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Information Excellence 2015 Spring Summit Business Analytics
Dr. Bindu Narayan, Strategic Projects, HP Global AnalyticsMeasuring Loyalty, Real Time: An HP Case Study
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Measuring Loyalty Real Time An HP Case Study
Bindu Narayan
HP Global Analytics
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 2
Agenda
Loyalty and Net Promoter Score (NPS)
Measuring NPS real time – The genesis
NPS Real Time Solution – The journey
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 3
Loyalty…
Willingness of a person ( a customer/ an employee/ a friend) to make an
investment or personal sacrifice in order to strengthen the relationship
(Reichheld, 2003).
Usage of a product/service
Satisfaction Continued patronage
Dissatisfaction Exit Decreased Revenue
Increased Revenue
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 4
Measuring loyalty
Loyalty
Repurchase
Intention to repurchase
Intention to recommend
Price Tolerance
Preference
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 5
Fred Reichheld and Net Promoter Score (NPS)
"One Number You Need to Grow". Harvard Business Review, December
2003
“Intention to recommend is the best proxy for Loyalty”
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 6
A 10-point scaled single question on intention to recommend
What is NPS?
0 1 2 3 4 5 6 7 8 9 10 Not at all likely Extremely likely
Based on your experience, how likely is it you would recommend HP to a friend?
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 7
How is NPS calculated?
0 1 2 3 4 5 6 7 8 9 10 Not at all likely Extremely likely
Based on your experience, how likely is it you would recommend HP to a friend?
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 8
NPS Stars by Industry
Source: Satmetrix 2010 NPS Benchmark Study of US
Consumers
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 9
Net Promoter Score…
…is a clarion call within our company
… NPS is the single greatest predictor of whether they will return and promote
our company, or defect to a competitor and detract from it.
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 10
• NPS survey has an annual cadence
• BUT, markets and consumer preferences keep changing fast
• NPS survey doesn’t capture customer loyalty in real time
Is something missing?
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 11
• Unstructured data from various online sources
• There is a global coverage
• Accessible anytime
• Real time reflection of consumer preferences
• Survey NPS vs. Social NPS
Is there an alternative?
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 12
Developing a POC…
Objective: To develop a tool which can leverage unstructured data and estimate NPS on a real time basis, without dependency on surveys
Proof Of Concept
Scope Country | United States
Business Group | Personal and Printing Systems
Customer Segment | Consumers
Platform | Big Data Platform of HP Quality Office
Data Type | Customer Review comments
Language | English
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 13
Analytical Journey
POC developed for US Consumer
using customer review comments
Exploration of algorithms
Exploration of data sources
Initial success in modeling
Deep dive on data sources (US) Monthly &
Product level analysis
Calibration
We are here
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 14
It makes an inference on the author’s propensity to recommend by reading a text
How does the algorithm work?
Text data
Will he/she
recommend
HP’s products?
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 15
Words are interconnected through meaning and semantic similarity
Leveraging Wordnet published by Princeton
Product
Technology
Science
Learning
Knowledge
Skills
Perception
Conceive
Aware
Industry
Sale
buy purchase
Re-purchase
Value
Service
assistance
Resource Need
Satisfaction
help
Contribution
Effort
Usage
Preference Like
Suggest
• Project started in mid-1980s at
Princeton University
• Large lexical database of English.
Nouns, verbs, adjectives and
adverbs grouped into sets of
cognitive synonyms.
• Tree structures on Pedagogical
relation, Product relation , Animal
relation, Disease Ontology,
Historical Ontology available
• Used mostly by researchers who
work in linguistics and NLP.
