CSC-EBG-Digital Channel Management

CSC Proprietary and Confidential 1May 12, 2016
CSC - EBG
Digital Channel Management Marcelo Baptista

Context – Are You learning from Your Digital Channels?
A common challenge for most of the companies is to convert access from their
traditional channels to their web site, or mobile apps, however there are some
important questions to be consider along this transition:
• Am I collecting behavior data from my digital
channels?
• Is there a process to consolidate this data in one
single federative DB?
• Is there a capability to segment my costumer by their
digital trial in my eco-system?
• Are my digital channels life cycles considering this
information?
• Am I feeding back this behavior information into my
channels?

Digital Strategy contextualization based on customer behavior
in a Bank environment…...
Branches Internet
Banking
ATM Call Center Mobile Wearables Comunication
Technology Customer
Marketing
Risks Predective Benchmark
Market
Cloud
Info
Decision
Comunication

• Focus on how to shift digital channel consumption from
adoption to engagement , according to customers behavior
• Support our clients to understand customer’s “digital-self” and
convert this awareness into accurate actions
Our Value Proposition

Digital Channel Life Cycle
• Adoption – Intention to Use: 90% of the apps are
opened just once. Adoption is the intention to use an
app, but not necessary, a relationship creation between
the app and the user. At this stage, technical monitor
such as downloads, sessions, time to logon,
transactions performance, crashes are very important
• Engagement – Recurrent Use: Is the recurrent use of
the App. How the user is consuming business
transactions and how the stability (?) contributes to
gain trust on the app, for instance, the app provides
business transactions but the users only are just
querying information.
• Evolution – Feed back the Channel: By incorporating
new features based on channel usage
Adoption
Engagement
Evolution
ROADMAP
• Comparative between
Sessions vs. Download
• Recurrence use
calculation
• Dropout rate
• Conversion rate for
business transactions
• App errors
• Downloads
• Sessions Numbers
• Transactions
Performance
• Logon time
• Crash by Business
Transactions
• e
• Transform feedback
into features
• New Features
Recommendations
• Focus on user
continues satisfaction
• Crossing data with
other web channels /
call center
Goals
App Lifecycle ... Belongs to the user

Solution View and Scope
We believe that Digital Channel Management represents only part of the cycle
Our offer aims to enable our clients to consolidate traditional and digital profile in
order to feed back their channel itself….
Marketing
Campaign
Digital
Channel (mobile)
Monitoring
Customer Profile
Federative View
Push Notifications Management
Services already being provided
to Santander Bank
Under Development for
Santander Bank

Architecture
Our platform runs in AWS and collects in a automated way raw data provided by a MBaaS
platform

Digital Channel Management front-end

CSC Proprietary and ConfidentialCSC Proprietary and Confidential
Deliverable
Examples
Santander Case

Transfers to
another accounts
18.63%
16.79
%
16.79
%
15.99
%
15.73
%
17.05
%
10.05
%
7.60%
Monday Tuesday
Wednesday Thursday
Since the application release, the days
of bigger use are on:
Monday, Tuesday and Fridays
Executive summary
Weekly Average Use
Usage TimeInstallations Sessions
15.204
3.712
703.914
301.281
2.44 min
2.12 min
Biggest financial transactions in the week (all systems)
Payment of Bills
37.85%
Payment of Credit
card
11.57%
 The average of installations and sessions in all systems have decreased on this week.
 Payment of bills increased 5% in relation to the last week. The payment of credit card came back to
the third position in the place of credit to cell phone that decreased 2 positions, this behavior use to
happen in every month beginning.
Days of bigger use
827 18.734 2.14 min

 On November 12nd we identified a unusual high level of finance investments in all the systems, the
numbers achieved only on this date is similar to the average of an entire week
 The average response time per hour and day in all systems (slide 09) were very good during the
week. The areas represented in red color does’t represent gravity, they’re only the higher times
among those on a week with low times. Another point is that they indicate the period of time when
the application is more active.
 The requests with bigger time on slide 18 were caused by few occurrencies and didn’t impact the
performance.
Executive Summary

