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Friday 10th October 2014
London, Southbank
Predictive Analytics for Retail: Understanding
Customer Behaviour
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Agenda
• 9:30 – 10:00: Arrival, Registration & Coffee
• 10:00 – 10:20: Introduction: What is predictive analytics and is it realistic for you?
• 10:20 – 11:00: Applying Advanced Analytics in Retail
– Predictive Applications for Retail
– Customer Examples
• 11:00 – 11:30 Refreshment Break
• 11:30 – 12:15 Technology overview:
– Demonstration of tools and techniques
• 1215 – 12:30
• Getting started: what to consider and how to begin
• Lunch
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• Premium, accredited partner to IBM specialising in the SPSS Advanced
Analytics suite.
• Team each has 15 to 20 years of experience working in the predictive
analytic space - specifically as senior members of the heritage SPSS team
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Current Themes In UK Retail…
• Retail is important to the UK economy, with turnover in 2012 of £310 billion
• The value of internet retail sales in 2012 was £29 billion, around 9% of total retail sales
• Seven retailers (between them worth around £76 billion) are in the FTSE 100, and many pension and investment funds have significant retail holdings
• It is the largest private sector employer – around 3 million people
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Current Themes In UK Retail…
• Examples of UK retail news in the last 14 days…
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Current Themes In UK Retail…
• Hot off the press…
• Evidence of changing consumer behaviour in retail
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Current Themes In UK Retail…
• Source Deloitte
• http://www.deloitte.com/view/en_GB/uk/industries/consumer-business/retail/cost-effectiveness-in-retailing/the-six-key-pressures-driving/
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grow
risk
fraud
attract
retain
So how can Predictive Analytics help? Demand Side…
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Forecasting & Pricing
risk
Internal fraud
Product Lifecycle Planning
Store / channel segmentation & profiling
So how can Predictive Analytics help? Supply Side…
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Predictive Analytics for Retail
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What do we mean by ‘Predictive Analytics’?
Predictive analytics encompasses a variety of techniques from
statistics and data mining that analyze current and historical
data to make predictions about future events
Analysis of structured and unstructured information with mining,
predictive modeling, and 'what-if' scenario analysis.
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What do we mean by ‘Predictive Analytics’?
• It’s different from Business Intelligence or MI reporting
• Actually, it’s not always about prediction
• However, Predictive Analytics does creates important new data
• These data take the form of estimates, probabilities, forecasts, recommendations, propensity scores, classifications or likelihood values
• Which in turn can be incorporated into key operational and/or insight systems
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Types of Predictive Modelling…
Propensity/ Classification Clustering
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
– Who is most likely to respond / convert based on historical response data and the array of behavioural data we have about them?
• Clustering
– How can I divide my prospects/ client base into meaningful and usable groups as a framework for marketing planning / customer insight?
• Association & Sequence
– What is the optimal sequence & frequency of events and interventions that lead to a response / purchase / dormancy?
• Time Series
– What will site traffic / revenue be next day / week / month?
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Analytics in Retail
• Propensity Modelling
– Response
– Cross-Sell
– Churn
– Reactivation
– Voucher Redemption
• Segmentation
– Life Time Value
– Loyalty
– Purchase Behaviour
– RFM
– Store clustering
• Other Applications
– Affinity/Basket Analysis
– LTV prediction
– Forecasting
– Text Mining
– Satisfaction Modelling
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Why is this important to organizations?
• Acquiring customers is expensive
– Not unusual to cost 6 times as much as retaining them
– Understanding who is most likely to convert is very cost effective
• 80% of a company’s profits come from 20% of its customers
– Need to understand these customers needs
– How they behave and what keeps them happy
• Increasing customer retention rates by 5% increases profits by 25% to 95%.
– Study by Bain & Company, working with Earl Sasser of Harvard Business School
– http://hbr.org/1990/09/zero-defections-quality-comes-to-services/ar/1
• Customer Lifecycle Economics is amplified for e-businesses (B2B & B2C)
– http://hbr.org/2000/07/e-loyalty-your-secret-weapon-on-the-web/ar/1
– by Frederick F. Reichheld and Phil Schefter
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Even when the application isn’t a predictive model…
• We can still use historical data to better understand our customers
• Find strong correlators or drivers of behaviour
• Carry out text mining and analyse changing sentiment
• Develop a segmentation strategy that is data-driven
• Go beyond recency, frequency monetary to incorporate
– Who – Demographics
– What – Product/Service Categories
– Interactions – Website, Social Media, Call Centre, Payment methods
– History – Tenure, Customer Journey,
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Transaction DataCom
petit
ive
adva
ntag
e
Degree of intelligence
By exploiting a wide data landscape
Descriptive Data
Interaction Data
Social Media Data
How do our clients maximise success?
