Data Based Marketing

Long Time No See

presented by: Paresh Patel

Using predictive modelling to win back long lapsed customers

• Long Lapsed Customers – The Theory– Definition of long lapsed– The information and data you need

• Long Lapsed Customer Marketing – Real World Case Study– The background– The process– The results

• Recap, hints and tips• Q&A

Agenda

Data Based Marketing

• Lapsed customer:“An individual or business who is no longer considered active and no longer purchases from your company”

• Long:“A significant amount of time”

• Examples— Mail Order Customers who purchased over 37 months ago— Charity Supporters who have last donated over 5 years ago— Online customers who last ordered products over 2 years

ago

Long Lapsed Definition

Data Based Marketing

• Product /Service offering or Charity ask?• Relevant now as it was it when the customer was

active?• Response or Value? Maybe both? • What is the planned customer journey?

What you need

Business Objective

Data Based Marketing

Customer Data

Data Based Marketing

Data quality matters

Get buy in from other owners of the database

Know the data or the activity that took place at the time when the data was active

Understand the history. Communications, financial purchases, visits…… beware of inconsistent data!

1

2

3

4

Clean Data = Better Results• PAF Validation, name

verification, telephone number clean

Apply Standard Suppressions • Consumer - Movers, goneaways

or Deceased• Business - Movers, Trading

Status

• Your data has been dormant for a long time, consider….• Appending demographics (Household composition, wealth indicators, SIC,

employees, turnover……..) • Appending behaviours (Media consumption, purchases, event visits,

website visits……)• Appending share of wallet or other customer transaction behaviour

(Abacus)

• What do you already know about your lapsed customer?• Tenure, Time since last purchase, previous baskets, mailing history……

• Segment customers• Product , Service, Communication activity

Data Enhancement

Data Based Marketing

Database marketing tool - For data mining and analysis developed by Apteco

Faststats delivers…

Data selections and Campaign management

Business reporting

Statistical modelling and clustering

Apteco FastStats

Data Based Marketing

Long Lapsed Customer Reactivation CampaignCase Study – Barnardo’s

Data Based Marketing

• 25% of children in the UK eat their only hot meal at school

• 31% of children in Inner London live in poverty

• 33% of British families are surviving on just £10 each day

• Help support vulnerable children across the UK, by making a donation to Barnardos, visit http://www.barnardos.org.uk

The Cause

Data Based Marketing

• Qbase working with Royal Mail, Qbase Direct, Call Credit & An Abundance

• Identify dormant data Qbase can reactivate for a cash donation mailing in February 2011.

Cash Fundraising Mailing

Consists of mailing scored long lapsed supporters Cold data Packs are split between a letter and a Box Pack Prompt ask is £20, £50 and £100

Real example

Background

Data Based Marketing

Process

Warm Results

insight

profile

model

output

Real exampleData Based Marketing

Insight-The Data

The Data

Warm Results

• Total supplied 2.7 million supporters (individuals only)

• 17 million payments, made by 1.4 million Supporters

• 10 million communication appeals to 1.1 million supporters

• Other Tables include:• Forms of Support • Letter logs • Membership• Communication History Real exampleData Based Marketing

Insight-The Data

Objectives

Warm Results

• Primary objective - Warm cash appeal• Audience

• reactivate long lapsed cash supporters• Identify and score supporters who are

likely to give cash gift but have not done previously

• By...• Using FastStats Marketing Database

containing• Demographic data enhancements

(Lifestyle variables)• Financial variables• Audience net of Barnardo’s exclusions

are scored

Data Based Marketing

Insight-The Data

Audience

Warm Results

• Standard suppressions (GOA, Deceased, Mailing flags)

• Challenge events• Supporters with active relationships are

excluded ( for example MEM, SAP, CHI, Pledges enquirers etc.)

