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1Stadt Wien – Wiener Wohnen Kundenservice GmbH
Using semantic technologies to identify the
content of a call in the contac...
2Stadt Wien – Wiener Wohnen Kundenservice GmbH
Agenda
1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice
2....
3Stadt Wien – Wiener Wohnen Kundenservice GmbH
Well known municipal housings in Vienna
Karl-Marx Hof
• Built 1927-1930
• L...
4Stadt Wien – Wiener Wohnen Kundenservice GmbH
Social housing in Vienna
Facts & Figures
220.000 flats
200.000 sponsored co...
5Stadt Wien – Wiener Wohnen Kundenservice GmbH
Number One in social housing
Subsidiary:
Stadt Wien - Wiener Wohnen
Kundens...
6Stadt Wien – Wiener Wohnen Kundenservice GmbH
Agenda
1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice
2....
7Stadt Wien – Wiener Wohnen Kundenservice GmbH
Customer service
Simplified Process flow
Tenant calls and
tells his/her
con...
8Stadt Wien – Wiener Wohnen Kundenservice GmbH
Customer service – former solution
Call Agent listens
Agent makes
notes on ...
9Stadt Wien – Wiener Wohnen Kundenservice GmbH
Former search solution
Keyword
Topic tree
E x a m p l e
10Stadt Wien – Wiener Wohnen Kundenservice GmbH
Challenges of the former solution
• Difficult search because the customer'...
11Stadt Wien – Wiener Wohnen Kundenservice GmbH
Agenda
1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice
2...
12Stadt Wien – Wiener Wohnen Kundenservice GmbH
Customer service – new solution
Call
Agent
listens
Agent
makes notes
and t...
13Stadt Wien – Wiener Wohnen Kundenservice GmbH
Goals of the solution
• Documenting the business case from the customer‘s ...
14Stadt Wien – Wiener Wohnen Kundenservice GmbH
Agenda
1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice
2...
15Stadt Wien – Wiener Wohnen Kundenservice GmbH
Catalog of business cases
Examples of business cases that refer to defects...
16Stadt Wien – Wiener Wohnen Kundenservice GmbH
Ticket (UI of the issue management system)
User Interface
Contakt
Person
L...
17Stadt Wien – Wiener Wohnen Kundenservice GmbH
Identification of the topic (via API)
Highlights
• Description of the topi...
18Stadt Wien – Wiener Wohnen Kundenservice GmbH
Interpretation of text in real-time
Textinput
Stop-word
filtering
Semantic...
19Stadt Wien – Wiener Wohnen Kundenservice GmbH
Unified knowledge via DEEP.knowledge
20Stadt Wien – Wiener Wohnen Kundenservice GmbH
Highlights
• Contains 90.000 words and their
relationships
• About 4.000 a...
21Stadt Wien – Wiener Wohnen Kundenservice GmbH
Configuration of DEEP.assist
• Basis: Catalogue of standardized business c...
22Stadt Wien – Wiener Wohnen Kundenservice GmbH
Agenda
1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice
2...
23Stadt Wien – Wiener Wohnen Kundenservice GmbH
Benefits
• High user acceptance
• Acceleration of the business process
• R...
24Stadt Wien – Wiener Wohnen Kundenservice GmbH
Lessons learned
• Misspellings tolerance is very important
• Search with s...
25Stadt Wien – Wiener Wohnen Kundenservice GmbH
Next steps
• Implementation of DEEP.assist for other departments (on
going...
