Consumerization is driving transformational change for every enterprise and therefore, customer intelligence is a core capability for every company to compete in the New Era.

1. Real-time Customer Intelligence:
The New Heartbeat For Growth & Profitability
INSTANT INTELLIGENCE
Balu Rajagopal
brajagopal@vmware.com
VMware and Cetas Confidential; Do NOT Distribute
Web: www.cetas.net
Twitter: @CetasAnalytics
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© 2009 VMware Inc. All rights reserved

2. What is Big Data?
DATA
VOLUME 2.0 Zettabytes in 2011
Enterprise Data Machine
Zettabyte To
Machine
Exabyte
Petabyte
Interactions
Terabyte
Transactions
Mainframe PC Internet Mobile Machine Time
Chart based on IDC and UC Berkeley Data Growth Estimates, Source: IDC & CosmoBC.com:
http://techblog.cosmobc.com/2011/08/26/data-storage-infographic/
2

3. What is “Big Data Analytics”?
Volume Velocity Variety Value
$
From Terabytes to 10’s of Billions Multi-Structured Business
Petabytes of Daily Records Insights
ADAPTABILITY SCALABILITY FLEXIBILITY ACTIONABILITY
3

4. The Perfect Storm for Retailers & Brands
MARKET FORCES BUSINESS CHALLENGES
① Consumerization ① “Show Rooming”
② Mobility & Connectivity ② Margin Pressure
③ Purchase Choices ③ Demand Predictability
④ Social Influences ④ Customer Defection
⑤ Price Sensitive Economy ⑤ Differentiation Difficulties
4

5. Path To Retail Growth & Profitability
BUYER Individual Search
Who, What, How Micro Segments Learn
Macro Consult
Real-Time
Customer
Intelligence
What/When/Whom? Multi-dimensional Buy
Stock Patterns/Trends Tell
Offer Activity Tracking
Influence Monitoring
SELLER SHOPPER
5

6. Big Data Analytics Retail Context
Shopper  Customer centric: Who is the customer, his/her
Behavior Analysis patterns, trends, likes, dislikes, frequencies, ...
Product Mix
 Customer-Product relationship: Categories
Analysis interested? products/games/apps favored? Ad
potential? ….
Market Basket  Customer-Product relationship: Categories
Analysis interested? products/games/apps favored? Ad
potential? ….
Social & Other  Social: Who’s influencing whom? Network size?
influences Type of interactions? …
Instant Decision  Actionable insights: offerings to present? What to
Making stock? Ad placement? Purchase Likelihood? …
6

7. SOLUTION
REAL-TIME CUSTOMER INTELLIGENCE
USING BIG DATA ANALYTICS
7

8. Customer Intelligence Frame
ADAPTED
FROM
INSTANT INTELLIGENCE
8

9. About the Big Retailer
 Revenue
• Top 20 Global Retailer
• Generates over 10 billion in revenue worldwide
• Average margin per transaction - ~ 10%
• Average Market Basket Size - ~ $ 50
 Product Categories and SKUs
• Over 10,000 product categories
• Millions of SKUs, 1000s of stores
 Shopper Base
• Millions of registered online and mobile app shoppers
9

10. Questions That Needed Answers (in Real-time, with Drill Down)
 Behavioral
• Do Women generate more revenue than men ?
• Do Women buy products that are of higher margin than men ?
• Does Demographic patterns for age, income and household size
roughly match US population (e.g., % with income $100K+)?
 Online Shopping Trends
• Is Average weekend revenue higher than weekday revenue?
10

11. Retailer Dataset Summary
① Time series data of Click & Purchase activity by user ID for April
2012
② Customer master, customer behavioral interests, behavioral interest
taxonomy
③ Mobile App & Website analytics (real-time streams)
④ Loyalty Data
⑤ Number of shopper related events: Over 10M
11

12. Click and purchase activity Data
Column Field Name Description Value
1 Event Timestamp Timestamp of click or purchase event. m/d/yyyy h:mm
2 User ID Unique user ID 6 digit numeric
17 and under
18-24
25-44
45-64
3 Age Group Quintile of age range 65 and over
$0-$25,000
$25,000- $50,000
$50,000- $75,000
$75,000- $100,000
4 Income Group Quintile of household income range $100,000 and over
Male
Female
5 Gender Shopper's gender Unknown
East
Central
6 Region Shopper's georaphic location in United States West
7 Household Size Quintile of household size 1-5
1 if click event
8 Click Event Event is a click 0 if not click event
1 if purchase event
9 Purchase Event Event is a purchase 0 if not purchase event
10 Revenue Total shopping cart revenue if purchase event USD
11 Margin Gross margin after COGS if purchase event USD
12

