how to use text mining in social and crm to improve ... · all the time in social / text mining...
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How to use Text Mining in Social and CRM to Improve Quality Control and Save Money
Olle Hagelin
Field Data Mgmt
© Sony Mobile Communications
Olle Hagelin
• 20+ years within Mobile Industry
• 10 years working in Field Feedback
• 9 years in Social Reporting
• 4 years working with finding issues via Social
• 2 years using Text Mining and to understand details in CRM data
Short history about Social within Sony Mobile
20052009
2010
2011
2012
2013
All the time in Social / Text Mining working with
Integrasco/Confirmit as provider of
Social information, Support, Education and Tools2014
• Genius start to
be used cross
Sony Mobile
• Ongoing
development to
analyze Survey
free text
• People educated to be
able to use Genius
fully
• Text mining of CRM
data in Genius
• 2013 Regular “simple”
brand comparisons
started*
• 2013 Free text from
CS surveys transferred
to Genius for
analyzes*
• Limited use of
Social tool Genius• Voice of the Consumer
project for better
understand issues with
Service and Support
• Initiated project about
getting Text Mining
based on same principle
as information finding in
Social
• Deep dives
specific issues
• Competitor
comparison
• Issue tracking
• Weekly reporting
- product issues• Listen to brand
buzz
• Monthly reports -
brand / product
awareness
Several TM tools tried and disregarded, usually due to bad coverage of languages
Social Media
Information
Social Media Mobile
business
Internal
Data
Internal
Data
Internal
Data
SoMC use of Customer data
Verified Unique Issues
Social Media related
to Sony Mobile
Is social data valid information?Is it technically correct?
3 650 000 000 posts / year
10 000 000 / day
480 000 000 posts / year
Positive
Neutral
Negative
Possible Issues
4 800 000/year
25 000/year
A. Collect information in Social media
B. Identification of relevant information
C. Sorting out information and clean up
D. Issue identification step one
E. Issue identification step two
Numbers are from 2013
Issue definition process
B. Identification of relevant information
Automated methods are used to search out and filter data with taxonomies
C. Sorting out information and clean up
Confirmit cleans the data, stores it at a conversation level that allows Confirmit to attribute each comment to individual users as part of a
conversation (including information about when and where the conversation took place).
In this process, Confirmit system will filter out duplicates and at a basic level remove spam postings
E. Issue identification step two
Confirmit and Q&CS FDM dedicated issue tracking team will always manually assess each individual issue indicator before they are approved as a
real issue. In this process, our analyst would discard duplicates from the same user, and also flag potential fake or unreal issues.
D. Issue identification step one
Break down a cleansed result set of potential issue descriptions. The quality output starts with Confirmit storage and indexing, and is further refined
with sophisticated industry and client specific taxonomies
A. Collect information in Social media
Social Media Mobile
business
Internal
Data
Support
Web
Support
ForumInternal
Data
Internal
Data
Why use the same tool to do “text mining” of CRM data
Vast amount of unstructured text data in CRM
In 302 days, from 2014-01-01 to 2014-10-29, Sony Mobile recorded on average • ~14 000 interactions every day in Contact Centers
Useful for text mining, ~4 900/day and growing• Email 19,9%• Letters 0,1%• Chat 12,4%
Current CRM data hierarchy
• Categorized into:
25 fundamental categories
113 sub-categories
More than 400 symptoms which are still growing/changing
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Symptom, structured data
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Symptom, structured and un-structured data
Adding the unstructured information
0
20
40
60
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Symptom, structured data
Adding the Unstructured data can make changes of
the severity order.
Using unstructured CRM data adds functionality
• Cross product understanding of how customer describe an issue
• You identify a few key words like Wi-Fi, Router X and channel Y
• This was impossible before the use of text mining
Product A Product B Product C Product D Product E Product F Product G
Wi-Fi, Router X AND channel Y <text> A <text> B <text> C <text> D <text> E <text> F <text> G
No# A No# B No# C No# D No# E No# F No# G
Benefits of using unstructured data
• Reduced Costs / Increased
Capacity
• 75% reduction in time spent for
large Analytics projects to get to
more accurate results
• Faster response time - 1 hour to
resolve internal queries that
would previously take 1 day
Case study 1 – Wi-Fi, accuracy of information
• This case study describes the benefit of the information in the free text
from Contact Centers
Is the information enough to identify and solve problems?
