Traditional Market Research and Big Data Integration

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1 Traditional Market Research and Big Data Integration Unlocking the Secrets to Why Customers Act the Way That They Do Greg Mishkin Vice President, Research and Consulting Market Strategies International Don Hodson Director, Research and Analytics AT&T Mobility ISO

2 2013 was the year of Big Data. Privacy became a topic of kitchen table conversation. People are realizing that big data are everywhere.

3 Big data can tell you a lot about people. When they do it What they do Who they are doing it with Where they are doing it

4 But it can t tell you the why. When they do it What they do Why they are doing it Who they are doing it with Where they are doing it

5 Without knowing why they do what they do you cannot effectively get them to modify their behaviors. Ultimately, isn t that a marketer s primary job to influence how people think of your brand? to get customers to buy more of your offerings? to be more loyal to your brand? 5

6 current needs income Listening Posts Traditional Quantitative buying patterns aspirations Traditional research reveals fears why customers feel the way they do. Ethnography education Traditional Qualitative communication cultural identity Mobile Analytics Syndicated Sources future needs Biometrics location Web Analytics reachability behavior Big data reveals what customers do. receptiveness social network brand loyalty media community brand awareness motivations attitudes

7 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Initial Survey Append Experimental Design Profiling Product Development/ Remediation Research Survey Apply Algorithms

8 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Initial Survey Append Identify the problem Experimental Design Product Development/ Remediation Research Survey Apply Algorithms Profiling

9 Continuous Improvement Cycle TM Application to Entire Customer Base Initial Survey Identify the problem Experimental Survey Append Determine who is impacted Experimental Design Product Development/ Remediation Research Survey Apply Algorithms Profiling

10 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Initial Survey Append Identify the problem Determine who is impacted Find out how to modify actions and attitudes Experimental Design Product Development/ Remediation Research Survey Apply Algorithms Profiling

11 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Initial Survey Append Identify the problem Determine who is impacted Find out how to modify actions and attitudes Experimental Design Profiling Product Development/ Remediation Research Test and confirm efficacy and ROI Survey Apply Algorithms

12 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Apply to customer base Initial Survey Append Identify the problem Determine who is impacted Find out how to modify actions and attitudes Test and confirm efficacy and ROI Experimental Design Profiling Product Development/ Remediation Research Survey Apply Algorithms

13 Continuous Improvement Cycle TM Experimental Survey Application to Entire Customer Base Initial Survey Append Identify the problem Determine who is impacted Find out how to modify actions and attitudes Test and confirm efficacy and ROI Experimental Design Product Development/ Remediation Research Survey Apply Algorithms Profiling Apply to customer base

14 Continuous Improvement Cycle TM Customer Experience Targeted Messaging Customer Satisfaction / NPS Upsell / Cross Sell / Upgrade Loyalty Actionable Segmentation Churn Product Improvement / Features Promotions Brand Management New Product Development A MULTI-PURPOSE TOOL

15 INCREASE BUSINESS IMPACT Customer Experience Targeted Messaging Customer Satisfaction / NPS Upsell / Cross Sell / Upgrade Loyalty Actionable Segmentation Churn Product Improvement / Features Promotions Brand Management New Product Development DECREASE BUSINESS RISK

16 When the right people, companies, infrastructure and data come together, it s magic. We can begin to see the world in ways that were previously impossible. AT&T Case Study

17

18 A B C

19 AT&T Retail Agent Nat l Retail Indirect/ Direct Marketing Warranty Replacement Call Centers Chat On Line Usage Data Network Performance Enables insight into detailed customer experiences across virtually all touch points Billing Profile/ Characteristics Many Other Data Sources

20 New $ $ $ $ April May June 20

21 The first months are critical for new activation retention and NPS NPS by Period Into Contract NPS Among Actives

22 The first months are critical for new activation retention and NPS NPS by Period Into Contract NPS Among Actives NPS Among Ultimate Churners

23 You never get a second chance to make a good first impression * NPS by Period Into Contract NPS Among Ultimate Churners *Quote from Will Rogers Importance of initial provisioning

24 How does the provisioning process leverage learnings from millions of experiences?...? ? ?......?...

25 The path of a churner can be detected and prevented in the provisioning process...? ? ?......?...

26 Maybe we can Get a second chance to make a good first impression NPS by Period Into Contract NPS Among Ultimate Churners Importance of initial provisioning Importance of refinement

27 Are early NPS decline and cost/billing churn driven by provisioning that didn t address customer needs? Wrong Plan Plan limits too low resulting in usage alerts and $ overages Consistently greater than plan limit PLAN LIMIT Range of Usage: Months Following Activation Mixed Plan xx% OF PLAN LIMIT Right Plan Plan limits appropriately sized based on usage Wrong Plan Plan limits too high given usage Consistently less than plan limit

28 Plan fit translates into higher NPS Right Plan Wrong Plan Mixed Plan Limits too: High Low NPS

29 Right-sizing makes the ultimate difference in NPS Mixed Plan NPS Right Plan Wrong Plan Period 1 Period 2 Period A Period B & C NPS by Time in Contract and Plan Fit

30 Proactive right-sizing could have great impact on retention and NPS? New $ $ $ $ SMS Direct Mail Early after activation, examine experiences of new customer Identify specific resolution(s), if needed, to improve fit Proactively reach out to propose solution

31 Feedback refinements maximize ALL performance measures New $ $ $ $ Learning is used to refine Activation provisioning tools. Constant refinement maximizes efficiencies, reduces post-activation follow-up and system costs, maximizes early retention and improves customer NPS and AT&T financials.

32 Integrating traditional research with big data is happening today and is transforming the way AT&T and other companies do business. You should be taking advantage of this innovation, you can be sure your competition will.

33 Thank You! Greg Mishkin Vice President, Research and Consulting Market Strategies Don Hodson Director, Market Research and Analytics AT&T Mobility #

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