Big Data Analytics for TELCOs Customer Experience Management Permission Based Marketing for Location and Movement Data Data Modelling Business Use Cases Data Mining BSS OSS COTS OTT Churm Modeling Markov Processes HANA HADOOP INtegration Video Streaming Test cases

1. TELCO Big Data Churn Analytics
Identifying revenue growth opportunities and
strengthening CEM customer retention policy
BSS OSS COTS OTT CHURN DATA MODELING DATABASE CREATION
ACCURATE CHURN PREDICTION USING MARKOV PROCESS CHAINS
Prepared and presented by
Prof. Dr. Dipl. Wirtsch. Ing. Mehmet Erdas
MBA B.Sc. M.Sc. METU Ph.D. TU Braunschweig Germany
mehmeterdas@outlook.com
erdasmehmet23@gmail.com
Mobile: +49 (0)1789035440
+43(0)6509111090
+90(0)5374154413
1

2. 2

3. 1. Objectives
2. Revenue growth and retention – our scope of work 2015
3. How to participate; Our reference architecture – guide
Identify Use Cases& Define the Business Use Cases, KPIs
3.1 Upsell
3.2 Cross Sell
3.3 Retain Customers: Churn Minimization
4. LOCATION
5. MOVEMENT
6. NEXT BEST COURSE OF ACTION TIMING&SPEC.
7. Movement Solution Scope
8. Role Based HR Project Resourcing and Budgeting
9. Use Case Identification SLA VIP etc..(by Presentation)
3

4. Why Big Data Analytics CEM Churn Project?
We will drive the development of appropriate technology and
steer technology & Service Quality delivery models for new
services and products based on deep profile customer
inspection/experience i.e big data subscriber profile involving
social networks and word of mouth after integrating structured
and unstructured data using in-memory HANA and Hadoop MR
1. Mobile operator agrees to participate in
use case focused workshops
 Mobile operator supplies customer data
samples
 Customer identity encrypted by operator
2. Provider builds and deploys operational
prototype for one or more of the use
case listed
 Operator can validate business value
3. Provider and operator agree solution,
service and technology roadmap
4

5. 1. Customers benefit from the data they
Generate: Permission Based Marketing
 My mobile operator treats me as a person, not a KPI.
 They will try to understand what is important/relevant for me
- they will genuinely offer me the best deals.
 This includes not just their own services, but they will find and
provide me access to the best deals out there that improve my
life.
 Perhaps an easier/more economical route to work. Perhaps
access to a pay as you use car insurance scheme, or a life
insurance scheme that takes into account the amount of
sleep, exercise that I routinely partake of etc.
2. Operators generate more value for
customers by new APPS: CONTENT_
META_MASTER _TRANSACTIONAL DATA
 All commercial business needs to generate profit.
 There are two philosophies on this:
Inside-out:
We reduce costs, increase revenues, profit is the difference
between the two.
Outside-in
We generate value for our customers, profit is the natural
consequence of this.
5

6. The challenge – right data!
1. Volume, Variety, Veracity, Velocity
χ It is neither possible nor beneficial to store all
data.
 It is important to store the right data: First
Achieve the Highest Data Quality Measures
2. Value
 To identify the right data, experts are
required.structured
un-structured
Data
Tsunami
Continuous Ingestion Continuous Queries /Analytics on
data in motion
$
$$$
Right Data
= Profit
Big Data
= Cost
6

7. Our Big Data solution roadmap proposal
2015
2016
2017 • .
2018
Data Workloads
Scope Def.n
Analytics
Platf. Spec.
HANA Hadoop Sys Int.
Automation
Processing
structured&unstructuredd
ata combined
7

8. 1. We are strategically committed to help
our customers increase their
profitability
2. In support of this we will present an
overview of the work programs that we
are under taking in 2014 that focuses on
revenue growth and customer
retention
3. Reference Primer Architecture
We hope to solicit feedback from key
TELCO customers and identify
customers that are willing to participate
in a joint work program next year - 2015
1. OBJECTIVES
8

