Long Time No See                       Using predictive modelling to win                       back long lapsed customersD...
Agenda• Long Lapsed Customers – The Theory   – Definition of long lapsed   – The information and data you need• Long Lapse...
Long Lapsed Definition•    Lapsed customer:     “An individual or business who is no longer considered     active and no l...
Business Objective    •   Product /Service offering or Charity ask?    •   Relevant now as it was it when the customer was...
Customer Data            1       Data quality matters Data = Better Results                                   Clean       ...
Data Enhancement•   Your data has been dormant for a long time, consider….    •   Appending demographics (Household compos...
Apteco FastStats                       Database marketing tool -                       For data mining and analysis       ...
Long Lapsed Customer Reactivation Campaign Case Study – Barnardo’sData Based Marketing
The Cause• 25% of children in the UK eat their  only hot meal at school• 31% of children in Inner London  live in poverty•...
Background       •    Qbase working with Royal Mail, Qbase Direct, Call            Credit & An Abundance       •    Identi...
Process                            Receive Barnardo’s Data Extract          insight                           Build FastSt...
The Data                                                               Insight-The Data                                   ...
Objectives                                                               Insight-The Data                                 ...
Audience                                                               Insight-The Data                                   ...
Audience Behaviour                                                                                                     Ins...
Analysis, Profile, Model                                                                   Insight-The Data               ...
Profile Variables                                                                     Insight-The Data•   Profile needs to...
Model Curve                                                                    Insight-The DataPropensity model created13 ...
Model Deciles                                                                                                  Insight-The...
Mailing Cells                                                                                 Insight-The Data•     Top de...
Model ResultsOverall Warm Model Response Analysis                                                                         ...
ModelbyResultsResponse Split Type-Warm                                                                                    ...
Model Results   Warm Model Gift size by Decile and Mail Type                                                              ...
Model ResultsWarm vs. Cold                                      Insight-The Data                          Long Lapsed supp...
Model ResultsWarm vs. Cold                                 Insight-The Data                       Long Lapsed supporters  ...
Creative Profiles                                                          Creative Profiles -                            ...
To Recap, hints and tips                                                               Strategic                          ...
To Recap, hints and tips                                                                Strategic                         ...
Q&A                                           Strategic                                       recommendations           Pa...
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Data & Marketing Analytics Theatre; Long time no see: using predictive modelling to reactivate long lapsed customers

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  • You need someone in your organisation who knows the data or the activity that took place at the time when the data was active
  • Data & Marketing Analytics Theatre; Long time no see: using predictive modelling to reactivate long lapsed customers