• Current structure allows us to spot emerging trends at higher
levels
• But generic categories and broad symptom descriptions do not
reveal enough information to identify emerging issues
SymptomSymptom
Sub-CategorySub-Category
CategoryCategoryConnectivity & Data transfer
WLAN
Any inquiry/problem
GPRS data transmission
etc.
It tells us nothing more
than there is a problem
The answer lies in the vast amount of unstructured text
• Take this post as an example:
• The problem is related to Hotspot function of the X10 mini
• It is much more useful than “Any inquiry/problem”
• How did we find it?
• How can we find them in the future?
• Is it possible to find all of them?
Comparison between Social Media search and CRM tagging system – WLAN issues
100,748
95,033
78,481
Only tagged by CRM
system with “WLAN”
Searchable by using
Social Media keywords
Combining Social Media
keywords and CRM tagging
• How many entries can be found
with more detailed information
+ 21.1%
+ 28.4%
• How many symptoms with
detailed information can be
found?
• Are there more symptoms to be
found?
By introducing Social Media symptoms into CRM data, we have found there is much more actionable information available to us!
WLAN
Hotspot related
General update related
Router related
Mobile data
related
Battery life
relatedCharging related
Proxy related
ICS update related
SSID related
Adhocnetwork related
Call quality related
4814
313368
3222
1400
592
5611
116
238
371
406
China• Wi-Fi, could not connect to router• Channel 11 not active on phone as it
was for military use• Not correct info about China market• Standard setting on routers sold in china
is channel 11• Change setting in SW and update of all
phones
Case study 2 –Coating of the Keypad was peeling off
June 2008
• K800, 2 weeks after launch, we heard first indication in social media
• Initial answer to consumers was “Not a product issue but a customer issue”
• Few days 15 people in several countries complained and could be used as proof
that it was an issue
• In short, new keyboards was offered to customers
• Root cause found in manufacturing of keyboard and actions taken
ROI?
• Savings 10s if not 100s of thousands considering not only Warranty Costs, but
also Brand damage and coming sales
Case study 3 –Invisible crack in front
July 2011• arc / arc S - arc S started to sell in Germany
• Initial response to customer: “Not an valid warranty issue” = customer abuse
• Rapid growth in discussions on social media about a crack in the front on the arc. Furious customers completely stopped sales on arc S
• SoMS changed communication within days – “It is an issue and SoMC will change the front if you want, but there is no impact on functionality due to crack”
ROI?
• Sales took off on arc S
• Brand image increased, “SoMC take care of their customers”
• Returns then? Extremely few!
Lessons Learned
• Very important to
acknowledge issues that
evidently exist
• Handled correctly it might
not become an issue but a
Brand Builder
Case study 4 –Speed
Two cases where looking at the incredible speed Social can help you to
understand and solve issues
I can’t tell you the worth of speed, but every hour you can “earn” is worth
lots and lots of Euro
Google Play Services Battery Issue
2 weeks
Escalation First escalation
meeting Hits on Social
Media
New version of
Chrome released
by Google
7/3
6/35/3 9/3 18/3
Statement sent out to
Call Centers
Updated statement to Call
Centers and Service
Centers
Call answered in speaker mode
x x x x x
What is the next steps?
• Text mining on Survey comments – under implementation
• To understand what's behind the customers rating of questions
End objective is to skip symptom code input and use text mining to sort out the symptoms
Contact Center• Focus on helping the consumer
• No time to report a symptom
• Data about issues not skewed by CC Agent
interpretation of the issue
Knowledge Management• Secure correct search by using natural
language search
• Search often
• Increase the first resolution rate
CRM• Secure good information collection
• Secure that you catch the sentiment,
new type of issues and behaviors
Register the consumer complaint / question exactly as the consumer express it.
Final word
The information is out there.
You just need to take the time and effort to listen.
SONY is a registered trademark of Sony Corporation.
Names of Sony products and services are the registered trademarks and/or trademarks of Sony Corporation or its Group companies.
Other company names and product names are registered trademarks and/or trademarks of the respective companies.