'Predictive Analytics'?


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Predictive Analytics for Digital Channels Friday 7th March 2013

London, Southbank www.sv-europe.com

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Agenda • • •

• • •



9:30 – 10:00: Arrival, Registration & Coffee 10:00 – 10:15: Introduction: What is predictive analytics and is it realistic for you? 10:15 – 1100: The Predictive Potential in Digital channels – Predictive Applications – Segmentation and prediction – Digital and multichannel prediction 11:00 – 11:15 Refreshment Break 11:15 – 11:45 Customer Examples 11:45 – 12:30 Technology overview: – Demonstration of tools and techniques – Getting started: what to consider and how to begin Lunch A SELECT INTERNATIONAL COMPANY

• 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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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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Predictive Analytics for Digital Channels

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Types of Predictive Modelling… Predict a particular type of outcome

Propensit y/ Classificati on

Forecast a future value over a defined time period

Time Series

Clustering

Association / Sequence

Identify groups within a population displaying homogeneity (based on a wide array of data)

Identify repeatable patterns of behaviour or sequence… A SELECT INTERNATIONAL COMPANY

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 / cancellation?

• Time Series – What will site traffic / online revenue be next day / week / month?

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Core Applications in (Digital) Marketing Acquire customers: attract

• • •

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: grow

• • •

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: retain

• • •

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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Typical Analytical Applications •

Segmentation – Customer Behaviour – Life Time Value – Loyalty



Predictive Modelling – Campaign Response – Cross-Sell/Up-Sell – Customer Acquisition – Retention Scoring – Satisfaction Modelling – Real Time Recommendations



Other Applications – Basket Analysis – Sequence Modelling – Sentiment Analysis

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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 Incremental gains is one business area lead to extremely compelling ROI

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Push Marketing vs. Pull Marketing •



Push Digital Marketing: – The marketer sends a message without the recipient actively seeking the content... – Display advertising on websites and news blogs. – Email, text messaging can also be classed as push digital marketing when the recipient has not actively sought the marketing message. Pull Digital Marketing: – The consumer actively seeks the marketing content, often via web searches or... – Opening an email (subscribed), text message, web feed, websites, blogs and streaming media (audio and video) are examples of pull digital marketing. – In each of these, users have to navigate to a location to view the content. A SELECT INTERNATIONAL COMPANY

Proactive vs. Dynamic Deployment •



Proactive (push) – Integrated with existing campaigns – Can set the decision agenda – Can be planned in advance – Less costly and simpler than dynamic but also less timely & accurate Dynamic (pull) – Offers based on new data in real time – Opportunity to gather new, important information – Opportunity to revise offer/action – e.g. retain, cross-sell – Outcome can be captured immediately – Requires greater investment than proactive approaches

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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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How do our clients maximise success? By exploiting a wide data landscape

Competitive advantage

Social Media Data

Interaction Data

Descriptive Data

Transaction Data

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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

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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 A SELECT INTERNATIONAL COMPANY

The Predictive Potential in Digital Channels…

www.sv-europe.com

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Digital – The Predictive Potential John McConnell www.sv-europe.com

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Some objectives and actions from predictions • •

• • •



Deliver differentiated content – Personalisation – Recommendations Change sites more fundamentally – Revise through information architecture – Provide multiple journeys – Build/re-build Have more informed communications with advertisers – Enumerate customer value Stop fraudulent activity Optimise the whole thing (multichannel) – The CRM process – The Job Seeker process – The customer service process Predict Next Best Action (NBA) by channel by device – Which campaign/offer and which channel/device A SELECT INTERNATIONAL COMPANY

The CRISP-DM process 1.Business Understanding

6.Deployment

2.Data Understanding

5.Evaluation

3.Data Preparation

4.Modelling A SELECT INTERNATIONAL COMPANY

3. Data Preparation 1.Business Understanding

6.Deployment

2.Data Understanding

5.Evaluation

3.Data Preparation

4.Modelling

Data Prep takes more time in most predictive model builds – especially the first time In Digital volumes and complexity often compound this A SELECT INTERNATIONAL COMPANY

6.Deployment 1.Business Understanding

6.Deployment

2.Data Understanding

5.Evaluation

3.Data Preparation

4.Modelling

Digital platforms have the “potential” to be automated deployment platforms Delivering personalised content/offers/experiences in real time

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At the heart of Predictive Analytics is the model • Predictive Analytics uses historical data from many people/incidents • Age, Gender, Average Spend, Product Category, Region, Tenure etc. • With known outcomes/results • Responded, upgraded, defaulted, recommended, cancelled, donated, failed, renewed etc. • To build a reusable model

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At the heart of Predictive Analytics is the model • We can take new data from individuals or incidents… • Age, gender, average spend, sentiment, tenure, time since last visit

