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Customer Insight and Prediction for B2B Marketing22nd November 2013
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Agenda• Arrival, registration and coffee
• Introduction: what is predictive analytics and is it realistic for you?
• Typical predictive analytics applications: where is it being used, for what and with what results?
• Break
• Analytics in action: Roger Watson – Chubb Fire and Security
• Technology overview: demonstration of tools and techniques
• Getting started: what to consider and how to begin
• Close and Lunch
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Aims and Objectives
• Introduce Smart Vision, IBM and predictive analytics
• Share practical approaches to using advanced analytics for improved business to business marketing and customer management
• Encourage discussion and debate with us and within the group
• De-bunk some myths and set expectations
• Suggest practical approaches for getting started
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About Smart Vision…
• Premier IBM Partner• Accredited Support Providing Partner for SPSS• Specialist in SPSS analytical products and BI
– Currently resourcing the IBM SPSS public training schedule
• Staffed by former SPSS employees– Former UK General Manager– Former SPSS Brand Leader, EMEA– Former UK Head of Professional Services– Senior Professional Services Specialists
• Experience in advanced analytics– B2B, retail, telecommunications, leisure and hospitality, financial services,
publishing, utilities…
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Projects in the last 12 months….
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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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Core capabilities driven by advanced predictive analytics
grow risk
attract
retain
BehaviouralDescriptive
InteractionSentiment
When?
How frequent?
No. contacts?
Feedback?
SIC, Credit Rating,
Employees, T/O,
EBIT...
Offers?
Payment history?
How often?
How long?
What value?
Feedback/Surveys
Social Media
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Types of predictive modelling
Propensity/
ClassificationClustering
Association / Sequence
Time Series
Identify groups
within a
population
displaying
homogeneity
(based on a wide
array of data)
Identify
repeatable
patterns of
behaviour or
sequence
Forecast a
future value
over a defined
time period
Predict a
particular type
of outcome
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Types of predictive modelling• Classification / Propensity
– What customer account is most likely to respond / convert / cancel based on historical response data and the array of behavioural data we have about them?
• Clustering
– How can I divide my account base into meaningful and usable groups as a framework for marketing communications? By value, by service or product portfolio
• Association and Sequence
– What is the optimal sequence and frequency of events and interventions that lead to a one off purchase from a company becoming a high value account?
• Time Series
– What will product demand be next month / quarter / year?
• Optimise product sourcing decisions
• Identify pricing errors during product lifecycle
• Predict end of life products in time to divest stock held
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Is it practical for your business?
• Do you need to be statistically trained?
– No…
– Appropriate skills can be transferred and learnt
– Business understanding is just as important as technical skill
– Analytical skills can be taught
• Data readiness?
– You need some data, certainly…
– Does not need to be organised into a tidy single supporter view
– Data is often [always] fragmented and disparate (occupational hazard)
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Is it practical for your business?• How do we get started?
– You can start small and develop in phases
– Demonstrate initial ROI before releasing further investment
– Plan your first projects using a recognised analytical methodology
• Cost to get started?
– Approximate, minimum investment £20 – 30k
• Key analytical tools implemented
– Data access, management and manipulation
– exploratory analysis, profiling and modelling capabilities
• Skills transfer and knowledge to continue independently
• First project (s) completed
• Actionable results, deployed in the business
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John McConnell - Services
Typical
predictive analytics
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Interest in predictive analytics
‘Predictive Analytics’
‘Business Intelligence’
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So who uses predictive analytics?
• Commercial Sector
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• Not-for-profit sector
So who uses predictive analytics?
