How to use Text Mining in Social and CRM to Improve Quality Control and Save Money

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 All the time in Social / Text Mining working with Integrasco/Confirmit as provider of Social information, Support, Education and Tools 2005 Listen to brand buzz Monthly reports - brand / product awareness 2009 Issue tracking Weekly reporting - product issues 2010 Deep dives specific issues Competitor comparison 2011 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 2012 Limited use of Social tool Genius 2013 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* 2014 Genius start to be used cross Sony Mobile Ongoing development to analyze Survey free text Several TM tools tried and disregarded, usually due to bad coverage of languages

SoMC use of Customer data Social Media Mobile business Social Media Information Internal Data Internal Data Internal Data

Is social data valid information? Is it technically correct? 3 650 000 000 posts / year 10 000 000 / day Numbers are from 2013 A. Collect information in Social media Social Media related to Sony Mobile 480 000 000 posts / year B. Identification of relevant information C. Sorting out information and clean up Positive Neutral Negative Possible Issues 4 800 000/year D. Issue identification step one E. Issue identification step two Verified Unique Issues 25 000/year

Issue definition process A. Collect information in Social media 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 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 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.

Why use the same tool to do text mining of CRM data Social Media Mobile business Support Web Internal Data Support Forum Internal Data Internal 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: Symptom, structured data 120 100 80 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 25 fundamental categories 113 sub-categories More than 400 symptoms which are still growing/changing

Adding the unstructured information Symptom, structured data 80 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Symptom, structured and un-structured data 100 80 60 40 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 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 Category Connectivity & Data transfer Sub-Category WLAN GPRS data transmission etc. Symptom Any inquiry/problem 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 How many entries can be found with more detailed information 95,033 Combining Social Media keywords and CRM tagging 78,481 Searchable by using Social Media keywords + 21.1% Only tagged by CRM system with WLAN + 28.4%

By introducing Social Media symptoms into CRM data, we have found there is much more actionable information available to us! How many symptoms with detailed information can be found? Are there more symptoms to be found? 5611 31 Hotspot related Call quality related General update related Router related 4814 3368 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 116 238 371 406 SSID related Adhoc network related ICS update related WLAN Proxy related Charging related Battery life related Mobile data related 3222 1400 592

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

Call answered in speaker mode Statement sent out to Call Centers 7/3 2 weeks Updated statement to Call Centers and Service Centers 5/3 6/3 9/3 18/3 Escalation First escalation meeting x x x x x Hits on Social Media New version of Chrome released by Google

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 Register the consumer complaint / question exactly as the consumer express it. 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

Final word The information is out there. You just need to take the time and effort to listen.

Olle Hagelin Questions olle.hagelin@sonymobile.com @OHagelin +46 70 565 41 26

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