Smarter Decisions and Better Outcomes with Predictive Customer Intelligence
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Transcript of Smarter Decisions and Better Outcomes with Predictive Customer Intelligence
1 #SmarterEnterprise
Smarter Decisions and Better Outcomes with
Predictive Customer Intelligence
Vivian Braun
WW Solutions Marketing Executive; Customer Service & Marketing
2 #SmarterEnterprise
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
© 2014 IBM Corporation18
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
© 2014 IBM Corporation19
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
© 2014 IBM Corporation20
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
© 2014 IBM Corporation21
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
© 2014 IBM Corporation22
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
© 2014 IBM Corporation23
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
24 #SmarterEnterprise
Industry Vignettes
© 2014 IBM Corporation25 #SmarterEnterprise
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
© 2014 IBM Corporation26 #SmarterEnterprise
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
© 2014 IBM Corporation27 #SmarterEnterprise
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
34 #SmarterEnterprise
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
35 #SmarterEnterprise
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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