Let’s understand how a conversational AI can help connect the problems to the solutions effectively in real-time. Take an instance where there is a conversation between a customer care executive of a fashion store and a customer that is effectively solved with the help of a conversational AI.
The conversational AI uses sentiment analysis to understand the customers’ state of mind, makes recommendations to the executive for better solutions, and quick recommendations related to the offers of the brands the customer liked or has in their wishlist increases the overall satisfaction of the customer.
This is how conversational AI helps in solving customers’ issues in real-time. Many contact centers are already infusing AI into their operations and help desk tasks. Although AI is not going to eliminate the staff, it is going to aid a huge scale-up of the help desk services.
Are you a contact center organization looking for real-time & customized analytics solutions for your business?
You would have learned how Advanced Analytics aids the contact centers to enhance the customer experience thereby converting contact centers to revenue centers. Our Conversational AI product FLASH comes in handy providing meaningful insights in just a matter of seconds.
Conversational AI-led analytics is indeed the game-changer for contact centers was a 3 part blog. The part 1 concentrated on the problems faced by the contact centers which was followed by the solutions conversational AI could offer in part 2 and how the conversational AI solved the problems in real-time in part 3 of the blog.
BPO – Analytics: A prerequisite for operational efficiency:
The contact center has extensive information about customers, their behavioral and non-behavioral data, product experience, and the interactions with the frontline staff. With appropriate analytics, the information could be modeled to predict very useful characteristics to promote the product, up-sell/cross-sell, predict the intent of the customer call, etc.
BPO sector sets forth the following expectations which can be achieved through Advanced Analytics:
The historical and current data needs to be analyzed to uncover the masked opportunities for the strengthening of the organization.
The potential customers have to be identified, segmented, and targeted for the marketing campaign.
The unstructured data from the customer complaints have to be extracted and analyzed further to enhance customer service strategies.
The data from social media requires to be mined to determine emotions and opinions about brands, products, and services.
The customers who are likely to cease the service from the business need to be predicted.
The intent of the customer has to be predicted to give what exactly the customer wants before they ask for it.
There is a need for prediction of the future using extensive evaluation of the past performance
Recurrent issues of a product should be categorized for future enhancements.
The best products to be recommended to customers are to be predicted so that they can easily find their desired product.
The total value a customer can bring to a company throughout their lifetime is to be measured.
Analytical Solutions for Contact Centers:
geniSIGHTS understands the pressing needs of Business Process Outsourcing companies and has come up with a handful of analytical solutions. These intelligence solutions yield valuable insights to organizations ensuring the highest level of customer satisfaction.
Some of the analytical solutions offered are:
Uplift Modelling
The Uplift Modelling ensures that the customers who have higher propensity scores are rightly identified and targeted for the marketing campaigns. The customers are segmented into four categories based on their behavior after being encountered with a marketing action.
Sure Things – Customers who would have responded whether they were targeted or not.
Lost Causes – Customers who will not respond irrespective of whether or not they are Targeted.
Sleeping dogs – Customers who are less likely to respond because they were targeted.
Persuadables – Customers who only respond to the marketing action because they were Targeted.
Cross-sell/Upsell recommendation
The cross-sell recommendation suggests the customers for the complementary product when they purchase a new product. The upsell recommendation shows up the customers with a better version of the product that they plan to buy. This system uses affinity analysis/association rules mining to analyze the co-occurrence of relationships among activities performed by customers and discover the cross-sell/up-sell opportunities accordingly.
Recommendations Systems
The recommendation system exceptionally helps the business to predict the most likely product that the customer will buy. This system can therefore provide more personalized offers for the items of the customer’s interest.
Predicting customer churn rate
Customer Churn analysis predicts the customers who are likely to cancel their subscription from the company. The analysis also helps to understand why the customers are leaving and the possible ways to reduce the churn.
Sentiment mining
Sentiment mining extracts the necessary information from social media to understand what people think about a product or how they look up to a brand. The emotional tone behind each mention in social media can be derived from this analysis.
Customer Effort Analysis
The customer effort analysis brings out the amount of effort a customer puts in getting his issues resolved through various touchpoints.
