IBM Transforming Customer Relationships Through Predictive Analytics
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Join SFIMA and AMA (American Marketing Association) as we host IBM for a very special September event dedicated to Predictive Analytics.
![1. Customer
Intelligence
&
Insight
14
Generate
a
more
complete
360-‐degree
view
by
amalgama2ng
mul2ple,
disparate
data
sources
and
appending
predic2ve
insights.
Advanced
analy2cs
finds
hidden
pa]erns
and
predictors
in
large
amounts
of
structured
and
unstructured
data
that
are
most
relevant
to
customer
profiles.
Use Case #1: Know Your Customer!](https://image.slidesharecdn.com/sfima-9-10-2015predictiveanalyticspresentation-v2-1-150916214903-lva1-app6891/85/IBM-Transforming-Customer-Relationships-Through-Predictive-Analytics-14-320.jpg)
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IBM Transforming Customer Relationships Through Predictive Analytics
- 1. Transforming Customer Relationships and Experiences Through Predictive Analytics South Florida Interactive Marketing Association
- 2. Today’s agenda 2 De Dawkins NA Sales Leader IBM Predic2ve Customer Intelligence Speaking to you today… 1. How Analy2cs can add value to six key use cases in the marke2ng lifecycle 2. Iden2fy basic predic2ve analy2cs techniques and concepts 3. Define an end to end data driven, advanced analy2cs powered customer engagement architecture 4. Review a real-‐life case study This session will cover the following areas…
- 3. Leaders leverage big data and analytics for innovation in marketing and creating a superior customer experience 3 Source: 2014 IBM Innova2on Survey. IBM Ins2tute for Business Value in collabora2on with the Economist Intelligence Unit. 3
- 4. Predictive Analytics Leveraging technology and applied mathematics to learn from the past in order to predict the behavior of individuals and outcomes of events in order to drive better business decisions.
- 5. Acquire, Grow & Retain customers by improving customer interactions and relationships by harnessing all customer data ACQUISITION RETENTION PERSONALIZATION PROFITABLE GROWTH
- 6. To create a superior customer experience and effective marketing campaigns, you must start with a complete view of the customer Transac?onal data • Orders • Transac2ons • Payment history • Usage history Descrip?ve data • AVributes • Characteris2cs • Self-‐declared info • (Geo)demographics AFtudinal data • Opinions • Preferences • Needs & Desires Interac?on data • E-‐Mail / chat transcripts • Call center notes • Web Click-‐streams • In person dialogues WHY? WHAT? HOW? WHO? 6
- 7. A Living Customer Profile Base Customer Profile DataWhat We Know What They’ve Told Us How They’ve Responded What They Are Doing How They Feel Living Customer Profile (360°) Transactional Data Explicit Preferences and Permissions Contact & Response Data Behavioral Data Social Insights What They’ve Purchased Predictive Customer IntelligenceHow will they Act 7
- 8. Predictive Analytics enables marketers to extract deep insights from data and better understand customers in order to send more relevant offers. Consume greater amounts of data VOLUME Make sense of data more quickly VELOCITY Amalgamate more types of data VARIETY Examine and validate uncertain data VERACITY Data mining: The self-organizing use of algorithms to interrogate data and uncover hidden patterns, associations, and key predictors. Great for large data sets. “Who are the most likely consumers of organic granola bars, and what else do they typically buy?” Statistical analysis: Tests hypotheses about your data to drive confidence in business decisions “I think 35-year old single women in urban metro areas are the largest consumers of organic granola bars.” 8
- 9. Type Classification Identify attributes causing likelihood of something occurring Segmentation Find patterns and clusters of similar things, and outliars Association Discover associations, links, or sequences in your data Types of models Rule deduction, Regression, Time Series, Decision, Trees, ANN, SVM, KNN, ... K-Means, Kohonen SOM, Correspondence Analysis, Anomaly Detection, .... Association, Sequence, Correspondence Analysis,...... Examples § What signals a customer leaving? § How many umbrellas will we sell in the next three months in Chicago? § Who is likely to respond to a marketing campaign? § Which insurance claims should we investigate? § What products are purchased together? § What is the series of clicks on my web page that leads to a sale? Use to Build alerts for call centers to take corrective action on customers identified as at risk for going to a competitor. Increase ROMI and reduce opt-out rate by reduce the number of people you market to by selecting only those most likely to respond. Increase average sales by building campaigns and promotions that combine items offered or provide recommendations for purchase Algorithms find the relevant data among the noise 9
- 10. Example models for customer analytics • Propensity Modeling, Campaign Response Models, Product Affinity Models, Up Sell/Cross Sell Models – Knowing who is most likely to respond to a campaigns, offers or product recommendation increases campaign returns without increasing cost. It reduces customer fatigue by not bothering customer with unnecessary messaging. • Churn Models – Knowing who is likely to attrite, cancel contracts or buy from competitors allows customer communication to be oriented to retaining the customer. • Customer Value, Life-time Value – Knowing which customer are valuable or have the potential to be valuable changes the way markets will communicate to them and what incentives and programs should be aligned. • Segmentation Models – Segmentation models cluster customers into homogenous groups for improving marketing tests and align offers based on common behaviors. • Pricing Sensitivity – Insure marketing incentives are a aligned with customers sensitivity. Protect margin by not discounting products to customers that are not driven by price. • Sentiment Analysis – Negative sentiment aligns with churn analysis above. Positive sentiment helps marketers which customer may become social advocates.§ © 2015 IBM - Internal Use 10
- 11. Customers Contacted Total Sales 0 100% 100% Rule 1: Target Hot Leads (Life Events, Enquirers) Rule 2: Affinity Targets Rule 3: High Value Mul2-‐Buyers Rule 4: Exclude “Bad” Prospects 50% Coverage = 50% Total Sales 100% Coverage = 100% Total Sales Baseline Gains Rule Gains Marketing Segments and Predictive Models Working Together – Gains Chart
- 12. Customers Contacted Total Sales 0 100% 100% Some improvement due to beVer op2miza2on of exis2ng rules Most improvement ader core rules are exhausted Some improvement through beVer exclusion of weak prospects 40% 70% Rule Gains Baseline Gains Marketing Segments and Predictive Models Working Together – Gains Chart Predic2ve Model
- 13. 1. Customer Intelligence & Insight 6. Marke?ng Offer Selec?ons Creating an analytically-powered marketing platform: six key use cases 13 5. Real Time Customer Analysis 2. Campaign Targe?ng 3. Campaign Automa?on (in-‐line scoring) 4. Marke?ng Op?miza?on
- 14. 1. Customer Intelligence & Insight 14 Generate a more complete 360-‐degree view by amalgama2ng mul2ple, disparate data sources and appending predic2ve insights. Advanced analy2cs finds hidden pa]erns and predictors in large amounts of structured and unstructured data that are most relevant to customer profiles. Use Case #1: Know Your Customer!