9. 1. Tradition/Off-line use cases
 Up-sell based on usage analysis – i.e. sell the
customer more of what they already consume
 Cross-sell based on usage analysis – i.e. sell the
customer additional products and features
 Targeted retention of existing customers based
on churn analysis
2. Next generation/On-line uses cases
 Targeted marketing of customer segments based
on location through event calendar correlation
 Targeted marketing of customer segments based
on movement along a transportation corridor
 Enhanced customer care handling through next
best action suggestion
2. Revenue growth and retention
- business focused use cases
9

10. 10

11. Next generation
technology
Traditional
technology
Event Factory
Statistical and
Mathematical
Functions
Raw Data
Reader
Sockets
Event
Writer
Sockets
Web based
Graphical
Context
Data production
Analytic
Database
 Unpredictable Queries
 High Responsivesness
Data analyticsCollection
Filtering
Enrichment
Event Correlation
Event Aggregation
Network
Mgt - Stats
Device
Inventory
Network
Inventory
Data Presentation
 Dashboard
 Report
Production
TT,
Workflow
CDR, Logs
NE
UE
Probes
IOT Sensors
Short lived data
BSS
Immediate
User Equipment
Configuration Mgt
NW Policy Control
Equipment
Notification API
Implementatio
n
Event
Driven
Rules Logic
Data
Automation
Service Subscription
Databases
 Predicate based
Group/Set Logic
Periodic
 Fast Retrieval Option
 Standard Retrieval Option
Data storage
 Real-time
Streaming
Immediate
Immediate
On-demand
On-demand
Periodic
Periodic
Immediate
 Consolidation,
Filtering and
Correlation
Immediate
Information Element Event
Repository
On-demand
Network
Mgt - Event
BSS
3. Our reference architecture - guide
Long lived data
11

12. The objective of this use case is to proactively identify customers who have exceeded one
or more elements (e.g. mobile data) of their contracted tariff plan, and proactively offer
them additional capacity for an incremental fee.
For example:
Customer complains they have unexpectedly incurred additional charges for mobile data
usage. We verify through usage analysis that the charges are due to legitimate
downloading from Google market. We can offer a more suitable tariff plan based on the
actual usage profile.
3.1 Up-sell
The
business
case
12

13. 3.2 Cross-sell
The objective of this use case is to identify customers who are likely to purchase
additional products.
For example:
Through usage analysis we identify those customers who are routinely downloading
music from iTunes. We then offer them an alternative subscription to Spotify
highlighting how much they would have saved based on recent purchases.
The
business
case
13

14. 3.3 Retain customers
The objective of this use case is to establish the propensity of our customers to
churn through the identification and analytic modeling of churn indicators
For example:
We can produce a predictive model that encompasses both OSS and BSS data
sources that identifies customers most likely to churn. This data can be used to
inform retention policy within a mobile operator.
POSTPAY PREPAY
Top Up Frequency
Avg. Credit Value
Top Up Method
ServiceLength Of
ReasonDisconnect
Contract Stage
No. Of Upgrades
BSS (customer facing) – i.e. billing and CRM data
No. OB Calls [Delta
Discount
Avg. Inactive Time
Device
No. Of Products
Geo [Urban Rural]
Tariff Band
X-Net Ratio
Initiation Credit Value
BandAge
Unpaid Balance freq.
Complaints Flag
Promo Flag
Type
Competitor
Loyalty
Sphere of influence
PREPAYPOSTPAY
Calls to customer service
The
business
case
14

15. 4. Location
The objective of this use case is to correlate customer location, pre-provisioned events
and a customers profile, for specific promotions and communications.
For example:
Based on customer usage we establish Frank is a Man United football fan. Correlating
this information with his cell location (e.g. while attending a football game) and known
football fixture timetable can be used to route him towards an accessible but relevant
offering - e.g. sale on Man United club merchandise.
1. Frank has regular
access to Man
United app
2. System provisioned with
event calendar (e.g. Man
United versus Barcelona @
Location, date, time)
3. Correlate with location
actual data
User Preference Event Calendar Actual location
4. Timely and tailored
promotion
Tailored promotion
15