13,735
19,168 19,078
22,100 22,900
16,624
13,335
17,962 17,696
15,742 15,290
16,469
12,558
10,714
3,268
5,082 4,878 5,024 4,987
3,754 3,132
4,424 4,803 4,039 3,647 3,893
2,719 2,458
890 1,286 1,162 1,294 1,296 907 796 912 1,007
860 820 868 713 609
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
-
5,000
10,000
15,000
20,000
25,000
Android iOS Windows Phone Accumulated
Daily installations and Total Accumulated (iOS + Android + Windows Phone)
“Are people installing the application?”
Current Week installations (Nov/09th –
Nov/15th)
Total Accumulated (since Nov/2014)
132.414 7.412.550
 Presents the total of daily installations and total accumulated since the beginning of the project

428,824
817,008 816,969
937,769
1,066,086
714,863
493,785
855,063
880,871
767,531
731,933
801,856
507,676
382,467
166,321
361,255 346,078 376,344
402,160
224,585
190,366
365,833 396,139
343,056 325,537 338,159
191,583
148,660
11,259 20,370 20,966 24,103 26,845 17,344 13,704 22,218 23127 19415 19246 21292 14249 11589
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Android iOS Windows Phone
 Presents the daily sessions for Android.
Application Usage History (iOS + Android + Windows Phone)
“Has the application been used?”

1.36 1.35 1.36 1.34
1.42
1.35
1.22
1.35 1.33 1.31 1.32 1.29 1.26 1.25
2.02
2.21
2.18
2.24
2.23
2.11
2.34 2.22 2.21
2.33 2.37
2.16
2.00 2.02
1.59
2.07
1.59
2.11
2.72
2.12
2.91
2.66 2.7
2.61
2.79
2.48 2.4
2.33
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
Nov/02 Nov/03 Nov/04 Nov/05 Nov/06 Nov/07 Nov/08 Nov/09 Nov/10 Nov/11 Nov/12 Nov/13 Nov/14 Nov/15
Average Usage time (Minutes)(Android)
Average Usage time (Minutes)(iOS)
Average Usage time (Minutes)(Windows Phone)
 Presents the average usage time that each user keep on application and compares it with the average response time
experienced in each day.
Minutes
Average Usage Time and Response to Requests
“is the app performing well and with constant use?”
Seconds

Average response time by hour and week day
“is the application performing well?”
 Presents the average response time for each system separated by day and hour in order to identify the best and the worst
perfomace hours and improve an investigation.
Average response time by hour and day – Nov/09th – Nov/15th

0.25%
0.32%
0.26%
0.30%
0.24%
0.20%
0.25%
0.35%
0.38%
0.24% 0.23% 0.22%
0.17%
0.23%
0.42%
0.49%
0.44%
0.48% 0.49%
0.47% 0.46%
0.58% 0.61%
0.42% 0.40% 0.42%
0.37% 0.39%
0.00% 0.00% 0.00% 0.01% 0.00% 0.01% 0.00% 0.01% 0.01% 0.00% 0.00% 0.01% 0.00% 0.01%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
% iOS % Android % Windows Phone
 Presents the percentage of finance transactions by system in relation to all transactions in the application independent if they’re
financial or not. (iOS + Android + Windows Phone).
% of Financial Transactions by system
“Are the users putting Money with the application?”

CHECK
BALANCE
12.68%
(197.721)
Logged in
82.33% (1.559.125)
Financial Transaction Flow - Android Nov/09 – Nov/15
Map visualized
55.28% (1.059.986)
App started
100% (2.027.397)
Logged: 1.559.125
Prepared profile
93.91% (1.893.839)
TRANSFERS
1.04% (16.207)
PAYMENT OF BILLS
4.23% (65.909)
Prepared
100%
(17.705)
Concluded
91.54%
(16.207)
Prepared
100%
(70.500)
Concluded
93.49% (65.909)
Prepared
100%
(24.431)
Concluded
95.02%
(23.215)
CELL PHONE CREDIT
1.49% (23.215)
SAVINGS
0.44% (6.828)
LOAN
0.01% (230)
Prepared
100% (7.232)
Concluded
94.41%
(6.828)
Prepared
100% (3775)
Concluded
6.09% (230)
PAYMENT OF
CREDIT CARD
1.06% (16.586)
Prepared
100%
(17.552)
Concluded
94.50% (16.586)
Logged: 1.559.125
INVESTMENTS
1.06% (16.586)
Prepared
100%
(17.552)
Concluded
94.50% (16.586)

