AI for Customer Service: How to Improve Contact Center Efficiency with Machine Learning
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The document discusses strategies for improving contact center efficiency using AI and machine learning, with presentations from experts in the field. Key highlights include the application of natural language processing for automated customer service inquiries, and the advantages of a unified machine learning platform for data management and collaboration. The session includes live demos, polls, and discussions on the challenges organizations face while implementing AI and the benefits of machine learning in customer service.
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, Insurance, Manufacturing, Retail and Ecommerce to define and implement AI strategies Nisha Shoukath Co-founder, People10 & Skyl.ai The Speaker
- 3. 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. Shruti Tanwar Lead - Data Science The Speaker
- 4. Bikash Sharma CTO and Co-founder at Skyl.ai 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
- 5. Getting familiar with ‘Zoom’ All dial-in participants will be muted to enable the presenters to speak without interruption 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 automated routing of customer service inquiries using NLP How contact centers are leveraging AI & Machine learning to improve efficiency How to quickly overcome the challenges in building ML models 1 2 3 ...In the next 45 minutes
- 7. Machine Learning automation platform for unstructured data A quick intro about Skyl.ai 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 contact centers are leveraging AI & Machine learning to improve Efficiency01
- 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 Jessica 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. Live Demo of automated routing of customer service inquiries in Contact Center02
- 18. 8 stages of Machine Learning workflow
- 19. Live Demo on automated routing of customer service inquiries using NLP
- 20. POLL #2 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 ⊚ Data Security ⊚ Lack of knowledge of ML tools ⊚ Lack of end to end platform ⊚ Lack of expertise ⊚ Choosing the right algorithms
- 21. Advantages of a unified platform Speed, Visibility, Quality, Collaboration, Flexibility 03
- 22. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 23. Data Collection - Flexible options (CSV bulk upload, APIs, Mobile capture, Form based…)
- 24. ⊚ On-prem solutions - Data stays in your own servers, and in your own databases, giving you complete control over your data. ⊚ Controlled access flow - Defined and controlled access flow allows selective restriction so that you have full command to regulate who can view or use resources in your ML projects. ⊚ Encrypted data sources - All data sources are encrypted in Skyl thus giving users an additional layer of security, making sure your data stays safe and protected. DataSecurity- on premise solutions (encrypted data sources, access controlled flow..)
- 25. Data Labeling - Simple 4 steps process (collaboration jobs, guided workflow…)
- 26. Data Labeling - Real-time early visibility (class balance, missing data…)
- 27. Data Labeling - Early Visibility (data frequency, data intuition, outliers, trends, labeling accuracy…)
- 28. Data Labeling with Effective Collaboration (Job allocation, trends, statistics, interactive messaging…) ⊚ Analyse trends and progress of your data labeling job in real time with statistics and interactive visualizations ⊚ Manage collaborator progress, activity, interactive messaging
- 29. Data Visualization to build strong data intuition ( visuals for data composition, data adequacy)
- 30. Data Visualization to build strong data intuition (visuals for data composition, data adequacy...)
- 31. 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.
- 32. 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.
- 33. ⊚ 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. Model Evaluation - Release Confidently (Accuracy, Precision, Recall, F1 Score)
- 34. No upfront cost in Infrastructure set up (no DevOps needed, auto-deploy, SaaS & On-prem models…) No DevOps required 01 Latest tech stack 02 On premise and saas models 03 Scalable On demand 04
- 35. Skyl.ai - as ML automation platform Efficient Data Management Solve your data issues; collect and manage data efficiently Accuracy & Quality Maintain accuracy and quality; train and test faster; monitor quality Effective Collaboration Collaborate and manage projects efficiently Early Visibility Get early visibility; visualize and affirm correctness on every step of the way Scalable High - Performance Access on-demand and scalable, high- performance infrastructure Reduce Cost Reduce cost of implementation; do it with less specialized resources
- 36. ⊚ Personalised demo ⊚ 15 days free trial with data credits ⊚ Complimentary consultation on pilot project ⊚ AI Implementation Playbook www.skyl.ai contact@skyl.ai Offers for you...
- 37. Questions? ?
- 38. We hope to hear from you soon Thank you for joining!
