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Big Data has Big Implications for Customer Experience Management


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This presentation covers the application of Big Data principles in Customer Experience Management. I present data models to help companies integrate, organize and analyze their disparate data sources (e.g., operational, financial, constituency and customer feedback) to improve the customer experience and customer loyalty.

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Big Data has Big Implications for Customer Experience Management

  1. 1. Using Big Data to Improve How may we help? Customer Loyalty Spring 2012 Bob E. Hayes, PhD
  2. 2. Overview1. Big Data2. Analytics3. Data Integration4. Customer Loyalty5. Customer Experience Management6. Linkage Analysis7. Implications Copyright 2012 TCELab
  3. 3. Search Volume Index 0 1 2 3 4 5 Jan 2006 March 2006 6 May 2006 July 2006 Sept 2006 Nov 2006 Jan 2007 March 2007 May 2007 Big Data July 2007 Sept 2007 Nov 2007 Jan 2008 March 2008 May 2008 July 2008 Sept 2008 Customer Experience Nov 2008 Jan 2009Copyright 2012 TCELab March 2009 May 2009 July 2009 Sept 2009 Nov 2009 Jan 2010 March 2010 May 2010 July 2010 Sept 2010 Nov 2010 Jan 2011 March 2011 Big Interest in Big Data: Google Trends May 2011 July 2011 Sept 2011 Nov 2011 Scale is based on the average worldwide traffic of customer experience in all years. Jan 2012 March 2012
  4. 4. Big Data• Big Data refers to the tools and processes of managing and utilizing large datasets.• An amalgamation of different areas that help us try to get a handle on, insight from and use out of data Copyright 2012 TCELab
  5. 5. Three Vs of Big Data• Volume – amount of data is massive• Velocity – speed at which data are being generated is very fast• Variety – different types of data like structured and unstructured Copyright 2012 TCELab
  6. 6. Disparate Sources of Business Data Customer Operational Feedback Financial1. Call handling time 1. Customer Loyalty2. Number of calls until 2. Relationship resolution satisfaction 1. Revenue3. Response time 3. Transaction satisfaction 2. Number of products purchased 3. Customer tenure Employee 4. Service contractPartner Feedback renewal Feedback 5. Number of sales1. Partner Loyalty 1. Employee Loyalty transactions2. Satisfaction with 2. Satisfaction with 6. Frequency of partnering relationship business areas purchases Copyright 2012 TCELab
  7. 7. Value from Analytics: MIT / IBM 2010 Study Top-performing organizations use analytics five times more than lower performers Number one obstacle to the adoption of analytics in their organizations was a lack of understanding of how to use analytics to improve the business winter/52205/big-data-analytics-and-the-path-from- insights-to-value/ Copyright 2012 TCELab
  8. 8. Three Big Data Approaches1. Interactive Exploration - good for discovering real-time patterns from your data as they emerge2. Direct Batch Reporting - good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports3. Batch ETL (extract-transform-load) - good for analyzing historical trends or linking disparate data Copyright 2012 TCELab
  9. 9. Data Integration is Key to Extracting Value Copyright 2012 TCELab
  10. 10. CEM and Customer Loyalty• Customer loyalty is key to business growth• The process of understanding and managing your customers’ interactions with and perceptions of your brand / company Copyright 2012 TCELab
  11. 11. Linkage Analysis• Linkage analysis answers the questions: – What is the $ value of improving customer satisfaction/loyalty? – Which operational metrics have the biggest impact on customer satisfaction/loyalty? – Which employee/partner factors have the biggest impact on customer satisfaction/loyalty? Operational Transactional Metrics Satisfaction Relationship Financial Satisfaction/ Business Loyalty Metrics Constituency Satisfaction/ Loyalty Copyright 2012 TCELab