Login Effectiveness (%)
 Presents the login success percentage by two different views: weekly and monthly. The LOGIN EFFECTIVENESS means how
many users had success to login after to click in the application and pass for all steps until the main screen.
0
10
20
30
40 50 60
70
80
90
100
Login Android
82,3%
(From 1.8M access)
De
5.8 M
De
6.0 M
De
4.8 M
De
4.2 M De
8.7M
De
6.6 M
0
10
20
30
40 50 60
70
80
90
100
Login Windows Phone
96.2%
(From 29.4 K access)
90% 99%
82%
88% 79% 96%
0% 0%
95%
0%
50%
100%
150%
Aug/2015 Sep/2015 Oct/2015
Login Android
From:
34.8 K
From:
2.5 M
From:
5.4 M
From:
644 K
From:
6.8 M
From:
4.5 M From:
4.4 M
0
10
20
30
40 50 60
70
80
90
100
Login iOS
96.3%
(From 662.3K access)
Period: Nov/09 – Nov/15
Monthly View

Technical View

History of Average Load Time by Network (seconds)- Android
 Presents how long the application takes to start until to show the main screen.
13.38 13.6 13.58 13.81 13.66
13.36
12.04
13.23 13.26 13.22 13.46 13.38 13.77 14.22
4.87
5.14 5.06 5.18 5.21 4.88 4.48 4.93 4.82 5 4.97 4.86 4.64 4.6
3.05 3.2 3.18 3.23 3.25 3.03 2.86 3.16 3.06 3.09 3.1 2.99 2.88 2.87
1.14 1.14 1.22 1.24 1.22 1.18 1.03 1.16 1.18 1.19 1.11 1.04 1.02 1.050
2
4
6
8
10
12
14
16
2G 3G
22.37 22.23 21.87 21.56
19.14
13.718.51 8.56 8.55 8.57 7.49 5.086.12 6.36 6.44 6.35 5.27 3.192.67 2.96 2.76 2.88 2.45 1.18
0
15
May/15 Jun/15 Jul/15 Aug/15 Sep/15 Oct/15
Average by month
2G 3G 3G+ 4G
Seconds
Seconds

Performance by network – ResponseTime - Android

Top 10 Requests with bigger Response Time: Android
3.58 getPublicKey/
getPublicKey
buscarFavorecidosOutrosBancos
simularAlterarProgramacaoRecar.
01
listaTodosBancos/
02
03
04
05
buscarFavorecidosSantander
simularCancelarContratoEmpre.
validarSessao
faturaPagamentoConsultar.
buscarPorCoordenada
06
07
08
09
10
2.9
2.59
2.24
2.13
1.94
1.78
1.72
1.69
1.66
TOP 10 average response Time (seconds) in the week – Nov/09 – Nov/15
27.59
4.56 3.61 3.02 2.92 2.50 2.46 2.39 2.17 2.06
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Oct
getPublicKey/ pagamentoTituloCartorio
recargaImp simularAlterarProgramacaoRecarga
simularCancelarContratoEmprestimo simularResgate
Top 10 Average Response Time (seconds) with Higher time by Month
Seconds

Active users X Heavy Users
“How many users use Supermóvil constantly?”

