On Monday April 11 2016, we have demonstrated how we applied Process Mining to improve the customer experience in a crucial customer-facing process at ING Belgium. In this case we compare traditional (six sigma-style) approaches with more modern techniques to help ING Belgium serve its clients better. We will illustrate the benefits, milestones, requirements and potential pitfalls we encountered. Presentation held on the INFORMS conference on Business Analytics and Operations Research in Orlando (USA).

1. Process Mining – the road to a superior
customer experience
A case by ING Belgium and Python Predictions
Pieter Van Bouwel - Python Predictions
Wouter Buckinx - Python Predictions

2. What the fuss?
2

3. ING Belgium
3
Universal direct bank
2.4 mio active retail
customers
730 branches and an
expanded digital
network
Extremely digital
Extremely personal

4. ING Belgium & analytics
4
Human
Resources
Operations
Marketing
Risk
Analytical
Maturity

5. 5
SUBMIT ACCEPT OFFER SUCCES
• Complete documents
• Signatures
• Follow-up incomplete
submissions
• Complete request
• Assess application
• Seek additional
information
• Investigate for fraud
• Prepare a competitive
offer
• Application approval
• Signatures
• Complete file
A fictive example to get us started
Mortgage Loan: The Process
Operations

6. 6
Throw over the wall

7. Operational Excellence
7
 Focus on Customer Journey
 End 2 End measurement: from initial request until the client needs are satisfied
TODAYYESTERDAY
Measure subprocesses Measure E2E process capability
Monitor and control
Root cause analysis
Operations
 Define promises to customer

8. Process mining
8
“Process Mining helps organizations
to discover, monitor and improve
real processes by extracting
knowledge from event logs”
(Wil van der Aalst)

9. Process mining
9
Source: http://fluxicon.com

10. Other examples
10
Call center
Infrastructure
HealthcareAdministration
Production IT

11. 11
What are the symptoms?
Define
Improve
How to cure
our patient ?
How sick is our patient?
MeasureControl
Is our patient
healthier?
More info: cfr. Lean Six Sigma DMAIC circle
What explains
the symptoms?
Analyse

12. What are the symptoms?
12
 It’s not clear what ING
expects from me
 It takes too much time before
everything is up and running
 Who is actually taking care of
my request?
 Our customers do not provide us
with all the information we need
 Our customers do not sign the
documents correctly
 We acknowledge that there’s a
problem but lack the total picture
There’s a clear need to discover and measure the
end-2-end process as experienced by our customers
Point of view Customer Point of view ING

13. How sick is our patient?
13
PAC
EB
Marketing
data
platform
1 base table
holding information
on the process (steps,
times), customers
and resources
Process discoveryData preparation
and consolidation
LAMS
CCCV

14. How sick is our patient?
14
26%
23%
16%
11%
7%
5%
3% 2% 2% 1%
5%
5 10 15 20 25 30 35 40 45 50 > 50
Weekdays
Online Branch
Starting point1 Starting point2 Starting point3
Back Office1
Back Office2 Endpoint2
Endpoint3
Endpoint4
(1) (2) (3) (1)
(4)
(3)
(2)
Median weekdays
5 8
1
1 hr
2
5
1
5
Simplified process
The median End-2-
End throughput time
is 11 weekdays, but
there’s a high
variation
25%
% Files Closed

15. What explains the symptoms?
15
49%
17%
11%
7%
4% 3% 2% 2% 1% 1%
4%
5 10 15 20 25 30 35 40 45 50 > 50
First part: Process Start
There’s a lot of
variation, however
we lack the data
to further
investigate
Weekdays
% Files

16. Last part: Process End
What explains the symptoms?
16
90%
6% 2% 1% 0,4%
5 10 15 20 > 20
Weekdays
The process end
is predictable, no
use to further
analyse this part
% Files

