Smarter Decisions and Better Outcomes with Predictive Customer Intelligence

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1 #SmarterEnterprise Smarter Decisions and Better Outcomes with Predictive Customer Intelligence Vivian Braun WW Solutions Marketing Executive; Customer Service & Marketing [email protected]

description

IBM's Vivian Braun on using predictive analytics to transform the customer experience. Presented at the Smarter Enterprise Summit in Edmonton, October 3, 2014.

Transcript of Smarter Decisions and Better Outcomes with Predictive Customer Intelligence

Page 1: Smarter Decisions and Better Outcomes with Predictive Customer Intelligence

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Smarter Decisions and Better Outcomes with

Predictive Customer Intelligence

Vivian Braun

WW Solutions Marketing Executive; Customer Service & Marketing

[email protected]

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Agenda

• Why are we here?

• Predictive Customer Intelligence solution overview

• The IBM difference

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Age of the Empowered Customer

Stay Relevant

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Anticipate and service my purchasing needs

Make it convenient and easy to interact with you

Be there when I need you, in real-time

Know me in context, remember all of our interactions

Empowered Constituents

DEMAND MORE FROM BRANDS AND SERVICE PROVIDERS

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Ensuring a positive experience is an enterprise-wide priority

Increase conversion, margin

Reduce sales cycle time

Increase brand preference, especially for

customers with the greatest lifetime value

Improve customer

satisfaction and loyalty

Enable flexible payment

options based on need

Enable employee access

to usable analytics

Hire capable employees and enable

them with appropriate skills

Manage inventory and

optimize supply chainUnderstand the

met and unmet

needs

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Execute single view of the customer

Develop integrated customer insight engine

Deploy spectrum of approaches to segmentation

Adopt agile and connected approach to segmentation

Deliver seamless cross channel experience /digital/physical merge

Got chat?

Architect customer experience

Build an experience that connects emotionally

Providing the customer with the right offer, right time, right

place, in real time if necessary

Right Service

Customer Service

Right Place

Omni-Channel Optimisation

Right Time

Customer Insight

Right Customer

Customer Segmentation

How should the brand respond & redesign their customer experience?

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Marketers and customer service departments face similar and

intertwined customer challenges

Marketing

Transforming the customer experience

Incomplete customer view

Improving customer loyalty

Improving customer lifetime value (CLV)

Delivering value across customer touchpoints

Marketing innovation

One-to-one marketing, personalization

Customer service

Transforming the customer experience

Incomplete customer view

Improving customer loyalty

Minimizing customer churn

Delivering value across customer touchpoints

Customer-service innovation

Acting on customer feedback

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Key challenges uncovered

Difficult to deliver end-to-end customer analytics solution

able to consume, integrate, analyze, score and determine

most appropriate action with individual customer

Inability to gather and synthesize insights from analysis

of social, text and transactional customer data to generate

real-time information to predict customer sentiment and

needs

Incomplete view of customer information at the time of

interaction, resulting in inappropriate or incomplete offers,

communications or both

Inconsistent service delivery and weak customer

relationships, resulting in high churn

Lack of channel integration and siloed lines of business,

causing inconsistent or tactical customer interactions

Focus on uncoordinated marketing offers — one-hit selling,

as opposed to lifetime value

Challenged in using analytics to add short-term value or

enhance long-term strategy

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Real-time marketing is a disruptive force to embrace now

“In 2013 we’ll see many more marketers take advantage of

the power of real-time communications to grow business. In

2013, buyers instantly engage with brands on their websites,

talk back via social media like Twitter and Facebook, and

follow breaking news in the markets they are interested.

Clearly, the opportunities to grow your business in 2014 and

beyond mean real time is key.

Success comes from engaging your buyers when they’re

ready, not when it’s convenient for you.”

—David Meerman Scott, Real-Time Marketing & PR

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• Reduce operational costs• Improve asset productivity• Increase process efficiency

AccelerateTime to Value

Big data, predictive and advanced analytics, decision management, scoring and business intelligence

Real-time capabilities

Industry-specific samples: retail, telco, insurance and banking

IBM Interact, IBM Campaign and GBS Lifetime Value Maximizer connectors

Open architecture

SingularSoftware Capabilities

(IBM SPSS, IBM CognosBusiness Intelligence)

CustomizableCross-IBM, Software and GBS Solution

(Analytics With Real-time Data Integration With CLTV)

PackagedCross-IBM, Software Product

(Analytics With Real-time Data, Connectors and Industry Samples)

