Big VimpelComRussia

Transcription

1 Big VimpelComRussia Cases, processes & business integration Sergey Marin Program Manager Big Data 1

2 2

3 The global program positioned outside of core business functions allows to satisfy needs of all internal & external customers Big Data customer departments Big Data program Target marketing/cbm Big Data Idea Factory Big Data Platform Retail Customer experience B2B Network development Customer care Demand for analytics Use cases generation and analysis Advantages Systematic approach towards business discovery ensures continues flow of project initiatives Overall guidance takes care of cross-department integration & solution re-use Implementation projects Storage & integration of big quantities of unstructured unrelated data, connection of the data sources Advantages Working with unstructured data minimizes effort to make data available Storage of unrelated data allows for cross-department & crossfunctional integration Overall platform ensures quick time to market of new initiatives Overall Big Data Advantages: Satisfy demand of all Vimpelcom departments due to integration of data sources & analytical expertise Continues idea generation & process for idea assessment & further implementation Environment for quick piloting, tailoring & value confirmation of marketing & analytical initiatives & ideas Quick time to market for new ideas & re-use of analytical tools & sources across all departments Governance over all Big Data related activities for correct prioritization & to avoid double spending 3

4 The following roles are involved in Big Data analytics projects & activities Role Idea generation Project implementation Business Owner Authorize project implementation based on provided Business Case Acceptance of created solution Business Representative Confirm initial viability of Big Data idea Contribute revenue part of financial justification Oversee implementation from department perspective Perform acceptance verification Business Analyst Propose Big Data idea Justify viability Create combined business case including revenue & costs Formulate exact requirements Guide implementation from content perspective Define acceptance criteria Data Scientist Assess feasibility of idea implementation in models Assess effort for data model creation Create statistical or data model Implement model using Big Data techniques Data Engineer Assess feasibility of idea implementation in software Assess effort for implementation Connect & integrate required sources for idea implementation Project Manager Project team forming Responsible for idea implementation 4

5 We leverage Big Data Analytics to both improve offerings of our core services and to create independent source of revenue & Data monetization Internal use Revenue streams based on core services, customer experience, retention, loyalty as well as cost & efficiency optimization based on more detailed customer, network & services insight External use Aggregated customer analytics to businesses & government agencies Partner loyalty programs where customers can opt-in and receive relevant offerings 5

6 We use Big Data for a variety of Business application areas UpSell Customer experience Call center Retention 3 rd party analytics Big Data business areas Network developemnt Antispam/antifraud Partner services Retail network М2М 6

7 Big Data ecosystem CDR Geo-locational data Subscriber profile/crm IVR Data sources Self service/mobile application Social networks Payments Network events Big Data program Idea factory Big Data platform SMS Self service Outgoing calls CRM IVR Communication channels Social networks Mobile applications Self service 7

8 Big Data sample cases at VimpelCom Geolocation services 1. Upsells using geolocation services: Roaming offer when arriving at airport Next best action offer when subscriber is near office LTE promotion in areas with good coverage Main challenges: Monitoring of total client base movement in real time Analysis of historical data in order to filter groups of people (often located in the zone) which are not suitable for offer Client streams management 1. Own shops traffic balancing 2. Monitoring of customer contact points with the company (self service, call centre, visit in office, no optimal allocation of flows) Main challenges: Location monitoring across total client base Integration of data from all sources containing information about the contacts with clients Customer experience management 1. Monitoring and management of the client s connection quality: To inform and generate retention offer in order to retain customers in case of service degradation Mapping of service quality to customer complaints & NPS score Call centre 1. Call-center load optimization and customer experience improvement: Adaptation of IVR using information about the client's problems (call drops, rapid balance changes) Customer preferred language determination based on client calls profile and adaptation of IVR Main challenges: Service quality information in real time across the total customer base on customer level 8