Building world class analytics capability at Global In-house Centre. Advisory, India Analytics

Transcription

1 Building world class analytics capability at Global In-house Centre Advisory, India Analytics

2 Accelerate growth Improve business performance Sales Cost Support business growth, product development and pricing strategy through analytics: Customer analytics Pricing analytics Social media and digital analytics Protect Secure what can be a threat to your business with analytics including: 500 million 90% 40ZB US$34 billion 4.4 million 2 Building world class analytics capability at SSCs

3 Strategy: People: Process: Technology: Building world class analytics capability at SSCs

4 Sector knowledge: Domain knowledge: Analytics knowledge: IT enablement: Building world class analytics capability at SSCs

5 Customer Age (owner) Customers gender (K=Female, M=Male) Type of residence (appartment, house.) number of DSL subscriptions (ADSL + VDSL) on owner Number of fiber subscriptions on owner Numer of POTS (plain old telephony) subscriptions on owner estimated household size (customers and others) number of Mobile Broad band subscriptions on owner Number of Mobile Post paid subscriptions on owner number of mobile pre paid subscriptions on owner Did the subscription get cancelled in the following month? Device category (smartphone, feature phone etc) Does the device support HD Voice? Does the device support LTE (4G)? The model name of the device The operating system of the device The manufacturer of the device Does the device support touch screen? The form factor of the device Number of days the phone insurance have been active Number of days FriFamilie has been active on the subscription Sum NOK voice 1 month ago (last month) Sum NOK voice 2 months ago (last month -1) Sum NOK voice 3 months ago (last month -2) Sum of total MB last month Sum of total MB 2 months ago (last month -1) Sum of smallscreen MB last month Sum of smallscreen MB 2 months ago (last month -1) Sum domestic voice minutes last month Sum domestic voice minutes 2 months ago (last month -1) Sum domestic voice minutes last 3 months (sum of.. LAST1,LAST2,LAST3) Number of days the subscription has been active Sum of SMS (domestic) last month Sum of SMS (domestic) 2 months ago (last month -1) Sum of SMS (domestic) 3 months ago (last month -2) The name of the price plan Churn pressure. The number contacts that churned last month Weighted 'churn pressure', ie the sum of relational strength to all contacts Churnet last month As above but weighted in relation to the total relational strength to all friends. (Volume ratio of churna contacts) number of contacts Weighted degree, ie the sum of all relational forces. (Derived from total sms + voice volume to friends) Share contacts outside MNO Overall strength relational contacts who are MNO(off-net) Number of your ten closest friends who Churnet last month Share sms to kontaketer after 17 Share of voice calls to contacts by 17 KPIs Advanced analytics approach and data models Ftth Rollup based on Customer Value Qos for Customer Experience Short Description Overview of our Proposed Approach Following a segmentation analysis, High Churners can be identd. Using propensity modeling, accurate estimations of a customer s probability to the rm can be provided. Its prevalent importance is due to its potential to assist with the design of proactive retention campaigns. Key Benets The customer s current status of activity is treated as the dependent variable (i.e. 1: in-active, 0: active) We conduct an iterative model development process to graduall introduce and exclude variables The nal propensity models that are selected bring together statistical accuracy and business sense The accuracy of the models is assessed on the basis of the validation dataset Identify, define and generate variables Specify & Revise the Model Commercial Dashboard Network Influencer discovery based on CDR's Obtain accurate estimations of a customers probability to churn Identify the customers with the highest probability to churn and, thus, sort by decreasing value order Detect the factors with a signcant effect on a customer s probability to churn Therefore, we can indirectly infer the most important causes that may lead to a customer s possible departure, so that we can design the most appropriate retention campaigns Train the model on the actual and test datasets Iterate until satisfactory results are achieved Process Description Mapping Customer Journey Hypothesis development Data Preparation Propensity Modeling Customer Targeting Interpret the results Methodologies Case studies Frameworks Tipping Point Conjoint analysis to identify improvement levers for the offer portfolio of a broadband operator Geomarketing & analytical market sizing for a greenfield broadband operation in Nigeria Customer segmentation for the launch of a French MVNO Conjoint analysis and product comparison for a leading UK operator Conjoint analysis and product comparison for a Jordanian operator Geomarketing & analytical market sizing for a greenfield broadband operation in Indonesia Marketing segmentation of Broadband customers for a leading French operator Customer Analytics implementation with daily monitoring for a Moroccan operator QoS report of strategic BTS for a leading Algerian operator Direct marketing allocation effectiveness for a leading French operator Marketing & communication spending effectiveness roll-out for a major handset manufacturer Implementation of a tool to assist the diagnosis and offer migration of customers for a French telco operator Diagnosis of customer database and performance for a telecom B2B distributor Shift from an audience assessment to an RoI assessment in digital marketing for a French operator For an operator in North Africa, setting up ATL and BTL campaigns to stimulate usage, facilitate service migration, and increase customer retention For a leading operator in Ivory Coast, defining and establishing the CBM unit (mission, roadmap, organization and procedures) For a French operator, specify 180 campaigns to be tested & perform business modeling ( value, usage, migration, retention recapture) Assist challenger operator in Kenya to initiate CBM: segmentation, campaigns design & tests, process & organization set-up, action plan, training Optimizing RoI of Direct Marketing Budget for a Telco Operator Quantitative analysis and recommendations to optimize the level of handset subsidies Route optimization of maintenance technicians Social Media Analytica CoE Digital Analytics CoE EY Data Centre EY Data Centre Global Talent Hub Global Talent Hub EY Data Centre Building world class analytics capability at SSCs

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7 Ahmedabad 2 nd Bengaluru 6 Chandigarh st Chennai Hyderabad Kochi 9 Kolkata Mumbai NCR 6 Pune N Balaji Kunal Mhaske Jayendran GS Jasjeet Singh Upendra Sai Building world class analytics capability at SSCs 7

8 Ernst & Young LLP EY About EY