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The return on social intervention in family welfare customer life cycle, paper no. 43, jonna heliskoski, hall a, 23.9.2016
1. J O N N A H E L I S K O S K I
M . S C . ( E C O N ) , M B A , P H D C A N D I D A T E
H A N K E N S C H O O L O F E C O N O M I C S , H E L S I N K I , F I N L A N D
C O N F E R E N C E P A P E R N O . 4 3
E U R O P E A N S O C I A L M A R K E T I N G C O N F E R E N C E 2 0 1 6
The Return on social intervention in
family welfare customer life cycle
2. The Challenge The Solution
1. Increasingly unfavourable
dependency ratio.
2. Siloed and fragmented
welfare services.
3. Little knowledge of cost
generation.
4. Resources allocated to late
phase remedial services.
1. Improve cost efficiency in
resource allocation.
2. Approach welfare services
from a system level.
3. Analyse and model cost
generation.
4. Motivate earlier and cost
effective interventions.
Model to motivate
Model to motivate!
3. Presentation Outline
1. Background
2. Research
• Aim of the study
• Method and data
• Modelling
3. Conclusions
9 challenging situations in life
• Child welfare
• Family counselling
• Family and home help services
• Remedial youth work
• Services for the disabled
• Unemployment
• Problems with livelihood
• Serious illness
• Parenting deficit
• Divorce
• Substance abuse
• Mental problem
• Marginalization
• Education deficit
• Social work
• Mental health services
• Substance abuse services
• Health services
• Remedial and special education
325 families
10 categories of welfare services
4. Aim of the study
The aim of the study
An explorative study aiming to induce structures explaining and
predicting the cost generation in family welfare services in the City of
Varkaus.
Research questions
1. How do the families differ in their use of welfare services?
2. Are there any conventionalities explaining the differences?
3. If there are conventionalities, how could they be used (A) to segment
customer groups, (B) to target interventions, and (C) to calculate the
return on social intervention.
5. Research method and data
Method
— Survey research conducted September 2015 in the City of Varkaus.
— Correlation and cluster analyses where used to explore the research
questions.
Data
— Random sample of 325 families using at least one of the welfare services.
— The surveyed categorical data explored:
Ø The use of 33 social, educational, and health services.
Ø The presence of 9 different challenging situations in life.
Ø The length of the use of welfare services in years.
6. K-Means clustering Profiling clusters
Induced customer groups
Indicative
(0-2 years)
Extensive
(3-6 years)
Settled
(6+ years)
The use of welfare services
Challenging situations in life
Cluster 1
“Indicative”
Cluster 2
“Extensive”
Cluster 3
“Settled”
The length of the
customer
relationship in years
0 ≤ x ≤ 2 2 < x ≤ 6 x > 6
Average number of
services
1.9 5.4 2.9
Number of families 188 51 85
The share of families .58 .16 .26
7. The total costs generated by
the extreme and the model
cases.
The number of extreme
and model cases.
The structure of the customer base
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Indicative
(0-2 years)
Extensive
(3-6 years)
Settled
(6+ years)
Extreme cases
Model cases
The length of the customer relationship in years
5 %
47 %
X % Share of all cases
6 %
16 %
26 %
8 %
8 %
6 %
23 %
55 %
8. Customer segments
Indicative (0-2 years) Extensive (3-6 years) Settled (6+ years)
Segment 2
5 % of customers
8 % of costs
16 000 € per year
Segment 1
47 % of customers
8 % of costs
1 600 € per year
Segment 4
6 % of customers
23 % of costs
31 500 € per year
Segment 3
16 % of customers
6 % of costs
3 100 € per year
Segment 5
26 % of customers
55 % of costs
19 600 € per year
ModelcasesExtremecases
T h e l e n g t h o f t h e c u s t o m e r r e l a t i o n s h i p
Thelevelofthetotalcosts
9. Intervention points, themes, and objectives
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Indicative
(0-2 years)
Extensive
(3-6 years)
Settled
(6+ years)
The length of the customer relationship in years
Indicative customers (S1+S2)
Customer identification and early
intervention
Extensive customers (S3+S4)
Relationship management
Settled customers (S5)
Controlled ending of the customer
relationship
1
2
3
1 2 3
Extreme cases
Model cases
The objective of the interventions is to minimize the total value
of the customer base on both the medium and the long term basis.
This is done by (1) reducing the total costs of services used by
targeted customers and by (2) shortening the length of the
customer relationship.
10. Phase 1
Create the intervention
Example
Family coach service concept
1. Create intervention service concept.
2. Estimate intervention unit costs.
3. Set intervention objectives.
4. Decide the level of interventions.
5. Target interventions to customer
segments.
1. Family coach service concept.
2. 2 300 € per coach per year.
3. 10 % of yearly cost reductions
and shortened length of the
customer relationship in 20 % of
the cases.
4. 15 family coaches working with
300 families in a 10 year time
period.
5. Family coaches are allocated to
work with 120 (40 %) indicative,
120 extensive (40 %), and 60 (20 %)
settled target families.
Modelling the return on social intervention 1/2
11. 6 000 000 €
7 000 000 €
8 000 000 €
9 000 000 €
10 000 000 €
11 000 000 €
12 000 000 €
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Phase 2
Calculate the impact
Example
Family coach service concept
6. Projections of yearly savings
from targeted interventions (€)
6. Calculate and illustrate, how the
interventions affect the value of the
customer base.
7. Calculate the return on social
intervention (ROSI).
Return on social intervention (ROSI) =
(cost savings from targeted interventions
– cost of interventions) /
cost of interventions
Total costs without interventions
Total costs with interventions
7. ROSI1 year = - 112 %
ROSI5 years = 42 %
ROSI10 years = 126 %
Modelling the return on social intervention 2/2
12. Conclusions
— This study presents a new approach to analyzing, modeling, and
calculating the return on social intervention (ROSI).
— The results from this study can be used to
¡ analyze and model welfare service cost generation from a customer
life cycle perspective.
¡ predict how social interventions impact the total value of the
customer base over time.
¡ motivate welfare promotion and early intervention.
— This research contributes theoretically to service system, market
segmentation, and social marketing research areas.