Editor's Notes
- #2: Hello everyone and welcome. Thank you for joining today’s webinar on How to Improve Contact Center Efficiency with Machine Learning? My name is Edwin and I’ll be your host today. First off, I’d like to introduce 3 expert speakers for today’s webinar..
- #3: First we have Nisha Shoukath - Nisha is a technology entrepreneur with background in investment banking. She’s co-founded two successful technology startups and has worked with a wide variety of global organizations from different industries. She helps enterprises with defining AI strategy, and AI adoption roadmaps. Welcome, Nisha!
- #4: Next we have Shruti Tanwar - Shruti is an expert in data science who is a veteran in building SaaS products using Machine Learning and AI. Her expertise includes Deep Learning and Data Analysis, as well as full stack development and building tech products in various different fields such as ecommerce, travel, and healthcare. Welcome, Shruti!
- #5: Finally, we have Bikash Sharma joining us today. Bikash is CTO and Software Architect with over 15 years of experience in leading innovative software projects and solutions. He’s co-founded Skyl with his expert knowledge in AI and Machine Learning. Welcome, Bikash!
- #6: Before we begin, I’d like to briefly talk about some relevant Zoom features. All participants in the webinar will be muted to avoid any interruptions during the session. Any questions you might have can be submitted to the Zoom Questions chat window in the control panel, located on the bottom of the screen. We’ll make sure to address your questions during the Q&A session. Also, the recording of the webinar will be emailed to you afterwards, just in case you’ve missed any talking points or wish to view it again. So that’s all for the introduction - now we’ll get started with the webinar and I’ll hand over the session to Nisha
- #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: How
- #20: 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
- #21: We don’t have any AI projects yet Practitioner - Data Science/ Engineering background Sponsor Product Manager Project Manager Student Others
- #22: Benefit
- #25: On-prem solutions For industries, where business depends upon sensitive data, Skyl provides the provision of on-prem solutions. Your data stays in your own servers, and in your own databases, giving you complete control over your data. Access controlled flow Defined and controlled access flows with different organizational roles like business owner, project lead, collaborators etc. allow for selective restriction so that you have full command to regulate who can view or use resources in your ML projects. Encrypted data sources All data sources are encrypted in Skyl thus giving users an additional layer of security, making sure your data stays safe and protected.
- #36: Now, we
- #37: Thank you Nisha and Shruti, for the wonderful presentation and demo. As mentioned earlier, the recording of the webinar will be emailed to you afterwards. [pause] Before we get to the Q&A, I want to mention some of the offers Skyl has for those of you that are curious about incorporating Machine Learning to your business. Skyl offers a personalized demo as well as a 15 days free trial. You’ll be able to interact with real data on the screen, just like we showed in the demo. You’ll experience the process of going from collecting & labeling the data… all the way to deploying a model! Skyl also offers a complimentary consultation on a pilot project of your choice and an AI implementation playbook to go along. This is a great opportunity to see how Skyl can provide Machine Learning solutions to your challenges. If you’re interested in finding out more, please visit the skyl.ai website or you can send an email directly to contact@skyl.ai.
- #38: Alright, now it’s Q&A time! As a reminder, if you have any questions, go to the question box in your control panel - located on the bottom of your Zoom screen. We’ll try to answer as many questions as possible in the time that we have left. So let’s answer some questions. Sample questions: For me/ Bikash: -(Olivia) How do I know if my model performance is going down, and how do I fix it? -(Freddy) Can Skyl help me in figuring out if my data needs re-labelling? - How much is the devops effort in building a model deployment pipeline in Skyl? For Nisha: -(Emma) Can I upload unlimited data? Or is there some kind of subscription system I need, to use Skyl? -(Alem) Apart from text, can I use Skyl for image based data, like screenshots, to build model for my customer center?? Ok, that’s all the time we have for questions today, but feel free to contact us with your specific questions and we’ll make sure to get them answered.
- #39: All right, so we have reached the end of the webinar. We hope you enjoyed it. We have a lot more webinars coming up on different machine learning topics and how they can be implemented into different businesses and industries, So don’t miss out and make sure you sign up for upcoming webinars as well Thank you for joining and I hope you have a wonderful day.