• Any form of communication made with the supporter last 18 months (Appeals, Letter logs, Me contacts)

• Any postal donation in the last 72 months• Any Lottery generated income in the last

96 months • Lapsed supporter types such as CG with a

form of help

Data Based Marketing

Insight-The Data

Audience Behaviour• Total contactable audience is 905K, however...• 62% (561K) have no payment date (archive legacy data)

Top 90% of Forms of support

Order FOH from Payment table Supporters % %cumulative1 Barnardo Cata logue Purchase 98,019 18% 18%2 GENERAL LOTTERY INCOME 74,651 13% 31%3 GENERAL HOUSE TO HOUSE INCOME 71,268 13% 44%4 Limericks Prospective 42,975 8% 51%5 Posta l Appeal Annual Subscribers 36,255 6% 58%6 Genera l Box Individuals 33,056 6% 64%7 Reta i l Va lue of Donated Goods 29,414 5% 69%8 Posta l Appeal Donations 25,224 5% 73%9 Barnardo Trading Donation 23,647 4% 78%

10 Gardeners Arcade Prospective 21,742 4% 81%11 Limericks Purchaser 13,794 2% 84%12 GENERAL DONATED INCOME 13,396 2% 86%13 GENERAL H2H 11,580 2% 88%14 Genera l Box Group 9,974 2% 90%LE

19901992

19941996

19982000

20022004

20062008

05,000

10,00015,00020,00025,00030,00035,00040,00045,00050,000

No of supporters with Last Pay Date

Data Based Marketing

Insight-The Data

Analysis, Profile, Model

Warm Results

• Over half of the supporter audience have no known financial payment

• Those that do, the majority have made a payment over 8 years ago. Look at• Tenure/Loyalty, Value, Frequency, First,

Last, Average Values

• Look at other supplied attributes:• Channel of recruitment• No of class codes• No of derived relationships (has letter log,

sent appeal, has contact)

• Apply demographics such as lifestyle attributes

Data Based Marketing

Insight-The Data

Profile Variables

Warm Results

• Profile needs to consider supporters with and without financial values

Sex

Cameo Financial Level 1

LifestyleSegment Lifestage Level 1

LifestyleSegment Houshold Age bande Level 1

FinancialSegment Savings Level 1

FinancialSegment Attitudes Level 1

Acquisition Segment

0 50 100

150

200

250

300

350

400

Variable predictive weight

Profile of Cash supporters (Final attributes)

Data Based Marketing

Insight-The Data

Model Curve

Warm Results

Propensity model created

Percent of supporter base

Perc

ent o

f Cas

h gi

vers

Model curve shows good fit at identifying cash givers

13 variables went into the model

Data Based Marketing

Insight-The Data

Model Deciles

Warm Results

Highest 2 3 4 5 6 7 8 9 Lowest0

2

4

6

8

10

12

14

16

18

0

10

20

30

40

50

60

70

% of Cash Givers % of CG within

% of CG within Decile % of total CG

Decile score

% of Cash giver comparison by banded score(decile size 95K)

Highest decile contains over 60% of cash

Data Based Marketing

Insight-The Data

Mailing Cells

Warm Results

• Top decile selected (Decile 1: 75K)• Decile 1 then split into deciles again• Then split between Box Pack and Letter

ScoreMailed

Box Pack Letter TOTAL

Seg 1 & 2 Decile 1 (Highest) 8,817 8,808 17,561

Seg 3 & 4 Decile 1 8,739 8,799 17,499

Seg 5 & 6 Decile 1 3,477 2,824 6,295

Seg 7 & 8 Decile 1 964 961 1,925

Seg 9 & 10 Decile 1 (Lowest) 952 955 1,903

TOTAL 22,949 22,347 45,183

• Volume determined by charity based on cold/warm appeal mix• 50/50 split between type• Cell sizes split proportionality within deciles based on cash supporters

Data Based Marketing

Insight-The Data

Model Results

Warm Results

Overall Warm Model Response Analysis

• Model response curve shows that best scoring supporters are more likely to respond and is flat from segment 3 with no real decline, however...• Looking at the curve by segment code, the curve flattens at 4, then no real change at from 6 onwards• Sample size for segment 6 to 10 are 1K, next time we should consider having an equal proportions across cells for

validation

Data Based Marketing

Insight-The Data

Warm Results

Response Split by Type-Warm

• Box pack Curve inline with identifying cash givers based on model• Letter response significantly lower