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Predictive customer analytics: driving actionable insight
Acquire customers: • Understand who your best customers are
• Connect with them in the right ways
• Take the best action maximize what you sell to them
Grow customers: • Understand the best mix of things needed by your customers and channels
• Maximize the revenue received from your customers and channels
• Take the best action every time to interact
Retain customers: • Understand what makes your customers leave and what makes them stay
• Keep your best customers happy
• Take action to prevent them from leaving
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Predictive operational analytics: driving operational efficiency
Manage operations: • Maximize the usage of your assets
• Identify the impact of investment
• Ensure inventory and resources are in the right place at the right time
Maintain infrastructure: • Understand what causes failure in your assets
• Maximize uptime of assets
• Reduce costs of upkeep
Maximize capital efficiency: • Improve the efficiency and effectiveness of your assets
• Reduce operational costs
• Drive operational excellence in all phases: procurement, availability and distribution
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Predictive threat and fraud analytics: driving mitigation and prevention
Monitor environments: • Identify leaks
• Increase compliance
• Leverage insights in critical business functions
Detect suspicious activity: • Identify fraudulent patterns
• Reduce false positives
• Identity collusive and fraudulent merchants and employees
• Identify unanticipated transaction patterns
Control outcomes: • Take action in real-time to prevent abuse
• Reduce Claims Handling Time
• Alert clients of transaction fraud
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Typical Application Aims
Grow Defend
Market
Share
Profit
Acquire More Customers
Build a Reputable Brand
Anticipate Demand
Maximise Satisfaction
Lower Cost of Acquisition
Cross Sell
Up Sell
Maximise Lifetime Value
Maximise Loyalty
Address Poor Satisfaction
Lower Churn Rates
Reactivate Customers
Minimise Defaults
Prevent Fraud
Prevent Waste
Maintain Availability
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Typical predictive analytics applications
• Predictive modelling
– Marketing Response
– Customer Acquisition
– Cross-Sell/Up-Sell
– Customer Retention
– Asset Failure
– Fraud Detection
– Satisfaction Modelling
• Segmentation– Cluster Analysis
– Life Time Value
– Loyalty
– Store Clusters
• Other applications– Basket Analysis
– Forecasting
– Sentiment Analysis
– Anomaly Detection
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What sorts of people use predictive analytics?
• Marketing Managers/Analysts
• Fraud Officers
• Insight Directors
• Segmentation Strategists
• Asset Risk Managers
• Data Scientists
• Market Researchers
• Assortment Managers
• Credit Risk Officers
• CRM Analysts
• Information Service Managers
• Heads of MI
• Data Mining Consultants
• Head of Supporter Relationships
The most effective instigators of predictive analytics are
very often data-literate, business-focused people
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Brammer
• Leading distributor reduces cost of carrying surplus stock and improves customer service
• Applications and Benefits– IBM/SPSS predictive analytics helped Brammer to manage its inventory more
efficiently, significantly reducing the need to carry surplus stock, resulting in a total inventory reduction of £31.1 million in one year
– Inventory turnover improved from 3.2 times at the end of 2008, to 3.7 times at the end of the first half of 2009
– Greater understanding of patterns and trends in customer purchasing data helps Brammer forecast marginal stock products more accurately and improve customer satisfaction by making a wider product range available for immediate dispatch
– Detailed insight into inventory requirements has helped Brammer develop closer relationships with strategic suppliers leading to further cost benefits
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Case Study: Cablecom
• Based in Switzerland
• Core business: cable TV
• Diversified into:
–Broadband internet access
–Digital phone
–Pre-paid mobile
• Business issue: retention of broadband customers
–High cancellation rate at end of initial contract
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Identified crucial point in lifecycle
• Created customer satisfaction survey, run at month 7 of initial contract
• Ten “0-10” questions, one free text
• Converted to single “satisfaction score”, 0-100
–100 = perfectly satisfied
– 0 = totally dissatisfied
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Predictive approach
• Combined satisfaction scores with other data assets:
–Demographics
–Product ownership and usage behavior
• Built models to predict satisfaction score for all customers
• Used predictive satisfaction score to drive retention marketing
• Result: churn reduced from 19% to 2% in treated group
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Cablecom example 2: NPS plus text mining
• Net Promoter Score (NPS)
“On a scale of 0-10 how likely is it that you would recommend Cablecom to a friend or colleague?”