  12. 12. Integrating your Business Data Customer Feedback Data Sources Relationship Transaction (satisfaction with specific (satisfaction/loyalty to company) transaction/interaction) • Link data at customer levelBusiness Data Sources Financial • Quality of the relationship (sat, (revenue, number of N/A loyalty) impacts financial metrics sales) • Link data at transaction level Operational • Operational metrics impact (call handling, response N/A quality of the transaction time) • Link data at constituency level • Link data at constituency level Constituency • Constituency satisfaction impacts • Constituency satisfaction (employee / partner customer satisfaction with overall impacts customer satisfaction feedback) relationship with interaction Copyright 2012 TCELab
  13. 13. Financial Metrics / Real Loyalty Behaviors• Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes• Retention Relationship Financial Satisfaction/ Business – Customer tenure Loyalty Metrics – Customer defection rate – Service contract renewal • Purchasing• Advocacy • Number of products – Number of new customers purchased – Revenue • Number of sales transactions • Frequency of purchases
  14. 14. VoC / Financial Linkage Customer Feedback Financial Metric for a specific for a specific customer (account) customer (account) Customer x1 (Account) 1 y1 Customer x2 (Account) 2 y2 Customer x3 (Account) 3 y3 Customer x4 (Account) 4 y4 . . . . . . . . . Customer xn (Account) n yn xn represents customer feedback for customer n. yn represents the financial metric for customer n. Copyright 2012 TCELab
  15. 15. ROI of Customer Loyalty Additional Software Percent Purchasing 55% increase Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10) Customer Loyalty Copyright 2012 TCELab
  16. 16. Operational Metrics• Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty Operational Transactional• Support Metrics Metrics Satisfaction – First Call Resolution (FCR) – Number of calls until resolution – Call handling time – Response time – Abandon rate – Average talk time – Adherence & Shrinkage – Average speed of answer (ASA) Copyright 2012 TCELab
  17. 17. Operational / VoC Linkage Operational Metric Customer Feedback for a specific for a specific customer’s interaction customer’s interaction Customer 1 x1 Interaction y1 Customer 2 x2 Interaction y2 Customer 3 x3 Interaction y3 Customer 4 x4 Interaction y4 . . . . . . . . . Customer n xn Interaction yn xn represents the operational metric for customer interaction n. yn represents the customer feedback for customer interaction n. Copyright 2012 TCELab
  18. 18. Identify Operational Drivers Copyright 2012 TCELab
  19. 19. Identify Operational Standards Number of Calls to Resolve SR Sat with SR 1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls Number of SR Ownership Changes Sat with SR 1 change 2 changes 3 changes 4 changes 5+ changes Copyright 2012 TCELab
  20. 20. Constituency Metrics• Linkage analysis helps us understand how constituency variables impact customer satisfaction/loyalty Customer Employee Satisfaction/• Satisfaction Metrics Metrics Loyalty – Employee satisfaction with business areas Partner Metrics – Partner satisfaction with partner relationship• Loyalty Metrics • Other Metrics – Employee/Partner loyalty • Employee training metrics • Partner certification status Copyright 2012 TCELab
  21. 21. Constituency / VoC Linkage Employee Customer Satisfaction Satisfaction / Loyalty with the Company _ x1 Employee 1 y1 _ x2 Employee 2 y2 _ x3 Employee 3 y3 _ x4 Employee 4 y4 . . . . . . . . . _ xn Employee n yn 1Analysis typically conducted for B2B customers where a given employee (sales representative, technical account manager, sales manager) is associated with a given customer (account) _n represents employee satisfaction score for Employee n. x yn represents average customer satisfaction scores across survey respondents for Employee n. Copyright 2012 TCELab
  22. 22. Identify Employee Drivers Copyright 2012 TCELab
  23. 23. Validate Training Standards Copyright 2012 TCELab
  24. 24. Implications• Ask and answer bigger questions about your customers – Explore all business data to understand their impact on customer experience / loyalty• Build your company around your customers – Greenplum social network platform – Cross-functional teams working together to solve customer problems• Use objective, “real,” loyalty metrics Copyright 2012 TCELab
  25. 25. @bobehayes Big Data to Improve How may we help? Customer Loyalty Spring 2012 Bob E. Hayes, PhD