16. Source device
location and
movement
data from
available
sources
Track device
location and
movement of
segmented
users
Maps these
segments to
commercially
relevant areas
Publish the
local analytic
data
Retailers act
upon
opportunities
Source device
location and
movement
data from
available
sources
Track device
location and
movement of
segmented
users
Maps these
segments to
commercially
relevant areas
Publish the
local analytic
data
Retailers act
upon
opportunities
The objective
of this use
case is to
correlate
customer
movement
with
customer
profile for
specific
promotions
and
communicatio
ns.
The objective
of this use
case is to
correlate
customer
movement
with
customer
profile for
specific
promotions
and
communicatio
ns.
5. Movement
Source device location and movement data from available sources
Track device location and movement of segmented users
Maps these segments to commercially relevant areas
Publish the local analytic data
Retailers act upon opportunities
Source device location and movement data from available sources
Track device location and movement of segmented users
Maps these segments to commercially relevant areas
Publish the local analytic data
Retailers act upon opportunities
The objective of this use case is to correlate customer movement with customer profile for
specific promotions and communications.
The objective of this use case is to correlate customer movement with customer profile for
specific promotions and communications.
16

17. The objective of this use case is to enhance customer care handling through next best
action suggestion.
For example:
Mark rings first line customer care. He explains he is dissatisfied with his quality of his
mobile data service. Our system has validated that the download speed is below the
norm for Mark. It has correlated this with the application of a new software configuration
on his handset. Updating the configuration to the latest available version resolves the
issue for Mark.
6.Next best action
Validate
problem
Communicate
next best
action
Improved First
Call Resolve
ratio
17

18. Mobile operator agrees to participate in use case focused workshops
Mobile operator supplies customer data samples
Customer identity encrypted by operator
Provider builds and deploys operational prototype for one or more of the
use case listed
Operator can validate business value
Provider and operator agree solution, service and technology roadmap
7. Movement> Solution Scope
18

19. Role Based Project Resourcing &Budgeting
Names Focus Profile/Role Onboard
NN Use Case Design(Campaign/Churn) 20 years experience. SQM/CEM product management Now
AB Use Case Design(Campaign/Churn) 20 years experience. OSS/SQM/CEM product architecture and design. Now
CD Use Case Design(Campaign/Churn) 20 years experience. Operator marketing operations management. June
EF Use Case Design(Campaign/Churn) 10 years experience. Marketing campaign design. Now
GH Use Case Design 20 years experience. Telcordia SQM/CEM market management and solution design. June
PK Use Case Design(Customer Care) 10 years experience. Huawei Core Network R&D Now
LM Use Case Design(Customer Care) 15 years experience. NSN SQM/CEM solution architect July
NO Analytics Model Design(Churn/ Campaign) 20 years experience. Data mining, familiar with Chun/Campaign Now
PQ Analytics Model Design(Churn/ Campaign) 20 years experience. Data mining, familiar with Chun/Campaign July
HF Service Modelling 10 years experience Huawei Core network R&D and SmartCare product management Now
PQ Service Modelling 10 years experience. Huawei Core network R&D and SmartCare Service modeling Now
FM Service Modelling/Transformation 20 years experience. IBM COTS service modeling design June
XY Service Modelling/Transformation 15 years experience. IBM COTS service modeling design July
UV reference architect 20 years experience. OSS/SQM/CEM product architecture and design. Now
Dr. Mehmet In-memory architect 30+ years experience of Data ware housing and SAP HANA in-memory database professor Now
NE systems architect 10 years experience. Business intelligence expert Now
NM streaming architect 10 years experience. Ericsson OSS/SQM/CEM research and application architecture June
NN DWH + ETL architect 15 years experience. Netezza Big Data system architect July
NN data mining architect
15 years experience. Online analytics and quantitative modeling of high-performance low-latency
systems. June
Jingjin portfolio architect 14 years experience. Huawei R&D. Now
Use case Blue
Analytics Model Yellow
Service Model Red
BigData Platform Blue
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20. 20
UC-1 Customer complaint handling
• Scenario 1
– Clearly demarcate Server (Video) issues beyond operator control
• Scenario 2
– Convert contact into additional revenue
• Scenario 3
– Clearly demarcate UE (APP) issues beyond operator control
• Scenario X1
– Improve TT handling efficiency (automatically insert technical
detail)

21. 21
Intermittent problems
with content server e.g.
Youtube in this case
Complaint Handling #1– Prevent ticket creation with rapid
customer insights

22. 22
Complaint Handling #2– Upsell premium QoS package
User doesn’t have a
profile suitable for
viewing HD video’s.
Upsell a premium QoS
package to provide
better QoS

23. 23
Complaint Handling #3 – Customer Overcharged ?
Looking at the detail we
see a number of
downloads from Google
Market are the cause of
the data usage.

