Smarter Decisions and Better Business Outcomes with Predictive Customer Intelligence

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1 Smarter Decisions and Better Business Outcomes with Predictive Customer Intelligence Jason Salares Techincal Solutions Sales #SmarterBiz

2 Agenda Why are we here? Predictive Customer Intelligence solution overview The IBM difference 2 #SmarterBiz

3 Age of the Empowered Customer Stay Relevant 3 #SmarterBiz

4 Empowered Consumers DEMAND MORE FROM BRANDS 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 4 #SmarterBiz

5 Ensuring a positive customer experience is an enterprise-wide priority Manage inventory and optimize supply chain Understand the met and unmet needs Hire capable employees and enable them with appropriate skills Improve customer satisfaction and loyalty Enable employee access to usable analytics Increase conversion, margin Reduce sales cycle time Enable flexible payment options based on need Increase brand preference, especially for customers with the greatest lifetime value 5 #SmarterBiz

6 How should the brand respond & redesign their customer experience? Right Time Customer Insight Right Customer Customer Segmentation Right Place Omni-Channel Optimisation 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? Right Service Customer Service 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 6 #SmarterBiz

7 Marketers and customer service departments face similar and intertwined customer challenges Marketing Transforming the customer experience Customer service Transforming the customer experience Incomplete customer view Incomplete customer view Improving customer loyalty Improving customer lifetime value (CLV) Delivering value across customer touchpoints Marketing innovation Improving customer loyalty Minimizing customer churn Delivering value across customer touchpoints Customer-service innovation One-to-one marketing, personalization Acting on customer feedback 7 #SmarterBiz

8 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 8 #SmarterBiz

9 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 9 #SmarterBiz

10 New analytics offering for marketing, customer service and sales 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 Reduce operational costs Improve asset productivity Increase process efficiency Q22014 Accelerate Time 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 10 #SmarterBiz

11 A new, aligned marketing and customer service approach is needed Many views of the customer Past (historical, aggregated) Present (real time) Future (predictive) Virtually all staff, not just customer service Marketing Sales IT executives Front-line employees Different decisions Major and minor Strategic and tactical Routine and exceptional Manual and automated FROM TO Reactive, siloed, single view of customer, problem oriented Proactive, multidimensional view of customer, engagement oriented Data Media Content Machine Social Many media 11 #SmarterBiz

12 IBM Predictive Customer Intelligence drives optimized customer interaction at the point of contact based on predicted outcomes and behavior to achieve desired results Marketing Customer Service 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 Marketing Sales 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 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 upsell and cross-sell 12 #SmarterBiz

13 Growing, retaining and satisfying customers can require improved interactions and real-time actions across channels IBM Predictive Customer Intelligence Helps Develop Strategic Lifetime Value, Profitability and Loyalty Improves Delivery of Service and Customer Satisfaction Enhances Events That Drive Enterprise Business Results One Interaction, One Decision At a Time Increases Revenue-generating Actions, Such As Upsell, Cross-sell and Retention 13 #SmarterBiz

14 IBM Predictive Customer Intelligence key capabilities 14 #SmarterBiz

15 IBM Predictive Customer Intelligence delivers intelligence to marketing and operational systems Data HOW? Interaction Data 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 IBM Predictive Customer Intelligence 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) IBM EMM/ Third-party Marketing Campaigns Offers Messaging Lead Management Cross-channel Campaign Management Real-time Marketing Marketing Event Detection Digital Marketing Multichannel Customer Interactions Web Mobile Apps Social Media Voice Interactive Voice Response Short Message Service Chat 15 #SmarterBiz

16 IBM Predictive Customer Intelligence analyzes data from any customer source, generates predictive models and scores in real time recommended actions, enabling optimized customer interactions every time Segmentation Model SPSS C&DS Sentiment Analysis 3 Models available in real time Real Time Customer Database 2 Churn Model Acquisition Model Up-sell / Cross-sell Model Store models in SPSS C&DS Acceptance Likelihood Model IBM Predictive Customer Intelligence 4 Key customer data made available in real time 1 Build models using customer data stored in Analytics Customer DB Structured, Unstructured, Streaming, Hadoop, Batch User-friendly model creation Interactive dashboards Real time recommendations in context 5 Real time channels use context data from interaction + historic data stored in Real time Customer DB to request score/recommendati on from SPSS Real time Scoring Real Time Customer Database Chat session Web Mobile apps Call center 16

17 First you build the algorithms Demographic segmentation Churn Modeling, Next Best Offer Real-time decision management Social Network Analysis Customer Value Calculation Campaign Management Loyalty Segmentation 17 #SmarterBiz