These are some of the advanced analytical solutions that the conversational AI could offer for the problems faced by contact centers. Wait for edition 3 to find out how Conversational AI resolves all the problems of contact centers in real-time.
Are you a contact center organization looking for real-time & customized analytics solutions for your business?
Learn how Advanced Analytics aids the contact centers to enhance the customer experience thereby converting contact centers to revenue centers. Our Conversational AI product FLASH comes handy providing meaningful insights in just a matter of seconds.
Conversational AI-led analytics is indeed the game-changer for contact centers is a 3 part blog. Part 1 concentrated on the problems faced by the contact centers which is now followed by the solutions conversational AI could offer in part 2 and how the conversational AI solves the problems in real-time will be in part 3 of the blog.
We feature Mr. Dorai Thodla in our monthly Feature corner, He’s Founder of Technology Strategies LLC and iMorph Innovation Center Pvt. Ltd. this month. He has co-founded 4 software companies – two in India and two in the USA. Mr. J. S. Sridharan, our Co-Founder, and Senior Vice President ask a few questions to Mr. Dorai Thodla. You may follow Dorai on Twitter (@dorait) and LinkedIn.
He helps organizations leverage emerging technologies for building skills and creating innovative products. His companies build Information Assistants, a collection of tools for gathering and analyzing information.
He considers the ability to do informal research and tracking technology trends as important skills for the future of work.
Sridharan – With reference to the informal research process, could you tell us more about which areas this would apply to?
Dorai – There are a few areas where quick informal research can help.
1. A product team trying to validate a set of ideas. They may need to know what already exists and whether they should build a product.
2. A startup with reasons similar to product groups, but may need additional information about the market size, how the market is growing, current players, and whether there is a viable business model.
3. Finding gaps and opportunities in emerging technology trends. Inferring latent needs from discussions and analyzing competitive market spaces.
Sridharan – Why do you think organizations would undertake informal research and at what level?
Dorai – It depends on the size of the organization. Small to medium organizations may use it at a tactical level. For example, a small product group may use it to do competitive analysis, opportunity analysis for product extensions, etc.
Sridharan – We know that we can leverage Twitter as a tool for any informal research. Can you share insights on how twitter hashtags will help organizations in this research?How can we leverage LinkedIn for this? What are some other tools we could use for doing this informal research?
Dorai – Hashtag’s usage is not uniform. Some of the most influential tweeters (like Paul Graham) do not use hashtags at all. An analysis of hashtags shows that they are used when you want to join conversations or when you are in a conference or other similar events.
Linkedin
Twitter is a broad platform serving a wide variety of people. You can see authors, journalists, news organizations, technology people from the small, medium, and large organizations. LinkedIn is different. It started serving the needs of HR and recruiters. For a while, now LinkedIn is broadening the base but the type and quality of conversations are very different. It is easier to find influencers and people from large organizations. The profiles on LinkedIn are much richer.
The major problem with LinkedIn is a highly restricted API. So gathering information using automated tools is a bigger challenge.
With respect to other tools, If you cannot afford to subscribe to expensive research resources from experts in the field (students and startups have this problem), there are many tools you can cobble together to do your own research. You can call this DIY Research or Informal research. These tools include:
– Search engines
– Blogs and syndicated feeds
– News sources (both raw and aggregated)
– Websites and Portals
– Social media tools like Twitter but also LinkedIn and to some extent Facebook
– Wikis including Wikipedia and its properties
– Trade and Research publications
– Data and Scholarly search engines
Sridharan – How can startups leverage social media for tracking technology trends?
Dorai – If you want to speak the language of your customers, you need to use the vocabulary of your market place. These include idioms, phrases, and memes in those places. For example, tl;dr (too long did not read) is a common term among software developers. Other terms include “code smells”, “tech debt” etc. You can gather tweets of developers, do a frequency analysis of the terms to understand the terms of the group.
The importance of understanding the vocabulary of users may be much fold. If you want to use content marketing, you may want to use that language in your content.
There are a few ways. The more open the social media, the better. That is why Search and Twitter are our primary tools of choice.
