Category Archives: Analytics

Feature Corner – Supercharging Businesses with Artificial Intelligence

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With huge quantities of data generated every day, and sophisticated algorithms fuelling data modeling, we look at the scope of the expanding AI market in North America.  In our monthly Feature corner, our Co-Founder and Senior Vice President, Mr. J. S. Sridharan asks a few questions to Mr. Pat Krishnan, a seasoned C Level executive, from Bay Area, California, who has worked with leading organizations with disruptive technologies and has also provided strategic direction and leadership to various organizations in India and the US.

Sridharan – Having worked in AI, cybersecurity, analytics, and various other innovative ideas, please tell us how the pre-COVID and future priorities are changing for organizations in terms of adopting technologies like AI.

Pat Krishnan – AI as one knows is the “mantra” for every organization and every domain such as healthcare, cybersecurity, retail, financial to name a few. I think the market has been growing regardless of the COVID situation as far as AI goes.  With COVID-19 and scientific community still trying to understand the nature of this RNA genome, AI and mathematical models come in very handy to provide more insights into this Coronavirus. The models can get very advanced once they decipher the pattern and I personally think that we will be able to predict the future mutations of this SARS COV 2. This is a prediction with some level of certainty that is only possible with  AI models.

Sridharan – What are the problem areas you think AI can solve in the coming 3 to 5 years for these organizations?

Pat Krishnan – With COVID 19, new normal is happening and it will only be time before we get adapted to this. That said, Pharma (drug development), healthcare, retail, financial, insurance, real estate, entertainment, and food industries will go through a massive transformation and their reliance on AI and machine learning models will increase monumentally to an extent where vital decisions will be taken using data and technology.  Problem areas AI can solve are InSilico drug modeling and profiling, Telemedicine, Disease mgmt such as Diabetes (CGM) and terminal illness, Money movement and payments, Insurance Risk and underwriting, Food deliveries, and Online Retail merchandise (from groceries to apparel and the likes).

Sridharan – With more conversational agents like Alexa and Echo, and while reports state that 70% of the total US smart speaker owners will continue to use an Amazon Echo device, tell us more about the adoption of conversation AI and how conversational AI is seen as the next paradigm shift in the AI technology in the North American market?

Pat Krishnan – I consider the consumer-driven lifestyle type applications such as Alexa, Ok Google is the guinea pig for the Conversational AI technology which is about to invade into enterprises. Increasing working patterns like WFH have pushed the envelope to adopt Conversational AI in call centers and contact centers. According to reports, Conversational AI can save the industry $150 billion by eliminating the drudgery of communication and enhancing the customer experience in leaps which will benefit the business.  The next 5 years will see a lot of growth in conversational AI and related technologies in enterprises.

Sridharan – What according to you, are the areas organizations -small and big can supercharge customer engagement with the help of virtual agents and intelligent automation tools?

Pat Krishnan – Telehealth and continuous monitoring use cases, disease management are plum for engaging virtual agents and tools. Where there is a need for continuous engagement with the customer, automation tools will come in handy. The beauty of virtual agents stems from the fact that the back end Ai engine does the heavy lifting converting the customer queries and intent to tangible actions. The day will not be far off when most of the invasive questions are answered by virtual agents themselves.

Sridharan – How do you think organizations can leverage AI for higher levels of stakeholder engagement?

Pat Krishnan – Stakeholders live and die by making decisions that are important for the company be it operational or strategic level. AI is so democratic and objective that it does not differentiate any area of specialization. Where there is data, there possibly could be a need. Stakeholders should engage with AI service providers and work closely to close the gap that exists between science and technology. It will become increasingly necessary for stakeholders to come up to speed on the science aspect of AI which can help implement effective AI systems and platforms in the organization. Operationally the IT organization in the company should get aligned with the stakeholder and service provider.

Sridharan – What do stakeholders need to do to jumpstart their conversational AI journey?

Pat Krishnan – Stakeholders should start engaging with AI vendors and get the problem statement clearly laid out. This cannot happen in a silo and AI vendors should handhold the execs and help refine the problem. This is key to the successful implementation of an AI solution. This calls for stakeholders like the C suite giving enough time to go thru the implementation process which alone will pay rich dividends in the future.

Connect to us at info@genisights.com if you are looking at jumpstarting your AI journey

Conversation AI: Chatbot Series- Understanding Chatbot -part 1 of 2

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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.

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.

Effective Decision Making

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  FLASH CORNER

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The BI Survey research found that 58% of the surveyed companies relied more than half of their decisions on gut feel or experience over data. But two-thirds believe that information will be valued for decision making in the future. Highly data-driven organizations are 3X more likely to report significant improvement in decision-making, according to a survey by PWC.

The HiPPO effect, coined by Avinash Kaushik and reiterated by Bernard Marr in Forbes, is usually the most experienced, most powerful, or highest paid in the room. Once they voice their opinion, others find it disrespectful to voice their own. This may not lead to desired results. Let’s think if the decision would go on a similar path if the time and resources would be different. Given more time, you would probably rely on the opinion of a board of directors or your experienced colleagues. Will you still be completely confident?