• Sun Microsystem Labs &
Laboratory Informatics Nantes
Atlantique among users
Ontology – “A set of concepts within a domain, using a shared vocabulary to
denote the types, properties and interrelationships of those concepts”
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 16
Exploration of Data Sources
Social
NPS
Call Centre
Linked in
Forums
Blogs
Social Media (FB, Twitter)
e-Commerce websites
Challenges with data
• Social Media Grammar
• Twitter word limit
• Only queries on forums
• Complaint bias in call centers
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 17
List
Detractors
List
Passives
List
Promoters
Algorithm
Comments
Text
Review
Modeling
hp.com
Reviewer
classification
Rules
Model Development
Model Validation
Classification Rules
Algorithm
Review Comments
Hp.com
Compare with
self classification
from hp.com
Reviewer classification
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 18
US Consumer PPS Model
Initial results in modeling
24
30
38 40
0
5
10
15
20
25
30
35
40
45
FY 12 FY 13
Survey NPS vs. Social NPS (US Consumer PPS)
Survey NPS Real Time NPS
Online Store No of review Comments (FY12)
Amazon 4517
Best buy 4811
e-bay 1002
Hewlett-Packard 320
Total 10650
Online Store No of review Comments (FY13)
Amazon 9322
Best buy 9735
e-bay 1191
Hewlett-Packard 867
Total 21115
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 19
Monthly & Product Level Sample Size Analyses
• Annually viewed NPS is a culmination of monthly trends
• Extreme values can have an impact
• Product lines with <100 review comments not be considered
Sample Size
Laptops Desktop
computers PhotoSmart DeskJet OfficeJet Laser
272 80 10 9 51 108
390 173 24 27 45 124
435 175 30 29 69 143
402 129 25 20 55 161
458 912 15 18 72 158
473 240 17 17 53 131
352 118 4 18 49 131
392 132 7 18 79 111
392 120 12 18 61 119
479 231 16 18 66 145
487 118 7 9 56 171
613 206 11 14 74 186
5145 2634 178 215 730 1688
Social NPS
Laptops Desktop
computers PhotoSmart DeskJet OfficeJet Laser
Adj NPS(All
sample sizes
considered)
Adj NPS(Sample
Size>100
Considered)
Nov-12 26.10 18.75 0.00 22.22 25.49 25.93 14.95 26.09
Dec-12 23.08 11.56 4.17 25.93 22.22 28.23 15.85 19.08
Jan-13 22.30 24.57 6.67 3.45 31.88 40.56 20.28 23.96
Feb-13 20.40 34.11 40.00 35.00 27.27 28.57 30.18 26.80
Mar-13 22.93 39.69 33.33 44.44 23.61 41.14 29.36 25.71
Apr-13 31.29 29.58 17.65 11.76 26.42 28.24 24.82 30.58
May-13 22.44 37.29 25.00 11.11 28.57 35.11 26.37 29.38
Jun-13 22.19 26.52 0.00 55.56 24.05 30.63 11.56 24.41
Jul-13 25.00 33.33 0.00 -5.56 31.15 21.85 17.14 28.66
Aug-13 28.60 29.87 0.00 -27.78 6.06 26.21 13.93 28.94
Sep-13 27.31 19.49 -28.57 22.22 17.86 29.82 -5.15 23.40
Oct-13 27.73 36.41 27.27 -7.14 14.86 34.41 27.39 31.76
Yearly 25.21 31.93 12.92 15.81 23.01 31.28 21.11 27.72
Std Dev 3.13 8.19 18.16 22.41 7.03 5.61 9.52 3.41
Survey NPS (2013): 30
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 20
Calibration
• Only product lines considered in survey to be included
• Only product lines with >100 monthly review comments considered
• Sample sizes adjusted to reflect market reality
24
30 29 29 28 27
0
10
20
30
40
FY 12 FY 13 FY14 (YTD)
Survey NPS vs. Social NPS (US Consumer PPS)
Survey NPS Real Time NPS
Minimum Sample
Size
Survey Alignment
Market Size
correction
Social NPS
-6.9% -6.7% +20.8%
Calibration Factors
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 21
Platforms Architecture
CXA Big Data Platform
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
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