1. Google, Facebook, LinkedIn, Twitter will provide you data about the size of a specific niche audience.
2. The search parts of Google, Twitter, and others will provide discovery tools. I prefer tools that provide good APIs. These tools make data-driven decisions easier. For example, on Twitter, I can type a search, find popular hashtags, lists, and mentions, and can mine them for more information. This is not so easy to do for example in LinkedIn (despite their having an API) and Quora. So people resort to using scrapers and RPA (robotic process
automation) tools.
3. Blog searches or Feed Searches
4. Job searches – for certain types of research jobs are a leading indicator
5. Conferences, meetups, and discussion forums are useful tools for identifying watch signals.
6. Funding patterns, especially Angel-funding are leading indicators. You can also track other rounds of funding and private equity to understand the market space.
7. Books and articles are mostly lagging indicators but they are good sources to track the popularity of certain technologies.
Sridharan – How can start-ups and organizations leverage this informal research for new product markets and finding new opportunities?
Dorai – There are several stages for a product startup. Let us look at the needs at each stage.
1. You come up with an idea for a product. You need to find out whether products similar to yours are there already and what are the pros and cons of each product (an analysis of alternative solutions). Having some competition validates that there are a problem and the need for a solution. But a lot depends on how established the competition is. At the end of this research, you identify a niche and some potential early adopters.
2. You build a prototype or a minimum viable product. You do this in stages. Once you have a proof-of-concept prototype or a functional, usable prototype, you need to validate that it solves the problem. You need research to identify these users, ways to reach them, and interact with them.
3. Once you have a few users using your product, you need to expand the number and also identify paying customers. You need research to go from 100s of users to thousands or tens of thousands of users.
4. Once you have sufficient users to feel comfortable with your validation, you need to price the product. If similar products exist, you need to research their pricing models. If there are no similar products but the market is being served by services, you need to research those pricing models.
As the number of users and types of users grows, you need more research to get to the next stage. Most of this research is not available in the market. Even if available, it may not be current.
Sridharan – Could you elaborate on some new research methodologies that can help startups?
Dorai – Startups have several free and paid resources for product discovery, content discovery, keyword discovery, and competitive analysis. Informal research strings together available tools to create your own Research Assistants. I see this as a multi-stage process.
– Info tools – tools to gather data from a variety of sources – Web, RSS feeds, Tweets, Posts, Discussion boards, Forums, and Blogs.
– Analyzers – Analyze information gathered via info tools – segment/cluster, discover topics, mine entities, derive the vocabulary of conversations, etc. The analyzers use Machine Learning, Deep Learning, Natural Language Processing, semantic tagging, and other emerging techniques.
Sridharan – With reference to businesses tracking technology trends, how do you think digital analytics help track the trends?
Dorai – Data gatherers will bring in a lot of data in various digital forms – text, images, speech (podcasts), videos. You need analytics for different levels of filtering. The first level simple analytics are early filters to separate signal from noise. Next level analytics provide good inputs for inference and prediction.
Sridharan – To understand customers and market size, which tools or platforms to look for?
Dorai – We can start with some of the available free tools. These include search engines, Twitter, LinkedIn, Facebook. They help you understand the market sizes since they need to know it to guide you in advertising. For example, using Facebook or Google ad products you can understand the size of a reachable market.
Using product hunt, beta list, and other similar services you can identify similar products. But nothing beats search. You can come up with a list of key terms that describe your product and try searches and analyze the results. Google provides a search API that you can use to automate this process.
You can use services like angel list to locate startups. You can use search to locate directories, professional associations in your product or technology space.
Twitter is becoming a great resource for discovery. Twitter APIs allow you to automate searches. Using Twitter you can find companies, products, trends, research reports, influencers, and discussions. You can create lists to manage the vast amount of information, retrieve tweets and links, and analyze the results.
Sridharan – How does text analytics help organizations in the changing business landscape?
Dorai – The outcome of searches (web search, Twitter search, Blog Search, Product Search, etc.) is short documents.
You need to text analysis to mine useful information from documents. Let us take an example of a blog comparing several products. You can use topic and keyword extraction techniques to derive useful information. You can use entity extraction to identify companies, products, and events mentioned in the article.