26Stadt Wien – Wiener Wohnen Kundenservice GmbH
27Stadt Wien – Wiener Wohnen Kundenservice GmbH
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Roland Fleischhacker (Sonja Kabicher-Fuchs): Using semantic technologies to identify the concerns of a caller in a big contact center of Stadt Wien – Wiener Wohnen

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http://2015.semantics.cc/roland-fleischhacker-dr-sonja-kabicher-fuchs

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Roland Fleischhacker (Sonja Kabicher-Fuchs): Using semantic technologies to identify the concerns of a caller in a big contact center of Stadt Wien – Wiener Wohnen

  1. 1. 1Stadt Wien – Wiener Wohnen Kundenservice GmbH Using semantic technologies to identify the content of a call in the contact center of Stadt Wien – Wiener Wohnen Semantics, Vienna 2015
  2. 2. 2Stadt Wien – Wiener Wohnen Kundenservice GmbH Agenda 1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice 2. Challenges and former solution 3. Goals of a new solution 4. Finding the solution in DEEP.assist 5. Advantages 6. Lessons learned 7. Next steps 8. Demo
  3. 3. 3Stadt Wien – Wiener Wohnen Kundenservice GmbH Well known municipal housings in Vienna Karl-Marx Hof • Built 1927-1930 • Longest contiguous residential building worldwide, looks like a castle • 1.482 flats • 5.000 tenants • nurseries, advice centre for mothers, youth club, lending, library, dentist, drugstore, post office, doctors‘ surgeries, coffee shops, ... WHA Friedrich-Engels-Platz 1-10 • Built 1930-1933 • Second largest social housing • 1.400 flats https://www.wienerwohnen.at/wiener-gemeindebau.html
  4. 4. 4Stadt Wien – Wiener Wohnen Kundenservice GmbH Social housing in Vienna Facts & Figures 220.000 flats 200.000 sponsored cooperative apartments 500.000 tenants (1 out of 4 lives in a municipal housing complex) 13.500.000 square meter of floor space 1.800 municipal housing complexes 1.300 playgrounds 7.600 lifts 6.000 retail units 5.500 tumble-dryers 1,8 Mio shrubs 3.043 caretakers http://www.wienerwohnen.at/ueber-uns/ueber.html
  5. 5. 5Stadt Wien – Wiener Wohnen Kundenservice GmbH Number One in social housing Subsidiary: Stadt Wien - Wiener Wohnen Kundenservice GmbH (responsible for customer services and public communication) https://www.wienerwohnen.at/ueber- uns/organisationsstruktur.html
  6. 6. 6Stadt Wien – Wiener Wohnen Kundenservice GmbH Agenda 1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice 2. Challenges and former solution 3. Goals of a new solution 4. Finding the solution with DEEP.assist 5. Advantages 6. Lessons learned 7. Next steps 8. Demo
  7. 7. 7Stadt Wien – Wiener Wohnen Kundenservice GmbH Customer service Simplified Process flow Tenant calls and tells his/her concerns. Agent creates a ticket, chooses the right business process and starts the workflow Department staff opens the task in workflow system, solves the problem and closes the ticket Tenant Call Center Agent Department of WW
  8. 8. 8Stadt Wien – Wiener Wohnen Kundenservice GmbH Customer service – former solution Call Agent listens Agent makes notes on a piece of paper Agent reads his/her notes and searches in the topic tree Agent decides which business case to use Opens a ticket
  9. 9. 9Stadt Wien – Wiener Wohnen Kundenservice GmbH Former search solution Keyword Topic tree E x a m p l e
  10. 10. 10Stadt Wien – Wiener Wohnen Kundenservice GmbH Challenges of the former solution • Difficult search because the customer's language is not the language of the system. • The caller asks questions- the knowledge base describes answers. • No unified handling of similar business cases. • Long search times with a high error rate. • Uncommon business cases were difficult to find.