2012

2013

Q22014

IBM Predictive Customer Intelligence

Real-time, optimized recommendations at point of decision

Personalization with rich 360-degree view of customer

Exemplary customer experience — every interaction in context

New analytics offering for marketing, customer service and sales

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IBM Predictive Customer Intelligence drives optimized interaction

at the point of contact based on predicted outcomes and

behavior to achieve desired results

Loyalty and profitability

Target cross-selling and up-selling of

customers, based on loyalty and

profitability, to grow customer

relationships

Churn and lifetime value

Respond to customer needs and sentiment

during the engagement to proactively

decrease churn, continuous CLTV

calculations

Market-basket analysis

Tailored offers are targeted to

a customer’s basket of existing

or new goods and services—at

the point of purchase for up-

sell and cross-sell

Customer acquisition

Identify and segment

customers, target them for

profitable marketing and

acquisition efforts (wisdom of

the crowd)

Offer optimization

Develop offers, tailored to business objectives

and targeted to customer’s unique profile

Ma

rke

tin

g

Cu

sto

me

r S

erv

ice

Ma

rke

tin

g

Sa

les

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IBM Predictive Customer Intelligence key capabilities

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Chat

Voice Email

Social Media

Interactive Voice Response

Mobile Apps

Short Message Service

Web

IBM Predictive Customer Intelligence delivers intelligence to marketing and operational systems

DataIBM Predictive

Customer Intelligence

IBM EMM/

Third-party Marketing

Multichannel

Customer Interactions

HOW?Interaction Data

• Email and chat

transcriptions

• Call center notes

• Web click-streams

• In-person dialogues

WHY?Attitudinal Data

• Opinions

• Preferences

• Needs and desires

WHO?Descriptive Data

• Attributes

• Characteristics

• Self-declared information

• Geographic demographics

WHAT?Behavioral Data

• Orders

• Transactions

• Payment history

• Usage history

Acquisition Models

Campaign Response Models

Churn Models

Customer Lifetime Value

Lifetime Value Maximizer (GBS)

Market Basket Analysis

Price Sensitivity

Product Affinity Models

Segmentation Models

Sentiment Models

Up-sell/Cross-sell Models

IBM Predictive Customer

Intelligence Available Both

Inbound (Real Time) and

Outbound (Batch)

Campaigns

Offers

Messaging

Lead Management

Cross-channel Campaign

Management

Real-time Marketing

Marketing Event Detection

Digital Marketing

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First you build the algorithms…

Real-time decision management

Customer Value Calculation

Demographic segmentation

Loyalty Segmentation

Campaign Management

Churn Modeling, Next Best Offer

Social Network Analysis

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%? $?

%? $?

%? $?

?

?

?

42

87

A

B

C

Possiblecampaigns

200

100

Revenue

25%

50%

ResponseProbability

40

30

(Prob * Rev) - Cost

Businessrules

A

B

C

Possiblecampaigns

10

20

Cost

Constraint:Budget Exceeded

Campaign Optimization maximizes return across the entire campaign

Customer Interaction application maximizes return for each customer

…Then you optimize

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3

Real-time decision loop allows predictive models to get

even smarter

1. An activity occurs that calls for a

decision.

2. The context from the activity is

passed to the decision process.

3. The decision process augments the

context with stored information and

runs the decision model.

4. One or more actions are

recommended to the activity.

5. The activity feeds back the results to

help tune the model over time.

Context

Action

Decision

Activity

Feedback

Information

Facts,

recent events,

options

Decision

input, actions

and outcomes

3

5

41

2

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Key Industry Use Cases

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Millennial Female Shopper Lily is a good, long time shopper making a new move.

She leaves her footprints in many places…

Lily

VT Living Marketing ExecutiveTasked with driving personalized offers and successful segment

migration campaigns

John

VT Living Marketing AnalystRuns analysis on data to deliver actionable customer insights

Marketing needs to execute personalized messages and offers

Ann

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Lily launches VT Living app,

browses the natural bamboo

cutting boards and organic

cotton sheets

Drops cotton sheets in her

basket, but closes app without

making a purchase

LilyLily browses products online, but does not

complete the transaction

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Lily

Opt-in

Offer 1

Lily receives a concierge

text message at the entrance

Welcome to the Brooklyn store,

Lily! We have the organic cotton

sheets in the Your Home

Department. Click here to view

our Brooklyn Store layout for

your convenience.

Offer 2

After wandering more than

10 minutes in Your Home zone

Receive 10% off select Bamboo

cutting boards. Valid today only!