With the recent technology advancements in Artificial intelligence (AI), neuro-linguistic programming, machine learning, the internet of things (IoT), and web speech APIs, it is now possible to streamline interactions between computer and human languages more effectively. Every day practically we see Google Home or Alexa ads on social media and on television.
We dig deeper into the world of chatbots in this interesting series on
Conversation AI: Chatbot Series- understanding chatbot -part 1 of 2
What is a chatbot?
A chatbot is a service that people interact with via a chat interface. You can ask questions using your voice or by typing in the same way you would ask a person. The chatbot will usually respond in a conversational style, and it may carry out actions in response to your conversation. It answers your question, rather than directing you to a website.
Chatbots are great conversational assistants for businesses. From simple conversation to complex transactions, a chatbot can perform everything. AI technologies like Machine learning, RPA, and NLP can take chatbots to the next level with intuitive capabilities.
Chatbot Adoption
Chatbot adoption is happening across all industries and functions because of its scalability and assistance capabilities. Chatbots live inside instant messaging apps such as website chatbots, dashboard chatbots, and integrated into third-party applications, with which users interact daily. This enables businesses to leverage the digital spaces that are already growing.
These messaging platforms which support AI chatbots are a more socially acceptable form of personal interactions than social networks. While social networks still connect users with brands, messaging platforms via personalized offers, photos, videos, GIFs, memes, links, and documents create one-on-one interaction between brands and users.
AI chatbot solutions have the transformative power to provide end-to-end services ensuring the business objectives of different industry sectors. Whether it is retail, customer service, or hospitality, AI-powered chatbots are building a vast channel ecosystem. Today’s chatbots are powerful personal assistants. They can call a Cab, place a lunch order, make a hotel reservation, order office supplies when you get low, and even help manage your calendar.
Broadly Chatbots powered with AI can be used for two purposes:
For Enterprise – In the last few years, the e-commerce market has grown a lot, so does the chatbot market. Businesses are inclining towards bots for jobs like order tracking, delivery confirmation, customer support, feedback & reviews, and surveys.
Undoubtedly, the applications of chatbots are also benefiting the education sector. Enterprises can automate their admission registration system, provide virtual assistants to their staff for decreasing their workload, and enable parents to connect with teachers in real-time with chatbots
For Consumers – Chatbots provide better service to users. Mainly in these following ways:
Quick Response time – Compared to a human being, a smart computer program will answer queries much quicker. This, coupled with an AI chatbot’s machine learning and multitasking abilities makes it a highly efficient virtual assistant that can revolutionize customer interaction metrics.
Improved Conversions – Chatbots can study past search data and offer more personalized shopping options via retargeting. The AI is constantly learning, and it can pull up multiple options that will be more appealing to the user based on their search history and captured data points. This can lead to increased conversions for you in the long term.
The demand for chatbots is increasing at a tremendous pace across verticals. In a recent survey, Gartner predicted that 85% of their engagement with businesses will be done without interacting with another human by 2020. Instead, we’ll be using self-service options and chatbots.
Chatbots are being adopted to automate processes like voice-driven assistants, sales, marketing, lead generation, and customer service. During this survey, 42% of participants responded that automation technologies in these areas will improve the customer experience. 48% said that they already use automation technology for various business functions, and the other 40% said they are planning to implement some form of automated technology by 2020.
The shift towards intelligent assistants
The massive adoption of AI in the user’s everyday lives and businesses is fuelling the shift towards voice applications and conversational agents.
Powered by Conversational AI platforms, chatbots are becoming smarter and more human-like in engaging customers with smooth, conversational messages. Conversational interfaces are not limited to chatbots. With the rapid developments in voice technology that converts speech to text capabilities, businesses can build applications that can engage in voice-based conversations. Voice-enabled assistants will be the drivers for improving customer engagement for organizations and value to customers. Voice-enabled applications can not only accurately understand the requirements, but can also understand the intent or the context in which they are said.
There are a lot of opportunities for much deeper conversational experiences with customers with the evolving technology space.
We uncover advanced AI-powered chatbot applications in part 2 of this series. So watch this space for our next article on chatbots.
