Airtel iCreate National Wildcard Winners 2019

1. airtel iCreate 2018 Wild Card Case Study : Strengthening the Customer Base (Finance) Team Name: StormTroopers Campus: IIM Rohtak Members: Naveen Kumar, Saurav Sarkar

2. 1. Poor Customer Additions Financial & Non-Financial Metrics 2 Fintel has seen Competitive boundaries shifting as it’s core businesses comes under intense competition due to the emergence of a new competitive player in the market & regulatory pressures such as MNP resulting in low switching costs for users. This resulted in Poor Customer additions with low value per user affecting Fintel’s Financial & Non-Financial Metrics Increasing CapEx puts redundant pressure to satisfy increasing data needs Financial Terms Implications on Fintel Non Financial Terms Implications on Fintel LeadingIndicatorsofFuturePerformance Subscriber Acquisition Cost : Dealer Commission, Terminal Subsidy, Sales & Distribution This is the direct measure of the Fintel’s expenditures on acquiring new customers through it’s operators trade channels. Due to the arbitrage opportunities present in the Customer hopping between multiple network operators, this GAC cost has risen in the last few quarters 101 84 87 80 0 100 200 Q4 GAC Q3 Q2 Q1 Operating Profit margin is taken a direct hit due to unstructured payouts affecting Fintel’s retention efforts & increasing CAC Fintel’s Business strategy of giving high offers to Low Customer life time value segment with high switching periods directly affects EBITDA Margins Low Capital Productivity Low ROIC (EBIDTA/ Gross Capex) Decreased Gearing (Debt/ EBIDTA) Take Rate Adoption rate Take Rate signifies how many customers/prospects act on the offers rolled out, sign up for new products & Innovation is the ability to bring new products/services to market successfully -> Both these metrics reflect the company’s ability to bring value to your customers & the market. Revenue Diversification “Foot in the door” Channel Expansion Revenue Synergies Subscriber growth is one of the main Non Financial Metrics affected by the poor customer additions which directly affects the operating margins and the portfolio of services that Fintel has in it’s Pipeline threating sustainability New Revenue models: for future rewards • High leveraged capital structure • Focus more on OPEX & CapEx uses Measure of customer satisfaction across the Organization Decreasing focus on improving customer satisfaction due to latency affects the High CLV Segment Subs Segmentation: Postpaid to Prepaid switching is increased due to switching of price sensitive -> Increased churn (Dual Sim Mobile Factor)  No longer makes sense to invest as heavily as once made sense, since revenue gains will underperform the intensity of the investment.  That is a strategic issue of paramount importance to Fintel in face of rising data & voice usage by Customers Customer Switching hits the overall strategy which affects future growth The Level of new product rollouts which Fintel can bring to the market is affected due to low reserves Drop in the redirection time and CLV usage of customers resulting in high Churn Rates of Fintel Share of revenue declining due to the rotational sim resulting in bottom line getting affected Increased spending and decreasing productivity threatens long term sustainability encumbered by debt costs reducing expansion plans Source: https://www.ey.com/Publication/vwLUAssets/EY-telecom-analytics-advisory/%24File/EY-telecom-analytics.pdf https://smallbusiness.chron.com/adjusting-paper-margins-powerpoint-29281.html

3. 2. Trade Channel Fraud Analytics Management System Solutions 3 Fintel’s Incentive payout schemes has been taken advantage of by players & the problem is widespread throughout the Trade Channel resulting in increased Customer Acquisition costs for Fintel slashing it’s profit margins. The commingled effect of rotational sims give rise to a significant arbitrage opportunities for traders creating a vital need for a targeted action program Intensive Growth Strategy Customer Integration Trade Channel Diversification Base Case Strategy Planning Gap Targeted Action plan to ensure accountability is in absentia! Increase Customer acquisition Decrease subscribers acquisition costs (SAC) Arrest Churn rates Trade Channel Fraud Tactics Study 1. Distributors and POS activating SIMs previously to selling them through non- regular distribution Networks at a lower price 2. Fake activations and signing fake contracts 3. Distributors making Offnet calls to the previously activated sim to get the consumption commission on top the activation and contract fulfillment ones Point of establishment at 3 check points on the sales & distribution chain 1. SIMs delivery by the Logistic Operator to the Distributors 2. Activation 3. RGS (register of revenue generation call) & Receipt of the signed contract Wallet Size Strategy A predictive analytics model can be built using network event data (mediation devices), billing data (billing system), customer data (CRM) and payments data (accounts receivable) to predict the likelihood of potential fraud in the future. Source: https://pdfs.semanticscholar.org/33c6/186be37dfd1422c04dedb8b8f20bede95d33.pdf http://www.flytxt.com/blog/predictive-customer-churn-modelling-in-telecom-industry-with-greater-accuracy/ https://www.urbanairship.com/blog/churn-prediction-our-machine-learning-model