Crashes
Reports

Total Crashes – Android (Top 20)
321 297
258 278 268 272 263
279 285 281 288 295 301 308
0
100
200
300
0
100
200
300
400
500
600
Oct/06 Oct/13 Oct/20 Oct/27 Nov/04 Nov/10 Nov/17
Number of Crashes X Life time
Number of Crashes Age of Crashes (days)
645,661 654,727 655,261 655,751
656,348 657,293 658,157
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
Total of users affected
Total users affected

Life Time – Crash 01
java.lang.IllegalArgumentException unresolved View=com.android.internal.policy.impl.PhoneWindow$Decor
View{41f8e170 V.E..... R.....ID 0...
97% 2% 1% 4.4.
0
51% 49% 0%
4.4.2
4.4.4
4.4.3
SM-G530BT GT-I9192 XT1025 SM-G530H XT-1022Most affected Devices *
CRASH 01
Most Affected
Operational System *
* Values for the last 30 days.
Most Affected App
Versions *
88,543
88,633 88,689 88,750 88,804 88,859 88,908
13.59% 13.63% 13.53% 13.54% 13.55% 13.56% 13.53%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
-
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
100,000
Users affected

Total Crashes – iOS (Top 20)
4,464 4,605
4,482
4,049 3,802 3,735 3,798
287.05 294.05 301.05 308.05 287.86 293.86 300.86
-100
100
300
500
700
0
1,000
2,000
3,000
4,000
5,000
Number of crashes X Life time
Number of Crashes Age of Crashes (days)
231,003 231,190 231,416 233,118 238,265 233,118 236,283
0
40,000
80,000
120,000
160,000
200,000
240,000
280,000
320,000
360,000
400,000
Total of users affected
Total users…

Life Time – Crash 01
43,291 43,301 43,316 43,324 43,330 43,334 43,344
18.74% 18.73% 18.72% 18.58% 18.28% 18.28% 18.34%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
22.00%
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Users affected
NSRangeException unresolved *** -[__NSArrayM objectAtIndex:]: index 0 beyond bounds for empty array
100% 1.0
31% 18% 11% 40%
8.3
5 GSM+LTE 6
5s
CDMA+LTE 4s
5s
GSM+LTEMost affected Devices *
CRASH 01
Most Affected
Operational System
*
* Values for the last 30 days.
Most Affected App
Versions *

Crashes Occurrences by Device – Daily View
Nov/0
3
Nov/0
4
Nov/0
5
Nov/0
6
Nov/0
7
Nov/0
8
Nov/0
9
Nov/1
0
Nov/1
1
Nov/1
2
Nov/1
3
Nov/1
4
Nov/1
5
Nov/1
6
GT-I8552B 2 8 9 11 5 7 14 8 7 0 5 5 9 6
XT1069 12 7 16 29 11 8 16 12 19 11 16 8 2 12
XT1033 29 26 28 42 14 16 36 33 27 35 21 21 13 21
XT1068 12 8 14 16 6 9 6 7 17 3 9 4 3 8
SM-G530BT 10 10 17 12 15 10 11 15 10 12 9 10 3 10
0
5
10
15
20
25
30
35
40
45

% of Crashes Occurrences by Device – Monthly View
46
40%
24
21%
20
17%
19
16%7
6%
XT1033
XT1069
SM-G530BT
XT1068
GT-I8552B
Devices
0
5000
10000
15000
Apr/15 May/15 Jun/15 Jul/15 Aug/15 Sep/15 Oct/15 Nov/15
GT-I8552B
XT1069
XT1033
XT1068
SM-G530BT
Device
s

Crashes Occurrences by Device – Daily view
Nov/0
3
Nov/0
4
Nov/0
5
Nov/0
6
Nov/0
7
Nov/0
8
Nov/0
9
Nov/1
0
Nov/1
1
Nov/1
2
Nov/1
3
Nov/1
4
Nov/1
5
Nov/1
6
4S 331 377 206 463 201 177 395 416 291 315 307 145 150 307
5 GSM+LET 235 183 230 186 69 95 212 205 181 168 157 66 70 177
5S CDMA+LTE 303 283 310 301 152 109 310 284 232 231 256 118 110 222
6 547 416 417 432 158 151 404 452 374 338 400 148 126 409
5C CDMA+LTE 130 103 150 144 66 80 138 124 107 109 112 71 43 104
0
100
200
300
400
500
600

% of Crashes Occurrences by Device – Monthly View
2.760
29%
2.399
26%
1.980
21%
1.343
14%
893
10%
6
4S
5S CDMA+LTE
5 GSM+LTE
5c CDMA+LTE
Devices
0
2000
4000
6000
8000
10000
12000
14000
16000
4S
5 GSM+LET
5S CDMA+LTE
6
5C CDMA+LTE
Devices