If you are willing to score in on the opportunities in the world of conversational businesses, connect with us for a chatbot or voice-enabled assistant experience at info@genisights.com.
geniSIGHTS is proud to announce the launch of FAST(FLASH As Service Tuned)! FAST is an artificial intelligence-powered business intelligence suite designed to help small and medium enterprises perform data analytics through voice-powered dynamic query solving. The tool is an effort from geniSIGHTS to make analytics accessible to entrepreneurs even with a tight budget.
FAST was launched as a virtual event by Lakshmi Narayanan, Emeritus Vice-Chairman, and former CEO of Cognizant. In his address, he pointed out the importance of ease of use and seamless software installation for the rising demand for data-driven tools. Impressed by the new benchmarks established in analytics platforms and the delivery of insights through BI tools with voice commands, he stressed on the user’s demand for independent setup without involvement from support teams which FLASH aims to provide for modern technology-driven organizations.
FAST is a focused solution on SMBs that comes as ‘A Do It Yourself kit’ from the FLASH team to jumpstart world-class AI experience in 30 seconds. At this lightning-fast set-up time, the conversational tool FAST provides customizations at the hands of the decision-maker on KPIs, Charts, etc. FAST renders actionable insights and views to the business user. The AI also suggests the type of charts and what level of detail is required for the business user. The users then can further upgrade/downgrade the features according to the AI learnings for the program to tune into the user’s choice. Now that’s the truly world-class platform genISIGHTS has built for small businesses.
FAST gives leaders the ability to analyze business performance with advanced AI capabilities especially during these testing times with the COVID-19 pandemic causing economic and global distress. The setup is seamlessly designed to avoid the need for any external support. It not only visualizes your data but can also render real-time actionable insights from your data with AI-based algorithms. For businesses that need to adopt data-driven strategies in uncertain times, we created FAST as a solution that is the need of the hour.
FAST is built on the geniSIGHTS flagship product ‘FLASH’ which was launched two years ago as an enterprise model and has served many large organizations in the US and India. FAST is a subscription model improved from FLASH, utilizing its advanced analytics algorithms and machine learning ability and simplifying the process of installation. Lalit Kumar, who is among the first users of our product, and heads the statistics department within the quality function in Renault Nissan Technology and Business center India addressed everyone in the launch, communicating how FAST has helped his organization handle data from different sources. It streamlined the data into a dashboard with actionable insights for the management, available at a quick glance.
We were glad to have with us Pat Krishnan, an entrepreneur, and visionary from the Bay Area, with a proven track record of creating value through innovative ideas. He has been a significant part of our journey as a customer, an advisor, and now an active partner for our business expansion for FLASH in the US. Pat in his address appreciated the contribution of the geniSIGHTS team, who helped him conceptualize disruptive business strategies by the use of advanced data analytics. He also shared his strong belief in conversational AI and its contribution to enhancing the customer experience.
Our power team was represented by our Founder and CEO Rajesh Kumar, Co-founder and CAO, Parvathy Sarath, and our Marketing Head, Srividhya M. Rajesh, who was delighted with the amazing response to the launch invite, said that FAST has come at a time when the COVID-19 pandemic has impacted businesses significantly. The thought behind the product was to assist organizations that are looking to save operational costs and use agile insights to drive business decisions. Parvathy, who is the brain behind the success of the project, in her crisp demonstration showed how FAST can be set up in minutes and guide businesses with actionable insights powered by advanced analytics.
FAST features:
Cloud-based subscription model
30 seconds setup with no additional support required
Custom KPIs and metrics
Conversational AI for insights at the speed-of-your-thought
AI-based actionable insights to adapt to your business
Impressive visualization techniques to understand your data
FAST is an AI-driven conversational BI suite designed to make advanced analytics accessible to small and medium enterprises at an affordable price. A dashboard that brings to you analytics at the speed-of-your-thought!
