Predictive Marketing. Digital Intelligence. Igniting Customer Engagement.

Transcription

1 Predictive Marketing. Digital Intelligence. Igniting Customer Engagement. Suneel Grover, M.B.A., M.S. Sr. Solutions Architect SAS CCE Customer Intelligence Adjunct Professor - Dept. of Decision Science The George Washington University Copyright 2010, SAS Institute Inc. All rights reserved.

2 Presentation Agenda 1. The Changing Face of Data-Driven Marketing 2. Marketing Modernization Meets Digital Intelligence 3. Predictive Marketing Unleashed 4. Key Considerations: Personalization Copyright 2010, SAS Institute Inc. All rights reserved.

3 THE CHANGING FACE OF DATA-DRIVEN MARKETING. Copyright 2010, SAS Institute Inc. All rights reserved.

4 Part of what we've had to do is rebalance, not by bringing the data side down, but by elevating the creative. Tariq Shaukat EVP and CMO of Caesars

5 while creative marketers and data scientists are both tremendous organizational assets, it is the alliance between these two segments that will push marketing forward...

6 The 2014 Digital Trends report from Econsultancy is based on a global survey of more than 2,500 marketers and Internet professionals carried out at the end of 2013.

7 29% INDICATED AN EVEN MIXTURE

8 25% INDICATED HAVING MORE QUANTITATIVE RESOURCES

9 73% LACKING ANALYTICAL SKILLS TO FULLY ANALYZE & OPTIMIZE PROGRAMS

10 Econsultancy CEO Ashley Friedlein Pi-shaped marketers (as in the Greek letter Π) are those who are analytical & data-driven, yet understand brands, storytelling, and experiential marketing. This special mix of talents and attributes, roughly categorized as leftbrain & right-brain abilities, will be in more demand than ever

11 THE SCIENCE OF ANALYTICS

12 THE NEED FOR INTERPRETABILITY

13 we are living in the age of the customer, where customer obsession is the new frontier of competitive differentiation scaled & fueled by customer insights

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16 Source:

17 WHAT IS GIVING MARKETERS NIGHTMARES?

18 MARKETING MODERNIZATION MEETS DIGITAL INTELLIGENCE.

19 St. Peters Square The Vatican 2013

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21 DIGITAL INTELLIGENCE DEFINED (2014) The capture, management, & analysis of customer data to deliver a holistic view of the digital customer experience that drives the measurement, optimization, & execution of digital customer interactions. Source:

22 DIGITAL INTELLIGENCE: KEY OBSTACLES FOR MARKETING MODERNIZATION Digital & customer analytics teams are attempting to work together, but their projects struggle to get off the ground due to a clash of approaches & culture. Data types structured vs. unstructured, known vs. anonymous Skills data scientist vs. web geek Analysis advanced analytics vs. good enough analytics

23 Data Scientists (Statisticians, Data Miners, etc.) GROWING UP SEPARATELY Web & Digital Analytics (Digital Ninjas, Web Analysts, etc.) 1. Mature advanced analytic practices 2. Powerful databases & tools 3. Growing experience with digital data 1. BI-centric analytic practices 2. Lack tools for multi-channel analytics 3. Growing awareness of predictive analytics

24 DIGITAL FUNCTIONS HAVE SPLINTERED 1. Isolated functional silos 2. Web, mobile apps, social, , & other teams are islands unto themselves 3. Each builds out its own skills & experiences its own successes & challenges, without a thought to what is known by others

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26 Web/Social Analytics Digital Intelligence

27 Big Data Big Data Big Data Big Data

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30 IS THERE A BETTER WAY TO COLLECT DIGITAL DATA?

31 WHAT HAVE WE UNLOCKED ANALYTICALLY?

32 Digital Visitor Stitch with Other Data Sources Dynamic Data Collection Normalized for Digital 1:1 Data Predictive Analytics & Visualization

33 PREDICTIVE MARKETING UNLEASHED.

34 INTERACTIVE VISUALIZATION: MULTIPLE DATA SOURCES

35 FORECASTING + WHAT-IF + OPTIMIZATION

36 DECISION TREES: PREDICTIVE SEGMENTATION

37 VISUAL CLUSTERING: ANALYTIC SEGMENTATION

38 PATHING: CUSTOMER EXPERIENCE

39 SOCIAL MEDIA: LISTENING VS. MINING

40 CONSIDERATIONS FOR MARKETING & PERSONALIZATION.

41 PERSONALIZATION: CAN WE DO BETTER? 1. A/B Testing 2. MVT (Multivariate Testing) 3.Predictive Modeling

42 A. Creative Displayed B. Offer & Display Price Info C. Call-to-Action Attribution D. Consumer Behavior A/B Testing, MVT, & Predictive Modeling can help understand the relationship between the prospect, the customer experience, the influence of offers, & the likelihood to engage/convert.

43 Thank You Suneel Grover, M.B.A., M.S. Sr. Solutions Architect SAS CCE Customer Intelligence Adjunct Professor - Dept. of Decision Science The George Washington University Copyright 2010, SAS Institute Inc. All rights reserved.