Applying Analytics with Big Data for Customer Intelligence. Seven Steps to Success

Transcription

1 Applying Analytics with Big Data for Customer Intelligence Seven Steps to Success

2 Sponsors: 2

3 Speakers David Stodder Research Director, Business Intelligence TDWI Dan Potter VP, Product Marketing, Datawatch Tamara Dull Director, Emerging Technologies, SAS Sri Raghavan Senior Global Marketing Manager, Teradata Hannah Smalltree Director, Treasure Data 3

4 Agenda The big trend: data-driven customer intelligence Gaining a comprehensive view of data Roundtable discussion with guest speakers Customer analytics strategies and practices Roundtable discussion Speed to insight, visualization, and governance Roundtable discussion Seven steps: Concluding thoughts Your Questions 4

5 Data-Driven: Key to Customer Intelligence Customers are empowered: more opportunities to learn before buying; competitors a click away Companies seek clues to increasing loyalty, stickiness, and attraction Not just efficiency but intelligence: Firms don t want to waste money, but more important is not to waste opportunities Data analysis critical to targeting and personalization Customers are influencers: what is social influence on brand, marketing effectiveness?

6 Customer Insight & Engagement: CMOs Move Past Gut Feel Decision-makers want data: 75% in TDWI Research indicate acceptance of data-driven insights over gut feel Data insights help those with fresh ideas to challenge authority Seeking speed to insight: Leaders can t wait out long cycles to understand market behavior Budgets grow for customer intelligence solutions that are easier to deploy Research still suggests that greater success comes with CMO/CIO collaboration 6

7 Big Data: About Going Beyond Beyond relational: flow of semi- or unstructured click streams, sensor, machine data Beyond structure: interest in raw and/or complex data streams, not transformed info Beyond BI and DW: Demand for Hadoop & NoSQL; is transformation necessary? Not just for big companies: Even small and midsize firms confront big data issues Can also tap external big data

8 #1: Gain a Comprehensive View Perfection: A complete, 360 degree view of customers across channels Integrating transactional, behavioral, and demographic views of customer data Sharing insights with business partner networks Develop an strategy to enable analytical depth and breadth Silo consolidation into DW Data virtualization/federated access Appliances and cloud solutions

9 Hybrid: Integrating Big Data, EDW Emerging hybrid architectures: supporting a variety of BI and analytics processes Addressing demand for different types of reports, visualizations, and complex analysis In-memory computing as part of the arsenal; moving more data closer to users Integrating cloud and onpremises solutions Strategy for agility and scalability Credit: Fotalia

10 Integrating Views of Big Data: Discussion with Guest Speakers What strategies should organizations take to support greater depth and breath of BI and analytics on big data sources for customer intelligence? David Stodder TDWI Dan Potter Datawatch Tamara Dull SAS Sri Raghavan Teradata Hannah Smalltree Treasure Data 10

11 #2: Implement Predictive Analytics Customer analytics: learning More about customers Their paths to purchase What increases loyalty among the most valuable The goal: To derive accurate insights from integrated transaction, service, behavioral, data To better attract, retain, interact with, and expand customer relationships Statistical analysis Why this is happening? Forecasting or extrapolation What if trends continue? Predictive analytics/modeling: What will happen next? (E.g., churn analysis)

12 Customer Analytics: Which Business Marketing: Pursuit of efficiency and achievement of measurable results; testing hypotheses Sales: Improve forecasting based on deeper knowledge of priority opportunities Finance: 2/3 in TDWI Research said that customer analytics important to finance function Functions Benefit? Service/Order Management: Gain view of what actions most impact customer satisfaction; tune agent performance 12

13 Varied Benefits of Customer Analytics From Customer Analytics in the Age of Social Media, TDWI Best Practices Report, Third Quarter

14 Tools and Techniques Applied From Customer Analytics in the Age of Social Media, TDWI Best Practices Report, Third Quarter

15 #3: Deploy Analytics for Personalized Marketing & Engagement Goal: Increasing intimacy and knowledge through data-driven insight Predictive view: Modeling and understanding segments propensity to buy to improve targeting of upsell and cross-sell offers Big data: Capturing behavioral data, including from real time event streams, from first contact to successive engagements Behavior-based segmentation: Analytics for getting beyond simple demographics to one based on actions and preferences recorded over time

