Customer analytics - now it's personal

© 2015 IBM Corporation

Customer Analytics: Now it’s personal!


Of mobile users keep their device within arm’s reach 100% of the time

In the last 10 years the average attention span has dropped from 12 to 5 minutes

The average office worker checks his/her email box

40 times an hour - once every 1.5 minutes

2


Today’s “empowered customer” puts businesses to the test

4 in 10Smart phone users search

for an item in a store

86%use multiple

channels

4-5xmore than average is spent by

multi-channel buyers

78%of consumers trust peer

recommendations

58% are more price-conscious

today than a year ago

75%do not believe companies

tell the truth in ads

Source: Sources of statistics from “Smarter Commerce Stats and Facts”

80%of CEOs think they deliver a

superior customer experience

8%of their customers

agree

3


Digital disruption is not just about incremental productivity gains

Processes & Data

Process and content digitization - along with the resulting data - is disrupting how traditional processes are delivered

New business models are reshaping processes, companies and industries

Enterprises

Enterprises are creating new business models, becoming ISVs and adopting to changes in value streams

Industries Industries are being disrupted and transformed as processes, data and customers begin to connect freely across boundaries

1. Analytics becomes central & pervasive while data fuels business outcomes

2. Increasing & diversified competitive pressures (technology, data & services)

3. Analytics skills are scarce and expensive & not likely to be fulfilled in the next 5 years

The Analytics Marketplace is Radically Transforming Organizational tensions are fueled by Big Data

Line of Business IT

Accuracy &

Security

Value &

Speed

4


Traditional

Internaldata warehouse, transactions, descriptive

Distributed datamarts, spreadsheets

EmergingUnstructurednotes, logs

Social Mediapulse, emerging issues

Survey Researchattitudes, opinions

Sensors

volu

me

- v

eloc

ity -

var

iety

- v

erac

ity

Sense-making

LEARN

5

Big Data & Analytics: the value is in “actionable”

New Mix of Data

Exploration

Recommendations

Auto-Analysis

Unified UX

Redefining the Experience

people, process

DECISIONS

Mobile Dashboard

InsightWhat-if?

differentiated analytic solutions

Auto

mat

eEm

bed

“in the business moment”

Plan

, Sim

ulat

eCo

llabo

rate

Case MgmtDecision Mgmt

“consumer oriented agile insight”

ACTION

Actionable insight

“data is the new oil”

© 2015 IBM Corporation 6

• Fast Data– Real-time analytics - from Big Data to Fast Data

• Exogenous & Right Data– The Internet of Everything (e.g., things, social, environment…)

• Decision Management 3.0 – Analytics on a need-to-know basis

• Emotional Data– No (buying) decisions without emotions

Advanced themes in advanced (customer) analytics


Glo

bal

Dat

a V

olu

me

in E

xab

ytes

Sens

ors

(Inte

rnet

of T

hing

s)

Multiple sources: IDC,Cisco

100

90

80

70

60

50

40

30

20

10

Agg

rega

te U

ncer

tain

ty %

VoIP

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

2005 2012 2017

By 2017 the number of networked devices will be more than double the entire global

population.

Social Media

(video, audio and text)

The total number of social media accounts exceeds the entire global

population.

Enterprise Data

The uncertainty is growing alongside its complexity

7


The problem is not really Big Data… it is Fast Data

10/07/2014

TimeLa

g tim

eR

eal t

ime

Operational data Big data

hour

sse

cond

sm

illise

cond

s StreamAnalytics

Real-TimeAnalytics

OperationsAnalytics

BatchAnalytics

OLTP: On-line Transaction ProcessingOLAP: On-line Analytical ProcessingRTAP: Real Time Analytic Processing

OLTP: On-line Transaction ProcessingOLAP: On-line Analytical ProcessingRTAP: Real Time Analytic Processing

Complex Event Processing

In-Memory DB

OLTP Reporting

OLAP

RTAP

Analytic “on flight”

Map Reduce Batched(NoSQL)

SizeGigabyte Terabyte Petabyte

8


Most of the data you might need… you do not own

60% of determinants of health Volume, Variety, Velocity, Veracity

30% of determinants of healthVolume

10% of determinants of health

Variety

Clinical data

Genomics data

Exogenous data(Behavior, Socio-economic, Environmental, ...)

1100 Terabytes Generated per lifetime

6 TBPer lifetime

0.4 TBPer lifetime

Source: "The Relative Contribution of Multiple Determinants to Health Outcomes", Lauren McGover et al., Health Affairs, 33, no.2 (2014)