- 15. 2. Campaign Targe?ng Advanced analy2cs models help improve accuracy of targe?ng. This allows markers to send fewer offers with higher predicted conversion rates, lowering marke?ng costs and improving ROMI. Use Case #2: Present Offers and Messages that Resonate 15
- 16. 3. Campaign Automa?on (in-‐line scoring) Predic2ve Customer Intelligence scores can be embedded in Campaign flows and scored at any 2me during campaign processing, making analy?c sophis?ca?on immediately available to the marke2ng lifecycle. Use Case #3: Automate Campaigns 16
- 17. 4. Marke?ng Op?miza?on Combine predic?ve analy?cs scoring to reveal likelihood of certain events (e.g. propensity to accept an offer, risk of aVri2on, etc.). Evaluate predic2ve scores alongside business constraints and within business rules to op2mize decisions. Use Case #4: Optimize Through Business Rules, Constraints, and Analytics 17
- 18. 5. Real Time Customer Analysis Predic2ve Customer Intelligence’s real 2me scoring engine allows the power of the deep algorithms to be introduced at the moment of impact, including the inclusion of contextual data -‐ informa2on collected as the interac2on is happening. This again adds depth and accuracy to the understanding of the customer profile, which supports campaign execu2on. Use Case #5: Interact in Real-Time and Considering Context 18
- 19. 6. Marke?ng Offer Selec?ons Predic2ve Customer Intelligence scores provide an alternate recommenda2on for marketers to consider alongside standard naive bayes/self learning algorithms for offer selec2on, grounded in mul?ple algorithmic techniques that examine many dimensions of data. This empowers the marketers with op2ons that may improve accuracy of offer selec?on. Use Case #6: Add Predictive Layers to Offer Selection 19
- 20. STEP V Measure & Refine Business Intelligence Engine STEP II Generate Insights Customer Intelligence Segmentation Offer Propensity Churn risk purchase predictors Customer profile Etc… STEP I Gather Data Data Integration Customer Analytics Platform STEP IV Act Delivery STEP III Decide Campaign Execution Campaign Targets Customer analytics produces data for targeted campaigns Predictive INSIGHTS PROFITABLE ACTIONS Real-‐Time Push Batch Real-‐Time Interac?ve Real-‐Time Campaign Cross Channel Offers Event Offer Channel 20
- 21. Acquisition models Campaign response models Churn models Customer lifetime value Price sensitivity Product affinity models Segmentation models Sentiment models Up-sell / Cross-sell models Etc. Campaigns Offers/Messaging Customer experience design Omni-channel campaign management Contact optimization Real time marketing Lead nurturing Marketing event detection Digital marketing Customer insights drive optimized, integrated decision making Big Data Predictive Customer Insight Real time or historical Enterprise Marketing Solutions Chat Voice Email/SMS Social media IVR & Call Center Web and Mobile apps Outbound, Mail, etc. Omni-channel Customer Interactions Integrated Decisioning Shared Contextual View of the Customer HOW? Interaction data • Email & chat transcriptions • Call center notes • Web clickstreams • In-person dialogues WHY? Attitudinal data • Opinions • Preferences • Needs and desires • Sentiments WHO? Descriptive data • Attributes • Characteristics • Self-declared information • Geographic demographics WHAT? Behavioral data • Orders • Transactions • Payment history • Struggles • Interests POS, Kiosk ATM 21
- 22. 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% increase in cross-sales of accessory products Increased satisfaction by creating a more personalized customer experience 50% increase in effectiveness of customer retention campaigns Excellent buy-in from front-line crew
- 23. Connecting more closely to customers What should we offer this customer? • Use models to predict churn risk, propensity to respond to different offers • Use rules to enforce eligibility, policy, and regulatory compliance “We’re not only getting a more complete picture of our customers’ needs, we’re translating those insights into a higher-value customer experience.” - Justin Croft, Manager of Brand Platforms and Analytics Systems of record PULSE database is constantly updated with every customer interaction – including purchases, demographics, and prior offers / responses Systems of engagement Personalize interactions across all touch points Connect CRM, Web and mobile into one seamless experience Point of Sale Web IVR Email SMS
- 24. © 2015 IBM - Internal Use 24