customerintel direct social communication analyticsinfundraising_ludo_l_geertv
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Customer Intelligence
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Ludo LonginAdministrateur Délégué Direct Social Communications
My favourite song: Queen
Geert VerstraetenPartner at Python Predictions
My favourite song: Absynthe Minded
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Ludo LonginDirect Social Communications
How knowing donors helps in growing donors
Geert VerstraetenPython Predictions
Analytics in Fundraising
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= communication agency
°1985
1 activity:fundraising for humanitarian organisations
Team:12 enthousiastic people
Direct Social Communications nv
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Health FoodBelgium Belgian Assocation for Burn Injuries
Belgian Cystic Fibrosis AssocationKom op Tegen Kanker (Cancer Assoc.)
Food BanksRestaurants of the Heart
World Mercy ShipsChain of HopeDamien FoundationMedics Without Vacation
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Children AnimalsBelgium Collective Research and Expression
Youth VillagePelicano Foundation
World If Child Help Veterinarians Without Borders
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Handicap PeopleBelgium Blind Care Light & Love Flemish Autism Association
World Handicap InternationalSensorial Handicap Cooperation
Pilots Without BordersMamas for AfricaThe Voice of the Lebanese WomenFriends of Sister Emmanuelle
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± 400 fundraising campaigns using dm
> 8,000,000 letters to private individuals
> 1,500,000 inserts in newspapers
> 600,000 donations per year
> 24,000,000 euros
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Female
60+ years old
religious
Fully owned appartement/house
Adult children have left the house
Typical dm-donor in Belgium
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Response rate in housemailing:◦ 8 %
◦ 10 %
◦ 12 %
◦ 14 %
◦ More than 15 %
Normal results of dm-campaigns
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Response rate in Acquisition campaigns:◦ 2 %
◦ 2,5 %
◦ 3 %
◦ 4 or More %
Normal results of dm-campaigns
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Recruitment campaignsWhere do we get our donors from?
D.S.C.
± 220,000addresses
CONSUDATA+ criteria
6 millionaddresses
Otherdatabases
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Our Prospection ApproachCustomer/Donor Cloning
Use of analytics: advanced (predictive models, similarity models)
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Core business: Predictive AnalyticsSince 2006Based in BrusselsReferences:
Python Predictions
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STEP Select the best donors:
Cloning in a Nutshell
1
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STEP Calculate area conversion rate:
Cloning in a Nutshell
2
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STEP Calculate area profile
Cloning in a Nutshell
3Residents
Housing
Neighbourhood
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STEP Build Predictive Model
Cloning in a Nutshell
4Residents
Housing
Neighbourhood
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0,0%
1,0%
2,0%
3,0%
4,0%
0 50000 100000 150000 200000 250000 300000
CloningGeo SegmentationAge
Number of Households Targeted
Response
STEP Validate Predictions
Cloning in a Nutshell
5
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0%1%2%3%4%5%6%7%8%9%
0‐4 5‐9 10‐14 15‐19 20‐24 25‐29 30‐34 35‐39 40‐44 45‐49 50‐54 55‐59 60‐64 65‐69 70‐74 75‐79 80‐84 86‐89 90‐94 >95
Age categoryHouseholds we would not target
Households we would target
STEP Validate Predictions
Cloning in a Nutshell
5
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8%26%
0 1 2 or more
Number of bathrooms
9%
21%
N/A < 35 m2 35 till54 m2
55 till84 m2
85 till104 m2
105 till124 m2
> 125m2
House surface
STEP Validate Predictions
Cloning in a Nutshell
5 Households we would not target
Households we would target
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1,98% 2,29%2,71%
Age > 55y CloningTop 100.000
CloningTop 10.000
Response Percentage
21,6 €25,5 € 28,6 €
Age > 55y CloningTop 100.000
CloningTop 10.000
Average Donation Amount
improvement of 37% in response rate
improvement of 32% in donation amount
Campaign Results (First Test)
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0,43 €0,58 €
0,78 €
Age > 55y CloningTop 100.000
CloningTop 10.000
Break(revenue per letter sent)
improvement of 82% in revenue per letter sent
Current and Future Usage
Campaign Results (First Test)
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Geert [email protected]
Tel +32 2 762 69 00
Python Predictionswww.pythonpredictions.com
QUESTIONS? Or later?
Ludo [email protected] +32 2 280 00 74
Direct Social Communicationswww.dsc.be