roland fleischhacker (sonja kabicher-fuchs): using semantic technologies to identify the concerns of...

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


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


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

https://www.wienerwohnen.at/wiener-gemeindebau.html


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

http://www.wienerwohnen.at/ueber-uns/ueber.html


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

https://www.wienerwohnen.at/ueber-uns/organisationsstruktur.html


Agenda




4. Finding the solution with DEEP.assist

5. Advantages

6. Lessons learned

7. Next steps

8. Demo


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


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


Former search solution

Keyword

Topic tree

E x a m p l e


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.


Agenda





5. Advantages

6. Lessons learned

7. Next steps

8. Demo


Customer service – new solution

CallAgent listens

Agent makes notesand the textis analyzedin realtime

System proposessolutionsand agent

decides

Agent collectsmore detailed

data for thespecific

business case

• Search and documenting business case in one step: acceleration

• Standardization of business cases: reduction of errors


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


Agenda





5. Advantages

6. Lessons learned

7. Next steps

8. Demo


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


Ticket (UI of the issue management system)

User Interface

Contakt

Person

Location

Concern

...E X A M P L E


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


Interpretation of text in real-time

Text

inp

ut

Sto

p-w

ord

fi

lter

ing

Sem

anti

c n

orm

alis

atio

n

Sem

anti

c an

alys

is

Sem

anti

c se

arch

Cal

cula

tin

g re

leva

nce

Sort

rel

evan

ce

Ou

tpu

t o

f p

rop

ose

d

solu

tio

ns

t0 Ø 16 ms


Unified knowledge via DEEP.knowledge


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


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


Agenda





5. Advantages

6. Lessons learned

7. Next steps

8. Demo


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.


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


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)

roland fleischhacker (sonja kabicher-fuchs): using semantic technologies to identify the concerns of...

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