This is the National wildcard winning solution for Airtel iCreate competition. We were selected amongst Top 5000 teams participating from all B-schools over India. We presented the only analytical segmentation driven customer strengthening model.
Our proposal was to use Gradient boosting to identify churn, positive revenue customers to turnaround the customer strategy.
We wanted to drop the low value customers and focus on increased services to the high value customers by offering value driven offerings.
Business Process as a Service for Utilities: From Meter to CashCognizant
As utilities adopt smart meters, smart grids and other innovative digital technologies, deploying business process as a service (BPaaS) offers great benefits for operational efficiency, customer service and controlling IT expenses.
Enhancing the Utility Customer Experience: A Digital FrameworkCognizant
For utilities organizations, providing a digitally enhanced customer experience and journey is essential to remain competitive in the world of new age energy prosumers.
Business Process as a Service for Utilities: From Meter to CashCognizant
As utilities adopt smart meters, smart grids and other innovative digital technologies, deploying business process as a service (BPaaS) offers great benefits for operational efficiency, customer service and controlling IT expenses.
Enhancing the Utility Customer Experience: A Digital FrameworkCognizant
For utilities organizations, providing a digitally enhanced customer experience and journey is essential to remain competitive in the world of new age energy prosumers.
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Ex...cVidya Networks
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings – a presentation by Efrat Nissimov, cVidya’s Director of Product Management at the “Big Data and Analytics for Telecom & Mobile Carriers” event in Atlanta.
Understanding revenue risk is central to implementing effective RA controls, and to establishing a company-wide revenue and cost risk management framework.
Identifying and closing control gaps should be the highest priority for revenue assurance managers today.
This webinar with Geoff Ibbett, independent consultant and TM Forum Trainer, discussed an approach to risk mitigation that will allow managers to optimise costs at the same time.
Covered in the webinar:
- An introduction to RA maturity
- Basic risk management concepts
- Preventative vs. corrective techniques
- The importance of primary and secondary controls
- Dynamic risk management
ANA programmatic-financial-fog 22-5-17Brian Crotty
A new study of programmatic media trading was released last week. The study, entitled “Programmatic: Seeing Through The Financial Fog” is a joint initiative between the primary US advertiser trade group the ANA, its Canadian equivalent the ACA, Ebiquity (EBQ.L, N/R) and Ad/Fin, and was undertaken to investigate costs and economics of programmatic advertising ecosystem. The study is relevant to digital media technology owners large (i.e. Alphabet’s Google (GOOGL, Hold) and small (i.e. The Trade Desk (TTD, N/R), to digital publishers, agency holding companies including IPG (IPG, Hold), Omnicom (OMC, Hold), Publicis (PUB.PA, Hold) and WPP (WPP.L, Hold) and providers of measurement and data services such as Nielsen (NLSN, Hold) because of the benchmarks the study provides and because of the implications the study suggests about the industry’s evolution.
Energy Trading and Risk Management (ETRM) solutions have now been a part of the broader wholesale energy trading application landscape for around 20-years, having evolved in step with both business and technology trends over that time period. As a result of this evolutionary process, there are a large number of diverse solutions on the market that address any number of combinations of industrial segments, energy commodities, geographic locations, and functional reach.
Prepare for your interview with these top 20 SAP SRM interview questions. For more IT Profiles, Sample Resumes, Practice exams, Interview Questions, Live Training and more…visit ITLearnMore – Most Trusted Website for all Learning Needs by Students, Graduates and Working Professionals.
Looking to add weight to your resume? Check out for ITLearnmore for varied online IT courses at affordable prices intended for career boost. There is so much in store for both fresh graduates and professionals here. Hurry up..! Get updated with the current IT job market requirements and related courses. For more information visit http://www.ITLearnMore.com.