Next day, Lily goes to the store

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VT Living Marketing Executive

Tasked with driving personalized offers and successful

segment migration campaigns

John

VT Living Marketing Analyst

Runs analysis on data to deliver actionable customer insights

Marketing needs to execute personalized messages and offers

Ann

LilyHow it works

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PCI uses a breadth of data to build predictive

models – that are used to determine the right

action at the right time through the right channel

CoreMetricsPOS

Smarter

Commerce

Big Insights(Hadoop)

Predictive

Customer

Intelligence

Presence

Zones

IBM Campaign/

IBM Interact

ESP/

eMessage

Metadata

Manager Publisher

Analytic

Manager

Customer Insights

and Reporting

Lily

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Real time scoring using contextual data to

determine the right interaction – Lily’s location

in the store, stock levels, promotions etc. Lily

• Last item browsed

• Discount response

• Passion for Eco-friendly products

• SMS offer response rate

• PZ focused shopper-N

10% Sample Predict Persona Next

PERSONA_NEXT_BEST_AC

Predict Persona Next Format Scored Data PERSONA_NEXT_BEST_AC

Opt-in

Receive 10% off select Bamboo

cutting boards. Valid today only!

Receive 10% off select

Bamboo cutting boards.

Valid today only!

Product, Promotion

Availability

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Industry Vignettes

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Scenario 2: Real-time Offers and Cross-sell

• Anne’s product portfolio shows she recently bought a new home

• Recent spending patterns in her demand account and her bank card show she’s made a number of

large household purchases recently

• Real-time transaction data shows Anne just purchased a kitchen appliance

Scenario 1: Optimizing Offers

• Pete called the bank contact center today to ask about loan processing times

• He checked mortgage rates on the bank website three times

• He tweeted for information on buying a second home

IBM Predictive Customer Intelligence in action: Banking

Bank Action

• Proactively sends Anne an offer to her smartphone for an increase in her credit line and a

reduction in interest rate—while she is still shopping. This heads off possible card offers

from retailers.

• Alerts Anne to the bank’s secure digital vault service by simply taking pictures of important

documents with a smartphone

Bank Action

• Sends an offer to Pete, via his channel of choice, for a mortgage with special terms

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Scenario 1: Churn Reduction

• Roger called the contact center today to talk about poor reception at his home

• Roger has had 10 dropped calls in last week in his home area – that’s 10x the average of 1 dropped call per week!

• Roger will be eligible for a new Smartphone under his contract in 6 months

IBM Predictive Customer Intelligence in action: Telco

Communications Service Provider (CSP) Action

• Customer service representative acknowledges the issue, apologizes to Roger for poor network service at his home, informs him of the estimated time for resolution of the issue

• CSR knows that the client is at risk of churning (due to PCI solution) and offers the client early upgrade to the latest Smartphone with a new signed contract (PCI solution recommendation).

• Roger accepts the offer, renews the contract for 2 years and is delighted at the exceptional service

Scenario 2: Improved Cross-sell / Up-sell

• Sara changes jobs and starts consuming media content over 3G in the public transport

• Sara tries 4G but decides not to subscribe to the Full High Speed Bandwidth for 4G Offer.

Communications Service Provider (CSP) Action

• CSP’s PCI solution suggests that Sara may like temporary 4G access. Based on Sara’s mobility profile (locations visited during the day, time spent at each location) & demographics, Sara is similar to Jane who utilizes high speed access during her commute to work on public transport.

• CSP offers Sara location based high speed access subscription to allow her to stream videos on the way and back from her using public transport (selected lines on partnered public transport network)

• The solution is custom-fit for Sara – Sara accepts the offer, CSP gets the additional revenue

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Scenario 1: Customer Lifetime Value & Customer Retention

• Karen scores high in the customer lifetime value scoring for Insurer. She pays premiums on time and has a good claims history as a 13 year policy holder.

• Karen and her husband have 3 cars, a home, a college-aged child’s renter’s policy, an umbrella policy, and 2 life policies all insured with Insurer. Karen and her husband have 3 children all who are insured on the auto policies.

• Karen posted a Facebook photo of her car with bumper damage that was hit in the parking lot while grocery shopping.

• She makes a call to her insurance company concerned about her insurance premium and repairs.

IBM Predictive Customer Intelligence in action: Insurance

Agent Action

• The call center agent (CSR) has visibility to relevant information about Karen and her claim:

- Automated FNOL (first notice of loss) initiated 2 hours ago through the telematics data and notes that the claims adjuster has been trying to reach Karen

- Sees Karen’s Facebook post

- Online police report showing other driver is at fault and his insurance will cover damages

• The CSR informs Karen that they have been trying to reach her to make sure she was okay and that her premium should not be impacted. CSR transfers her to the adjuster so that Karen can get her car repaired.

• Karen is delighted with the exceptional and caring service received.