24. 24
Complaint Handling #4 – Populate TT with accurate
customer data for problem resolution
Auto populate ticket to
ensure accurate data for
engineer to resolve
issue.

25. 25
Complaint Handling #4 – Demarcate the problem

26. 26
UC-2 Monitoring Top-up – Individual Retailer
Verifying that this
retailer has been
experiencing a number
of delays with their top
up service

27. 27
UC-2 Monitoring Top-up – Individual Retailer
Drilling down identifies
the specific transactions
that have been
impacted

28. 28
UC-2 Monitoring Top-up – Individual Retailer
Drilling down on the
specific transaction
identifies delays on the
billing interface. Doing
this for multiple
transactions shows this is
a common problem with
all of the delays

29. 29
UC-2 Monitoring Top-up – Are there other retailers
impacted by this same issue?
Drilling down provides
visibility to which
customers are impacted
delay

30. 30
UC-2 Monitoring Top-up – Analysis for all retailers
Multiple retailers are
impacted by the same
issue. With 4 retailers in
Xian (incl Retailer0561)
impacted.

31. 31
UC-2 Monitoring Top-up – Analysis for all retailers
Individual Retailer
Drilling into the
impacted customers
shows the different
retailers in this area
impacted.

32. 32
UC-3: Enterprise SLA Monitoring Use Case
Customer
Provider
A BANK
Business
Agreement
SLA
SLS
KPI KPI
KQI
SMS Origination Success RateBanking Transaction
E-commerce applications require a high quality and reliable real-time mobile services
that perform up to an operators SLA commitments.
C bank has implemented an online payment service for their customers. To guarantee
individual account security it is required to enter a verification code (sent via an SMS
by C bank) before confirming the online payment. It is necessary for the user to input
this code within 5 seconds or the payment transactions will timeout. C bank wants
the operator to guarantee a SLA (e.g. delay, success rate) for all SMS originating from
the C bank’s set of pre-defined number. This is especially critical during holidays and
special events

33. 33
UC-3 Enterprise SLA Monitoring
SMS Success Rate and
Delay have gone into a
warning state. Looking at
the recent history shows
that the declining over a
period of time
Lets drill into the most
recent period to
understand the root cause
behind the decline

34. 34
UC-3 Enterprise SLA Monitoring – Understanding SLA Breaches
Failure Analysis shows large
numbers of failures due to
capacity problems specifically
- ‘Submit Message Queue
Full’ and ‘Bandwidth Limit
Exceeded’
Drill down on the specific
regions having the lower
success rate

35. 35
Drill down on the
specific regions
having the
increased delays
Drill down into the
detailed transactions
shows the specific
transactions timing out

36. 36
UC-4 Enabling Network Operations to evaluate impact of
Marketing Promotions
Select the criteria to
identify target
customers
Target customer
segments based on the
selection criteia

37. 37
UC-4 Enabling Network Operations to evaluate impact of
Marketing Promotions
Adjustable
parameters –
which will be
directly
reflected in the
maps/graphs
on the bottom
of the screen,
to determine
the impact on
the network.

38. 38
UC-5 PSPU Service Quality Improvement
Page Response and
Page Browsing Success
Rate have breached
their thresholds

39. 39
UC-5 PSPU Service Quality Improvement
Server problems
constitute the majority
of the failures. Drill
down the specific
sessions impacted

40. 40
UC-5 PSPU Service Quality Improvement
Analyzing the failures
by web site and per
user identifies a
specific web site i.e.
CNPC

41. 41
UC-6 Real Time VIP Care

42. 42
UC-6 Real Time VIP Care

43. 43
UC-6 Real Time VIP Care

44. 44

45. Thank you.
Prof. Dr. Dipl. Wirtsch. Ing. Mehmet Erdas
MBA B.Sc. M.Sc. METU Ph.D. TU Braunschweig Germany
fatih91us@yahoo.de
mehmeterdas@outlook.com
erdasmehmet23@gmail.com
Mobile: +49 (0)1789035440
+43(0)6509111090
+90(0)5374154413 45