13. Customer Master Data
Column Field Name Description Value
1 User ID Unique user ID 6 digit numeric
2 Member Since Date of initial membership m/d/yyyy h:mm
Silver
Gold
Platinum
3 Loyalty Level Membership class Unknown
Bank Transfer
Paypal
Credt Card
4 Preferred Payment Method How customer typically pays Unknown
Poor
Fair
Good
5 Payment History Credit history with vendor Unknown
Low
Medium
High
6 Promotion Receptivity Demonstrated openness to offers Unknown
No
Yes
7 Mobile App Download Downloaded vendor app Unknown
13

14. Customer Behavioral Data
Interest Category
Arts & Entertainment
 Taxonomy:
Autos & Vehicles
Beauty & Fitness
Books & Literature
Business & Industrial • 25 top level categories
Computers & Electronics
Finance
• 249 sub categories
Food & Drink
Games
Hobbies & Leisure
Home & Garden  One shopper can have multiple
Internet & Telecom interests
Jobs & Education
Law & Government
News
Online Communities
People & Society
Pets & Animals
Real Estate
Reference
Science
Shopping
Sports
Travel
World Localities
14

15. Real-time Web Analytics Data
Column Field Name Description Value
1 Hour Summary Hourly bucket for site analytics aggregations m/d/yyyy h:mm
2 Average Revenue Per Visit Average transaction revenue per site visit USD
3 Average Item Value Average value of items checked out USD
4 Average Num Orders Per Visit Average number of orders per site visit Numeric
5 Average Num Items Per Order Average items in cart at checkout Numeric
6 Shopping Cart Abandonment Rate Percent of users who add to cart but don't check out %
7 Shopping Cart Session Percent Percent of users who add at least one item to cart %
8 Average Time On Site Average time spend shopping Minutes
9 Average PVs Per Visit Average page views Numeric
10 Percent Single Page Visits Bounce rate %
11 Percent New Users Percent first time users %
15

16. Customer Intelligence
THE ANALYSIS
16

17. Customer Intelligence Analysis Elements
① Multi-dimensional Views of Shopper
② Shopper Activity Analysis
③ Website Analysis
④ Loyalty Analysis (Using Batch Query)
⑤ Real-Time Dashboard Views
17

18. MULTI-DIMENSIONAL VIEW
OF SHOPPER
18

19. Multi-Dimensional View Analysis Steps
 Charts & Tables
• Measures of Interest: Revenue, margin
• Break down by dimension of interest
• Age, gender, HHsize, income group, or region
 Time trends
• Measure of Interest: sum of revenue (could also do avg
margin)
• Started with daily the use time pivot to drill down
• Break down by dimension of interest
• Age, Gender, HHsize, Income group, or region
 Custom measures
• Select a custom measure and operate on it like regular
measure
19

20. Multi-dimensional analysis
Total revenue by
gender and region
20

21. Multi-dimensional analysis with filtering
Total revenue by income group and region, with filters:
Age group = 18-24
Gender = Female
21
Household Size = 4

22. SHOPPER ACTIVITY
ANALYSIS
22

23. Shopper Activity Analysis Steps
 Summary aggregates
• Look at revenue broken down age, gender, other dims of interest
 Uniques aggregates
• Look at unique counts of shoppers by day
• Remove time trend and break down by age, gender, other dims of
interest
 Dashboards
• Leverage pre-defined dashboards and review
• Demo and geo theme
• Revenue theme
• Shopper theme
23

24. Activity analytics – simple time trend
Weekend
Total revenue (USD) by day.
Weekend revenue is approx
20% lower than weekdays.
24

25. Custom measures - create
Compute margin %
from margin and
revenue measures
25

26. Custom measures - chart
Custom measure margin %
broken down by gender.
Males generate lower margins.
26

27. Aggregates summary chart (1)
Avg. monthly transaction revenue (USD)
by age and gender. Male revenue is
significantly lower.
27

28. Aggregates summary chart (2)
Avg. margin (USD) by gender for three days in April.
Male revenue is significantly lower on each day.
28

29. REAL-TIME WEBSITE
ANALYTICS
29

30. Real-Time Website Analytics Steps
 Hourly aggregations from web site analytics tool that
shows shopping metrics of interest
 Use the date field called “Hour Summary”
 Correlate values from Cyber Monday industry reports
(e.g., shopping cart abandonment rate)
30

