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Teleperformance - Smart personalized service door het gebruik van Data Science
- 1. Collective Memory through Data Science Big Data Expo 19-9-2018 Tom Mac-Kenzie
- 2. outsourced omnichannel customer experience management. We are the worldwide leader in We are experts in people interactions, and this gives us the edge in delivering a superior customer experience in every contact.
- 3. Through our omnichannel customer experience capabilities, voice, e-mail, chat, click- to-call, social media, video chat, automation, face-to-face, and other channels that your customers use. we interact every year by VOICE EMAIL WEB FORM CHAT W/ LIVE AGENT SOCIAL MEDIA MOBILE APP SMS INSTANT MESSAGING AUTOMATION FACE- TO-FACE VIDEO KIOSK/ VIDEO TELLER/ VIDEO CHAT CLICK- TO-CALL
- 4. 4 Over the past 40 years we have worked tirelessly to build the largest Customer Experience company on the planet. We know how to create great & trusted service moments for the customers of our clients. 223Kpeople We are a team of 350facilities 76countries Present in 160markets Serving 265 We provide service in languages and dialects In 2017 Revenue of $4.720bn €4.180bn
- 5. Tom Mac-Kenzie Digital Project Manager, Teleperformance BLX Tom.Mackenzie@Teleperformance.com +31 (0)6 514 653 89 • Current: Digital Project Manager, Bus. Development • Previous: Senior Manager, Google BLX Project Sales Manager, Google BLX Project Manager New Teams, Google BLX Project • Based in Tilburg, NL • 7+years experience in outsourcing industry • Created Messaging-Playbook based on experiences with local and global partners. Created Competitor-Matrix to assess key-players in Messaging Software. Created and pitched client-specific Messaging Pitches to more than 20 Clients/Prospects. Researched and created main differences narrative between Chat & Messaging. • Specializes in providing effortless Customer Experience for enterprises through Instant Messaging & other innovations within customer contact • Extensive background within online Ad Sales, SEA, Sales & People Management • Specialties: Instant Messaging | WhatsApp Business | Facebook Messenger | Customer Contact | Sales | Effortless Customer Experience | Innovation | Online Marketing | e-Commerce | WhatsApp | Social Messaging | In-App Messaging Helping companies deliver an effortless customer experience through Instant Messaging Solutions A passionate online professional with a clear focus on achieving positive ROI and an effortless Customer Experience across all channels. Over 7 years proven experience in Customer Contact, Instant Messaging, Effortless Customer Service, Advertising, e-Commerce, Online Marketing & Sales Management. Teleperformance Experience: 7 Years Relevant Industry Experience
- 6. This is the new normal ...
- 8. 8
- 9. 9 THE CHALLENGE The costs of handling the channel Social Messaging are too high. The growth of the messages is massive and because of that, the workforce grows accordingly. A large number of questions asked by customers have been answered before, using this data would reduce time and effort for the agent, but also increase the satisfaction of both the customer and the employee.