4. 4 Fintel’s Analytical Capabilities are not robust enough to support effective monitoring in it’s trade channel. We have proposed an integrated Analytics solutions which Fintel must incorporate into it’s strategy to maximize it’s throughput. We recommend sophisticated fraud scoring engine applied with risk- and value-based scoring models to bolster sophisticated challenges 2 (Cont.) Analytical Management Solutions and related KPIs Channel Fraud KPI’s for Trade Channel monitoring Activations per POS / Distributor RGS = Revenue Generating g Subscriber RGS per POS / Distributor Activation and RGS location (city) RGS generated with incoming/outgoing calls Contract validity Telecom Policy and high license fees - - - - - - Uncertainty regulatory policy Worsening CapEx to support growth Access to advanced analytical capabilites Competition from new technologies Cost containment Customer shifts Human Capital deficitNew Operational challenges Price Volatility due to competition -More Same Less P R E D I C T E D R I S K L E V E L Increase collaboration between the fraud, marketing and finance/credit risk departments. Through efficient customer screening and scoring, all internal teams affected by fraud can start working together to solve these problems – with minimal interference to the customer journey. Analytical models can help prevent the use of high- end devices to fraudsters who should not become customers. They can help identify different offerings to different customers depending on their risk profile A Base case example of using Analytical capabilities to curb Multi-Sim Rotational Problem of Fintel These KPI’s serve as control points and when introduced on the chain will have full control over the chain’s activity. They would help monitor the implementation process, providing Fintel relevant feedback to fine tune the different actions to ensure efficiency. An Encompassing Analytical Customer quality model framework Leveraging Analytics to curb user switching Source: R. Fildes, “Telecommunications Demand Forecasting - A Review,” International Journal of Forecasting, vol. 18, 2002, pp. 489-522. Team StormTroopers Student Research

5. Customer segmentation 5 Segmentation based on customer value journey (LTV Model) Acquisition Growth RetentionOnboarding Efficient Acquisition Positive Engagement No Response Failed Costly reengagement efforts Continued engagement through VIP treatment Good quality custom ers with High- CLV Asking for more VIP plans with offers Mid- Level CLV Custom er Low- CLV custom ers RFM Model for Customer segmentation Customer Value Pyramid Top Big Medium Small Inactive Highly Active Medium Active R: Regency F: Frequency M: Monetary Hybrid Model for Segmenting customers Custome r Profile RFM Model Generic Algorithm Selection Crossover Mutation Fitness Value Customer Segmentation Strategic Design for segments Strategic Execution LTV Model Regency, Frequency and Monetary (RFM) model and lifetime value (LTV) model to evaluate proposed segmented customers. For selecting more appropriate customers for each campaign strategy, this work proposed using generic algorithm (GA) Behavioral Segmentation( 1st step to identify Customers) Plain Loyal:: A customer that has always been Active Not Dependable: reached the Churn status for the first time Fence Seated: Moved out of Active into Dormancy for first time Loyal Under Incentives: Became active again due to offers Due to inconsistent behavior of consumers, we need to look into Customer Lifetime Value We can easily segmented customers through their behavior. After that we can get the Life Time Value of each segment. Through this CLV model and RFM( Revenue, Frequency & Monetary) model we designed a model which will work on Generic Algorithm to segment the customers properly. After that we can sub-segment those based on their data usage basis. Thus we can get the proper view of segmentation Potential Customer Segmentation based on internal CLV Usage of Data Traffic Model Source: https://www.researchgate.net/publication/262056572_A_Customer_Churn_Prediction_Model_in_Telecom_Industry_Using_Boosting. Team StormTroopers Student Research

6. Predictive Churn Model 6 Historical Data New Data Test Set Model Training/ BuildingTraining Set Test Model Prediction Deployed Model CLV Prediction for next Quarter Customer Data CRM Call Centre Records Application Logs Web Clickstream Data Discovery Model Production Model Customer Lifetime Value Prediction for next Quarter Give rating according to CLV CA BA AA CB BB AB CC BC AC Customer Segmentation through rating Stickiness Index Revenue per Customer HCV Customer s LCV Custo mers Interdependent Data for Estimating Churn Probability Customer HLV MLV SLV ACCA BA AB BCCC CB BB Churn Rate Calculation= Average Churn Rate for the segment in previous quarter*Average CLV for the segment Decision Tree for Predicting the Customer Churn Predictive Positive(c=1) True Positive(TP) False Positive(TP) Predictive Negative(c=0) False Negative(FN) True Negative(FN) Actual Positive(y=1) Actual Negative(y=1) Accuracy = (TP+TN)/(TP+TN+FP+FN) Recall = TP/(TP+FN) Precision= TP/(TP+FP) F1 score= 2*(Precision- Recall)/(Precision+ Recall) Ca: Cost of contacting the customer Co: The average cost of the retention offer CLV: Customer Lifestyle Value (Considered as intangible Asset) New Customer Active Customers TP FP FN TN Effective Customer C0+Ca C0+Ca 0 CLV+Ca CLV Inflow Customer Base Predictive Churners Predictive Non-Churners Outflow Resource Lost Cost of Acquisition < Cost of Retention Increasing Churn Rate More spending on Customer Retention High Variable Cost Discount offering for satisfying customers High Fixed cost due to high Capex Difficult to reach Break-even point ARPU & ARMU goes down Operating Free Cash Flow declines EBITDA Margin comes down Profit will decline Non Controlling Interest increases Acquire customer from Competitor High Fixed Asset High cost for upselling to new customer High Marketing Cost High Maintenance Cost High Operating Cost Loss of Recurring Revenue Expense Increasing Voice Usage Call Recharge Pattern Data and VAS usage SMS Usage Location & Network Age Churn Rate affects the Bottom-Line Financially Exploratory data analysis (EDA) For predicting customer churn with previous quarter data we divide the customer in segments, then make sub segment of those. Then through decision tree & supervise machine learning we will predict the churn rate of each sub segment. We are giving grade to customers based on Revenue per Customer & Stickiness Index of Customers, A is the highest & C is the lowest EBITDA =(Revenue)-(cost of sales)-(service costs)-(network costs)-(bad debt expenses)-(marketing costs) Financial Impact of Cost of Churn Source: https://www.tcil-india.com/public/pdf/AR-2016-17.pdf Team StormTroopers Student Research

