AI for Customer Service - How to Improve Contact Center Efficiency with Machine Learning?
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About the webinar It only takes one bad interaction for a customer to abandon a service or product. Businesses are no longer just competing with other companies’ products, they’re competing with a customer’s last service experience. All contact centers worldwide are looking for new and strategic ways to increase operational performance, reduce cost and still provide high-touch customer experiences that improve customer loyalty and highlight ways to increase revenue and productivity. Through this webinar, we will understand how AI can augment the effort, focus and problem-solving abilities of human agents so that they can tackle more complex or creative tasks. With an abundance of data from logs, emails, chat and voice recordings, contact centers can ingest this data to provide contextual customer service at the right time with the right way providing satisfactory customer service and retain the brand value. What you'll learn: - How organizations are leveraging AI & Machine learning in Customer Service - Live Demo of AI & ML in Customer Service - Best practices to automate machine learning models To explore more, visit: https://skyl.ai/form?p=start-trial
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AI for Customer Service - How to Improve Contact Center Efficiency with Machine Learning?
- 1. AI for Customer Service How to Improve Contact Center Efficiency with Machine Learning?
- 2. Technology leader with 20+ years expertise in Product Development, Business strategy and Artificial Intelligence acceleration. Active contributor in the New York AI community Extensively worked with global organizations in BFSI, Healthcare, Manufacturing, Retail and Ecommerce to define and implement AI strategies Nisha Shoukath Co-founder, People10 & Skyl.ai The Speaker
- 3. Shruti Tanwar Lead - Data Science Extensive experience building future tech products using Machine Learning and Artificial Intelligence. Areas of expertise includes Deep Learning, Data Analysis, full stack development and building world class products in ecommerce, travel and healthcare sector. The Speaker
- 4. CTO & Software Architect with 15 years of experience working at the forefront of cutting-edge technology leading innovative projects Areas of expertise include Architecture design, rapid product development, Deep Learning and Data Analysis The Panelist Bikash Sharma CTO and Co-founder at Skyl.ai
- 5. All dial-in participants will be muted to enable the presenters to speak without interruption Getting familiar with ‘Zoom’ Questions can be submitted via Zoom Questions chat window and will be addressed at the end during Q&A The recording will be emailed to you after the webinar Please familiarize yourself with the Zoom ‘Control Panel’ on your screen
- 6. Live Demo of AI & ML in Customer Service ...In the next 45 minutes How organizations are leveraging AI & Machine learning in Customer Service Best practices to automate machine learning models 1 2 3
- 7. A quick intro about Skyl.ai Machine Learning automation platform for unstructured data Guided Machine Learning Workflow Build & deploy ML models faster on unstructured data Collaborative Data Collection & Labelling Easy-to-use & scalable AI SaaS platform
- 8. POLL #1 At what stage of Machine learning adoption your organization is at? ⊚ Exploring - Curious about it ⊚ Planning - Creating AI/ML strategy ⊚ Experimenting - Building proof of concepts ⊚ Scaling up - Some departments are using it ⊚ In production - Using it in product features ⊚ Transforming - AI/Ml driven business
- 9. How organizations are leveraging AI & Machine learning in Customer Service 01
- 10. Artificial intelligence (AI) is the ability of a computer to think and learn like a human ( understand sentiment, keywords, context etc, and respond appropriately…) Understanding the fundamentals Machine learning (ML) Train models using algorithms to learn and improve from data without explicit programming Natural Language Processing (NLP) Branch of machine learning that helps computers understand, interpret and manipulate human language (keyword extraction, etc..)