9


Expanding your data universe

10


Operational AnalyticsCustomer Analytics

AcquireGrowRetain

ManageMaintainMaximize

Threat & Risk Analytics

MonitorDetectControl

• Claims fraud

• Credit-card fraud

• Insider threat

• Signals analysis

• Cyber security

• Predictive maintenance

• Assortment planning

• Condition monitoring

• Reverse logistics

• Allocation management

• Up-sell/cross-sell

• Market basket analysis

• Churn prevention

• Customer segmentation

• Brand Monitoring

Smarter solutions

11


Customer engagement framework with OCM

Deliver engaging messages and capture reactions

12

Learn, Optimize and tune iteratively

Collect data that augments each customer profile

Build and integrate assets, offers, promotions for customer engagement

Analyze data to find actionable insights

Manage budgets, processes & measure results

Decide on the best offer, action or communication

for each customer


In customer analytics our focus is on the customer experience

Research Product

PurchaseProduct

UseProduct

Get Customer Service

AdvocateProduct

Up/CrossSold

Marketing

Sales

Support/Services

Feedback Management

Social Intelligence

Advocate Use

Research Purchase

13


Customer analytics complements the business cycle

Customer Analytics

ActionInsight

•Process Centric•Action•Target to Cash

• Data Centric• Insights• Integrated Consumer

Experience

Customer Analytics complements Customer Engagement Solutions

Customer Operations

The Business

Cycle

The Customer

Cycle

14


Condensing data sources to reduces uncertainty through context

Customer at Mall

Customer in Store #42Correlation

Data finds Data

Sense Making

Fact Discovery

Son

Mother

Birthday

Date

Spatial Reasoning

A&

Temporal Reasoning

&

Corroboration(Evidence Combination)

ETC.

MichaelSan Jose, CA

Credit Loyalty

Influencers

Buying DSLR today !

Buying DSLR today !

Intent

CO

ND

EN

SE

$999 $560

In-Store PricingAnd Discounts

Maximum ContextFor

Minimum Uncertainty

$999

$560OR

Buyinga DSLR today !

NY

15


What are the interests of a specific customer?

What products are relevant to what interest

groups?

How does context affect customer interest? Which product should a

customer be offered?

Fast Data… here today at a cell phone & coffee shop near

you

(e.g., predicting future customer propensities to drive channel optimization)

10/07/201416

Fast data: capturing each customers in context

16© 2015 IBM Corporation #ibmamplify


Fast Data… at a restaurant near you…

Targeted offers piloted by the marketing professional

Decision rules matrix controlled by marketing

Propensities determined through predictive analytics

models

17

© 2015 IBM Corporation 18

• Fast Data– Real-time analytics - from Big Data to Fast Data

• Exogenous & Right Data– The Internet of Everything (e.g., things, social, environment…)

• Decision Management 3.0 – Analytics on a need-to-know basis

• Emotional Data– No (buying) decisions without emotions

Advanced themes in advanced (customer) analytics


19

+ + =

An opportunity to think and act in new ways -economically, socially and technically.

Instrumented Interconnected Intelligent

Smarter Planet Premises

19


From Customers + Machine Behavior… predict & optimize

Machine_1Location_1

Central Database

• Maintenance history• Location• Daily usage data• Years of service…

Identify key components

propensity failure

Identify key components

propensity failure

Machine: Hydraulic ArmLocation: Section 022 floor 7Expected Error: 79012 (79.6%)Associated part:: 7097 Part description: Arm Rotator

Repair planning optimization• Crew scheduling• Repair cost by Location• Repair Person Availability• Display in Map• Optimal scheduling

Generates revenue analysis

Generates revenue analysis

20

Gather failures, associated actions &

areas + optimize repair schedule

Gather failures, associated actions &

areas + optimize repair schedule

Trigger repair rules associated to the probable failure

Trigger repair rules associated to the probable failure

Understand clients traffic patterns and revenue segments

Understand clients traffic patterns and revenue segments

1 2 3 4

Pre-emptiveMaintenance Ticket

Spender model

Business Rule

Schedule Repair Planning

Revenue Analysis


The Internet of Things landscape – at a customer near you

21Source: Goldman Sachs Global Investment Research


Which OneIs it?

Chef (Marmiton)

Comedian (MySpace)

CAO (IBM)

Apocalypto (WoW)

Captain Picard (ST:TNG)

User

Consumer

Customer

Participant

Influencer

Resonance (x,y,z…)

The Trek Tribe

In search of soul mates… the new tribes

22


• People do not make decisions without emotions

• Drivers: lasting fun, iconic simplicity, emotional feedback

• The tipping point of intimacy: “Starting to draw circles, not lines!”

• “People do not buy games, they buy experiences!”

23

No decisions without emotions…

fierofiero

curiositycuriosity

excitementexcitement

amusementamusement

Hard FunEmotions generated: • frustration• relief

Easy FunEmotions generated:• wonder• curiosity

Serious Fun

Emotions generated:• zen focus• relaxation

People Fun

Emotions generated: • admiration• naches


Example: at banks, emotionally charged interactions (e.g., replacing a stolen credit card or negotiating mortgages as opposed to buying travelers’ checks) can have dramatic impacts on the organization’s bottom line

(1) Source: Survey of 2,229 large banks customers - The McKinsey Quarterly - “The moment of Truth in Customer Service”

87% 72%

Moments of truth

24


… One more thing

25


Campaign

"Intrusive"1%-5% Response

Event-Driven

"Convenient"5x Success

Real-Time

"Appropriate"10x Success

Beware of the “creepy factor”

Too SlowToo Fast

Cost-Benefit

Response Time

+

-0

Creepy

IdealIdeal

Usable

IrrelevantIrrelevant

Event:

CustomerResponse:

26