Continue # Infosys Whitepaper 
 expression, objects, actions,  scenes, topic, and sentiment.<|endoftext|>Computer Vision Computer Vision helps to derive  meaningful information from images,  objects, and videos. With hyper  personalization of customer experience,  we need an intelligent and integrated  customer experience which can be  personalized by the people. While AI  plays an important role in analyzing the  data and recommend the corrective  actions, Computer Vision helps to capture  the objects, face expressions, etc. and  the image processing technology can  be leveraged to interpret the customer  response.<|endoftext|>Chatbot A chatbot is an artificial intelligence  software that can simulate a  conversation (or chat) with a user.  Chatbot has become a very important  mode of communication and most of  the enterprises use chatbots for their  customer interactions, especially in the  new normal scenario.<|endoftext|>Some of the metrics to measure  customer experience using a  chatbot are: 1. Customer Satisfaction: This metrics  will determine the efficiency and  effectiveness of chatbot. Questions  which can be included in this can be:  • Whether chatbot was able to  understand the query of the  customer?  • Was the response provided to the  specific query?  • Whether the query was  transferred to the specific agent  in case on non-resolution of the  query 2. Activity Volume: How frequently  is the chatbot used? Is the usage of  chatbot increasing or decreasing? 3. Completion Rates: This metric  measures the amount of time the  customer took. Also, the levels of  question asked by the customer.  It will measure the instance  when the customer opted to get  resolution from an agent and left  the chatbot. This will help identify  the opportunities to improve the  chatbot further, improving the  comprehension, scripts and adding  other functionalities to the chatbot.<|endoftext|>4. Reuse Rates: This metric will  provide the insight on the reuse of  chatbot by the same customer. This  will also enable to dive deep into  the results of customer satisfaction  metric, help us understand new  user v/s old user usage ratio and  allow us to conclude on re-usability  and adaptability of chatbot by  customers.  5. Speech Analytics Feedback: In this  speech analytics can be used to  examine customer interactions with  service agents. Some of the specific  elements to be noted include  tone of the call, frustration level  of customer, knowledge level of  customer, ease of use etc.<|endoftext|>Measuring Tools  Even though there are various  tools available from startups like  BotAnalytics, BotCore, CharBase,  Dashbot, etc., most of the QA teams are  measuring the Chatbot performance  parameters through AI/ ML utilities.<|endoftext|>

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 External Document © 2022 Infosys Limited Alternative Reality Alternative Reality includes augmented  reality (AR), virtual reality (VR) and mixed  reality.  AR is in many ways adding value to  the customer experience of an enterprise  by providing an interactive environment  and helps them to stay ahead of their  competitors. The data points used to  measure it overlap with those of website  and app metrics, with addition of a few  new points to be measured.<|endoftext|>Some of the additional metrics to  measure customer experience in  Alternate Reality: 1. Dwell time: Total time spent on the  platform. More time spent on platform  being the positive outcome 2. Engagement: Interaction with the  platform. More the engagement better  is the outcome.<|endoftext|>3. Recall: Ability to remember. Higher  recall rate indicates proper attention  and guides us on the effectiveness of  the platform 4. Sentiment: Reaction. Positive,  Negative and Neutral. This will assist in  understanding the sentiment.<|endoftext|>5. Hardware used: Desktop, laptop, tablet,  mobile etc.  Measuring Tools There is not much automation done in  AR/ VR experience validation. Custom  built utilities using Unity framework  can be explored to measure the AR/ VR  experience.  Brain computer interface A brain computer interface (BCI) is a system  that measures activity of the central nervous  system (CNS) and converts it into artificial  output that replaces, restores, enhances,  supplements, or improves natural CNS  output, and thereby changes the ongoing  interactions between the CNS and its  external or internal environment. BCI will  help in personalizing the user experience by  understanding the brain signals from a user.<|endoftext|>Metrics to measure customer  experience in BCI: 1. Speed - Speed of the user’s reaction.  Higher the speed, more is the user  interest on digital print.<|endoftext|>2. Intensity - Intensity of user’s reaction  towards a digital presence will help  understanding the likes and dislikes of user.<|endoftext|>3. Reaction - This will help understand  the different reactions on digital  interaction.<|endoftext|>Measuring Tools Open-source tools like OpenEXP,  Psychtoolbox, etc. can be leveraged  to build custom built utilities for  measurement of the above metrics {{ img-description : a person is holding an ipad with location & shopping app in the view, in the style of photobashing, rounded shapes, light emerald and violet, blink-and-you-miss-it detail, precision, david brayne, sharp focus }} 

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 External Document © 2022 Infosys Limited With multiple channels to interact with  the end customers, companies really  looking at ensuring the digital quality  assurance in a faster and in a continuous  way. To reduce time to market, customer  experience assurance should be automated  with more and more infusion of AI and  ML. Further, quality assurance should  be in an end-to-end manner, where the  developer can ensure the quality even  before the application is passed to QA.  With the adoption of DevSecOps, customer  experience assurance should be an  ogoing process which goes beyond the  conventional QA phase  Some of the technical challenges in  automation are: • Services offered by company should have  a seamless experience with all distribution  channels (Web, mobile, Doc, etc.).<|endoftext|>• Early assurance during development • Ensure regulatory compliance • Collaboration environment for  developers, testers, and auditors with  proper governance  • On demand service availability • Automating the remediation and  Continuous Integration • Actionable insights • Scoring mechanism to benchmark • Integration with Test and Development  tools The above challenges will call for a fully  automated customer experience platform  as depicted below: Automation in Customer Experience Assurance Figure 2 Automation approach for evaluating holistic customer experience An automation approach should be  comprehensive enough to provide a  collaboration environment between  testers, developers, auditors, and the  customers. It needs accelerators or  external tools to measure and analyze  various aspects of customer experience.  Cognitive analysis to ensure continuous  improvement in customer experience is  a key success factor for every enterprise.   As shown in the picture, complete  automation can never be achieved as  some assistive or manual verification is  required. For example, JAWS screen reader  to test the text to speech output. Also, the  platform needs to have the integration  capabilities with external tools for end-to- end test automation.<|endoftext|>IDE plugins for shift left  remediation Online Experience Audit  Services APIs and CI/CD plugins Accessibility Analyzer Sentimental Analytics Visual Consistency checker Google APIs Intelligent application  crawler Cognitive analysis  Dashboards & Reports Cloud Environments with  multi browser & device  PCloudy Applitools ALM JiRA Subscription &  Administration Scheduler Tool adapters External IPs Accelerators Usability Analyzer User touch  points Platform  component Accelerators/ tools Others  Manual Assistive  technologies 

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 Conclusion As the digital world is moving towards  personalization, QA teams should  work on data analytics and focus on  analyzing user behavior and activities,  leveraging various available testing  tools. They should also focus on  adapting new and emerging testing  areas like AI based testing, Persona  based testing, Immersive testing, 2D  illustration testing etc. These new  testing areas can help in identifying  the issues faced in providing the best  customer experience, quantify the  customer experience and can help in  improving it.<|endoftext|>Since there is considerable amount  of time, money and effort are put  into QA., for ensuring good ROI, QA  team should start taking customer  experience as a personality-based  experience and work upon all major  aspects mentioned above. QA teams  should look beyond the normal  hygiene followed for digital platforms,  dig deeper and adapt a customer  centric approach in order to make  digital prints suitable to the user in all  the aspects.<|endoftext|>{{ img-description : hands holding a phone with a star rating on it, in the style of interactive experiences, futurist claims, expert draftsmanship, meticulous attention to detail, precision and detail-oriented }}  External Document © 2022 Infosys Limited 

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