Category Archives: Deep Learning

Conversational AI-powered business intelligence suite -FAST Launch Event

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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.

Check out our feature on TOI:
https://timesofindia.indiatimes.com/business/india-business/genisights-startup-launches-ai-powered-business-intelligence-suite-for-small-businesses/articleshow/77238910.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst
Check out our feature on UNI:
http://www.uniindia.com/~/covid-19-ai-powered-business-intelligence-suite-launched-for-smes/States/news/2099769.html

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!

If you missed the web-launch you can click here

Contact us at info@genisights.com or log on to https://flash.genisights.com/ and request a demo today!

Feature Corner- Transforming the Digital Banking Landscape

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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.

7 questions Contact Centre Data Analysis can answer

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 Industry Watch Corner

The lack of access to physical stores due to the pandemic has increased the traffic for contact centres worldwide. Contact centres are bridging the gap to communicate with customers, emergency helplines, on-call services, and inquiries. Even before contact centre agents were required to work from remote locations, Cisco’s 2020 survey revealed that 62% of decision-makers plan to implement a cloud contact centre in the coming year. 93% agree that technology is very important in creating a better customer experience. Contact centres are a treasure of data waiting to be discovered. They have data acquired through daily processes like telesales, telemarketing, customer surveys, collections by means of outbound services and customer services, emergency response past inbound services. Analyzing contact centre data can help not only the customers but the contact centre decision-makers as well, to improve service and increase efficiency. Artificial Intelligence-based software improves the customer experience by monitoring key phrases and then prompting the agent in giving proactive responses. They can identify challenging calls for agents to handle and route the calls depending on complexity. AI is also gaining popularity through chatbots and solving issues without the need for agent interference. Chatbots need to understand the tone of the customer and respond accordingly. A healthy Contact Centre must conserve excellent hardware and software solutions providing a direct impact on quality assurance/success metrics, call statistics, customer implementation, and proficient Contact Centre agents. Contact centres today can answer more than just calls. Let us look at what other questions can be unraveled through analyzing contact centre data.

1. Is the Customer Satisfied?

According to a 2019 survey by Deloitte Digital, Customer experience or CX is a high priority (57%) among the surveyed centers. Harvard Business Review found in a study that a focus on increasing (Customer Satisfaction) CSAT will help businesses retain 74% of customers for another year leading to a substantial increase in revenue. We can increase customer lifetime value by looking after the key determinants like customer acquisition, customer retention, and customer margin. IVR (Interactive Voice Recording) Systems have been in use recently to reduce the number of calls reaching an actual agent. Valuable insights on the reason for the call, the caller, Opt-out Rate, self-Services rates, and incorrect routes can be evaluated through IVR data.

Representative customer satisfaction metrics from FLASH, the analytical dashboard

Representative customer satisfaction metrics from FLASH, the analytical dashboard

2. Is your Product facing a regular or common problem?

Collating and categorizing issues is a great way to understand the product. A repeated complaint of the product needs to be fixed for future versions so that customers do not engage in expressing distress and reducing brand value. Categorizing these issues also helps in creating a common answer pool for contact centre employees. AI can be used to direct the calls to Subject Matter Experts to handle issues. This will reduce escalations and help employees with better ways to tackle problems. We can also perform sentiment mining to analyze the sentiment of the customers on service level parameters such as resolution effectiveness, feature variety, etc. by leveraging social media sentiments of the public on various products or services of clients for improved business services.

3. Is your Contact Centre Efficient?

First call resolution (FCR) is an important metric that measures the efficiency of the contact centre to solve issues in the first call. A long waiting time is another factor for low customer satisfaction. Measuring the average delay of calls or the call abandon rate or the response time will give us an idea about the need for more staffing to handle the surge in calls. Response time metrics and call resolution metrics are indicators of efficiency(image below depicts metrics from an AI-driven dashboard, FLASH). Customer Effort Analysis can be performed to measure the efforts spent by customers to get various services rendered through various touchpoints. Customer journey analysis can be used to identify the pain points that customers face while traversing channels for a specific purpose. Path analysis can help identify the patterns in the menu/caller path in a time slot. All metric dimensions can then be evaluated to arrive at actionable insights about the customer segment in qualifying dominant paths, dependent paths, and average time spent on the path, etc.

Flash insights

Flash analytics

Flash dashboard

 

4. Is your Employee productive?

Average Sales per Agent is the measure of sales that an agent closes within a time period. We can use this metric to award agents performing well and provide targets for employees to work towards. An agent’s Utilization and Average Handle Time are two primary contact centre metrics to focus on when trying to measure an agent’s productivity. We must also be careful of the escalations that an agent faces and train employees who need to improve performance.

Flash AI board

5. How many Customers at risk of attrition?

According to contactcenterpipeline.com, Attrition is considered the No. 1 challenge 27% (up from 19.2%) for contact centres in 2020. Measuring the Net Promoter Score or NPS of customers helps us to plan a better approach for managing customers who are at risk of attrition. Predicting churn rate qualifies the steady-state level of customers at any point in the network.

