The return on social intervention in family welfare customer life cycle, paper no. 43, jonna heliskoski, hall a, 23.9.2016

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

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!

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

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.

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.

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

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 %

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

Mod

el c

ases

Extr

eme

case

s

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

Th

e le

vel

of

the

tota

l co

sts

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

31 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.

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

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

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.

Model &

Motivate

Thank you for your attention.

Questions?