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Customer Journey Analytics and Big Data

Companies that want to turn excellent customer experience into growth need to master Customer Journeys. Customer Journeys (the set of interactions a customer has with a brand to complete a task) and less moments of truth are what matter for a customer. Companies that master not only see an improvement in customer experience, loyalty, and operational productivity; they also see above-market growth.

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Customer Journey Analytics and Big Data

  1. Any use of this material without specific permission of McKinsey & Company is strictly prohibited McKinsey on Marketing & Sales – Slideshare Brief April 2013 Customer Journey Analytics and Big Data
  2. McKinsey & Company | 1 Across sectors, companies in the U.S. store at least 100 Terabytes of data – many have over 1 Petabyte . . . Average stored data per firm with more than 1,000 employees (Terabytes, 2009) 967 870 831 825 801 697 536 370 319 278 231 150 Discrete Manufacturing Securities and Investment Services Banking Communications and Media 1,312 3,866 1,931 1,792 Utilities 1,507 Government Healthcare Providers Education Professional Services Construction Consumer and Recreation Services Insurance Process Manufacturing Resource Industries Transportation Retail Wholesale WalMart data warehouse in 2004 ~500 TB Library of Congress collection in 2011 ~235 TB SOURCE: McKinsey Global Institute
  3. McKinsey & Company | 2 . . . and those that know how to use it have outperformed their respective markets and have created competitive advantage Percent Revenue 1999-2009 10-year CAGR EBITDA 1999-2009 10-year CAGR Financial Services IT Retail Consumer 11 10 6 4 13 20 8 10 19 9 6 6 7 8 6 6 Other companies Big Data leaders “Companies which are more data-driven are 5% more productive, and 6% more profitable” – MIT, Wharton, McKinsey, HBR SOURCE: Bloomberg; Datastream; annual reports; McKinsey analysis
  4. McKinsey & Company | 3 Value potential of Big Data affects virtually every part of the economy Wholesale trade Utilities Computer and electronic products Arts and Entertainment Educational services Real Estate and Rental Other Services Health Care Providers Construction Professional Services Natural resources Transportation and Warehousing Information Manufacturing Management of companies Financial Services and Insurance Government Admin, Support and Waste Management Accommodation and Food Retail trade Bubble sizes denote relative sizes of GDP High Low Big data value potential index1 High Low 1 Determined by industry average of transaction intensity, amount of data per firm, variability in performance, customer & supplier intensity, and turbulence Bigdataeaseofcaptureindex SOURCE: McKinsey Global Institute
  5. McKinsey & Company | 4 However, most companies face challenges with how to best extract real value out of Big Data Harnessing Big Data Driving to actionable insights rapidly Real-time execution 1 2 3 ▪ Massive volume, multiple sources and systems ▪ “Garbage-in / garbage-out” . . . data quality matters ▪ Knowing which data will drive impact ▪ Staying focused on priority business opportunities ▪ “Needle in a haystack” ▪ “Analysis Paralysis” ▪ Long wait and heavy analytical effort required ▪ Driving change across organizational silos ▪ Building the right capabilities infrastructure Typical challenges SOURCE: McKinsey & Co
  6. McKinsey & Company | 5 What is a Customer Journey, and how is it different? . . . an event that marks the defining experience of key life- cycles of a customer . . . defined around a discrete beginning and end across time . . . typically multi-touch, multi- channel and therefore cross- functional in nature . . . anchored in how customers think about it, not the way functional silos do Journeys represent an evolution in thinking over traditional touchpoint (or „moment‟) approaches A Journey is … Journeys are increasingly the way customers interact with companies of all customer interactions happen during a multi- event, multi-channel journey of all customer journeys involve more than one channel of interaction 56% 38% SOURCE: McKinsey & Co
  7. McKinsey & Company | 6 Customer Journey example Journey Touchpoint Example card on-boarding journey FAQs (agent) Retrieve customer ID/password (e-mail) Inquiry call (agent) FAQs (chat) Card activation (IVR) Rewards use (Web) Partner offers setup (Web) Feedback about experience (social media) Billing issue (chat) Opt-in alerts (mobile) Account info (mobile) Reposition mobile app (mobile) Credit increase request (agent) Retention call (agent) Payment (IVR) Autopay (IVR) Email Social & chat Retail/ branch Web Call center Field Mobile/ SMS CSAT data Set up a new account (branch) SOURCE: McKinsey & Co
  8. McKinsey & Company | 7 Journeys are 30-40% more predictive of customer satisfaction and churn Correlation analysis Correlation coefficient 0.43 0.530.53 0.23 0.58 0.65 0.58 0.52 +36% JourneysTouchpoints Customer satisfaction -0.25-0.26 -0.23 -0.33-0.33-0.32 +33% AverageAuto insuranceBankingPay TV Likelihood to cancel/churn SOURCE: McKinsey U.S. multi-industry survey