18 Then you optimize Customer Interaction application maximizes return for each customer Possible campaigns Business rules Response Probability Revenue Cost (Prob * Rev) - Cost A B 50% C 25% Campaign Optimization maximizes return across the entire campaign Possible campaigns A B C %? 42? $? %?? $? %? 87? $? Constraint: Budget Exceeded 18 #SmarterBiz

19 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. 2 3 Facts, recent events, options Context 1 3 Activity Information Decision Action Decision input, actions and outcomes 4 Feedback 5 19 #SmarterBiz

20 A closer look at the offer / decision process Customer profile Data from realtime service calls Can use common: segment definitions offers interaction history Real-time context Accept / Reject REAL-TIME LOGIC Uses combination of segmentation, rules and event pattern recognition candidate offers & scores ADD/REMOVE OFFERS Adjust list of offers using white/black lists and suppression rules white lists, black lists PRE- CALCULATED DECISIONS Builds white lists and black lists and factored into future batch campaigns. final offer(s) ARBITRATION Self-learning algorithm or external model adjusts scores revised offer list and scores Offers can be: global segment-level Records offer presentation and response or non-response Can be factored into selflearning for future scoring individual-level 20 #SmarterBiz

21 The future of shopping behind the scene When do I promote, alter inventory or sourcing? Real-time event correlation Low Latency Workload Management Low Latency Is the buzz relevant to my business? Quickly sift thru all relevant data Parallel Processing Data Optimization Parallel Processing Data Optimization How do I optimize all customer interactions on a consistent basis? Assured delivery of insights to all channels Scale & Performance High Availability Scalability 21 #SmarterBiz

22 Communications Scenario for Customer Service/Churn 22

23 THE CALL CENTER AGENT 23

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29 Industry Vignettes 29

30 IBM Predictive Customer Intelligence in action: Banking 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 Bank Action Sends an offer to Pete, via his channel of choice, for a mortgage with special terms 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 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 30 #SmarterBiz

31 IBM Predictive Customer Intelligence in action: Telco 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 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 31 #SmarterBiz

32 IBM Predictive Customer Intelligence in action: Retail 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 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 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. 32 #SmarterBiz

33 IBM Predictive Customer Intelligence in action: Insurance 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. 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. 33 #SmarterBiz

34 IBM Differentiators 34

35 Cross-channel, real-time delivery 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 Mobile apps Call center Cross-channel, real-time decisions Analytics Big data platform 35 #SmarterBiz

36 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 Systems management Application development Discovery Accelerators Apache Hadoop system Stream computing Data warehouse Information integration and governance Data Media Content Machine Social 36

37 IBM Predictive Customer Intelligence What s distinctive? Advanced analytics Marketplace-leading tools for predictive and advanced analytics Integrated optimization techniques that combine analytic output for the best answer Decision management Mature technology combining analytics and business-rules creation, integration and execution Near-real-time recommendations beyond just marketing offers 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 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 Multiple entry points Analytics, decision and information management for quantitative starting points for next best action project 37 #SmarterBiz

38 Customer examples 38

39 XO Communications takes control of customer satisfaction 142 percent reduction in revenue erosion for customers at most risk of churning $10 million+ savings/year from increased retention and reduced customer service costs 5 months to achieve full return on investment Solution components IBM SPSS Analytics Catalyst IBM SPSS Modeler IBM SPSS Modeler Server IBM SPSS Statistics IBM InfoSphere BigInsights 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 YTP03235-USEN #SmarterBiz

40 A multichannel Korean retailer combines marketing automation with analytics to personalize communications and optimize offerings Reliable insight provides decision support for senior management Targeted campaigns can be developed for marketing Precise measurement of cross-channel campaigns Business Challenge: As sales increased for this retailer s online shopping mall, management experienced increasing difficulty ensuring that an appropriate product mix was being presented to its customers. Solution Components IBM Cognos Business Intelligence IBM SPSS Modeler IBM Unica Enterprise Marketing Management IBM Netezza Data Warehouse IBM InfoSphere Warehouse IBM InfoSphere DataStage The Solution: The company adopted sophisticated analytics and marketing automation to understand, predict and act on consumer buying behavior with confidence. Real-time marketing automation delivers personalized content to each shopper, triggered by their interaction history. Delivered at the right place and time, these offers can move the shopper toward a sale and even increase the size of the purchase. We have greatly improved our understanding of our customers, which is helping us to make smarter decisions that significantly improve business performance. Spokesperson, multichannel Korean retailer 40 #SmarterBiz

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