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By utilising a powerful, proven methodology
• CRISP-DM: Cross-Industry Standard Process for Data Mining
• Each application can be developed and progressed through a series of key phases
• www.CRISP-DM.eu
How do our clients maximise success?
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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
• Minimum set up components
– Key analytical tools implemented
• Data access, management & manipulation
• exploratory analysis, profiling & modelling capabilities
• Offline deployment
– Skills transfer and knowledge to continue independently
– First project (s) completed
– Actionable results, deployed in the business
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Applying Predictive Analytics in Retail…
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John McConnell
Advanced Analytics in Retail
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Objectives
To talk about some of the things that have been done in retail with AA and to introduce more concepts to help you work out what (else) you might do
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SPSS Retail Users Include …
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The 3 pillars – top level AA types
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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 & 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 & Fraud Analytics: Driving mitigation & 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 to transaction fraud
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Some more example retail apps
• Operational
• Developing sales baselines
• Retail location
• Retail outlet segmentation
• Demand/stock forecasting
• Merchandising
• Store performance modelling
• Resource planning
• Customer
• Campaign response modelling
• All the web site stuff
• All the call centre stuff
• All the CRM stuff
• Customer segmentation
• Risk and Threat
• Shrinkage
• Click fraud
• Payment fraud
• Credit scoring
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1.Business Understanding
2.Data Understanding
3.Data Preparation
4.Modelling
5.Evaluation
6.Deployment
The CRISP-DM process
http://www.sv-europe.com/crisp-dm-methodology/
1 2 3. Data Preparation 4 5 6
Time
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Data at the heart of Predictive Analytics
Behavioral data- Orders- Transactions- Payment history- Usage history
Descriptive data- Attributes- Characteristics- Self-declared info- (Geo)demographics
Attitudinal data- Opinions- Preferences- Needs & Desires
Interaction data- E-Mail / chat transcripts- Call center notes - Web Click-streams- In person dialogues
“Traditional”
High-value, dynamic- source of competitive differentiation
Who? What?
Why?How?
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Inferential Statistics
Inferring parameter values in a target population based on
sample statistics. Often using parametric
assumptions.
Data MiningApplying historical patterns to
predict future outcomes. Tested empirically
Definition I
IBM/SPSS Statistics IBM/SPSS Modeler
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Data miningDiscovering previously
undetected patterns and relationships in data
Predictive analytics
Applying historical patterns to predict future outcomes
Definition II
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Types of Predictive Modelling
Classification
Value predictio
n
Association /
Sequence
Forecasting values
Predict a value
e.g. revenue,
visits to a web
site
Identify
repeatable
patterns
Forecast a
future value
over a defined
time period
Predict a
particular
class of
outcome e.g.
churn
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A classification of predictive types
Type Overview
Classification Probably the most widely applied.We are predicting a discrete set of targets (“categorical”). Typical examples of classes we predict are:• Good credit risk/bad credit risk (loss)/bad risk (profit)• Will buy/will not buy• Legitimate transaction/Fraudulent transactionWhen predicting classifications we create a propensity score for each class
Predicting Values Here the target is a real measurement (“continuous”). It can often take a wide range of values. For example sales my vary from 100 to 5.000 depending on the dayTypical examples are:• Revenue• Call centre calls• Demand in all its forms
Forecasting Values Predicting values where we explicitly account for previous values over time and look to predict at future points in time e.g. for the next 7 days.The example targets are largely the same as those for Predicting Values
Association/Sequence The most specific and therefore less common of the 4Looking for associations between entities. Most often used for Market Basket Analysis/product affinities.Sequences can look for event sequences that lead to successful outcomes or failures e.g. across repair processes or patient treatment paths
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Data mining
Discovering previously undetected patterns and
relationships in data
Predictive analytics
Applying historical patterns to predict future outcomes
Where is “Segmentation”?
