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

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. 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. 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. 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. 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. 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. 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. 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. 9Stadt Wien – Wiener Wohnen Kundenservice GmbH
Former search solution
Keyword
Topic tree
E x a m p l e

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. 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. 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. 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. 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. 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. 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. 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. 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. 19Stadt Wien – Wiener Wohnen Kundenservice GmbH
Unified knowledge via DEEP.knowledge

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. 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. 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. 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. 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. 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. 26Stadt Wien – Wiener Wohnen Kundenservice GmbH

27. 27Stadt Wien – Wiener Wohnen Kundenservice GmbH