# Infosys Whitepaper 
Title: Quantifying Customer Experience for Quality Assurance in the Digital Era 
Author: Infosys Limited 
Format: PDF 1.7 

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 WHITE PAPER QUANTIFYING CUSTOMER  EXPERIENCE FOR QUALITY  ASSURANCE IN THE DIGITAL ERA Abstract Post the pandemic, the new normal situation demands an increased  digitalization across all industry sectors. Ensuring top class customer  experience became crucial for all digital customer interactions  through multiple channels like web, mobile, chatbot, etc. Customer  experience is an area in which neither the aesthetics nor the content  can be compromised as that will lead to severe negative business  impact. This paper explains various automation strategies that  can enable QA teams to provide a unified experience to the end  customers across multiple channels.  The focus is to identify the key  attributes of customer experience and suggest metrics that can be  used to measure its effectiveness.<|endoftext|>{{ img-description : rateyourexperience with a palm and pen with four stars, in the style of interactive, smartphone footage, digital  }} 

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 External Document © 2022 Infosys Limited Introduction Customer experience has always been a dynamic topic as it is becoming more personalized day by day and varies according to individual  preferences. It is hard to measure customer experience which make the work even more difficult for Quality Assurance teams. The factors  which amplify the customer experience not only include the functional and visual factors like front end aesthetics, user interface, user  experience, etc., but also include non-functional and social aspects like omnichannel engagements, social media presence, customer  sentiments, accessibility, security, performance, etc.<|endoftext|>Enterprises encounter various challenges  in providing a unified experience to their  end customers across multiple channels  such as: • Lack of information or mismatch in  information • Quality of content is not up to the  standard • Lack of usability in cross navigation  to make it intuitive and self-guided • Consistent look and feel and  functional flow across various  channels • Improper content placement • Inappropriate format and alignment • Performance issues across local and  global regions • Violation of security guidelines • Nonconformance to Accessibility as per  the Web Content Accessibility  Guidelines  (WCAG) guidelines • Lack of social media integration Why do we need to measure the Customer Experience? Quality Assurance is required in all these areas of functional, nonfunctional, and social aspects of Customer Experience. Since, Customer  Experience is hyper personalized in the digital era, a persona-based experience measurement is required. Conventional Quality Assurance  practices need to be changed to evaluate all aspects of customers journey across multiple channels, comprehensively.<|endoftext|>Figure 1 Challenges in Quality Assurance of Customer Experience Lack of  single view of  factors afecting customer  experience.<|endoftext|>Traditional Testing fails to  adapt to real time learning,  lacks feedback loop Lack of persona based test strategy Quantifable CX  measurements not  available Adapting experience  unique to each customer Testing is inward focused  rather than customer  focused Testing based on biz/  technical requirements  resulting in gaps in  customer’s expectations Vast sea of social  messages  and user  feedback data from  social media platforms 

