Predictive Analytics for Retail: Understanding Customer Behaviour

Predictive Analytics for Retail: Understanding

Customer Behaviour

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

Current Themes In UK Retail…

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

Current Themes In UK Retail…

• Hot off the press…

• Evidence of changing

consumer behaviour in

retail

Current Themes In UK Retail…

grow

risk

fraud attract

retain

Forecasting & Pricing _{Internal fraud} Product Lifecycle Planning

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

Types of Predictive Modelling…

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

Analytics in Retail

– 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

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 Data

Com

pe

titive

ad

van

ta

ge

Degree of intelligence

Descriptive Data

Interaction Data

Social Media Data

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

Is it practical for your business?

– 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

Is it practical for your business?

– 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

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

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

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

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

Some more example retail apps

• 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

1.Business Understanding 2.Data Understanding 3.Data Preparation 4.Modelling 5.Evaluation 6.Deployment

The CRISP-DM process

Data at the heart of Predictive Analytics

-Needs & Desires

Interaction data

- E-Mail / chat transcripts

- Call center notes

- Web Click-streams

- In person dialogues

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 Mining

predict future outcomes. Tested empirically

Definition I

Data mining

relationships in data

Predictive

analytics

Applying historical patterns to predict future outcomes

Types of Predictive Modelling

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 transaction

When 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 day

Typical 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 4

Data

mining

relationships in data

Predictive

analytics

to predict future outcomes

Where is “Segmentation”?

Here we find segments without an outcome in mind

Here we find segments with an outcome in mind (e.g. repurchase

Creating meaningful segments

•Descriptive

–Gender, age etc –Lifestyle

•Behavioural

•Interaction

•Attitudinal

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

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%

Homemakers - demographics

Homemakers – browsing behaviour

0 100 200

Index vs repeat shoppers

Category browsing

Browsing 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.

Time Revenue

Loss Less Loss

Profit

Back to prediction - Predictive Analytics for CRM

Types of Predictive Modelling

The Business Analytics Continuum

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

Thank you

Contact us:

+44 (0)207 786 3568

[email protected]

Highly Effective Technology For

Predictive Analytics in Retail

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 item

s

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

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

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

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)

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

Thank you