- 11. ● Improve efficiency ● Provide personalized & Intuitive customer care ● Simplify jobs How to use AI to improve customer service? Quick & around the clock answers to customer questions/complaints Faster case closure by agents providing stellar customer experience Insights discovery into customer needs
- 12. AI for Customer Service Examples
- 13. Automated Call/Email Routing Laura Amy Sam Jessika Intelligent call routing to assign calls to relevant agents Identify customer issues with social listening and ticketing Scan and redirect Emails to the right office/department Assign queries to relevant customer support
- 14. Faster Resolution of Cases Intent discovery to know the context of the query Extract contextual data from the knowledge base “Hi! I What documents are needed to open my bank account? Priority High Inquiry Category Question Case Detail Account Opening Sentiment Neutral Sure. Please see the document checklist here. Automated response for user queries and complaints
- 15. Virtual agents Automate informational & transactional cases Ask for suggestions Report an issue Schedule a service call Transfer complex/unusual cases to human agents with contextual data
- 16. Customer Service Analytics Improve customer service & satisfaction with insights Advanced call/chat analytics to bring faster insight into customer needs Analyze text fields in surveys and reviews to find insights from customer feedback
- 17. POLL #2 State your role in the AI initiatives/ projects in your organization ⊚ We don’t have any AI projects yet ⊚ Practitioner - Data Science/ Engineering background ⊚ Sponsor / Executive ⊚ Product Manager ⊚ Project Manager ⊚ Student ⊚ Others
- 18. Live Demo of AI & ML in Customer Service 02
- 19. 8 stages of Machine Learning workflow
- 20. Live Demo on automated routing of customer service inquiries using NLP
- 21. Best practices to automate machine learning models 03
- 22. POLL #3 Some challenges that you are facing while implementing AI & Machine Learning ⊚ Not started yet, so no challenges` ⊚ Data collection ⊚ Data Labeling ⊚ Large volumes of data ⊚ Identifying the right data set to train ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
- 23. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 24. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
- 25. Data Labeling - Real-time early visibility (class balance, missing data…)
- 26. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
- 27. Data Labeling with Effective Collaboration (Job allocation, trend, statistics, interactive messaging…) Manage collaborator progress, activity, interactive messaging Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations
- 28. Data Visualization to build strong data intuition ( visuals for data composition, data adequacy)
- 29. One click training at scale (Easy feature sets, out of the box algorithms, API integration, hyper parameter tuning, auto scaling…) ● Train, Deploy and Version your models by creating feature-sets in no time with our easy feature selection provision. ● Choose from state-of-art neural network algorithms, tune hyperparameters and see logs for your training in real time. ● Integrate our powerful inference API with your application for AI- driven actionable intelligence. ● Auto scaling of model training based on data and hyperparameters
- 30. Model Monitoring of metrics in real-time (inference count, execution time, accuracy…) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
- 31. Model Evaluation - Release Confidently (Accuracy, Precision, Recall, F1 Score) ● Monitor your deployed models and analyse inference count, accuracy and execution time. ● See how your models are performing in real-time. No black boxes here.
- 32. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) No DevOps required - Incorporates automatic deployment and dockerization Scalable tech with latest stack Domain agnostic build by data type Scalable on demand On premise and saas models
- 33. Skyl.ai - as ML automation platform
- 34. Try out 15 days free trial with complimentary consultation on pilot project Register https://skyl.ai/form?p=start-trial
- 36. 85 Broad Street, New York, NY, 10004 +1 718 300 2104, +1 646 202 9343 [email protected] We hope to hear from you soon Thank you for joining!
Editor's Notes
- #3: She is a technology leader who wears multiple hats. From defining product strategy , developing product , accelerating AI adoption to scaling businesses, she knows it all. She is based in New york
- #4: She is a versatile person who builds scalable , high-performance solutions and shares expertise through blogs and is currently building future tech products using ML and AI
- #5: Innovator, problem solver and creator
- #9: Exploring - Curious about it Planning - Creating AI/ML strategy Experimenting - Building proof of concepts Scaling up - Some departments are using it In production - Using it in product features Transforming - AI/Ml driven business
- #12: No more phone trees or juggling with 5-6 cases at a time. AI can automate simple, common interactions, doing handoffs to live agents when needed. Crisp and increase the font size Speed up the recruitment process by automating time-consuming & repetitive tasks
- #14: Speed up the recruitment process by automating time-consuming & repetitive tasks
- #15: live agents get recommendations in real time about knowledge sources that can help resolve customer issues more quickly and helpfully. Speed up the recruitment process by automating time-consuming & repetitive tasks
- #17: Machine learning uncovers and categorizes popular customer questions along with all their variations, helping analysts more quickly formalize responses that will please those customers.
- #18: We don’t have any AI projects yet Practitioner - Data Science/ Engineering background Sponsor Product Manager Project Manager Student Others
- #19: How
- #21: 5 minutes intro - 10 industry awareness - 15 min demo - 20 minutes QnA Define problem - Features model - How this model is built using skyl.ai Add slide of Pneumonia detection
- #22: Benefit
- #23: Not started yet, so no challenges Data collection Data Labeling Data Bias Large volumes of data Identifying the right data set to train Lack of knowledge of ML tools Lack of end to end platform Lack of expertise Choosing the right algorithms Monitoring the model performance
- #34: Now, we
- #36: Thank you, Nisha and Shruti! We will go ahead and take some time for questions now. Just a reminder, please be sure to type your questions into the question box in your control panel.
- #37: Confidently - to be charge of / control of your AI projects. Script: https://docs.google.com/document/d/1NWGBbMg1SpePzvaFiO0gy_vkgoyAGGcwe1D2_OUcQIE/edit#