Presentation on theme: "Data Enhancement Panel. 1. What do we mean by data enhancement? Making more of your data – Cleaning deduping – Contact data e.g. email, suppressions Widening."— Presentation transcript:
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
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
Individual, household & postcode level Real & modelled Demographic & lifestyle Date of birth Income Household composition Interests Contact Infill information Email Phone (Landline or mobile) Behavioural & transactional Online browsing Buying behaviour Donation value Subscriptions Locational Distance to nearest store or town TV or radio region Property value & tenure Owner occupier/renter Owned outright Equity
Age Household composition Charitable donations Property tenure Length of residence Preferred channel £££ Useful variables Newspaper readership
Segmentation and Profiling Mosaic NameMosaic Description% Supporters % UK Index vs. UK Rural SolitudeResidents of isolated rural communities4.9%4.5% 110 Small Town DiversityResidents of small and mid-sized towns with strong local roots9.1% 99 Alpha TerritoryWealthy people living in the most sought after neighbourhoods5.2%3.6% 144 Professional RewardsSuccessful professionals living in suburban or semi-rural homes11.6%9.0% 129 Suburban MindsetsMiddle income families living in moderate suburban semis13.3%12.0% 111 Careers & KidsCouples with young children in comfortable modern housing7.7%5.5% 140 Liberal OpinionsYoung, well-educated city dwellers10.1%8.9% 113 New HomemakersCouples and young singles in small modern starter homes4.7%4.3% 110 Terraced Melting PotLower income workers in urban terraces in often diverse areas5.8%7.3% 79 Industrial HeritageOwner occupiers in older-style housing in ex-industrial areas7.5%7.9% 96 Ex-Council CommunityResidents with sufficient incomes in right-to-buy council houses7.0%9.4% 75 Active RetirementActive elderly people living in pleasant retirement locations3.9%4.0% 97 Elderly NeedsElderly people reliant on state support3.2%4.6% 70 Upper Floor LivingYoung people renting flats in high density social housing3.0%4.8% 63 Claimant CulturesFamilies in low-rise council housing with high levels of benefit need3.1%5.2% 59 100.0% 100 Age Band % Female UK Penetration % Male UK Penetration % Overall UK Penetration 0-143.9%0.1%2.0% 15-2417.3%1.5%9.2% 25-3429.0%4.3%16.8% 35-4428.4%6.0%17.5% 45-5924.4%6.8%15.9% 60-6422.6%8.3%15.9% 65-7416.2%8.1%12.7% 75+15.8%13.0%15.1% 23.7%7.7%16.4% 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
The Importance Of Postal Geography 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 Household Mr & Mrs Fowler 22 million households Data now available at person and household level
GOSH Liquid Assets (household) Household income (household) Lifestage (household) Experians Mosaic (household) Age (individual) Location data http://data.gov.uk/dataset/os-code-point-open http://data.gov.uk/dataset/os-code-point-open
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?)
Cash & Regular Giving GOSH Experience Appending internal survey data – Motivations – Attitudes – Interests After behaviour Liquid assets is one of the biggest drivers
Events Locality to event – Use open code-point and the Pythagorean theorem – Age – lifestage – Drive time
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
High Value High value profiling – Action Planning and Factory profiling Information on wealth, disposable income, director, individual or partner Combine with behaviour
Charity Shop Networks Create Town Types using Acorn Different stock offerings for different Town Types
What should you consider? What do you need from your supplier? How will you use it? What codes do you need? Which records must be appended? How has the data been collected & how long ago? What is the aim? What supporting information is provided? Cost Can billing be staggered? How quickly will the investment payback? Could you club together with another charity? What level of data is practical? What is the likely match rate?
What does it cost? Cost variables Volume Type of data Level (postcode/household) Number of variables Costs range from £3,350 to append postcode level codes to 99,999 records or £58,275 for appending 100s of lifestyle variables to millions of records
Data triggers Treadmill of campaign Feedback of data….. Collecting & using VPI (Volunteered Personal Information) Relevance of data to use…
Donor Lifecycle Analysis 1st Donation ROI Media Effectiveness Campaign Welcome Value Recency Repeat Gift Response Recency Frequency Value Complaint 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
Acorn profiler Profiler is Underlying data also VERY useful, see below Acorn data-set
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 herehere
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.
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