Here we find segments without an outcome in mindSegments used more strategically
Here we find segments with an outcome in mind (e.g. repurchase propensity or high value propensity)Segments used more tactically
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Creating meaningful segments
•Descriptive–Gender, age etc
–Lifestyle
•Behavioural–Browsing
–Purchasing
–Responding
–Converting
•Interaction
•Attitudinal–Brand empathy
–Satisfaction
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Segmentation strategies
Deterministic
Rules
Hierarchies
Filters
Discovery based
Clusters
Associations
Patterns
Correlations
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Behaviourally customer segments
Behavioural segmentation based on a core set of
attributes
Segments profiled using other behavioural data and also additional demographic, survey and/or customer data
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Homemakers – purchase behaviour•A mid sized segment with a profile bias towards younger females from low income families•Product purchasing:
–Domestic items–Furnishings–Nursery–Temperature control
•Purchasing profile suggests this segment are homemakers, probably buying appliances, furnishing and nursery items on a budget –low average item value•Within all repeat shoppers they are fairly average in terms of volume and frequency of ordering•Entry point is through ranges such as floor care and nursery•More likely than average to be acquired in Q1 and Q3
HD only
22%
CC only
58%
Mixed
20%
Order patterns Value Index
Number of orders 3.2 94
Number of items 6.2 92
Lifetime spend £175 75
Average order value £57 85
Average items/order 2.0 102
Segment size All shoppers Repeat shoppers
% shoppers 2% 9%
% orders 5% 9%
% items 5% 8%
% sales 5% 7%
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Homemakers - demographics
80 90 100 110 120
Symbols of Success
Happy Families
Suburban Comfort
Ties of Community
Urban Intelligence
Welfare Borderline
Municipal Dependency
Blue Collar Enterprise
Twilight Subsistence
Grey Perspectives
Rural Isolation
Index vs Our Brand online shoppers
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Homemakers – browsing behaviour
0 100 200
Index vs repeat shoppers
Category browsingBrowsing behaviour Value Index
Avg Number of visits 14 96
Visit lifecycle (days) 174 104
Categories browsed 4.3 101
Relative to other repeat shoppers this segment focus on browsing their core categories (Baby, Homewares and Household).
May also be browsing Furniture as part of their repertoire.
% B2B 1.2%
% opted-in 18%
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Time
Revenue
Loss
Less Loss
Profit
Back to prediction - Predictive Analytics for CRM
Attract Grow Retain
Think of events in the journey (visitor, customer, subscriber, applicant, product, machine, etc.) Events are the target for predictions.
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Types of Predictive Modelling
Classification
Value predictio
n
Association /
Sequence
Forecasting values
Predict a value
e.g. revenue,
visits to a web
site
Identify
repeatable
patterns
Forecast a
future value
over a defined
time period
Predict a
particular
class of
outcome e.g.
churn
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The Business Analytics Continuum
What is happening?
Why?
What will happen next?
What should I do about it?
Business Intelligence
PredictiveAnalytics
Sometimes the data mining approaches give us BI style analyses that BI doesn’t
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Time
Revenue
Loss
Less Loss
Profit
Thinking multi-channel means taking a broader view
Attract Grow Retain
The journey is more complex e.g. Research may happen on-line … acquisition conversion offline. If we weren’t at “Big Data” already … we probably are now.
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Predicting Life Time Value (Classification)
A retailer wants to identify - early in the relationship –customers who will go on to become high value customers
e.g. here!
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Brammer
• Leading retailer/distributor reduces cost of carrying surplus stock and improves customer service
• Applications & 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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Suggested further reading
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Thank you
Contact us:
+44 (0)207 786 3568
info@sv-europe.com
@sveurope
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Highly Effective Technology For Predictive Analytics in Retail
Jarlath Quinn
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• CRISP-DM: Cross-Industry Standard Process for Data Mining
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What do we offer Anna?
• 31 years old
• Estimated income > £28K
• On average spends £26
• Usually pays with credit card
• Not eligible for discount offer
• In the last 6 weeks bought these items -
What is the next most relevant product to offer her?
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Why IBM SPSS Modeler?• Most intuitive interface on the market
• Supports the entire process
• Data agnostic
• Automatic Model Generation
• Very powerful data manipulation/transformation functionality
• Direct scoring to multiple data formats
• Highly scalable
• Exploits existing database functionality
• SQL Pushback
• In-Database Mining
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Let’s look at an example…
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To finish…Options to get started!
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Common Misunderstandings
• Revolutionary results overnight!
• You’ll need a Ph.D.
– In fact , data–literate, business focussed people learn how to do this all the time.
• The more accurate the model the better
• You need a clean, single-customer-view warehouse
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Advice to get started
• Build Internal Credibility: Think about where you would get biggest impact for the least effort.
• Consider adopting a proven methodology e.g. CRISP-DM (www.CRISP-DM.eu)
• 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
• Don’t get hung up on modelling techniques - focus on Business Understanding and Deployment
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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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Options to get started…
Predictive Analytics for Retail (Starter Pack)
Single client software tools, set up, training & first project deployed
£10K to £20K
Predictive Analytics for Retail (Enterprise Automation Pack)
2 x client plus server software tools,
automation plus set up, training & first projects
£75K
Predictive Analytics for Retail (Enterprise Collaboration &
Deployment Pack)
4 x client plus server software tools, automation,
collaboration & deployment , set up, training & first project
£140K+
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Thank you
Contact us:
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