Data Enhancement Panel

1. Data Enhancement Panel

2. 1. What do we mean by data enhancement? • Making more of your data • Cleaning deduping • Contact data e.g. email, suppressions • Widening your own dataset • Using your own data first • Adding in external variables Fundraising Area – F2F, Legacy, RG, Trading

3. 2. External data: why buy it? • Where in house data is limited • Supporter understanding • Profile segments • Targeting • Developing and informing creative content/offers etc • Targeting • Up sell, cross sell, lapse model development • Static selections • Extending available contact channels • Segmentation mapping

4. What’s available?

5. What’s available? • Individual, household & postcode level • Real & modelled • Locational • Distance to nearest store or town • TV or radio region • Property value & tenure • Owner occupier/renter • Owned outright • Equity • Behavioural & transactional • Online browsing • Buying behaviour • Donation value • Subscriptions • Contact • Infill information • Email • Phone (Landline or mobile) • Demographic & lifestyle • Date of birth • Income • Household composition • Interests

6. Length of residence Charitable donations £££ Household composition Age Useful variables Preferred channel Property tenure Newspaper readership

7. 3. Application

8. Segmentation and Profiling • Age is integral to profiling, targeting and also applying supporter segmentations • Geodems also provide useful profiling for supporters and can be used to link online, market, non supporters and supporters

9. Data now available at person and household level The Importance Of Postal Geography Household Mr & Mrs Fowler 22 million households Postcode BS8 4RU 1.6 million postcodes 15 households in each Postal Sector BS8 4 9,000 sectors 2,600 households in each Postal District BS8 2,700 districts 8,600 households in each Postal Area BS 120 areas 194,000 households in each

10. Free data!

11. GOSH • Liquid Assets (household) • Household income (household) • Lifestage (household) • Experian’s Mosaic (household) • Age (individual) • Location data http://data.gov.uk/dataset/os-code-point-open

12. Cash & Regular Giving • External data - adds depth • Understand who your supporters are • Understand how they may behave • Determine next best action • Predictive modelling • Past behaviour > geodems (usually) • External data most useful when little behaviour • New recruits (no past to track) • Reactivation (No recent behaviour – are they still active elsewhere?)

13. Cash & Regular Giving • GOSH Experience • Appending internal survey data • Motivations • Attitudes • Interests • After behaviour Liquid assets is one of the biggest drivers

14. Events • Locality to event • Use open code-point and the Pythagorean theorem • Age – lifestage • Drive time

15. Legacy • TARGETTING • Those who are warmest to you (longevity and activeness of support) • Age • TIMING Identifying life changing -> Will rewrite • Buy house • Have a family • Spouse death • VALUE • Family composition • Value of assets

16. High Value • High value profiling – “Action Planning” and “Factory” profiling • Information on wealth, disposable income, director, individual or partner • Combine with behaviour

17. Charity Shop Networks • Create “Town Types” using Acorn • Different stock offerings for different Town Types

18. Financial Products • Insurance – Pet, Home, Motor, Travel • Funeral Plan • Credit cards • Equity release

19. Online & Social • Email appending • Twitter handles • Facebook flag • Inmem & tribute • Event • Hitwise profiling

20. Other questions

21. What should you consider? How will you use it? What do you need from your supplier? Cost What is the aim? How has the data been collected & how long ago? How quickly will the investment payback? What codes do you need? Which records must be appended? Can billing be staggered? What supporting information is provided? What level of data is practical? Could you club together with another charity? What is the likely match rate?

22. What does it cost? Cost variables Level (postcode/household) Number of variables Type of data Volume Costs range from £3,350 to append postcode level codes to 99,999 records or £58,275 for appending 100’s of lifestyle variables to millions of records

23. Data triggers • Treadmill of campaign • Feedback of data….. • Collecting & using VPI (Volunteered Personal Information) • Relevance of data to use…

24. Donor Lifecycle Analysis • 1st Donation • ROI • Media Effectiveness • Campaign • Repeat Gift • Response • Recency • Frequency • Value • Complaint • Welcome • Value • Recency • Regular Gift • Frequency • Payment Method • Response • Committed Giving • Frequency • Value • LTV • High value donors • Value Bands • Upgrades • Loyalty • Value • Uplift • Complaint • Legacy • Gender • Location • LTV • Demographics • Major Gifts • Value/LTV • External Research • Demographics • Lapsed Donors • Recency • Frequency • Value

25. Postcode-level geo-demographics eg Acorn

26. Acorn profiler • Profiler is  • Underlying data also VERY useful, see below • Acorn data-set

27. Individual-level geo-dems e.g Ocean • Individual level data • More ‘attitudinal’ • Reflects the fact that all people • living in the same postcode will be • Different • Full listing of variables here

28. DISCUSSION • What geodems to people use and do they find them effective? • What variables have people found effective for targeting models? • What suppliers have people used for HV prospecting and how much success have they had in gennerating new high value prospects? • What other variables have people found useful to append to their data? • How have people used their own data/ collected data effectively? • What suppliers of data are good and how do you get the best deal? • What are the main challenges people find in completing their view of the customer.