Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
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The document outlines a comprehensive strategy for utilizing big data analytics in telecom to enhance customer retention and identify revenue growth opportunities. It discusses the application of churn modeling techniques, including the use of Markov processes, to predict customer behavior and create targeted marketing campaigns. Additionally, it emphasizes the importance of leveraging customer data for personalized service offerings and outlines specific use cases for enhancing customer experience through proactive engagement.
![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
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Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
- 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 [email protected] [email protected] Mobile: +49 (0)1789035440 +43(0)6509111090 +90(0)5374154413 1
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- 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
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- 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 19
- 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
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- 45. Thank you. Prof. Dr. Dipl. Wirtsch. Ing. Mehmet Erdas MBA B.Sc. M.Sc. METU Ph.D. TU Braunschweig Germany [email protected] [email protected] [email protected] Mobile: +49 (0)1789035440 +43(0)6509111090 +90(0)5374154413 45