   Data however changes this. You have an unbiased opinion based on facts that leads to better results. Data analytics uses information from all sources and simplifies it to visual representations that highlight what you need. You are now capable of viewing all your information in just one screen. But data is useless until interpreted. Data-driven decision making or DDMM lies in interpreting this data for actionable insights. A term fondly called as data storytelling is considered an art. Artificial intelligence-based complex algorithms make sure to see more than what the human eye and mind are capable of. These insights help drive your decisions in directions that lead to success. So you have got your data, got your insights from this data, why then have you not used it to make the decision given to you 10 minutes ago?

The problem with many AI solutions is the inability to get exactly what you want when you want. You have a ton of data and a ton of insights but how does your BI tool know what you need to reach the answer for the question given to you 10 minutes ago. You have knocked on the door of data analytics and are seeking the solution. But can’t you just ask?

FLASH, by geniSIGHTS, gives you the power to simply talk to your BI tool and seek the information you need to come to the decisions that will open the right doors. In turn, FLASH also talks back to you with insights. FLASH is a one of a kind voice-powered dashboard that provides quick insights into your organization’s metrics. It goes beyond the functionality of Business Intelligence and uses conversational AI in the dashboard to respond to queries and generates real-time insights for actionable decision-making.

FLASH is AI-driven, lightweight, and easy to use.  It can easily be integrated with your existing data systems without having any extra software. It is also accessible from any location and insights can be exported and shared with the members of your organization. The visualization can be customizable and easily understood without any expert knowledge of data. FLASH ensures that your entire team is on the same page, making your user experience smooth and without any delay. Unlike other solutions, FLASH learns continuously to understand what you need for your business and becomes your virtual data assistant over a period of time.

FLASH is your AI agent who has all the answers you need to make decisions in a FLASH. We have seen how Amazon’s Alexa has made life easier. Shouldn’t your dashboard be able to do that? Now you can make your decisions within 10 minutes and be sure of it.

FLASH gets you what you need, you simply need to ask it. Check out FLASH at https://flash.genisights.com/

12 KPIs to measure if you are in the Manufacturing and Automobile industry

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 Industry Watch Corner

You can’t improve what you don’t measure. KPI or Key Performance Indicators are the metrics used to measure how your company is performing. If you are part of the business world, you are probably familiar with the term. It is important for every business to choose the right metrics that can measure the success of your company and work towards improving your performance. It is important to understand that KPIs vary with organization, department, and even team. There are many types of KPIs. They can be quantitative, measured as a number or qualitative, based on standards set by expert opinion. They can be leading, predictive of future success and failure or they can be lagging, reflective of success and failure of past events. But the best way to categorize KPIs is by industry.

Just like any other industry, KPIs are important to measure the health and progress of the Manufacturing and Automobile industries. Besides knowing what your KPIs are, you will need to work on ways to improve these KPIs. KPI real-time dashboards monitor your KPIs and give you insights to improve them. They help in maintaining your supply chain and manage your logistics by retrieving data of your systems in real-time. By using Artificial Intelligence they run complex algorithms of predictive analysis to forecast demand of your vehicles. They analyze your customer data and give you insights at the right time to increase and decrease production depending on the demand of the customers. They help in customer segmentation and analysis of regions to convey high performing regions. These regions will need higher demand than other low performing regions.

Let us look at key performance indicators examples for the Manufacturing and Automobile industry

  1. Overall Equipment Efficiency: It is the product of availability, performance, and quality of equipment. It measures the overall effectiveness of equipment and can even be calculated for the factory. It highlights poor performance and quantifies improvements.

  2. Production Volume: No. of units manufactured in a time period. This helps us determine if the supply meets the demand of vehicles

  3. Production Downtime: Analysing your planned downtime and optimizing it for better productivity.

  4. Utilization Rate: Measuring the capacity of production against the actual production. The utilization rate will vary according to demand during the year but low utilization for long periods may indicate losses.

  5. Cycle Time: The average time required for making a single unit of a vehicle. Recording data of the production line will help us determine if optimal time is maintained in each process and point processes that take longer. We can then optimize production time.

  6. Yield: Measure of the number of vehicles that are made without any defects in one attempt. We can measure the yield for individual processes to highlight faults in the production line.

  7. Recall Rates: It is the percentage of vehicles or parts that were sent back due to defects amongst total vehicles produced.

  8. Inventory Turnover Ratio: This is a measure of the number of vehicles sold and replaced in a time period. It is a ratio of vehicles sold and vehicles in the inventory.

  9. On-time Delivery Rate: The percentage of time that a manufacturer delivers a product on time to the customers.

  10. Back order rate: The number of orders that cannot be fulfilled when the customer places an order.

  11.  Mean time between failures (MTBF): It is the average time between failures of a system. It can be calculated by dividing the total operation hours by the number of failures during this time.

  12. Mean time to repair (MTTR): It is the average time to repair failure and return to production. A good MTTR will tell you your readiness in emergency situations and how your response is to failure.