31. Unique Daily Visitors by Gender
Unique daily visitors
to site by gender.
31

32. Unique Daily Visitors by Age Range
Unique daily visitors
to site by age range.
32

33. Unique Monthly Visitors by Income Group
Unique monthly visitors
to site by income group.
33

34. Unique Monthly Visitors by Purchase Made
Unique visitors to site who
made a purchase on any day.
34

35. Out-of-Box Vertical Insights Dashboard
35

36. Loyalty Analysis (Batch Query)
36

37. Loyalty Analysis Using Batch Query
 Query “Revenue by Loyalty Level” - a simple join of event
level data stream with customer master (profile) data
 Query “Join with Customer Master” and customer profile
dimensions
 Query “Join with Customer Interests” - a more complex
multi-join with customer master and interest categories.
37

38. Batch query definition
Data sources
Drag and drop canvas
Expression builder
38
HiveQL expression

39. Batch query result
Joining event
level data with
customer master
(profile) data
Most revenue
comes from
“Silver” members
39

40. Batch query result (2)
Combining dimensions
from event level data and
customer master (profile)
data
Most revenue comes
from $25k-$50k group
40

41. Batch query result (3)
Female top interests by revenue Male top interests by revenue
41

42. Batch query result (4)
Good credit top interests by revenue Poor credit top interests by revenue
Joining event data with in-house profile data
helps you understand your customers more.
42

43. REAL-TIME DASHBOARD
VIEWS
43

44. Dashboard (1) Demographic and Geo Focus
44

45. Dashboard (2) Revenue Focus
45

46. Dashboard (3) Shopper Focus
46

47. Customer Intelligence
APPROACH, ANALYTIC FINDINGS, TAKEAWAYS
47

48. Questions That Needed Answers (in Real-time, with Drill Down)
 Behavioral
• Do Women generate more revenue than men ?
• Do Women buy products that are of higher margin than men ?
• Does Demographic patterns for age, income and household size
roughly match US population (e.g., % with income $100K+)?
 Online Shopping Trends
• Is Average weekend revenue higher than weekday revenue?
48

49. The Answers (From Analytics)
 Behavioral
• Women generate more revenue than men
• Women buy products that are of higher margin than men
• Demographic patterns for age, income and household size roughly
match US population (e.g., % with income $100K+)
 Online Shopping trends
• Average weekend revenue is about 20% lower than weekday revenue
(Correlates with third-party data showing people shop at work)
49

50. Interesting Associations
Behavioral preferences by gender
Females prefer:
302 Shopping Apparel
310 Shopping Luxury Goods
315 Shopping Toys
135 Beauty & Fitness Face & Body Care
136 Beauty & Fitness Fashion & Style
138 Beauty & Fitness Hair Care
213 Home & Garden Bed & Bath
222 Home & Garden Kitchen & Dining
102 Arts & Entertainment Entertainment Industry
100 Arts & Entertainment Celebrities & Entertainment News
141 Books & Literature Children's Literature
197 Games Online Games
Male prefer:
317 Sports College Sports
322 Sports Motor Sports
323 Sports Sport Scores & Statistics
324 Sports Sporting Goods
173 Computers & Electronics Consumer Electronics
207 Hobbies & Leisure Outdoors
185 Finance Investing
227 Home & Garden Yard & Patio
219 Home & Garden Home Improvement
121 Autos & Vehicles Motorcycles
125 Autos & Vehicles Trucks & SUVs
114 Autos & Vehicles Boats & Watercraft
50

51. Interesting Associations
 Behavioral preferences in dataset by payment history:
Payment History = Poor
prefer:
181Finance Credit & Lending
242Law & Government Military
318Sports Combat Sports
322Sports Motor Sports
100Arts & Entertainment Celebrities & Entertainment News
Payment History = Good
prefer:
281Real Estate Real Estate Listings
282Real Estate Timeshares & Vacation Properties
330Travel Air Travel
334Travel Cruises & Charters
337Travel Specialty Travel
51

52. The Analytics Solution
Semi-
Structured structured
Products Transactions Logs E-mails In-Apps Sensors
….
…. Unstructured
Social Audio Photo & Video
Inventory Ad Impr.,
Clicks, Conv.
• Zero ETL
 Volume
 • No Copy
Velocity Cetas Real-time
 Variety • No DW
Correlate Analytics
 Variance • No Schema
Billion+ Events
per day
Customer Intelligence
52

53. 5 Takeaways – How To Extract Customer Intelligence
CONNECT THE DOTS (DECISION CONTEXT)
Profiles, Sessions, Activities, Items in Baskets/Carts, Purchases, …
① Big Data – Source, Know, Manage, & Govern your big data
② Customer – Reconcile different versions of the same customer
③ Collaboration & Sharing – Enrich with third-party and partner data
④ Data-Driven Decision Making (DDM) – Start w/small project successes
⑤ Collective Wisdom – Machine plus Human Intelligence Still Required !
53

54. Cetas User Interface
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