Transforming Customer Relationships and Experiences Through Predictive Analytics  

South Florida Interactive Marketing Association

Today’s agenda

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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…

Leaders leverage big data and analytics for innovation in marketing and creating a superior customer experience

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Source:  2014  IBM  Innova2on  Survey.  IBM  Ins2tute  for  Business  Value  in  collabora2on  with  the  Economist  Intelligence  Unit.    

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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.

Acquire, Grow & Retain customers by improving customer interactions and relationships by harnessing all customer data

ACQUISITION  

RETENTION  

PERSONALIZATION  

PROFITABLE  GROWTH  

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?  

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A Living Customer Profile

Base Customer Profile Data What 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 Intelligence How will they Act

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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.”

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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

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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

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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

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  

1.   Customer  Intelligence  &  Insight  

6.  Marke?ng  Offer  Selec?ons  

Creating an analytically-powered marketing platform: six key use cases

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5.    Real  Time    Customer  Analysis    

2.    Campaign  Targe?ng   3.    Campaign  Automa?on    (in-­‐line  scoring)  

4.    Marke?ng  Op?miza?on    

1.  Customer  Intelligence  &  Insight  

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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!

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

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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

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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

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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

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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

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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

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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  

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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% 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

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

© 2015 IBM - Internal Use 24