01 02 03 04 05 06 07 08 09 100.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

Box Pack Torn Letter

Segment Mailed

Resp

Model Results

Data Based Marketing

Insight-The Data

Model Results

Warm Results

Warm Model Gift size by Decile and Mail Type(Gift value GT £50)

• Half of the supporters who gave a cash gift over £50 came from best scoring segments (1&2)• Average gift value from the top segment is nearly 4 times the size for TLR compared to Box

Pack

Column % of Responders Column % of Income Average Gift Value

Decile groups Warm BPR Warm TLR TOTAL Warm BPR Warm TLR TOTAL Warm BPR Warm TLR TOTALSeg 1 & 2 Decile 1 (Highest) 51.06% 45.45% 50.00% 52.06% 77.42% 61.13% £60.42 £240.00 £91.38Seg 3 & 4 Decile 1 25.53% 36.36% 27.59% 24.60% 16.13% 21.57% £57.08 £62.50 £58.44Seg 5 & 6 Decile 1 17.02% 9.09% 15.52% 16.16% 3.23% 11.53% £56.25 £50.00 £55.56Seg 7 & 8 Decile 1 2.13% 0.00% 1.72% 3.59% 0.00% 2.31% £100.00 £100.00Seg 9 & 10 Decile 1 (Lowest) 4.26% 9.09% 5.17% 3.59% 3.23% 3.46% £50.00 £50.00 £50.00TOTAL 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% £59.26 £140.91 £74.74

Data Based Marketing

Insight-The Data

Model Results

Warm Results

Warm vs. Cold

Long Lapsed supporters

Average gift values higher for Warm selection than cold

Warm is performing better then banker lists

Data Based Marketing

Insight-The Data

Model Results

Warm Results

Warm vs. Cold

Long Lapsed supporters

Average Gift higher overall for Letter vs. Box Pack

Again long lapsed customers outperform cold data

Data Based Marketing

Creative Profiles - Gender

Creative Profiles

Cold List Profiles

Torn Letter has greater appeal to higher Social Classes

0.0%10.0%20.0%30.0%40.0%50.0%60.0%

Social Class

Box Pack Torn Letter Estab

lished

Wea

lth (£

75K+)

Secu

re Affluen

ce (£50K-£75K)

Rising P

rosp

erity

(£40K-£50K)

Comfortably

Secu

re (£30K-£40K)

Budgeted

Stab

ility (

£25K-£30K)

Limite

d Resource

s (£20K-£25K)

Uncertai

n Mea

ns (£15K-£20K)

Economica

lly Chall

enge

d (£10K-£15K)

Entre

nched

Stru

ggle

(<£10K)

0.0%

4.0%

8.0%

12.0%

16.0%

Household Income

Box PackTorn Letter

Torn Letter responders have a higher household income

Data Based Marketing

Strategic recommendations

Recommendations

• The right data is key to Modelling1. Know your data or find someone who does (knowledge is…)

2. Do you have confidence in the data quality?

3. Use a data mining tool such as FastStats to understand past behaviour or identify inconsistent data

4. Give your data a revamp- append information such as demographics. This can also fill in the gaps where you have bad data!

5. Remember suppressions but don’t over suppress!

To Recap, hints and tips

Data Based Marketing

Strategic recommendations

Recommendations

• Modelling works! • Through 1 model we have identified long lapsed supporters worthy of

communication and have shown to be better responders then banker lists!

• The letter appeals to more affluent households, therefore Qbase will be creating niche models that identify supporters who are more likely to respond to Torn letter vs. Box Pack

• Use modelling to identify and build customer or supporter relationships

• Remember Test, test and more test…

To Recap, hints and tips

Data Based Marketing

Strategic recommendations

Recommendations

Q&A

Data Based Marketing

Paresh PatelBusiness Insight Director paresh@qbase.net