Download - Smarter Decisions
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Smarter Decisions and Better Business Outcomes with Predictive Customer Intelligence
Jason SalaresSolutions Specialist
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Agenda
• Predictive analytics overview
• Predictive Customer Intelligence solution overview
• Demonstration
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“Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events.”
—Gareth Herschel, research director, Gartner Group
What is predictive analytics?
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Heads or Tails?
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Would you bet on it?Toss # Outcome
1 Heads
2 Heads
3 Heads
4 Tails
5 Heads
6 Heads
7 Heads
8 Tails
9 Heads
10 Heads
11 Heads
12 ???
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What if you had more data?Toss # Person Wind Surface Humidity # Rotations Outcome
1 Person A Light Grass 40% 5 Heads
2 Person B Heavy Asphalt 70% 6 Heads
3 Person B Light Sand 55% 12 Heads
4 Person A Light Asphalt 55% 15 Tails
5 Person A Heavy Asphalt 76% 9 Heads
6 Person B Heavy Sand 35% 8 Heads
7 Person B Medium Grass 36% 8 Heads
8 Person B Light Grass 44% 3 Tails
9 Person A Heavy Sand 46% 7 Heads
10 Person A Medium Grass 41% 6 Heads
… … … … … … …
99 Person B Medium Grass 36% 8 Heads
100 Person A Medium Sand 76% 10 ?
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Classification
(or prediction)
Autoclassifiers, decision trees,logistics, supportvector machinesand time series
Predict group membership (e.g., will this employee leave?) or a number(e.g., how many widgets will I sell?)
Technique Algorithms Usage
Segmentation
Autoclustersand K-Means
Anomaly detection
Classify data points into groups that are internally homogenous and externally heterogeneous
Identify cases that are unusual
AssociationApriori, CARMAand Sequence
Find events that occurtogether or in a sequence(e.g., a market basket)
Full breadth of predictive techniques
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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 ConsumersDEMAND MORE FROM BRANDS
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Key challenges uncoveredDifficult 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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IBM Predictive Customer IntelligenceCreating a memorable customer experience one interaction at a time
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IBM Predictive Customer Intelligence drives optimized customer interaction at the point of contact based on predicted outcomes and behavior to achieve desired
results
Loyalty and profitabilityTarget cross-selling and up-selling of customers,
based on loyalty and profitability, to grow customer relationships
Churn and lifetime valueRespond to customer needs and sentiment during the engagement to proactively decrease churn, continuous CLTV calculations
Market-basket analysisTailored 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 acquisitionIdentify and segment customers, target
them for profitable marketing and acquisition efforts (wisdom of the crowd)
Offer optimizationDevelop offers, tailored to business
objectives and targeted to customer’s unique profile
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DEMO placeholder – to be run from laptop hard wired at front of room
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Decision management An innovative business discipline that combines a variety of techniques to optimize actions and outcomes
Optimization Predictiveanalytics
Optimized decisions
Provide frontline employees and systems with recommended actions
Empower real-time and adaptive decisions to accommodate changing conditions
Optimize actions with resource constraints, aligning execution with strategy
Business rules
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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
Data IBM 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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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: In-store real-time contextual offer
• Lily browsed VT Living’s smartphone app today to check out organic cotton sheets & cutting boards. Puts sheets in her checkout cart and abandons it.
• Social activity in the past 3 months shows her tweeting pics of first ever flat purchase in Brooklyn
• Lily as an opt-in customer walks into the VT Living store and is recognized via her smartphone in real-time
IBM Predictive Customer Intelligence in action: Retail
Retailer Action
• Sends a welcome message to Lily via SMS, directing her where to find the cotton sheets. A today only special offer of 10% off bamboo cutting boards when found spending more than 10 minutes in the area.
• Lily makes her purchases, delighted VT Living is helping her save time and money.
Scenario 2: Segment migration campaign
• VT Living’s customer segmentation report shows Lily is in the medium value segment and scored as prime to move to high value segment.
• Recent new behavior attributes about Lily from social data shows she is passionate for earth-friendly causes; web activity shows interest in eco-friendly products. Sales transaction data shows recent household purchases .
• Customers identified with the same passion for eco-friendly products have been scored &analyzed to show what eco-friendly products have been purchased
Retailer Action
• Proactively send Lily an email offer thanking her for her recent store purchases and a special 2 day expedited free shipping offer for eco-friendly soda maker products.
• Lily tweets about it favorite social media channels she is interested in.
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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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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.
“We are only just starting to realize the true potential that IBM analytics holds across the business.”
— Bill Helmrath, Director of Business Intelligence, XO Communications • 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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• IBM® SPSS® Modeler• IBM SPSS Statistics • IBM Cognos® Business
Intelligence V10
The Florida Department of Juvenile Justice uses predictive analytics to put delinquents
on a path to prosperity
13 fewerdelinquency arrests per 1,000 juveniles in the first two years
Solution components
Smarter Government
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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IBM Predictive Customer Intelligence
What’s distinctive?
Accelerated time to value• Pre-packaged solution - easy and swift to implement – vastly accelerating time to market
Decision management• Mature technology combining analytics and business-rules creation, integration and execution• Near-real-time recommendations beyond just marketing offers
Industry specific content• Templates/models that are pre-built and geared towards 4 different industries
Advanced analytics• Marketplace-leading tools for predictive and advanced analytics• Integrated optimization techniques that combine analytic output for the best answer
Operational connectors• Deploying analytics within marketing and customer service operational systems at the point of
impact with each individual customer.
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Next Steps
• Meet me in the Social Café to continue the conversation
• Find more information about Predictive Customer Intelligence at https://ibm.biz/predictive-customer-intelligence
• Please join us at Insight 2014, the conference for Big Data and Analytics, at Mandalay Bay in Las Vegas, October 26 – 30. More information is available in the Social Café or you can visit https://ibm.com/ibminsight
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