6. Are there any opportunities for Upsell and cross-sell?

Affinity analysis/association rules of mining are used to analyze the co-occurrence of relationships among activities performed by customers and discover the cross-sell/up-sell opportunities accordingly. Recommendation Systems help identify products customers need and help contact centres create personalized offers based on purchase history and products commonly bought together. Use of Uplift Modelling can be used to identify the right set of customers(the persuadable) to be targeted for marketing campaigns.

7. Can your competitor be a reason for low sales?

As contact centres are a hub of engagement, customers often give honest feedback on products. You also find that many potential clients are already using your competitor products. Employee engagement plays an important role in analyzing these potential risks to the business. Predicting the customer churn rate helps to identify the customers who are likely to leave the network.

Contact Centres are now moving into a space of self-service where customers are adapting to emerging artificial intelligence software and chatbots. Customers today do not like waiting for calls to be answered, they would rather finish queries through chatbots that do not consume time. IVR systems are slowly being replaced by intelligent assistants that provide human-like interactions. Having an advanced chatbot and analyzing its response can help you understand your customers. With these forms of service, direct calling and email, data is being collected from multiple sources. Decision-makers need regular reports to keep track of their performance. While automating this data is now a necessity, the use of AI to analyze this data is a competitive advantage. AI-based dashboards give you the insights needed to understand your data. They highlight possible improvements and forecast staffing depending on query traffic. They group dissatisfaction and anger sentiments and analyze agents who deal better with these emotions. Customer sentiments change with time, people are more impatient and now more sensitive due to long hours indoors. Many factors can be associated with good or bad performance and identifying these factors can be tricky for decision-makers. AI learning, analyses your data in real-time and helps you get a holistic view of your business. Analyzing and processing this data turns your contact center into an answering hub. Companies that ignore customer service face an inevitable fate of resentment and destroyed brand image. FLASH, a product by geniSIGHTS ensures your contact centre does more than just calls. FLASH is a one of a kind voice-powered AI tool that gives your contact centre the power to stay ahead of the competition and give highly efficient results. Our solution uses advanced analytical techniques through various statistical models and machine learning algorithms to qualify underlying patterns and mine hidden insights from data. It is lightweight and cloud-based, facilitating analysis at your fingertips even as you sit at home during this lockdown. Contact us at https://flash.genisights.com/ for a demo today.

Conversational AI – Making your platforms more intelligent

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Analytics

Chatbots are an integral part of most websites and used by many marketers to interact with their customers on a regular basis in the digital space.  There has been a paradigm shift from web browsers in the late ’90s to chatbots in 2008 to the much-advanced voice-enabled artificial intelligence using the most intuitive interface- natural language.  Businesses and millennials today are already using conversational artificial intelligence (AI) platforms as they are easier, less intrusive and quicker.

“There has been a paradigm shift from web browsers in the late ’90s to chatbots in 2008 to the much-advanced voice-enabled artificial intelligence using the most intuitive interface- natural language”

Technology research company, Gartner, has predicted that 85 percent of all customer interactions will be automated by 2020, and consultancy Servion believes that artificial intelligence will power 95 percent of all customer interactions by 2025.

So what is the difference between a chatbot and conversational AI? Though both may sound similar, there is a huge difference in the customer engagement to customer satisfaction levels of both. This blog covers the differences between the two, how conversational AI surpasses chatbots with its features & performance and makes platforms more intelligent.

In conventional chatbots, the interactions are based on a set of predefined conditions, statements and/or queries. These chatbots require human intervention if the queries asked by the users are beyond the rudimentary questions. Chatbots have inherent rules with which they operate, to provide consistent and uncustomized responses.  They also fail to continue the long human conversation as they lack language processing skills. They are programmed to converse only in one language and pick only certain words from the user query and answer it according to the predefined statements.

Conversational AI is a wider term that covers chatbots, text assistants and more has a much advanced interactive platform. Artificial intelligence enables the learning by classifying the models in data. Without training, conversational AI can apply the model to new & varied queries. This ability enhances their task performance and problem-solving skills without human intervention. Unlike chatbots, conversational artificial intelligence is more user-oriented. It engages in long human conversations with users and also provides recommendations based on previous interactions. Machine learning helps to learn from the experience without any mediation and then utilize the learning in their next user conversation.  Natural Language Processing (NLP) used in conversational artificial intelligence helps to learn and imitate the methods of human conversation which lessens human intervention helping business transformation, customer engagement, and growth in the organization’s Return on Investment.

Conversational AI is a boon for businesses that involve tasks that are monotonous and tedious in industries such as e-commerce, retail, travel, tourism, banking, business processing and more. Where the customer interactions are more frequent, it is customizable according to the customer requirements, inquiries, complaints or orders.

We, at geniSIGHTS, as an emerging leader in this space, aid organizations adopt scalable advanced analytics with pre-canned advanced solutions with conversational AI capabilities that fit common business needs and provisions to build, integrate customized analytical solutions.