Crashes Occurrences by Operational System – Daily View
Nov/0
3
Nov/0
4
Nov/0
5
Nov/0
6
Nov/0
7
Nov/0
8
Nov/0
9
Nov/1
0
Nov/1
1
Nov/1
2
Nov/1
3
Nov/1
4
Nov/1
5
Nov/1
6
4.2.2 0 0 0 0 0 0 0 65 51 69 100 47 38 73
4.4.2 74 66 86 82 59 41 57 65 0 0 50 39 36 0
4.1.2 50 72 77 82 28 50 66 73 49 62 48 38 21 52
4.3 0 0 0 0 0 0 0 30 47 23 32 14 12 30
5.0 0 0 0 0 0 0 0 27 32 35 0 0 0 21
5.1 37 30 33 45 12 15 30 0 10 13 27 22 6 16
0
20
40
60
80
100

% of Crashes Occurrences by Operational System – Monthly View
0
5000
10000
15000
20000
25000
30000
35000
40000
4.4.2
4.1.2
4.4.4
5.0.2
5.1
5.1.1
OS
Versi
on
144
29%
124
25%
110
23%
58
12%53
11% 123
3%
4.4.2
4.4.4
4.1.2
5.0.2
5.1
5.1.1
OS Version

Crashes Occurrences by Operational System – Daily View
Nov/0
3
Nov/0
4
Nov/0
5
Nov/0
6
Nov/0
7
Nov/0
8
Nov/0
9
Nov/1
0
Nov/1
1
Nov/1
2
Nov/1
3
Nov/1
4
Nov/1
5
Nov/1
6
7.1.2 429 442 527 586 232 238 590 523 402 356 372 191 187 384
8.3 66 58 61 51 19 21 75 63 49 61 46 17 21 51
8.4.1 75 67 54 41 27 23 49 61 52 57 51 24 18 43
9.02 176 126 131 118 52 54 84 101 71 65 88 29 26 52
9.1 1070 965 1014 1044 450 434 1076 1067 902 821 926 432 369 919
0
200
400
600
800
1000
1200

% of Crashes Occurrences by Operational System - Monthly view
0
10000
20000
30000
40000
50000
7.1.2
8.3
8.4.1
9.0.2
9.1
OS
Version
1.629
56%
743
26%
3.686
7%
2.991
6%
2.319
4%
9.1
7.1.2
9.0.2
8.3
8.4.1
OS Version

Crashes - Highlights
Crashes Android
 Since the last application release for Android on September 18th, the amount of crashes reduced
considerably.
 The most part of Android crashes are concentrated in the follow versions: 4.4.2 (29%) and 4.4.4
(25%).
 The Devices XT-1033 (40%) and XT-1069 (21%) presents the most amount of crashes.
Crashes iOS
 Actually most part of iOS crashes are concentrated in operational system 9.1 (56%), but
compared with the previous versions, the amount of crashes reduced considerably.
 The devices iPhone 6 (29%) and iPhone 4s (26%) presents the most amount of crashes.

• We recommend to consider that mobile apps are the most used digital channel today. This represents an
enormous opportunity to profile your customers by monitoring your apps, considering the following cycle:
– Release Your App: The first release is just a bid, you think it will be downloaded and use (recurrent).
Same old “field of dreams syndrome”
– Monitor Your App: Collect raw data around downloads, sessions, crashes, functionalities.
– Generate Insights:
• Technical: To corrective maintenance. Remember: You control your app, but not the eco-system
where is installed.
• Business: Functionalities and Transactions funnel, heat map per location and schedule, are just a
sample of business insights you could consider in order to evolve your channel
– Learn: This is the best part! Where, When, How and what functionalities are being used from my
channel? This is the part of the life cycle where you realize that collecting tacit behavior from your users
give you the opportunity to contextualize the content for your channel, getting closer to 1:1 relationship.
– Release your NEW App: This time based on collective behavior.
Final Considerations – Are You learning from Your Digital
Channels?

CSC-EBG-Digital Channel Management

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