• Using a model based on the same information… • Generate probability values, likelihood scores and estimates • In other words…..predictions Predicted Lifetime Value = £938

32% chance of cancellation

Estimated NPS = 6

0.13 probability of defaulting

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At the heart of Predictive Analytics is the model • The resultant model is a pattern or formula that can be examined and tested • Moreover, it can be treated as a physical object • Or an important asset that can be deployed in a wide variety of ways

before being archived

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At the heart of Predictive Analytics is the model • We can then deploy the predictions through multiple channels to make better decisions

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Types of Predictive Modelling

Predict a particular class of outcome e.g. churn

Forecast a future value over a defined time period

Classificati on

Value predictio n

Forecasti ng values

Associati on / Sequence

Predict a value e.g. revenue, visits to a web site

Identify repeatable patterns

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Forecasting Visits

A big campaign happened here!

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Predicting Applications (Classification) •

A job site wants to: – Understand better what is driving applications – Predict the likelihood that a visitor/member will become an applicant – Tune marketing effort to drive more applications

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Data at the heart of Predictive Analytics High-value, dynamic - source of competitive differentiation Interaction data - E-Mail / chat transcripts - Call center notes - Web Click-streams - In person dialogues

Attitudinal data - Opinions - Preferences - Needs & Desires

Descriptive data - Attributes - Characteristics - Self-declared info - (Geo)demographics

Behavioral data - Orders - Transactions - Payment history - Usage history

How?

Who?

Why?

What?

“Traditional”

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Where is “Segmentation”?

Data mining Discovering previously undetected patterns and relationships in data

Here we find segments without an outcome in mind Segments used more strategically

Predictive analytics Applying historical patterns to predict future outcomes Here we find segments with an outcome in mind (e.g. subscriber renewal) 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 A SELECT INTERNATIONAL COMPANY –Satisfaction

Segmentation strategies Deterministic

Discovery based

Rules

Associations

Hierarchies

Patterns

Filters

Correlations

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Predictive Analytics for CRM

Attract

Grow

Retain

Profit

Revenue

Less Loss

Time

Loss

Think of events in the journey (visitor, customer, subscriber, applicant, product, machine, etc.) Events are the target for predictions A SELECT INTERNATIONAL COMPANY

Doesn’t “Web Analytics” do this?

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The Business Analytics Continuum

What is happening?

Business Intelligence

Why? What will happen next?

What should I do about it?

Predictive Analytics

Web Analytics is a special type of Business Intelligence A SELECT INTERNATIONAL COMPANY

The Predictive Advantage Predictive Models • Leverage current and historical data • Make robust predictions on current and future cases • Provide “actionable insight” to drive better decisions

M

“NOW” Predictive Analytics: • Algorithms automatically discover significant patterns • “Learn” from historical data – create predictive models

“NOW” Traditional BI and Web Analytics: • Insight, metrics, etc. up to this point in time • User initiative to explore data

KPI

KPI KPI

“NOW”

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Prediction in Web Analytics

There are a growing number of point applications often incorporating behavioural targeting and multivariate testing

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Thinking multi-channel means taking a broader view

Attract

Grow

Retain

Profit

Revenue

Less Loss

Time

Loss

The journey is more complex e.g. Research may happen on-line … acquisition conversion off-line A SELECT INTERNATIONAL COMPANY If we weren’t at “Big Data” already … we probably are now

Summary • • •



The Digital potential is based on the implicit collection of data in digital platforms The challenge is often marshalling the data to – Effort to insight Once models are built there is the potential for real-time deployment – Often presents technical challenges – Platforms exist to help overcome these challenges • E.g. IBM/SPSS Collaboration & Deployment Services But as always it starts with recognising the potential of prediction from a commercial perspective

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Customer Cases John McConnell www.sv-europe.com

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It generally takes more than one visit to get the conversion Car Insurance Cumulative % of customers

100%

86%

80% 60%

92%

96%

97%

72% 46%

40% 20% 0%

1

2

3 4 5 Number of visits to conversion

6

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Tracking visitor behaviour over multiple visits First visit •Source of first visit •Campaign visit? •Keywords used? •Day/time •Depth of visit •Tool used? •Entry page •Exit page

Second visit •Days since first visit •Entry page •etc

Subsequent visit

Purchase visit

•Days since first visit •Entry page •etc

•Days since first visit •Source of visit •Campaign visit? •Keywords used? •Tool used? •Email landing?