  11. 11. 11Stadt Wien – Wiener Wohnen Kundenservice GmbH Agenda 1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice 2. Challenges and former solution 3. Goals of a new solution 4. Finding the solution with DEEP.assist 5. Advantages 6. Lessons learned 7. Next steps 8. Demo
  12. 12. 12Stadt Wien – Wiener Wohnen Kundenservice GmbH Customer service – new solution Call Agent listens Agent makes notes and the text is analyzed in realtime System proposes solutions and agent decides Agent collects more detailed data for the specific business case • Search and documenting business case in one step:  acceleration • Standardization of business cases:  reduction of errors
  13. 13. 13Stadt Wien – Wiener Wohnen Kundenservice GmbH Goals of the solution • Documenting the business case from the customer‘s perspective and his/her language • Searching the solution and documenting the business case in one step • Acceleration of the business case • Centralizing of knowledge for all agents • Possibility of updating knowledge immediately • Use of a wide vocabulary and of associations, in order to avoid that the agent has to type in the „correct“ keyword to find the solution
  14. 14. 14Stadt Wien – Wiener Wohnen Kundenservice GmbH Agenda 1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice 2. Challenges and former solution 3. Goals of a new solution 4. Finding the solution with DEEP.assist 5. Advantages 6. Lessons learned 7. Next steps 8. Demo
  15. 15. 15Stadt Wien – Wiener Wohnen Kundenservice GmbH Catalog of business cases Examples of business cases that refer to defects Schaden aufgrund von Bruch des Abflussrohr Schaden an Bügelmaschine in der Waschküche Schaden aufgrund von Einbruch Schaden an der Eingangstüre im Stiegenhaus Schaden an der Eingangstüre in der Wohnung Schaden an einem Fenster im Gemeinraum Schaden an einem Fenster im Keller Schaden an einem Fenster in der Wohnung Schaden an dem Geländer außerhalb der Wohnung Schaden an dem Geländer innerhalb der Wohnung Schaden an dem Luftentfeuchter in der Waschküche Schaden im Müllabwurfschacht
  16. 16. 16Stadt Wien – Wiener Wohnen Kundenservice GmbH Ticket (UI of the issue management system) User Interface Contakt Person Location Concern ...E X A M P L E
  17. 17. 17Stadt Wien – Wiener Wohnen Kundenservice GmbH Identification of the topic (via API) Highlights • Description of the topic or symptoms in everyday language • Semantic search already analyses part of sentences. • Interpretation of the text in real-time, while typing • Tolerant to spelling errors • Learns from the request behaviour Input Solutions proposed by the expert system
  18. 18. 18Stadt Wien – Wiener Wohnen Kundenservice GmbH Interpretation of text in real-time Textinput Stop-word filtering Semantic normalisation Semantic analysis Semanticsearch Calculating relevance Sortrelevance Outputof proposed solutions t 0 Ø 16 ms
  19. 19. 19Stadt Wien – Wiener Wohnen Kundenservice GmbH Unified knowledge via DEEP.knowledge
  20. 20. 20Stadt Wien – Wiener Wohnen Kundenservice GmbH Highlights • Contains 90.000 words and their relationships • About 4.000 additional technical terms of facility services (e.g. Subsidiär Schutzberechtigter) • Usage of associations (e.g. smoke & fire) • Usage of variable data (e.g. names of employees) Unified knowledge via DEEP.knowledge
  21. 21. 21Stadt Wien – Wiener Wohnen Kundenservice GmbH Configuration of DEEP.assist • Basis: Catalogue of standardized business cases for several departments like: o Service- and complaint management o Property maintenance (Erhaltung) o Property management (Hausbetreuung) • Semi automated machine learning, based on the description of the business cases • Automatic control of the expert system‘s output quality • Configuration effort: about 2 ½ days per 100 business cases
  22. 22. 22Stadt Wien – Wiener Wohnen Kundenservice GmbH Agenda 1. About Stadt Wien-Wiener Wohnen und Wiener Wohnen Kundenservice 2. Challenges and former solution 3. Goals of a new solution 4. Finding the solution with DEEP.assist 5. Advantages 6. Lessons learned 7. Next steps 8. Demo
  23. 23. 23Stadt Wien – Wiener Wohnen Kundenservice GmbH Benefits • High user acceptance • Acceleration of the business process • Reduction of the agent’s talk time • Reduction of error rate (wrong business case chosen) • Reduction of training time for new agents in the contact enter • The agent can concentrate on the communication with the caller, whereas the expert system leads in finding the right solution.
  24. 24. 24Stadt Wien – Wiener Wohnen Kundenservice GmbH Lessons learned • Misspellings tolerance is very important • Search with sentences is a behavioural change for users (search with sentences versus search with keywords) • The configuration („training“) of the expert system (machine learning) results in better solutions, when it is done on thematically related topics. (We started with „training“ separated business cases). • Sometimes it is difficult, to decide which granularity is best in defining a business case
  25. 25. 25Stadt Wien – Wiener Wohnen Kundenservice GmbH Next steps • Implementation of DEEP.assist for other departments (on going) • The expert system measures its quality and optimizes itself (summer 2016) • Further optimization of the business case editor to minimized manual work (summer 2016)
  26. 26. 26Stadt Wien – Wiener Wohnen Kundenservice GmbH
  27. 27. 27Stadt Wien – Wiener Wohnen Kundenservice GmbH

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