search will dominate the Insight-driven era 

While semantic search has been prevalent for the past 3 to 4 years, advanced natural language processing and natural language understanding algorithms have fuelled its demand to be the most anticipated conversational analytics trends in 2021. This effective combination will ensure an in-depth understanding of prominent ideas within the search and facilitate an effective generation of insights. 

#4 Minimal or low code platforms would lead to democratization of AI 

Gone are the days when hundreds and thousands of code lines would be required to accommodate every change in user requirements obtained through conversations. Organizations can not only customize conversational AI bots, but also make it easier for even the naïve non-technical user to create, test, and improve a chatbot model using API integration with messaging applications. 

Gartner says: “The model will shift from one of the technology-literate people to one of people-literate technology.” 

#4 Chatbots and Virtual Assistants market will be more robust with developments in natural language processing technology. 

Despite the preference of a single conversational AI platform across the spectrum, enterprises are still looking forward to deploying chatbots and virtual assistants in their business operations.  

Figure below shows various factors causing usage of chatbots by businesses: 

Figure 2: Factors causing chatbot deployment

Future of Conversational Analytics 

Gartner says: “15% of all service interactions would be completely taken care by artificial intelligence.” 

Advancement of technology has made data much richer and actionable with the increasing popularity of conversational interfaces like Slack, Facebook, Alexa, Google Home, etc.Business communications would dramatically change with different systems and devices adapting conversational analytic in providing customized responses to customer queries.

AI based conversational analytics are helping businesses to perfect their customer interactions and also automate data collection. Real time, around the clock data collection would give enable enterprises to pay immediate attention to errors or issues, which can be immediately addressed by live agents. 

Conversational AI is the most important aspect of conversational analytics and according to a recent report, the Conversational AI market would grow from 4.2 billion USD to 15.7 billion USD by 2024, at a CAGR of 30.2%.  With analytics being adapted by every employee and not just being limited to power users and business analysts, conversational analytics has successfully resulted in higher business impact. 

Want to explore Chirpbot’s conversational analytics capabilities, click to learn more.