Digital technologies have been helping banks and financial institutions to seize new markets, grow the business, and cut costs by providing a competitive advantage. We see tremendous changes in the banking and financial services industry impact businesses and in an attempt to seek insights from business leaders from the industry, our Co-Founder and SVP Mr. Sridharan J. S has a conversation with Mr. Palani Balasubramanyam Nama, Head of Open Innovation and Digital Transformation, Societe Generale, who has over 21 years of experience working with Innovation & IT divisions of Investment Banks, Financial Institutions and European/American Multinationals, about the state of AI and other technologies in banking, in our exclusive ‘Feature Corner’. As the Head of Trade Execution, Palani is also accountable for the IT & OPS team ensuring on-time and secure trade execution across global exchanges, efficiently from India.
Sridharan – Being in the banking industry for more than a decade, tell us how banks fostered innovation before COVID and how the perspective has changed during this situation?
Palani – Pre COVID-19, innovation in banking was more focused on internal optimization, focused increasing digital footprint for efficiency, or improving customer journeys.
COVID has compelled to accelerate the adoption of contactless service delivery for customers, at a rapid pace. This constraint forced banks to partner with external eco-system for rapid transformation, to provide neo banks equivalent digital experience for customers. Apart from service channels, there is quite a bit of investment being done on employee experience, like enabling them to work from home, across multiple secure digital channels and collaboration tools.
Sridharan – Approximately what percentage of spend, do you know of, that banks spend on IT costs annually?
Palani – Large banks spend between 5 to 10%, small & medium banks spend between 20 to 30% of their total spend on IT costs.
Sridharan – Approximately What percentage of banks’ transactions or what areas are automated?
Palani – Above 70% of P2P transactions are automated, however, B2B transactions hover between 30 to 40%. B2B transaction STP % is catching up in the recent past with the evolution of Machine Learning & Smart Automation.
Sridharan – As more devices connect to the internet and the concept of the Internet of Things gaining rapid momentum, what are the preparations banks are involved in the rapid increase in equipping themselves with the huge amount of data generated from the devices?
Palani – BFSI has been slow in the adoption of IoT before COVID, given security threats and central bank regulations. However, post-pandemic, banks are more open to adopting digital channels to deliver contactless & remote client experience.
This unprecedented disruption has increased data volumes multifold. Most of the banks have established internal data analytics teams to address basic data processing needs. Despite interest in partnering with external partners, there are roadblocks to be addressed before it can become reality, especially around secure data sharing, multiparty computation & adherence to regulatory norms (GDPR).
Sridharan – Data, being the most important factor for the digital transformation for banks, what technologies are used to leverage data to meaningful insights for smarter decisions?
Palani – Large BFSI enterprises have multiple tech stacks based on needs. However, they have well defined IS strategies guiding the usage of tech stacks based on problem patterns being addressed.
While exploring partnerships with tech giants like ORACLE, Teradata, or IBM, there is an intrinsic push to adopt opensource tech stacks, to ensure open standards and cost optimization.
Sridharan – Do you think banks are struggling with the rapidly changing technological landscape in terms of which technologies to adopt and which ones not to?
Palani – It is a challenge to keep up with fast-paced Technology landscape evolution for any organization. It is more complex in BFSI given constraints around tech choices to be made keeping in mind the security and reliability of services. In BFSI, business capabilities and security take precedence over technology grandeur.
There are few instances of large banks partnering with external partners to disruptive technology transformation, however, results so far haven’t been very encouraging. BFSI transformations demand as business acumen and technology expertise. External partners tend to be good at technology expertise, however most of the time they lack business acumen.
Sridharan – AI has been long seen as the next big thing in the banking and financial industry be it in the area of planning, cost-saving, customer experience, fraud prevention, or anti-money laundering. What do you see as the future of banking given the exponential growth in IoT devices, cloud computing with Artificial intelligence capabilities?
Palani – AI, so far has been capable of helping in raising proactive alerts, simplifying decision making, and processing of large datasets. however, it is far from autonomous process execution or decision making, which limits the full-scale unsupervised deployment of AI-driven process flows, which in turn would help increase the straight-through processing rate.
Necessity is the mother of invention. Pandemic has increased BFSI’s appetite to adopt technologies like IoT, Cloud computing, and AI. The exponential growth of these technologies should be complemented by the rapid hardening of enterprise cybersecurity strategies, without which, banks may be hesitant to leverage the full potential of emerging technologies.