16 #4: Leverage Big Data Analytics for Social Media Strategies External perspective: Firms can gain a valuable outside-in views of brands, operations, and competitors Customers influence each other by commenting on brands, reviewing products, reacting to marketing campaigns, and revealing shared interests Analytics can help spot influencers and measure impact on social networks Filtering out the noise: But not too much; noise could be important signals Predictive analytics to discover patterns, anticipate product/service issues Metrics: Measuring share of voice, brand reputation Understanding sentiment drivers Determining marketing effectiveness

17 Social Media Analysis Objectives From Customer Analytics in the Age of Social Media, TDWI Best Practices Report, Third Quarter

18 Text Analytics: Deriving Value From Big Social Media Data Text analytics: umbrella term for natural language processing, entity/relationship extraction, modeling, and taxonomy/classification Big data scalability: key for text and social media analytics Applying data science, often requiring many passes through the data; testing, modeling, and testing again What s beyond the words: understanding polarity of sentiment Not exact science: Analytics should focus on intangibles of improving interaction, building reputation, and influencing the influencers

19 Big Data Variety: More to Come Location data analysis: Learning about customers by integrating data with maps Mobile computing adding new dimension to customer data Speech analytics: search and analysis for contact centers, field sales/service Machine data: sensors producing data for tracking human behavior Internet of things; wearable computing

20 Analytics and Big Data: Discussion with Guest Speakers What should organizations be thinking about as they expand their use of analytics and big data for customer intelligence? What can they do to make sure their journey delivers return on investment? David Stodder TDWI Dan Potter Datawatch Tamara Dull SAS Sri Raghavan Teradata Hannah Smalltree Treasure Data 20

21 #5: Reduce Latency to Improve Real-Time Insight Smart use of data and information can help reduce, if not eliminate inefficiencies caused by delays in: Customer service and interaction Adjusting automated response to customers self-directed actions Responding to events in markets, supply chains, processes Using information to guide product and service development Monitoring and tracking developing patterns and situations Delivering fresh data to decision makers 21

22 Real Time: What Does it Mean? Strong interest: Setting expectations and defining what real time means in terms of data currency and quality is critical to user satisfaction Capturing data in real time to run analytics: Models and algorithms my then run on a daily or hourly basis Hadoop and NoSQL stores of interest to hold the data Streaming analytics: Applying predictive models and scoring algorithms to observe and interpret patterns in real-time data and event streams Common sources: online behavior, gaming, mobile device use, machine data

23 #6: Improve Data Visualization and Analysis for All Users Visual presentation of customer intelligence: Spotting patterns, trends, or anomalies that are critical to understanding customer and market behavior Enabling nontechnical SMEs to consume and share insights Storytelling with data visualizations for colleagues, partners, and customers Operational alerting Event notification and actionable intelligence Visual discovery and analysis Moving beyond BI reporting to answer why questions Dynamic, on-demand data interaction (often supported by in-memory computing) Visualizations to fit the analysis: growing libraries of possible visual expressions Beware of clutter Poor visualizations can mislead users make the data tsunami worse; guidance key

24 Visualizations to Meet Users Needs Source: Data Visualization & Discovery for Better Business Outcomes, TDWI Best Practices Report, Third Quarter

25 #7: Balance Flexibility with Governance Customer data is often sensitive: data breaches are commonplace; firms must carefully oversee how they manage and analyze it Center of Excellence or governance committee of business and IT management can help A committee can help ensure a good balance between privacy and regulatory adherence and meeting business user needs Emerging hybrid data architectures: enabling firms to address volume, variety, and velocity of big data customer intelligence Integrating EDW, Hadoop, and cloud into one architecture Alternative to governance chaos

26 Applying Analytics with Big Data for Customer Intelligence 1. Gain a comprehensive view 2. Implement predictive analytics 3. Deploy analytics for personalized marketing and engagement 4. Leverage big data analytics for social media strategies 5. Reduce latency to improve real-time insight 6. Improve data visualization and analysis for all users 7. Balance flexibility with governance

27 Closing Thoughts: Discussion with Guest Speakers What can organizations do to improve speed to insight for customer intelligence? What strategies are best for balancing analytics flexibility with governance and privacy requirements? David Stodder TDWI Dan Potter Datawatch Tamara Dull SAS Sri Raghavan Teradata Hannah Smalltree Treasure Data 27

28 Your Questions 28

29 Thank You to Our Sponsors: 29

30 Contact Information If you have further questions or comments: David Stodder, Hannah Smalltree, Treasure Data Sri Raghavan, Teradata Tamara Dull, SAS Dan Potter, Datawatch 30