Help the Aged is one of UK’s largest and well known charities raising over £75 million in annual contributions specifically to address issues that matter to older people. Their four main priorities are: combating poverty, reducing isolation, defeating ageism and challenging poor care standards.
Since the fight for charitable donations is more competitive than ever before, utmost importance is placed on utilising marketing resources and budget as effectively as possible. One way to do this is to cut costs by reducing the number of recipients of their direct mail campaigns without potentially losing possible high value donors. Help the Aged are one of the leading pioneers in improving the targetting of fundraising activity.
Help the Aged’s old system was to cull supporters from direct mailing programmes using the 'Recency of Last Gift' principle common place amongst direct marketers. Their mailshots went out to anyone who had donated within the last 4 years. The problem with this exclusion principle was that it was too gross a measure. Stuart McCoy, Database Marketing Analyst at Help the Aged explains, “If we have 2 donors who last gave 4 years ago we would want to cull the one who gave £5 once much quicker than the one who until then gave £50 every year.”
However, by including “Frequency” and “Value” variables in their analysis, Help the Aged would expect to have a much better indication of a supporter's worth to the charity, be able to match this worth against their mailing cost at an individual level and determine whether or not they wish to retain the donor for their direct mail.
A Recency Frequency Value (RFV) model was therefore developed, with the explicit aim of retaining the vast majority of income whilst reducing mailing costs by an appreciable amount.
By using SPSS software, Help the Aged managed to segment their existing pool of donors by ranking the file containing millions of records from highest to lowest by Recency, Frequency and Value respectively, each file was then split into equal bands (5 x 20% “quintiles”) and a score assigned to each band from 1 down to 5. Frequency was defined as the number of gifts a supporter has made in their lifetime and Value was defined as the average (mean) gift over their lifetime, as this proved to be a more discriminating factor than total value of gifts.
Each supporter was then attributed with a 3 character RFV score giving a possible 125 combinations of individual RFV scores (5x5x5). “Over several historical campaigns, the income was then calculated for each RFV score and matched against the total mailing costs for that cell. This would enable us to generate values for net income and ROI so that cells with a negative net income and poor ROI could be identified and culled from the mailing program”, explains Stuart McCoy.
By implementing the RFV model and tailoring their selection criteria, Help the Aged found that they could cut costs by mailing less people but still manage to retain income levels from supporters. The savings that were realised from reduced mailings far outstripped the small loss in income from each campaign and so was deemed a great success.
As well as the ability to perform segmentation exercises like this, SPSS is an essential tool for basic exploratory data analysis. In addition to the modelling, some useful Key Performance Indicators were derived solely by looking at where the boundaries between the RFV scores lie, e.g. what proportion of the database had given more than once, or whose last gift was within the last 8 months.
Stuart McCoy concludes on a highly successful project by saying, “Without the use of SPSS our database was not flexible enough to allow us to manipulate transactional data to build the RFV scores, let alone compare costs and income for each cell, so it would have been impossible to duplicate this methodology. One mailing saw a doubling of both response rate and gross contribution per person mailed when compared to a similar campaign run two years earlier”.
The complete list of global SPSS success stories can be found here
“One mailing saw a
doubling of both
response rate and gross
contribution per person
mailed when compared
to a similar campaign
run two years earlier.”
- Stuart McCoy, Database
Marketing Analyst, Help the
Aged
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