Predictive Analytics for Retail: Understanding Customer ... · Predictive analytics encompasses a...

A SELECT INTERNATIONAL COMPANYwww.sv-europe.com

Friday 10th October 2014

London, Southbank

Predictive Analytics for Retail: Understanding

Customer Behaviour

A SELECT INTERNATIONAL COMPANY

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


• 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


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



• Examples of UK retail news in the last 14 days…



• Hot off the press…

• Evidence of changing consumer behaviour in retail



• Source Deloitte

• http://www.deloitte.com/view/en_GB/uk/industries/consumer-business/retail/cost-effectiveness-in-retailing/the-six-key-pressures-driving/


grow

risk

fraud

attract

retain

So how can Predictive Analytics help? Demand Side…


Forecasting & Pricing

risk

Internal fraud

Product Lifecycle Planning

Store / channel segmentation & profiling

So how can Predictive Analytics help? Supply Side…


Predictive Analytics for Retail


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.


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


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


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?


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


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

http://hbr.org/1990/09/zero-defections-quality-comes-to-services/ar/1

http://hbr.org/2000/07/e-loyalty-your-secret-weapon-on-the-web/ar/1


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,


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?


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?

http://www.crisp-dm.eu/


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)


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


Applying Predictive Analytics in Retail…


John McConnell

Advanced Analytics in Retail


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


SPSS Retail Users Include …


The 3 pillars – top level AA types


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


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


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


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


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


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?


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


Data miningDiscovering previously

undetected patterns and relationships in data

Predictive analytics

Applying historical patterns to predict future outcomes

Definition II


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


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


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


Creating meaningful segments

•Descriptive–Gender, age etc

–Lifestyle

•Behavioural–Browsing

–Purchasing

–Responding

–Converting

•Interaction

•Attitudinal–Brand empathy

–Satisfaction


Segmentation strategies

Deterministic

Rules

Hierarchies

Filters

Discovery based

Clusters

Associations

Patterns

Correlations


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


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%


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


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%


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.


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


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


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.


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!


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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