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Combine NPS (KPI) with
Customer Feedback (key to actions) and Text Mining
Do more of these things
Supplementary open ended question:- Promoters
“what is the single most important thing that makes you likely to recommend us?’’
- Passives“ what is the single most important thing we could do to make you more likely to recommend us?’’
- Detractors “ what is the single most important thing that currently makes you unlikely to recommend us?”
Fix these things
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Results• Macro level:
– Information from supplementary question, categorised, reveals areas for improvement (product owners, market managers, operations managers)
• Micro level:
–Customer issues drive 1:1 interactions and resolutions
• In 3 months:
–Satisfaction improved in > 50% of cases
–23% of detractors converted to promoters
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Will you buy a car today?
• Fiat identifies the most likely customers and prospects
• Applications and benefits
– Improved customer response rate to marketing initiatives by 15-20 percent.
– Improved customer loyalty by 7 percent.
– Supports continuous improvement of dealerships and repair facilities.
– Centralized analytical reporting and modeling system enhances productivity and lowers costs.
– Efficiently works with large Oracle database
• containing history on 64 million customers
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Predictive maintenance snapshots
Major helicopter manufacturer Individualized maintenance plans for each
helicopter based on history and operations Enables “just in time” inventory management
Manufacturer of mining equipment
Proactively identified problems and the best action before failure
Saved $1 million in repair costs in under 2 weeks
12-14x ROI (return on investment) in just 4 months
UK water companies Reduce internal flooding incidents by predicting
asset failure risk
Predict risk of pollution events and take pre-emptive action
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Retaining subscribers
• Annual Magazine Subscription Renewal Modelling• Predict the likelihood of each customer to renew at their next renewal
• Ensure predictive accuracy
• The model must make sense to the business – it must be usable and ‘deployable’
• Pilot ran across three major lifestyle titles
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Data sources and fields
De
scri
pti
veCompany/Individual
Company size
Business type
Job function
Age
Association membership
Gender
LocationV
alu
e Lifetime
Lifetime value
Annualised value
Back issue claims
Payment method
Time taken to pay
Amount paid last time
Mar
keti
ng Frequency of contact
Acquisition Channel
Renewal channel
Subscription term
Preferred response method
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Project Context and Results
• Challenges around gaps in data– Created proxy measures for annualised value
– Refined range of date used through EDA
• Analysis– Decision trees used because…
• Good accuracy (tested with a “holdout” sample)
• Visual representation
• Deployment– The winning model was deployed as a test/control 50:50 split
Business Understanding
Data Understanding
Data Preparation
Modelling
Evaluation
Deployment
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Results
• Revenue in the test groups is up 18%• Profit in the test groups is up 21% • The success of this test means it is being rolled out across
100% of records for participating titles• Approach implemented on further titles
– We are working collaboratively to further automate the model development and deployment process
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Challenge:
In a highly competitive environment, Banco Itau Argentina wanted to find a more
effective way to increase customer satisfaction and lifetime value while maximizing the
bank’s profitability.
Solution:
The bank chose IBM Business Analytics software and used it to create predictive
models that identified clients with a high probability of accepting marketing offers. It
also used sales optimization technologies to maximize the effectiveness of its
marketing campaigns.
Impact:
Improve understanding of the customer to provide an unprecedented level of
targeting and coordination of campaigns which decreased cost and increased
revenue
Implement 20-30 rolling sales campaigns
Increase revenue from existing clients by 40 percent
Increase the total retail customer contribution margin by close to 60 percent
Banco Itaú Argentina
Optimizing customer cross-selling and acquisition strategies
“We wanted to increase
customer satisfaction and life-
time value while maximizing
bank profitability through the
analysis and execution of
optimal cross-selling and
acquisition strategies. That’s
why we chose solutions from
IBM.“
Mauricio González Botto,
COO, Banco Itaú Argentina
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American Public University SystemUsed predictive analytics to boost student retention and academic excellence
“For the first time, we have a statistical
model that can show, based upon data,
which students are most susceptible to
attrition. Before we started using IBM
SPSS predictive analytics, we were just
guessing from among hundreds of
variables and trying to put them together
by hand.”
