201503 ieg preso_bindu narayan_hp_publish

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Information Excellence 2015 Spring Summit Business Analytics Dr. Bindu Narayan, Strategic Projects, HP Global Analytics Measuring Loyalty, Real Time: An HP Case Study

Transcript of 201503 ieg preso_bindu narayan_hp_publish

Information Excellence 2015 Spring Summit Business Analytics

Dr. Bindu Narayan, Strategic Projects, HP Global AnalyticsMeasuring Loyalty, Real Time: An HP Case Study

Information Excellence 2015 Spring Summit Business Analytics

© 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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