FLASH, a product by geniSIGHTS is a real-time dashboard that can help the automobile and manufacturing industries by assisting in supply chain management. Its voice-controlled AI model solves your queries on the go. You can monitor the data across regions in a single view. The simplified visual representation will help you identify red flags quickly so you can act on them and improve your manufacturing process. Using a KPI dashboard will not only assist you to monitor your KPIs but will give you the power to optimize them.

Read about flash at https://flash.genisights.com/

Future of AI and Conversational AI in the eCommerce sector

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Artificial intelligence provides a quicker, engaging and seamless experience in increasing operational efficiencies thereby leading to the transmutation of businesses across industries.  A source from Statista. com says AI will grow into a $118.6 billion industry by 2025.

Evolving AI not only includes search, facial/ voice recognition but also self-driving cars and chatbots.  Chatbots are computer programs that conduct a conversation with a human. Companies like Amazon, eBay; Sephora are already deriving a greater ROI from AI technologies.

 Some interesting facts of Artificial Intelligence from allied market research.com are:

  • 55.6% is the forecasted compound annual growth rate of the AI market.

  • Asia Pacific is expected to hold the largest AI market share.

  • Manufacturing is expected to experience the highest AI growth.

  • The scarcity of AI experts is seen to be a major obstacle to the AI market’s growth.

AI in eCommerce Industry

eCommerce

eCommerce sector is experiencing a major impact with AI with data showing that of all the industries, eCommerce is the one most ripe for AI investment. AI has been modeled to understand the customer, learn from the experience, provide customer satisfaction and generate leads.

Conversational Artificial intelligence in eCommerce is used to improve and develop business growth with a greater ROI with technologies such as Machine learning, NLP and data mining. These work together to learn about the user and provide the relevant information on time. Recommendations based on customer searches and previous purchases are also provided eventually becoming their virtual assistant.

Some of the key features of AI in the eCommerce sector are:

Predictive sales: AI derives deep insights about the customer and helps develop the sales by predicting the customer purchase using current data. As per Forbes, “87 percent of current AI adopters said they were using or considering using AI for sales forecasting and for improving email marketing”.

Product recommendations: Just like how we ask for recommendations from friends/ family before buying a product and the salesperson at the store recommends the product based on our preferences, conversational AI makes this an online engagement with the help of a smart search. The recommendation engine analyzes customer behavior and provides all similar products with different versions.

Warehouse automation: AI automates the warehouse and distribution operations to achieve greater outcomes. Warehouse automation reduces the operating expenses and minimizes the manual process. A survey conducted for Zebra’s Warehouse Vision Report found that 59% of IT and operations personnel in manufacturing, retail, transportation, and wholesale market segments planned to expand process automation between 2017 and 2022.

Inventory Management: With AI, the demand forecast becomes more precise and allows to control of the supply chain with ease thereby saving time and cost.

Chatbots: Chatbots are computer programs that simulate human conversation through voice commands or text chats or both based on a set of a predefined algorithm. They respond to most of the rudimentary queries raised by customers, resolve their issues and infer customer preferences to create a customized shopping experience.

Conversational AI: Amazon’s Alexa, Microsoft’s Cortana, Google Assistant, and other voice-enabled applications have been contributing to a rapid spread of voice-driven user experience in the last few years.

Conversational AI is not only redefining the way people shop but also establishing deeper connections with customers resulting in better customer experience and higher engagement rates.  This results in higher retention, conversion rates and thereby revenue. Voice-enabled AI is impacting the emotional connect of the customers and in their decision-making process.

Voice-enabled AI enhances customer experiences, builds brand reputation and leads to higher revenue earning capability in the eCommerce sector.

Gartner foresees that by the year 2020, 30% of all web-browsing sessions will be managed without a screen.

Through conversational AI is still in the nascent stage, it is anticipated to grow manifold in the next 1 to 5 years thereby making a paradigm shift in the way we communicate with the world around us.

Check out our product, Flash, AI-powered business intelligence suite for today’s business.

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.

Make your IoT more intelligent

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Have you built Analytics on your IoT? Analytics implementation on the IoT helps the organization to perform

  • aggregations on the data from various devices and provide periodic schedule reports (e.g.: Hourly / Daily / Weekly / Monthly)

  • machine learning, advanced analytical model to predict the likely failure time of the devices, expected service maintenance time, etc.

  • historical data analytics and gather data-driven insights from the past

  • real-time data analytics

  • and many more

Interested to develop Analytics on your IoT? Collaborate with Us.

Our Things team would be your one-stop solution to help you build your own IoT analytics.

Want to see a demo? Go to http://things.genisights.com/

Click on “Things Demo”

Request for the demo: contact@things.genisights.com

 

Demo Description: The demo consists of a data simulator which simulates the live factory environment and provides a live feed. There is an external data aggregator to get the external data from various other sites/sources. These inputs are then processed in an analytical engine where appropriate transformation, aggregation, and machine learning models are run to arrive at the desired business objective. The outputs are facilitated as streams and the stream could be visually seen in the dashboard.