- Dr. Phil Ice, Director of Course Design
for APUS.
The need:
APUC wanted to boost student retention and academic excellence by better predicting
the likelihood of students staying on course or dropping out.
The solution:
Data-driven analysis of student behavior provided the university administrators with an
understanding of the factors contributing to retention. The predictive analytical model
made connections across datasets, including those that were not readily apparent. The
university could spot, in real-time, anomalies in students’ behavior indicating whether
they are at risk of dropping out. Administrators can then quickly build intervention
strategies, including better course designs, to keep those students in the program and
on target to graduation.
Real business results:
Boosted student retention and academic excellence.
Predicted with approximately 80% certainty whether a given student is going to
drop out.
Protected university’s bottom line by establishing the conditions for improved revenue
flow.
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Centre Hospitalier de NiortForecasting medical demand
“Using IBM SPSS Modeler, the IBM
SPSS data mining workbench,
we now plan to forecast the medical
activity levels of selected hospital
departments over the period 2008 to
2012.”
—
Delphine Yzèbe, of the Niort DIM
The need:
To measure annual medical activity and trends. To produce
projections of medical activity for selected departments.
The solution:
The Centre Hospitalier de Niort uses IBM/SPSS Statistics to
centralize all medical practice data and cross-reference data items on
the basis of time parameters. IBM SPSS Modeler, the IBM SPSS data
mining workbench, will enable the hospital to run activity forecasts for
future years
Real business benefits:
• A user-friendly, intuitive, simple-to-use solution requiring no
programming
• Immediately operational
• Computing power and depth of detail in processing outcomes
Attentiveness and availability of local support from IBM/SPSS
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Suggested further reading
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Content
• An intro to Predictive Analytics (PA)
• How to execute a PA project Part 1
Break
• How to execute a PA project Part 2
• Techniques, terms and tools
Break
• A worked example
• Case Studies
• Wrap up
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Today’s objectives
• Provide a concise overview of what Predictive Analytics is all about
• To provide examples of what has been done and what can be done
• To help you start to decide what you might do … and how
• To provide a framework for the process involved and the resources required
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Our final recommendations
• Follow a process methodology
– E.g. CRISP-DM
• Make sure you can use (deploy) the results before going too far
• Prove it with a test
– Tests may take days, sometimes weeks, not months
• In testing or production start with business objectives that will bring most value
• Balance that against risk
– Have we done this before?
– Has anyone else?
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Advice to Get Started
Jarlath Quinn – Smart Vision Pre-Sales
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Advice to get started
• Consider adopting a proven methodology e.g. CRISP-DM
• www.CRISP-DM.eu
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Advice to get started
• Don’t get hung up on modelling techniques - focus on Business Understanding and Deployment
• Build Internal Credibility: Think about where you would get biggest impact for the least effort.
• Consider the full data landscape
• Consider the sorts of roles involved /impacted
• Consider integration with other business insight systems (e.g. MI/BI)
• How will you know its worked? Focus on measuring the benefit – e.g. response rate lift, increased cross-sell, revenue/profit impact
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Common Misunderstandings
• Revolutionary results overnight!
• You need a clean, single-customer-view warehouse
• The more accurate the model the better
• You’ll need a Ph.D.
– In fact , data–literate, business focussed people learn how to do this all the time.
• It’s a ‘Big Bang’ initiative
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Working with Smart Vision Europe Ltd
• As a premier partner we sell the IBM SPSS suite of software to you directly
– We’re agile, responsive and generally easier to deal with
• As experts in SPSS / Analytics / Predictive Analytics we will
– deliver classroom training courses
– offer side by side training support
– offer “skills transfer” consulting
– run booster and refresher sessions to get more from your SPSS licences
– Give no strings attached advice
• We are a support providing partner so if you already have SPSS you can source your technical support directly from us (identical costs to IBM)
– We offer telephone support with real people as well as web tickets / email queries
– We offer “how to” support to help you get moving on your project quickly
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Thank you
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