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 External Document © 2022 Infosys Limited Experience Validation Needs to Cover Multiple Areas of a Customer Journey  While organizations try to focus on enhancing the customer experience, there are various areas need to be validated and remediated  independently for functional, nonfunctional, and social aspects. The current testing trend covers the basic functional and statistical aspects,  emerging testing areas will cover behavioral aspects and focus more on providing customer centric approach like using AI for enhancing  the quality of digital impression with personalized customizations. Below table provides information on areas where quality assurance is  required along with the popular tools for automation.<|endoftext|>Sr No Area Key Aspects / Metrics Current Testing Trend Emerging Testing Trend Tools 1 Visual  Conformance  Webpage content alignment,  font size, font color, web links,  images, audio files, video  files, forms, tabular content,  color scheme, font scheme,  navigation buttons, theme  etc.<|endoftext|>A/B testing, Style guide  check, Font check, Color  check, Usability testing,  Readability testing Persona based testing Siteimprove  Applitools, SortSite 2 Content Checking whether the image,  video, audio, text, tables,  forms, links etc. are up to the  standards.<|endoftext|>A/B Testing, Voice quality  testing, Streaming media  testing, Compatibility  testing, Internationalization/  Localization testing Personalized UX Testing,  CSS3 Animation testing,  2D Illustrations, AI  powered translators Siteimprove, SortSite 3 Performance  of webpage Loading speed, Timeto
Title,  DNS lookup speed, Requests  per second, Conversion  rate, TimetoFirstByte,  TimetoInteract, Error Rate  Performance testing,  Network testing, cross  browser testing, multiple  device testing, multiple OS  testing Performance Engineering,  AI in performance testing,  Chaos Engineering GTMetrix, Pingdom  Tool, Google  Lighthouse, Web  Page Test, etc.<|endoftext|>4 Security Conformance with security  standards across geographies.  Secured transactions, cyber  security, biometric security,  user account security Application security testing,  Cyber Assurance, Biometric  testing, Payment Testing Blockchain testing, Brain  Computer Interface BCI  testing, Penetration  testing, Facial recognition Sucuri SiteCheck,  Mozilla Observatory,  Acunetix, Wapiti 5 Usability Navigation on website,  visibility, readability, chatbot  integrations, user interface  Usability testing, Readability  testing, Eye tracking, Screen  reader validation, Chatbot  testing AI led design testing,  Emotion tracking,  Movement tracking Hotjar, Google  Anaytics, Delighted,  SurveyMonkey,  UserZoom 6 Web  Accessibility Conformance to web  accessibility guidelines as per  geography Checking conformance to  guidelines [Web Content  Accessibility Guidelines  (WCAG), Disability  Discrimination Act (DDA)  etc.) Persona based accessibility  testing Level Access, AXE,  Siteimprove, SortSite.<|endoftext|>7 Customer  Analytics Net Promoter Score, Customer  Effort Score, Customer  Satisfaction, Customer  Lifetime Value, Customer Turn  Rate, Average Resolution  Time, Conversion Rate,  Percentage of new sessions,  Pages per session Sentiment Analytics, Crowd  testing, Real time analytics,  social media analytics, IOT  testing AR/ VR testing, Immersive  testing Sprout Social, Buffer,  Google Analytics,  Hootsuite.<|endoftext|>8 Social Media  Integration Clickthrough rate, measuring  engagement, influence, brand  awareness Measuring social media  engagement, social media  analytics AR/VR testing, Advertising  Playbook, Streaming Data  Validation Sprout Social, Buffer,  Google Analytics, etc.<|endoftext|>Table 1 Holistic Customer Experience Validation and Trends 

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 External Document © 2022 Infosys Limited Emerging Trends in Customer Experience Validation Below are few of the emerging trends that can help enhance the customer experience. QA team can use quantifiable attributes to  understand where exactly their focus is required.  Telemetry Analysis using AI/ML in  Customer Experience  Telemetry data collected from various  sources can be utilized for analyzing the  customer experience and implementing  the appropriate corrective action. These  sources could be the social media feeds,  various testing tools mentioned in Table 1,  web pages, etc. Analytics is normally done  through custom built accelerators using  AI/ML techniques. Some of the common  analytics are listed below: • Sentiment Analytics: Sentiment of  the message is analyzed as positive,  negative, or neutral • Intent Analytics: Identifies intent as  marketing, query, opinion etc.<|endoftext|>• Contextual Semantic Search (CSS):  Intelligent Smart Search Algorithm which  filters the messages into given concept.  Unlike the keyword-based search, here the  search is done on a dump of social media  messages for a concept (e.g Price, Quality,  etc.)  using AI techniques.  • Multilingual Sentiment Analytics:  Analyze sentiment based on languages • Text Analytics, Text Cleansing,  Clustering: Extracting meaning out of the  text by language identification, sentence  breaking, sentence clustering etc.<|endoftext|>• Response Tag Analysis: To filter pricing,  performance, support issues • Named entity recognition (NER): To  identify who is saying what on social  media posts and classify  • Feature Extraction from Text: Transform  text using bag of words and bag-of-n- grams • Classification Algorithms: Classification  algorithms assign the tags and create  categories according to the content.   It has broad applications such as  sentiment analysis, topic labeling, spam  detection, and intent detection.<|endoftext|>• Image analytics: - Identifying the  context of the image using image  analytics, categorizes the image and  sort them according to gender, age,  facial