  9. McKinsey & Company | 8 6.0 6.4 6.8 7.2 7.6 8.0 8.4 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 Overall CSAT 2011 Journey satisfaction2 2011 I H J G F E D L K C B A Journey performers win in Customer Experience . . . and Growth Example: PayTV – Journeys winners are overall customer experience winners R2 = 53% vs. 14% for single touchpoints satisfaction -2% 0% 2% 4% 6% 8% 8.0 8.2 8.4 8.6 8.8 9.0 9.2 9.4 Journey satisfaction2 2011 N M E K C B A Revenue growth 2011 vs. 2010 Example: Auto Insurance, a 1/10 of a point in journey sat worth a full point of revenue growth ($200M on average) SOURCE McKinsey U.S. multi-industry survey
  10. McKinsey & Company | 9 . . . and drive substantial impact along all key business levers Impact Service operations 10% to 20% of customer service cost trapped between channels 15-20% Call/visit reduction Retention, cross/up-selling Journeys account for 5% to 20% of churn / sale Cross selling 10-20% 10-25bps Churn decrease Customer experience (CE) Journeys are 30% to 40% more predictive of CE than touch points CSAT lift 5-10 ppts 5-15% Cost avoidance Business lever SOURCE: McKinsey & Co
  11. McKinsey & Company | 10 In each industry, only a limited set of Journeys really matter 1 Includes promos, DMs, etc. Retail banking Wireless Cable ▪ Account opening ▪ Fees, rates, and pricing ▪ Payments / transfers ▪ New card / mortgage ▪ Collections ▪ Lost / Stolen & Fraud ▪ Proactive selling1 ▪ New activation ▪ Billing ▪ Feature / plan change ▪ Suspend reconnect ▪ Device upgrade ▪ Device Repairs ▪ Network issues ▪ Proactive selling1 ▪ Install ▪ Billing ▪ Rate changes ▪ Collections ▪ Package change ▪ Repairs ▪ Move ▪ Proactive selling1 These opportunities account for the bulk of either: ▪ Transaction costs, and/or ▪ Opportunities to drive incremental value (loyalty, upsell, etc.) SOURCE: McKinsey & Co
  12. McKinsey & Company | 11 Example: Customer journey analytics identify cost and CX improvement opportunities for U.S. retail bank ▪ Large global bank; ~780MM multichannel customer interactions/year ▪ Looking for ways to decrease service costs while improving NPS Context Big data/ customer journey approach Map key journeys and quality check 1 Identify pain-points and root causes 2 Assess value at stake, develo p and prioritize initiatives 3 Continuous -ly assess impact with KPIs/ dashboard 4 Expected impactExample insights ▪ ~2MM customers abandon web registration, 23% of those call agent ▪ ~870MM calls/year after successful web payment ▪ ~510MM calls/year after abandoned web payment ▪ ~20% of callers setting up new account payment unsuccessful at secondary verification (CVV, SSN) ▪ 45% of users initiating a chat call to speak with an agent 20-30% Call reduction/ cost NPS improvement Up to 60% SOURCE: McKinsey & Co, ClickFox
  13. McKinsey & Company | 12 McKinsey’s core beliefs on Big Data Big Data is not about technology, but about the real- time use of data in the front-line execution - Data without execution does not bring any value 1 The most important success factor is the man-machine interface and the decisions made by humans based on analytic insights 2 Any Big Data solution has to be “business first”, hypothesis driven, rather than trying to gather all the data available – Half the battle is to identify and eliminate the data that is irrelevant 3 No matter how incomplete your data is right now, you can nevertheless use it better to create business value through the use of Big Data tools – no “Apollo” project is needed to get going 4 SOURCE: McKinsey & Co
  14. McKinsey & Company | 13 Whom to contact Dorian Stone - Partner Vera Tkach Maxence Vancauwenberghe Vera_Tkach@mckinsey.com Maxence_Vancauwenberghe@ mckinsey.com Dorian_Stone@mckinsey.com
  15. CONFIDENTIAL AND PROPRIETARY | Any use of this material without specific permission of McKinsey & Company is strictly prohibited Learn more on the Chief Marketing & Sales Officer Forum 1 www.cmsoforum.mckinsey.com WWW @McK_CMSOForum www.youtube.com/McKinseyCMSOforum marketingandsales@mckinsey.com
  16. McKinsey & Company | 15 APPENDIX
  17. McKinsey & Company | 16 The McKinsey / ClickFox partnership brings “Big data” insights to action in a distinctive way ▪ Quick set up (weeks) ▪ Prioritization of data to aggregate based on business case ▪ Integrating customers and touchpoint data in one single datamart ▪ Millions of touchpoints data ingested every day (4 bn p.a. in total) ▪ Managing disparate sources / messy data ▪ Automatic prioritization of Journeys / pain points driving cost, CSAT, churn with built- in algorithms ▪ Ability to drill / surface root causes of pain points ▪ Leverage of McKinsey Advanced Analytics and predictive modeling (e.g., churn, NPTB) ▪ Design of target Journeys with McKinsey benchmarks (e.g., ICE) and expertise ▪ McKinsey transformation / change approach – starting from the top, down to the “shopfloor,” and across silos ▪ Real time Journey performance tracking, across channels ▪ Test and learn with immediate feedback ▪ Integration with CRM systems / workflow (e.g., customer outreach alerts/ lists) 1. Stitching Journey data across channels – Everyday 3. Accelerating execution – Real time 2. Surfacing actionable insights – Fast