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David Dipple
   Fellow of Royal Statistical Society   Worked with Not For Profit and Charity Clients for over    25 years   Recognis...
An approximate answer to the right question is worth a great deal more           than the precise answer to the wrong ques...
Question           Gubbins                     Answer
The only point where there is interaction is at the start – no time isallocated for re-visiting the question
Needs, wants and requirements Question   Initial Brief       Marketing                    Analysis                        ...
   Forget complex relationships – simplicity is your friend   Analysis follows the 80/20 rule    ◦ 80% of the analysis c...
Binary Clustering: Charity Sector                                  Humanity                   3rd Word &                  ...
Our Target?              Or
   Traditionally many legacy campaign have been designed and    devised around a message they are not shaped around suppo...
   Method    ◦   Mail    ◦   Phone    ◦   Event    ◦   Online   The halo effect
Behavioural                         Recency, Frequency,                         Value, Forms of help.                     ...
Payment Type                               InterestsAmount                                     LifestyleDate              ...
Donor InformationCommunications                           Attitudinal               Donor &             Demographic       ...
   Geo-Dems are great for cold and certain aspects of    warm targeting   For small population analysis they tend to be ...
Academic Centres, Students and Young                Acorn Description  ProfessionalsPersonicx                         Reti...
   People tend to be interested in people    ◦ But why are they interested?    ◦ What aspects of your cause excites them?...
   What data do we currently have?    ◦ What is its quality   What data would we like to have?    ◦ What barriers are th...
   But what type of model?    ◦   Legacy    ◦   Pledger    ◦   Legacy & Pledger    ◦   Residuary/Pecuniary   The past de...
   SPSS   Excel   FastStats   MapInfo & MapPoint   My own software
   Modelling techniques    ◦ Binary Logistic    ◦ Discriminant    ◦ Multinomial Logistic    ◦ CHAID    ◦ Proxy
   Type of Data    ◦ Number of Relationships    ◦ Supporter Lifetime    ◦ Number of Gifts    ◦ Age of Supporter    ◦ Gift...
Beware of False Relationships             Gender Response Age                    Response             Male      8% Young  ...
c                                         Clas sification Table                                                           ...
Selected                        High Score    SupportersEven with a smallpopulation outcomemodels – test downthe model to ...
   Building legacy models has so far been carried out by    building statistical propensity models. These need    previou...
   The factors that increase propensity to make a pledge    or leave a legacy are fairly well know – as we saw    earlier...
   Analysis of a legacy campaign tends to be point based,    That is how many responded to being contacted   To truly un...
Message 1   Message 2      Message 3   Message 4Single model thatdetermines bothwho should be                             ...
   The biggest barrier to producing efficient models is lack of    data – especially demographic and attitudinal data   ...
David.dipple@adroitinsight.com
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
How to avoid some of the pitfalls when deploying legacy targeting models   david dipple - adroit data and insight
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How to avoid some of the pitfalls when deploying legacy targeting models david dipple - adroit data and insight

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Transcript of "How to avoid some of the pitfalls when deploying legacy targeting models david dipple - adroit data and insight"

  1. 1. David Dipple
  2. 2.  Fellow of Royal Statistical Society Worked with Not For Profit and Charity Clients for over 25 years Recognised as an expert data modeller Trained numerous analysts and fundraisers in the use of analysis in fundraising Worked with charities in UK and mainland Europe
  3. 3. An approximate answer to the right question is worth a great deal more than the precise answer to the wrong question. -The first golden rule to applied mathematicsThe formulation of a problem is often more essential than its solution which may be merely a matter of mathematical or mental skill. •A. Einstein
  4. 4. Question Gubbins Answer
  5. 5. The only point where there is interaction is at the start – no time isallocated for re-visiting the question
  6. 6. Needs, wants and requirements Question Initial Brief Marketing Analysis Initial Brief Brief Analysis Analysis reqs Initial results Final analysisMarketing Results AnswerAnalysis workshop
  7. 7.  Forget complex relationships – simplicity is your friend Analysis follows the 80/20 rule ◦ 80% of the analysis can be done in 20% of the time. ◦ The last 20% takes 80% of the time
  8. 8. Binary Clustering: Charity Sector Humanity 3rd Word & Overseas Environment Disability Cancer & Nature Health Medical Research Wildlife Animal Welfare
  9. 9. Our Target? Or
  10. 10.  Traditionally many legacy campaign have been designed and devised around a message they are not shaped around supporters needs and requirements To fully tap the legacy potential of the base a more supporter lead strategy would match supporter interests and propensity to legacy message
  11. 11.  Method ◦ Mail ◦ Phone ◦ Event ◦ Online The halo effect
  12. 12. Behavioural Recency, Frequency, Value, Forms of help. Segmentation Demographic AttitudinalLifestage, Age, Gender Questionnaires, Geodems Interests & Beliefs
  13. 13. Payment Type InterestsAmount LifestyleDate Cause Name Address Gender LTVs Donor & Age RFVs Demographic Income Scores Details Media codes Responses Method
  14. 14. Donor InformationCommunications Attitudinal Donor & Demographic Details Database Donations Derived
  15. 15.  Geo-Dems are great for cold and certain aspects of warm targeting For small population analysis they tend to be less useful ◦ For one model that I created by using a geo-dem it added 0.5% to the power of the model Take care with including or excluding people based on their geo-dem coding
  16. 16. Academic Centres, Students and Young Acorn Description ProfessionalsPersonicx Retired - Low income - Aged in the CityDescription Suburbs
  17. 17.  People tend to be interested in people ◦ But why are they interested? ◦ What aspects of your cause excites them? ◦ What motivates them to give you money?
