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TELCO Big Data Churn Analytics Identifying revenue growth opportunities and strengthening CEM customer retention policyBSS 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 [email protected]
Mobile: +49 (0)1789035440
+43(0)6509111090
+90(0)5374154413
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1. Objectives2. Revenue growth and retention – our scope of work 20153. How to participate; Our reference architecture – guide
Identify Use Cases& Define the Business Use Cases, KPIs3.1 Upsell3.2 Cross Sell3.3 Retain Customers: Churn Minimization
4. LOCATION5. MOVEMENT6. NEXT BEST COURSE OF ACTION TIMING&SPEC.7. Movement Solution Scope8. Role Based HR Project Resourcing and Budgeting9. Use Case Identification SLA VIP etc..(by Presentation)
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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
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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-inWe generate value for our customers, profit is the natural consequence of this.
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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
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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
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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
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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
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Next generation
technology
Traditional
technology
Event Factory Statistical and Mathematical
Functions
Raw Data ReaderSockets
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 SensorsShort lived data
BSS
Imm
ed
iate
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
Imm
ed
iate
Imm
ed
iate
On
-dem
and
On-demand
Periodic
Pe
riod
ic
Immediate
Consolidation,
Filtering and
Correlation
Immediate Information Element Event Repository
On-demand
Network Mgt - Event
BSS
3. Our reference architecture - guide
Long lived data
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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
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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
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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 BandX-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
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4. LocationThe 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
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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 devicelocation andmovementdata fromavailablesources
Track device location and movement of
segmented users
Maps these segments to
commercially relevant areas
Publish the local analytic
data
Retailers actupon
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
movementwith
customer profile for
specific promotions
and communicatio
ns.
5. Movement
Source device location and movement data from available sourcesTrack device location and movement of segmented usersMaps these segments to commercially relevant areasPublish the local analytic dataRetailers act upon opportunitiesSource device location and movement data from available sourcesTrack device location and movement of segmented usersMaps these segments to commercially relevant areasPublish the local analytic dataRetailers 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.
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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
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Mobile operator agrees to participate in use case focused workshopsMobile operator supplies customer data samplesCustomer identity encrypted by operator
Provider builds and deploys operational prototype for one or more of the use case listedOperator can validate business value
Provider and operator agree solution, service and technology roadmap
7. Movement> Solution Scope
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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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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)
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Intermittent problems with content server e.g.
Youtube in this case
Complaint Handling #1– Prevent ticket creation with rapid customer insights
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Complaint Handling #2– Upsell premium QoS package
User doesn’t have a profile suitable for viewing HD video’s.
Upsell a premium QoSpackage to provide
better QoS
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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.
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Complaint Handling #4 – Populate TT with accurate customer data for problem resolution
Auto populate ticket to ensure accurate data for
engineer to resolve issue.
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Complaint Handling #4 – Demarcate the problem
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UC-2 Monitoring Top-up – Individual Retailer
Verifying that this retailer has been
experiencing a number of delays with their top
up service
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UC-2 Monitoring Top-up – Individual Retailer
Drilling down identifies the specific transactions
that have been impacted
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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
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UC-2 Monitoring Top-up – Are there other retailers impacted by this same issue?
Drilling down provides visibility to which
customers are impacted delay
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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.
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UC-2 Monitoring Top-up – Analysis for all retailers
Individual Retailer
Drilling into the impacted customers shows the different retailers in this area
impacted.
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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
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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
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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
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Drill down on the specific regions
having the increased delays
Drill down into the detailed transactions
shows the specific transactions timing out
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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
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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.
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UC-5 PSPU Service Quality Improvement
Page Response and Page Browsing Success
Rate have breached their thresholds
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UC-5 PSPU Service Quality Improvement
Server problems constitute the majority
of the failures. Drill down the specific sessions impacted
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UC-5 PSPU Service Quality Improvement
Analyzing the failures by web site and per
user identifies a specific web site i.e.
CNPC
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UC-6 Real Time VIP Care
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UC-6 Real Time VIP Care
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UC-6 Real Time VIP Care
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Thank you.Prof. Dr. Dipl. Wirtsch. Ing. Mehmet Erdas
MBA B.Sc. M.Sc. METU Ph.D. TU Braunschweig Germany
Mobile: +49 (0)1789035440
+43(0)6509111090
+90(0)537415441345
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