Big Data has Big Implications for Customer Experience Management

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. For More, please visit
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  • 1. How may we help? Spring 2012 Using Big Data to Improve Customer Loyalty Bob E. Hayes, PhD
  • 2. Overview 1. Big Data 2. Analytics 3. Data Integration 4. Customer Loyalty 5. Customer Experience Management 6. Linkage Analysis 7. Implications Copyright 2012 TCELab
  • 3. Big Interest in Big Data: Google Trends 0 1 2 3 4 5 6 Jan2006 March2006 May2006 July2006 Sept2006 Nov2006 Jan2007 March2007 May2007 July2007 Sept2007 Nov2007 Jan2008 March2008 May2008 July2008 Sept2008 Nov2008 Jan2009 March2009 May2009 July2009 Sept2009 Nov2009 Jan2010 March2010 May2010 July2010 Sept2010 Nov2010 Jan2011 March2011 May2011 July2011 Sept2011 Nov2011 Jan2012 March2012 SearchVolumeIndex Customer Experience Big Data Scale is based on the average worldwide traffic of customer experience in all years. Copyright 2012 TCELab
  • 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. 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. Disparate Sources of Business Data 1.Call handling time 2.Number of calls until resolution 3.Response time 1.Revenue 2.Number of products purchased 3.Customer tenure 4.Service contract renewal 5.Number of sales transactions 6.Frequency of purchases 1.Customer Loyalty 2.Relationship satisfaction 3.Transaction satisfaction 1.Employee Loyalty 2.Satisfaction with business areas Operational Partner Feedback 1.Partner Loyalty 2.Satisfaction with partnering relationship Customer Feedback Employee Feedback Financial Copyright 2012 TCELab
  • 7. Value from Analytics: MIT / IBM 2010 Study Top-performing organizations use analytics five times more than lower performers Copyright 2012 TCELab winter/52205/big-data-analytics-and-the-path-from- insights-to-value/ 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
  • 8. Three Big Data Approaches 1. Interactive Exploration - good for discovering real-time patterns from your data as they emerge 2. Direct Batch Reporting - good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports 3. Batch ETL (extract-transform-load) - good for analyzing historical trends or linking disparate data Copyright 2012 TCELab
  • 9. Data Integration is Key to Extracting Value Copyright 2012 TCELab
  • 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. 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 Metrics Transactional Satisfaction Relationship Satisfaction/ Loyalty Financial Business Metrics Constituency Satisfaction/ Loyalty Copyright 2012 TCELab
  • 12. Customer Feedback Data Sources Relationship (satisfaction/loyalty to company) Transaction (satisfaction with specific transaction/interaction) BusinessDataSources Financial (revenue, number of sales) •Link data at customer level •Quality of the relationship (sat, loyalty) impacts financial metrics N/A Operational (call handling, response time) N/A •Link data at transaction level •Operational metrics impact quality of the transaction Constituency (employee / partner feedback) •Link data at constituency level •Constituency satisfaction impacts customer satisfaction with overall relationship •Link data at constituency level •Constituency satisfaction impacts customer satisfaction with interaction Integrating your Business Data Copyright 2012 TCELab
  • 13. Financial Metrics / Real Loyalty Behaviors • Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes • Retention – Customer tenure – Customer defection rate – Service contract renewal • Advocacy – Number of new customers – Revenue • Purchasing • Number of products purchased • Number of sales transactions • Frequency of purchases Relationship Satisfaction/ Loyalty Financial Business Metrics
  • 14. VoC / Financial Linkage Customer (Account) 1 Customer (Account) 2 Customer (Account) 3 Customer (Account) 4 Customer (Account) n Customer Feedback for a specific customer (account) Financial Metric for a specific customer (account) x1 x3 x2 xn x4 y1 y3 y2 yn y4 yn represents the financial metric for customer n. xn represents customer feedback for customer n. . . . . . . . . . Copyright 2012 TCELab
  • 15. ROI of Customer Loyalty Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10) PercentPurchasing AdditionalSoftware Customer Loyalty 55% increase Copyright 2012 TCELab
  • 16. Operational Metrics • Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty • Support Metrics – 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 Operational Metrics Transactional Satisfaction
  • 17. Operational / VoC Linkage Customer 1 Interaction Customer 2 Interaction Customer 3 Interaction Customer 4 Interaction Customer n Interaction Operational Metric for a specific customer’s interaction Customer Feedback for a specific customer’s interaction x1 x3 x2 xn x4 y1 y3 y2 yn y4 yn represents the customer feedback for customer interaction n. xn represents the operational metric for customer interaction n. . . . . . . . . . Copyright 2012 TCELab
  • 18. Identify Operational Drivers Copyright 2012 TCELab
  • 19. Identify Operational Standards 1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls SatwithSR Number of Calls to Resolve SR 1 change 2 changes 3 changes 4 changes 5+ changes SatwithSR Number of SR Ownership Changes Copyright 2012 TCELab
  • 20. Constituency Metrics • Linkage analysis helps us understand how constituency variables impact customer satisfaction/loyalty • Satisfaction Metrics – Employee satisfaction with business areas – Partner satisfaction with partner relationship • Loyalty Metrics – Employee/Partner loyalty Employee Metrics Customer Satisfaction/ Loyalty Partner Metrics • Other Metrics • Employee training metrics • Partner certification status Copyright 2012 TCELab
  • 21. Constituency / VoC Linkage Employee 1 Employee 2 Employee 3 Employee 4 Employee n Employee Satisfaction with the Company Customer Satisfaction / Loyalty x1 x3 x2 xn x4 y1 y3 y2 yn y4 . . . . . . . . . yn represents average customer satisfaction scores across survey respondents for Employee n. _xn represents employee satisfaction score for Employee n. 1 Analysis typically conducted for B2B customers where a given employee (sales representative, technical account manager, sales manager) is associated with a given customer (account) _ _ _ _ _ Copyright 2012 TCELab
  • 22. Identify Employee Drivers Copyright 2012 TCELab
  • 23. Validate Training Standards Copyright 2012 TCELab
  • 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. How may we help? Spring 2012 Using Big Data to Improve Customer Loyalty Bob E. Hayes, PhD @bobehayes
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