    1. 1. Long Time No See Using predictive modelling to win back long lapsed customersData Based Marketing presented by: Paresh Patel
    2. 2. Agenda• 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&AData Based Marketing
    3. 3. Long Lapsed Definition• 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 agoData Based Marketing
    4. 4. Business Objective • 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 Dataneed Marketing you Based
    5. 5. Customer Data 1 Data quality matters Data = Better Results Clean • PAF Validation, name verification, telephone number 2 Get buy in fromclean owners of the database other Apply Standard Suppressions Know the data or the activity that took place at the 3 • Consumer - Movers, goneaways time when the data was active or Deceased • Business - Movers, Trading Understand the history. Communications, financial 4 Status purchases, visits…… beware of inconsistent data!Data Based Marketing
    6. 6. Data Enhancement• 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 activityData Based Marketing
    7. 7. Apteco FastStats Database marketing tool - For data mining and analysis developed by Apteco Faststats delivers…  Data selections and Campaign management  Business reporting  Statistical modelling and clusteringData Based Marketing
    8. 8. Long Lapsed Customer Reactivation Campaign Case Study – Barnardo’sData Based Marketing
    9. 9. The Cause• 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.ukData Based Marketing
    10. 10. Background • 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 Data Based MarketingReal example
    11. 11. Process Receive Barnardo’s Data Extract insight Build FastStats Marketing Database profile Campaign Objectives model Know Your Audience output Analysis, Profile, Model Data DataWarm Marketing BasedReal example Results
    12. 12. The Data Insight-The Data • Total supplied 2.7 million supporters (individuals only) Receive Charity Data Extract • 17 million payments, made Build FastStats Marketing database by 1.4 million Supporters • 10 million communication Campaign Objectives appeals to 1.1 million supporters Know Your Audience • Other Tables include: Analysis, Profile, Model Data • Forms of Support • Letter logs • Membership • Communication History DataWarm Marketing BasedReal example Results
    13. 13. Objectives Insight-The Data • Primary objective - Warm cash appeal • Audience Receive Barnardo’s Data Extract • reactivate long lapsed cash supporters Build FastStats Marketing database • Identify and score supporters who are likely to give cash gift but have not done Campaign Objectives previously • By... Know Your Audience • Using FastStats Marketing Database containing Analysis, Profile, Model Data • Demographic data enhancements (Lifestyle variables) • Financial variables • Audience net of Barnardo’s exclusions are scoredDataWarm Results Based Marketing
    14. 14. Audience Insight-The Data • Standard suppressions (GOA, Deceased, Mailing flags) Receive Barnardo’s Data Extract • Challenge events • Supporters with active relationships are Build FastStats Marketing database excluded ( for example MEM, SAP, CHI, Pledges enquirers etc.) Campaign Objectives • Any form of communication made with Know Your Audience the supporter last 18 months (Appeals, Letter logs, Me contacts) Analysis, Profile, Model Data • 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 helpDataWarm Results Based Marketing
    15. 15. Audience Behaviour Insight-The Data • 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 % %cumulative No of supporters with Last Pay Date 1 Barnardo Catalogue Purchase 98,019 18% 18%50,000 2 GENERAL LOTTERY INCOME 74,651 13% 31%45,000 3 GENERAL HOUSE TO HOUSE INCOME 71,268 13% 44%40,000 4 Limericks Prospective 42,975 8% 51%35,000 5 Postal Appeal Annual Subscribers 36,255 6% 58%30,000 6 General Box Individuals 33,056 6% 64%25,000 7 Retail Value of Donated Goods 29,414 5% 69%20,000 8 Postal Appeal Donations 25,224 5% 73%15,000 9 Barnardo Trading Donation 23,647 4% 78%10,000 10 Gardeners Arcade Prospective 21,742 4% 81% 5,000 11 Limericks Purchaser 13,794 2% 84% 0 12 GENERAL DONATED INCOME 13,396 2% 86% LE 1992 1994 1996 1998 2000 2002 2004 2006 2008 1990 13 GENERAL H2H 11,580 2% 88% 14 General Box Group 9,974 2% 90% Data Based Marketing
    16. 16. Analysis, Profile, Model Insight-The Data • Over half of the supporter audience have no known financial payment Receive Charity Data Extract • Those that do, the majority have made a payment over 8 years ago. Look at Build FastStats Marketing database • Tenure/Loyalty, Value, Frequency, First, Las t, Average Values Campaign Objectives • Look at other supplied attributes: Know Your Audience • Channel of recruitment • No of class codes Analysis, Profile, Model Data • No of derived relationships (has letter log, sent appeal, has contact) • Apply demographics such as lifestyle attributesDataWarm Results Based Marketing
    17. 17. Profile Variables Insight-The Data• Profile needs to consider supporters with and without financial values Profile of Cash supporters (Final attributes) Acquisition Segment FinancialSegment Attitudes… FinancialSegment Savings Level 1 LifestyleSegment Houshold Age… LifestyleSegment Lifestage… Cameo Financial Level 1 Sex 100 150 200 250 300 350 400 0 50DataWarm Results Based Marketing Variable predictive weight
    18. 18. Model Curve Insight-The DataPropensity model created13 variables wentinto the model Model curve shows good fit at identifying cash givers Percent of Cash givers Percent of supporter baseDataWarm Results Based Marketing
    19. 19. Model Deciles Insight-The Data % of Cash giver comparison by banded score % of CG within Decile (decile size 95K) % of total CG 18 70 16 60Highest decile 14contains over 50 1260% of cash 40 10 8 30 6 20 4 10 2 0 0 Highest 2 3 4 5 6 7 8 9 Lowest Decile score % of Cash Givers % of CG within DataWarm Results Based Marketing
    20. 20. Mailing Cells Insight-The Data• Top decile selected (Decile 1: 75K)• Decile 1 then split into deciles again• Then split between Box Pack and Letter Mailed Score 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 DataWarm Results Based Marketing
    21. 21. Model ResultsOverall Warm Model Response Analysis Insight-The Data• 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 DataWarm Results Based Marketing
    22. 22. ModelbyResultsResponse Split Type-Warm Insight-The Data Box Pack Torn Letter Resp 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 01 02 03 04 05 06 07 08 09 10 Segment Mailed• Box pack Curve inline with identifying cash givers based on model• Letter response significantly lower DataWarm Results Based Marketing
    23. 23. Model Results Warm Model Gift size by Decile and Mail Type Insight-The Data (Gift value GT £50) Column % of Responders Column % of Income Average Gift ValueDecile 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 • 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 DataWarm Results Based Marketing
    24. 24. Model ResultsWarm vs. Cold Insight-The Data Long Lapsed supporters Average gift values higher for Warm selection than cold Warm is performing better then banker listsDataWarm Results Based Marketing
    25. 25. Model ResultsWarm vs. Cold Insight-The Data Long Lapsed supporters Average Gift higher overall for Letter vs. Box Pack Again long lapsed customers outperform cold dataDataWarm Results Based Marketing
    26. 26. Creative Profiles Creative Profiles - Gender Social Class Household Income60.0% 18.0%50.0% 16.0% 14.0%40.0% 12.0%30.0% 10.0% 8.0%20.0% 6.0%10.0% 4.0% 2.0% 0.0% 0.0% Box Pack Torn Letter Box Pack Torn Letter Torn Letter has greater appeal to Torn Letter responders have a higher higher Social Classes household incomeData Based Marketing Cold List Profiles
    27. 27. To Recap, hints and tips Strategic recommendations• The right data is key to Modelling 1. 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!Data Based MarketingRecommendations
    28. 28. To Recap, hints and tips Strategic 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…Data Based MarketingRecommendations
    29. 29. Q&A Strategic recommendations Paresh Patel Business Insight Director paresh@qbase.netData Based MarketingRecommendations

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