17. 83%
14%
2% 1% 0,6%
4%
53%
20%
12% 14%
0 5 10 15 > 15
Weekdays
% First Time Right Files
% Non First Time Right Files
Middle part: Back office
What explains the symptoms?
17
The quality of
incoming files has
a high impact on
the back office
throughput time

18. What explains the symptoms?
18
2,2
6,4
9,7
14,5
17,6
0
2
4
6
8
10
12
14
16
18
20
0
(62,4%)
1
(28,2%)
2
(7,1%)
3
(1,5%)
4
(0,8%)
# Request Info Iterations
(% of files)
Each request info iteration adds
on average 4 days to the total
Back Office 1 throughput time
If a file requires
no extra info, it
takes on average
2,2 days from
branch until the
end of Back
Office 1 process
Average
Throughput Time
Back Office 1
(Weekdays)

19. 19
What explains the symptoms?
9,5% Non Second Time Right
15,7%
16,0%
7,8%
0% 5% 10% 15% 20%
Channel = Webtool
Branch Experience < 25%
Language = French
The incoming quality
can be explained by
different parameters

20. 20
What explains the symptoms?
9,5%
8,6% 15,7%
24% 11%
Channel
Paper (87%) Webtool (13%)
Sender Type
Branch / Other (8,4%)Client (4,5%)
The incoming quality
can be explained by a
combination of
parameters

21. How to cure our patient?
21
Symptoms Medication
 Our customers do not provide
us with all the information we
need
 Our customers do not sign
the documents correctly
 It takes too much time before
everything is up and running
 Who is actually taking care
of my request?
 It’s not clear what ING expects
from me
 Welcome team that will provide
additional support
 Set KPI on quality of the files and
not on number of files
 The order of products X and Y are
no longer done in the branches,
but in one back-office team
 One team is the owner of the
customer request from the start
until the very end
 This team is staffed by native
sales people that speak the
“language” of our customers

22. Is our patient healthier?
22
 Direct feedback will be gathered via
Customer Effort Score measurements
Point of view customer Point of view ING
 Set new smart Key Performance
Indicators on End-2-End Throughput
Time, Incoming and Outgoing quality
 Monitoring continuously to be able
to react immediately

23. Is our patient healthier?
23
56
49
44
52
56
62
56
49
58
48
44 45
26
14
41
33 32
39
42
39 39
35
40
34 33 33
21
12
12 11 11 13 13 11 13
10 10 12 12 12
7
7
% Files closed within ... weekdays
95% closed within … weekdays
90% closed within … weekdays
50% closed within … weekdays

24.  Effect = Quality of the solution X Acceptance of the solution
Criteria for Success
24
 Involvement of process experts and the different players in the
End-2-End process
 Ability to access and link the different data sources
 A pragmatic, but critical data scientist with business affection to do the data
preparation , discover the End-2-End process and find the key root causes.
 An Operational Excellence project manager with analytical skills that knows
the business, challenge the status quo and drives for high impact solutions

25.  Add aditional data elements to further complete the picture
 Link process data to customer feedback data
 Monitor and adjust when necessary
Plans for the future
25
On this particular case
 Run process mining exercises on other key customer facing processes
 A new exercise is in the pipeline, comparable to the one presented today
 Build up the data, tools, skills and a data driven mindset within the organisation
In general

26. 26
 Focus on Customer Journey
 End 2 End measurement: from initial request until the client needs are satisfied
TODAY TOMORROW
Measure E2E process capability Forecast E2E process capability
 Pro-active  reach promise
 transparency to customer
 Workload balancing
Plans for the future

27. 27
Personal learnings
Define
Improve
MeasureControl
Analyse
Methodology Tools Skills

28. 28
Pieter Dyserinck
Project Manager Operational Excellence
ING Belgium
pieter.dyserinck@ing.be
Pieter Van Bouwel
Senior Analyst
Python Predictions
pieter.vanbouwel@pythonpredictions.com
Wouter Buckinx
Managing Partner
Python Predictions
wouter.buckinx@pythonpredictions.com