Advanced tools in real time analytics and AI in customer support - Milan Simakovic, Tom Zumer
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The document discusses the implementation of advanced real-time analytics and AI in customer support, highlighting tools like Apache Kafka and Lambda architecture for data processing. It specifically examines the case of EKWB, which utilizes Zendesk for customer interactions and has developed algorithms for automatic ticket classification, priority calculation, and agent assignment to enhance support efficiency. The document concludes with future plans for further automation in customer operations, emphasizing the ongoing evolution towards reducing mundane tasks.
Advanced tools in real time analytics and AI in customer support - Milan Simakovic, Tom Zumer
- 1. ADVANCED TOOLS IN REAL TIME ANALYTICS AND AI IN CUSTOMER SUPPORT TOM ŽUMER MILAN SIMAKOVIĆ
- 2. AGENDA • Lambda architecture • Stream processing pipeline • Apache Kafka • Realtime data processors • Graphic ETL tools • AI in Customer support AGENDA www.ibis-instruments.com
- 4. Our products SOLUTIONS IPI – Ibis Performance Insights FOI - Field Operation s Insights Description Ibis Risk Analytics Technologi es iCEM Mobile Network Advanced Analytics (5G) Machine Learning use cases Ibis Data Lake E2E Network monitoring and analytics solution. Deep monitoring for: Docsis/HFC, IPDR, PNM, WiFi optimization, MPL S, UPS, DSL, GPON, TR069, Mobile Network. Centralized monitoring and control of Field Operations. Analytics over measurement data. Risk analysis for banks: - Calculation of IFRS9 in one system. -Scoring/Rating tool. - EWS tool. Ibis Data Lake - a reference architecture for implementing Data Lake by Ibis best practice, allows data-based innovation. A modern solution for managing customer experience based on analytics for Telecommunication providers and other industries. An advanced mobile network analysis, with a focus on today's and future challenges. Realization of different use cases based on machine learning such as: - Churn prevention, - Smart pricing - Next best offer. Work in progress
- 6. Usecases DWH Card transactions Shopping Realtime processing Overdraft ? Credit card? Realtime credits – Is it possible?
- 8. Data ingestion / integration Data storing (Data lake) Data access and processing Data visualization Realtime Real-Time Stream Processing Data lake (HDFS) NoSQL (HBase) Relational (Kudu) Data analytics Batch processing (MapReduce, Hive Pig, Spark, Hue) SQL (Impala) Search (SOLR) Machine Learning, Data mining, Statistics Collaboration, Data exploaration Batch Shell Python Perl Custom Visualizations CRM Campaign mgmt SMSC Call center IVR Realtime Integrations BI CDH Unified Services (Resource management – YARN, Security – Sentry and Record) Kerberos – authentication Workflow scheduler Integrated data management and governance – Cloudera navogator Cluster management – Cloudera manager IBIS DATA LAKE ARCHITECTURE
- 9. A STREAM PROCESSING PIPELINE ASTREAMPROCESSINGPIPELINE collect log analyze serve and store
- 12. REAL TIME PROCESSING • Process continuous data streams • Reduce time increase information value • Filter only useful bits • Streaming is a much more natural model REALTIMEPROCESSING
- 13. APACHE FLINK • Framework and distributed processing engine • Process Unbounded and Bounded Data • Leverage In-Memory Performance • Building Blocks for Streaming Applications • How does Flink support data pipelines? APACHEFLINK
- 14. APACHE BEAM APACHEBEAM • open source unified programming model to define and execute data processing pipelines
- 17. NifiFlow
- 18. TomŽumer AGENDA •Implementation of AI in customer support www.creapro.si
- 19. EKWB • EKWB is a premium liquid cooling manufacturer • Founded in 1999 by Edvard König • Their products are available in more than 30 countries worldwide • EKWB offers a full range of products for end users and enterprise as well • Some of the products are cooling systems (CPU and GPU), fittings, radiators, water blocks, reservoirs, pumps EKWB
- 20. • EKWB uses Zendesk customer support software • It offers interactions between customers and EKWB support team through so called tickets • Customers create tickets where they describe their questions with EKWB products/orders Zendesk ZENDESK
- 21. Tickets • Tickets are resolved by EKWB support groups • There are two EKWB support groups: • Technical • Shipping • There are three different categories of tickets: • Technical • Shipping • AND GENERAL!!! TICKETS
- 22. Challenges Manual assignment of tickets to support groups Faster reply time to more critical tickets Which agent is going to reply unassigned tickets CHALLENGES
- 23. Solution • Automatic ticket classification to Support or Technical team • Calculation of ticket priority • Algorithm for automatic ticket assignment SOLUTION IS IT REALLY ?!? 84% 57% 25% 91% 55% 30%
- 25. Support Team Classification • Challenges: • Preparing training and test set • Validation of results • Implementation of model in working process • It‘s a NLP problem! • We used BERT embeddings for text representations • Model classifies tickets with SVM algorithm • We achieved balanced accuracy of 89,33%! SUPPORTTEAMCLASSIFICATION AND WE ARE IMPROVING IT!!!
- 26. Automatic Priority Calculation • Goal was to prepare ticket priority evaluation • EKWB team had to mark tickets on scale 1-5 for few months! • Tickets need to be ranked by their priority so that tickets with higher priority are solved sooner • QUESTION: classification || regression? • Model evaluates ticket on scale 1-100% PRIORITYCALCULATION 4 5 6,4% 40,2 % 5 34,7%
- 27. Automatic Agent Assignment • Who is going to solve next ticket? • Person with the least tickets? • Person with least high priority tickets? • How to know which agent is active? • Our system smartly assigns tickets to one of active agents • Trying to find balance AGENTASSIGNMENT
- 28. Future Work • Dockerize everything!!! • Automatic cancelation of orders based on tickets • Connection with ERP system • Automatic responses (or at least semi automatic) • DEEP THOUGHT *Hitchhikers guide to the galaxy FUTUREWORK
- 29. THANK YOU. Automation is driving the decline of banal and repetitive tasks. - Amber Rudd