The Tool is an on-line app that allows a visitor to generate and save a quote between visits A SELECT INTERNATIONAL COMPANY

Key drivers of First Visit Buyers All First Time Buyers Index = 100

Direct Landing Index = 131

Paid & Natural Search Index = 100

Branded keyword Index = 146

Affiliate Index = 46

Other Index = 77

Non-branded keyword Index = 46

If a first time visitor arrives on a Branded keyword s/he is 46% more likely to A SELECT INTERNATIONAL COMPANY purchase than the average

What are the main factors influencing purchases over multiple visits? Conversion amongst multi-visit visitors Index = 100

Used tool on first visit Index = 156

2nd visit on same day as first Index = 149

Second visit within 8 days Index = 174

Didn’t use tool on first visit Index = 69

Second visit after 8 days Index = 146

2nd visit 4 days or less from first Index = 73

2nd visit more than 4 days from first Index = 59

If a visitor uses the Tool on the first visit and returns within 8 days they are 74% A SELECT INTERNATIONAL COMPANY more likely to convert than the average

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A framework… Who visits the site?

Why do they visit the site and what do they think of it?

What do they do on the site?

?

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Developing the visitor segments Behavioural segmentation based on content consumption

Segments profiled using other behavioural data and also additional survey and/or customer data

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Different segments have different styles of engagement… Average time on site per visit 1,000

Regular posters

800

600

Frequent finders

Cottage Industrialists

400

Hobbyists Domestic PAFfers

Anxious trackers

200

0

Number of visits

Virgin posters

OHW 0

2

4

6

8

10

12

14

16

18

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Happy Trackers (6%) Happy Trackers mainly use the site for Track and Trace and little else In terms of profile they tend to have a stronger business slant and be slightly older than on average They are not heavy users of the site and their visits are relatively light and narrow – all they do is use Track and Trace However they are happy with what they do, they rate the site functionality the best out of all the segments

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Happy Trackers– 6%, Occasional information Top content

• Track & trace • Redirections • Customer services • Delivery services

Top searches

• Redirections • Recorded delivery • Redelivery

• 9th highest number of visits

Top campaigns

• Redelivery • XMAS • Smartstamp Key behaviours

• 4th most buyers; redirections • Key demographics & attitudes

• Older • More business than personal • Satisfaction above par • Highest site rating • Stated reasons for visit: Track & Trace

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Price Finders (10%) Price Finders are primarily concerned about finding our information on things like airmail services and prices as well as other delivery services and costs Quite often their visit has something to do with an online auction activity but they are possibly new to the game as this segment generally haven’t visited the site very often and a large proportion of them are new to the site

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Cottage Industrialists Cottage Industrialists are frequent users of the site and they mainly come looking for information on postal prices, delivery services, parcel information and the like.

Half of this segment are involved in some type of online auction related activity and over the course of their lifetime they tend to look at the broadest amount of content on the site. Quite often they will be using the search function to do this They are reasonably happy with the customer experience on the site and are more likely than on average to recommend the site to others A SELECT INTERNATIONAL COMPANY

The framework…in action Who visits the site?

Why do they visit the site and what do they think of it?

What do they do on the site?

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Segmentation for email targeting Segment 3:

Segment 5:

Average # orders

3.3

Avg # items

6.0

Avg spend

£178

Avg order value

£53

Avg items per order

1.8

Similar ordering patterns

Products:

Average # orders

3.3

Avg # items

6.4

Avg spend

£175

Avg order value

£54

Avg items per order

2.0

Products:

Different product purchasing

DIY

Domestic appliances

Car maintenance

Furnishings

Garden tools and furniture

Nursery

Index vs all online shoppers

Male

Female

Younger (<35)

87

Older (>35)

127

Index vs all online shoppers

Male

Female

78

Younger (<35)

83

122

97

Older (>35)

87

106

Different demographics

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On-line TV channel – Customer Sentiment form Social Media Data Harvesting

Sentiment Extraction / Customer Insight

Market Sentinel: Locate the Facebook url & add the page Scheduled, automated capture

IBM SPSS Modeler: Picks up the captured data as a file, from a database

With Meta data: post description parent ID (group if post, post if comment) post/comment ID posted time, likes, message body

Runs comprehensive, automated analysis: Data cleaning & manipulation Merging of data from multiple sources Creates categorised data Identifies triggers based on RoE

Allows for continuous imporvement

Executive Reporting

Intervention Triggers

IBM SPSS Cognos: Integrated, dynamic dashboard Event triggers (email, CRM etc.) A SELECT INTERNATIONAL COMPANY

Intervention Triggers (Deployment)... Customer Sentiment Database

c Does this post meet Rules of Engagement criteria?

Create an alert email and send it to Customer Service Agent

Create new case in the CRM system – synched to an existing customer record A SELECT INTERNATIONAL COMPANY

2 sites with more cases and examples

http://www.predictiveanalyticsworld.com/

http://www.kdnuggets.com/

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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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How do our clients maximise success?

By using powerful IBM advanced analytics technology

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How do our clients maximise success? By integrating the resultant insight with existing systems

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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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Contact us: +44 (0)207 786 3568 [email protected] Twitter: @sveurope Follow us on Linked In Sign up for our Newsletter

Thank you

www.sv-europe.com

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