  18. 18.  What data do we currently have? ◦ What is its quality What data would we like to have? ◦ What barriers are there to getting it?
  19. 19.  But what type of model? ◦ Legacy ◦ Pledger ◦ Legacy & Pledger ◦ Residuary/Pecuniary The past determines the future ◦ Lifetime Model ◦ Time Limited Model ◦ Something Else
  20. 20.  SPSS Excel FastStats MapInfo & MapPoint My own software
  21. 21.  Modelling techniques ◦ Binary Logistic ◦ Discriminant ◦ Multinomial Logistic ◦ CHAID ◦ Proxy
  22. 22.  Type of Data ◦ Number of Relationships ◦ Supporter Lifetime ◦ Number of Gifts ◦ Age of Supporter ◦ Gift Aider Time is not our friend!
  23. 23. Beware of False Relationships Gender Response Age Response Male 8% Young 12% Female 10% Old 12% Population Response: 10% Gender: Male Gender: Female Response: 8% Response: 12% Age: Young Age: Old Age: Young Age: Old Response: 15% Response: 5% Response: 10% Response: 16%
  24. 24. c Clas sification Table Predicted a b Selected Cas es Unselected Cas es Legator Percentage Legator Percentage Obs erved 0 1 Correc t 0 1 Correc t Step 1 Legator 0 776 134 85.3 908940 153597 85.5 1 173 725 80.7 83 272 76.6 Overall Perc entage 83.0 85.5 a. Selected c as es sel_var EQ 1 b. Unselected c ases sel_v ar NE 1 c. The cut value is .500Multiple ways of understanding if amodel has worked. Most of the outputcan be ignored by non statisticiansand the key – The key is finding whatneeds to be communicated tomarketers and in what form. used todetermine power.
  25. 25. Selected High Score SupportersEven with a smallpopulation outcomemodels – test downthe model to reducethe Tom Smith effect. Model Score
  26. 26.  Building legacy models has so far been carried out by building statistical propensity models. These need previous results to determine what will happen. But if there are no previous results you can’t build a model or can you?
  27. 27.  The factors that increase propensity to make a pledge or leave a legacy are fairly well know – as we saw earlier Create binary flags for each of the data items given earlier and then add them all up. The higher the result, the more likely to make a pledge (and it works).
  28. 28.  Analysis of a legacy campaign tends to be point based, That is how many responded to being contacted To truly understand the effect of legacy campaigning the relationship over time needs to be examined, including the effect on non legacy messages – that is the full supporter journey
  29. 29. Message 1 Message 2 Message 3 Message 4Single model thatdetermines bothwho should be No Contact Modelcontacted and with (at this point…)what message. Warehouse
  30. 30.  The biggest barrier to producing efficient models is lack of data – especially demographic and attitudinal data Understand what the data is saying and then use an appropriate model - There is no one perfect solution There is no certainty in modelling – models are built from past behaviour and if you change what you are doing it can take a while for the data to catch up Examine the whole supporter journey to understand the full relationship Define the question and the answer will be much easier – remember a model is not a panacea
  31. 31. David.dipple@adroitinsight.com
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