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IBM Differentiators

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Cross-channel campaign management delivers the best offer, via the appropriate channel, with a

feedback loop that helps ensure accuracy over time with each delivered decision

Chat session Web Email Mobile apps Call center

Cross-channel,

real-time decisions

Analytics

Big data platform

Cross-channel, real-time delivery

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Big data platform An innovative, foundational big data platform can help tackle big data’s four V’s (volume,

variety, velocity and veracity) with an integrated set of big data technologies to address

the business pain, reduce time and cost, and provide quicker return on investment

More cost-effectively analyze

petabytes of structured and

unstructured formation

Analyze streaming data

and large data bursts for

near-real-time insights

Access deep insight with

advanced in-database analytics

and operational analytics

Big data

platform

Accelerators

Data warehouseApache Hadoop system Stream computing

Data Media Content Machine Social

Systems management Application development Discovery

Information integration and governance

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What’s distinctive?

Cross-channel, real-time action

• Including customer service, issue resolution, account management, response and billing, with

touchpoints managed in near–real time via appropriate channel

Decision management

• Mature technology combining analytics and business-rules creation, integration and execution

• Near-real-time recommendations beyond just marketing offers

IBM big data platform

• Integration and management of the variety, velocity and volume of data

• Phased approach for enhanced 360-degree customer view

• Advanced analytics applied to information in its native form

Advanced analytics

• Marketplace-leading tools for predictive and advanced analytics

• Integrated optimization techniques that combine analytic output for the best answer

Multiple entry points

• Analytics, decision and information management for quantitative starting points for next

best action project

IBM Predictive Customer Intelligence

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Customer Examples

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142 percent reductionin revenue erosion for customers

at most risk of churning

$10 million+

savings/yearfrom increased retention and

reduced customer service costs

5 months to achieve full return on

investment

Solution components

The transformation: XO Communications had already taken the first steps in

identifying customer retention risks through analytics; now it wanted to seize the

opportunity to put these insights into action more effectively. By using IBM®

SPSS® solutions to hone its predictive models, the company built a richer, more

up-to-date picture of its client base and began delivering this data to a greater

range of employees.

• IBM® SPSS® Analytics Catalyst

• IBM SPSS Modeler

• IBM SPSS Modeler Server

• IBM SPSS Statistics

• IBM InfoSphere® BigInsights™ YTP03235-USEN-00

XO Communications takes control of customer satisfaction

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The Florida Department of Juvenile Justice uses predictive

analytics to put delinquents on a path to prosperity

• IBM® SPSS® Modeler

• IBM SPSS Statistics

• IBM Cognos® Business

Intelligence V10

13 fewerdelinquency arrests per 1,000

juveniles in the first two years

Solution components

Business challenge: When a delinquent enters the juvenile justice system, he is at a

crossroads, and the wrong decision could set him up for a life of crime. With limited time

and money to spend on each child, the Florida Department of Juvenile Justice (FDJJ)

sought a way to optimize each child’s chances without slowing the process.

The smarter solution: The FDJJ is using analytics to tap in to years of data about the

relative success of different interventions with different kinds of offenders. Decision

makers can now use the insight provided by predictive models and statistical analysis to

quickly and accurately determine the strategy that will work best for each young person

who enters the system.

“With powerful predictive analytics technology, we can gain the deep insight we need to

increase the chance of a successful outcome for each and every child.”

—Mark Greenwald, chief of research and planning

26% decreasein the number of juveniles

referred to secure facilities

15% lowerrate of referral to juvenile

probation programs

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Communications provider C Spire Wireless uses

predictive analytics and decision models to optimize

cross-selling and prevent churn

Business Challenge ⏐ Outcompete the resource-rich wireless giants,

C Spire Wireless needed to beat them at the small things that matter most: getting

closer to customers and keeping them satisfied. Its challenge was to convert what

it knows about customers into actionable insights that help account reps craft the

optimal offers that meet their needs and head off customer dissatisfaction.

Smarter Solution ⏐ C Spire Wireless is using predictive models to examine the

complexity of its customers’ behavior and determine which service mix is optimal

for each customer’s need, as well as the indicators of imminent churn. By

embedding these insights into its customer-facing processes, C Spire Wireless has

empowered its reps to optimize their interactions with customers.

270% increasein cross-sales of

accessory products

Increased satisfaction by creating a more

personalized customer experience

50% increasein effectiveness of customer

retention campaigns

Excellent buy-infrom front-line crew

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Next steps

• Visit us in the Social Café

• Additional Analytics collateral can be found on the tablets

• Find more information on IBM.com (look up “predictive customer

intelligence”

• Get in touch with your IBM contact at any time

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