7. Exhibits 7 Prevention & detection Tools Data Visualiz ation Behavio ral Analysis Deep Learning Forensic Tools Investig ation CellsFlexible Audit Plans Internal Controls Bench marking Real Time Screeni ng Automat ed Controls Complia nce Solution s Third Party Screeni ng Fraud risk Assess ment Vigil mechanis m Awarenes s Initiatives Governan ce EXHIBIT 1: Important fraud prevention and detection tools 1. Integration, quality and management of data. CSPs are facing a high volume of data internally (CDR, customer data, etc.). But as noted above, they can also use external data, including both structured (lists, demography statistics) and unstructured (social media information, web crawls) data. Combining these data sources into a single system for analysis requires a system that can access data sources transparently and handle the data quality tasks to make sure that the data is high-quality and ready for analytics. 2. Detection. Using a hybrid analytical approach that combines business rules, anomaly detection, predictive modeling and social network analysis, organizations can quickly detect various types of fraud. By helping the system “learn” from the results of each round of analytics 3. Alerts qualification and investigation. Alerts should be clear and understandable, giving fraud investigators the opportunity to visualize and prioritize the alerts. For each of the alerts, the investigator needs to clearly understand the reasons of the alerts to shorten the investigation time and process. After investigation, the outcome will be sent back to the system to increase the detection logic –also known as the feedback loop. 4. Monitoring and ad hoc analysis. It is critical to measure program performance and monitor key performance indicators of successful fraud prevention. Also, following up on existing fraud instances can be used by management to determine future fraud prevention approaches. Monitoring the program over time and updating fraud prevention models will also help formulate long-term strategies. 5. High-performance and near-real-time technology. The underpinning of the Fraud Framework is its ability to run analytics on a very large number of transactions consistent with the communications industry and do this in near-real time (intra-day) Source: Team StormTroopers Student Research

8. Exhibit 8 Target Customer New Customer Initial Customer High Value High Potential Low Value Voluntary Churn Forced Churn Acquisition Activation Relationship Prospect New Customer Established customer EXHIBIT 2: Customer Life-Cycle in Telecom Industry Win Situation for Company’s Mobile High Tech Price sensitive Foreigners Low Tech Loyal Analogue Treacherous Analogue Prevention Retention Latest technology Offers ( broadband, gadgets etc.) • Cross Selling • Up-Selling • Weekend, evening calls to bind • Discounts to push to high-tech segment Invest less to keep CustomerValue Propensity to churn Identify customer behavior Identify propensity to churn Identify customer profitability EXHIBIT 3: The customer portfolio based on customer value, propensity to churn and identified clusters Network Operation Business Support Function Collaboration across value chain Sales, distribution, and customer service IT Infrastructure Products, pricing, and marketing & End-to-end redesign IT Services, Billing , Customer Care Joint venture on marketing and media Digital distribution and customer service Outsourced customer service Joint procurement venture Managed services partnerships Managed services for finance operations Passive network sharing Active network sharing Operator • Higher share of variable cost • More flexible to fluctuating volumes • Cost and asset base become more flexible, enabling a better competitive stance outside of strategic capabilities • Value chain is significantly transformed, from mostly internal delivery into a flexible modular set-up EXHIBIT 4: How Cost of Churn Affects the Telecom Operators Customer data Offer acceptance prediction Collection risk estimate Revenue estimate Churn prediction Decision making Decision making Churn intervention Profitability optimization EXHIBIT 5: THE FRAMEWORK FOR CUSTOMER RETENTION AND PROFITABILITY MAXIMIZATION Source: Team StormTroopers Student Research

Airtel iCreate National Wildcard Winners 2019

Airtel iCreate National Wildcard Winners 2019

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Airtel iCreate National Wildcard Winners 2019