- 10. 10 Goal Reduce number of interactions and time spent to get from question to answer Maintain customer satisfaction and save time. THE CHALLENGE Question Answer
- 11. TELEPERFORMANCE DATA SCIENCE Finding the right partner with proven practice in customer contact
- 12. The data science life-cycle Assumptions were made to define question and answer patterns Question Answer Data formatting § Define question and answer § Move from “message” format to “conversation” format
- 13. The data science life-cycle Language complex to model, but we have found solutions Data cleaning § Product tagging § Text cleaning § Stop words § Synonyms § Spelling mistakes Wasmachine Wasapparaat washingmachineTAG Kapot Stuk Text vectorization § Bi-Grams § Term Frequency – Inverse Document Frequency Dimensionality reduction § Principal Component Analysis
- 14. Product tag clustering Question level clustering Cluster checks Model answers Cluster filtering All Smartphone TV Cut-off 28% Product tag clustering 36 clusters 72% Initial clustering First the conversations are linked to the corresponding products
- 15. Initial clustering Once the product is know, we cluster the specific questions Product tag clustering Question level clustering Cluster checks Model answers Cluster filtering All Smartphone Screen display Broken screen, fixing cost Other TV Cut-off 28%72% 26% Product tag clustering Question level clustering 74% 36 1361
- 16. Initial clustering The clusters are checked, to validate that there is only one question in them Product tag clustering Question level clustering Cluster checks Model answers Cluster filtering
- 17. Self-learning algorithm Starting situation Database containing historic conversations
- 18. Self-learning algorithm Defined question clusters Database containing historic conversations
- 19. Self-learning algorithm Clusters with model answers Database containing historic conversations
- 20. Self-learning algorithm New observation Database containing historic conversations New conversation
- 21. Self-learning algorithm Matching in cluster Database containing historic conversations New conversation
- 22. Self-learning algorithm Matching in cluster Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 23. Self-learning algorithm Select model answer Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 24. Self-learning algorithm Extend existing cluster Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 25. Self-learning algorithm Select answer of most similar question Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 26. Self-learning algorithm Start new cluster Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 27. Self-learning algorithm Reject and write custom answer Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 28. Self-learning algorithm (optional) remove from cluster, add observation to history Database containing historic conversations New conversation Agent options Give model answer Use answer of most similar question Reject and write custom answer
- 29. Self-learning algorithm New observation Database containing historic conversations New conversation
- 30. Self-learning algorithm Matching outside cluster Database containing historic conversations New conversation
- 31. Self-learning algorithm Matching outside cluster Database containing historic conversations New conversation Agent options Use answer of most similar question Reject and write custom answer
- 32. Self-learning algorithm Matching outside cluster Database containing historic conversations New conversation Agent options Use answer of most similar question Reject and write custom answer
- 33. Self-learning algorithm Start new cluster Database containing historic conversations New conversation Agent options Use answer of most similar question Reject and write custom answer
- 34. Self-learning algorithm Matching outside cluster Database containing historic conversations New conversation Agent options Use answer of most similar question Reject and write custom answer
- 35. Self-learning algorithm (optional) remove from history, add observation to history Database containing historic conversations New conversation Agent options Use answer of most similar question Reject and write custom answer
- 36. Self-learning algorithm Overview MatchingNew conversation In cluster Outside cluster Use answer of most similar question Reject and write custom answer Agent options Give model answer Use answer of most similar question Reject and write custom answer Learning Extend cluster New cluster (Optional) remove match, add to set New cluster (Optional) remove match, add to set
- 37. Road to production The developed solution can be used in other customer service situations Collective memory AgentBotFacebook
- 38. Road to production The developed solution can be used in other customer service situations Collective memory AgentBotFacebook Phone Agent Website (self help) Tweets and forums Commercial actions
- 39. SUPERVISED LEARNING RAW DATA DATA SCIENCE ALGORITHM SUGGESTED SOLUTION OPTIONS AGENT SELECTS BEST SOLUTION ALGORITHM IMPROVED AND IS AN OPPORTUNITY TO TRAIN YOUR MODEL!
- 40. THE CHALLENGE The costs of handling the channel Social Messaging are too high. The growth of the messages is massive and because of that, the workforce grows accordingly. A large number of questions asked by customers have been answered before, using this data would reduce time and effort for the agent, but also increase the satisfaction of both the customer and the employee. THE SOLUTION Teleperformance created a “Collective Memory Database” containing Historical Conversations. These conversations have been clustered with algorithms using Data Science Techniques. Model answers have been created for these clusters and 3 options are pushed to agents when a customer query comes in. These options are “Give model answer”, “Use historic answer of most similar question” and “Reject and write own answer”. SUMMARY Provide Suggested Answers to Agents IMPLEMENTATION TIME: 3 Months THE BENEFITS ü Cost Saving: Reduced 12% costs in PoC ü ROI: 6 Months ü Improved Response Time with 1,5 minutes ü Manual effort reduced by 14% ü Higher standardization of processes ü Expected increase of Employee Satisfaction ü Expected reduction of Agent Onboarding NEXT STEPS ü Integrate with Self-Service Portal ü Integrate in Voice Channel