Infosys - Telecom OEM Solutions | Quote to Cash White PaperInfosys
Telecom OEMs can create value in quote to cash cycle operations by implementing solutions to reduce order cycle time, cost per order and revenue leakage
The secondary market research challenge in Pharma - An inside view on how to overcome the perceived market monopoly
Data, Analytics and Consulting services are an integral part of a pharmaceutical company’s pre and post drug launch activities. These organizations spend anywhere between USD 30 million to USD 50 million on a single service provider while sourcing various secondary market research services. These services provide them with indispensable insights about patients, payers, prescribers, drug positioning and so much more.
However, the number of global service providers in the market has not grown in tandem with the increasing demand for quality pharmaceutical data, analytics and consulting. This, in turn, has resulted in not only the lack of choice of service providers at a global scale, but also single supplier dominance in the secondary market research space.
Watch Beroe’s Senior Research Analysts, Angad Singh as he shares insights on how to negate the stiff challenges that pharmaceutical organizations face in order to effectively source secondary pharmaceutical market research.
About the speaker:
Angad Singh - Angad Singh is a Marketing Services - Mass Communications expert at Beroe Inc. He specializes in providing primary & secondary market research related procurement intelligence to Fortune500 companies and identifying key trends with respect to several end-use industries of primary & secondary market research. In his 3 years at Beroe, Angad has built extensive knowledge and expertise in categories including printing and fulfilment, market research, in-store promotions and merchandizing. He has written and published several thought leadership papers. Some of the topics he has covered in his papers include “The secondary market research challenge in pharma - An inside view” and “Market Research - Emerging Markets and The Never Ending Role of Technology”.
Motorola Reinvents its Supplier Negotiation Process Using Emptoris and Saves ...Emptoris, Inc
Learn about a Fortune 500 company that reinvented its supplier negotiations and saved $600 million.
For more information, please visit:
Emptoris website: http://www.emptoris.com/
Emptoris blog: http://emptorisinc.blogspot.com/
YouTube channel : http://www.youtube.com/emptoris
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...cVidya Networks
The Revenue Assurance arena is going through significant changes. To learn more, see this webinar presentation by Dr. Gadi Solotorevsky, cVidya’s CTO and Chair of the Revenue Assurance Modeling Team of the TM Forum. Find out more about those changes, the reasons behind them, and how they come into play in the daily activities of Revenue Assurance departments.
For more information on revenue assurance: http://www.cvidya.com/
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Ex...cVidya Networks
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings – a presentation by Efrat Nissimov, cVidya’s Director of Product Management at the “Big Data and Analytics for Telecom & Mobile Carriers” event in Atlanta.
Understanding revenue risk is central to implementing effective RA controls, and to establishing a company-wide revenue and cost risk management framework.
Identifying and closing control gaps should be the highest priority for revenue assurance managers today.
This webinar with Geoff Ibbett, independent consultant and TM Forum Trainer, discussed an approach to risk mitigation that will allow managers to optimise costs at the same time.
Covered in the webinar:
- An introduction to RA maturity
- Basic risk management concepts
- Preventative vs. corrective techniques
- The importance of primary and secondary controls
- Dynamic risk management
ANA programmatic-financial-fog 22-5-17Brian Crotty
A new study of programmatic media trading was released last week. The study, entitled “Programmatic: Seeing Through The Financial Fog” is a joint initiative between the primary US advertiser trade group the ANA, its Canadian equivalent the ACA, Ebiquity (EBQ.L, N/R) and Ad/Fin, and was undertaken to investigate costs and economics of programmatic advertising ecosystem. The study is relevant to digital media technology owners large (i.e. Alphabet’s Google (GOOGL, Hold) and small (i.e. The Trade Desk (TTD, N/R), to digital publishers, agency holding companies including IPG (IPG, Hold), Omnicom (OMC, Hold), Publicis (PUB.PA, Hold) and WPP (WPP.L, Hold) and providers of measurement and data services such as Nielsen (NLSN, Hold) because of the benchmarks the study provides and because of the implications the study suggests about the industry’s evolution.
Energy Trading and Risk Management (ETRM) solutions have now been a part of the broader wholesale energy trading application landscape for around 20-years, having evolved in step with both business and technology trends over that time period. As a result of this evolutionary process, there are a large number of diverse solutions on the market that address any number of combinations of industrial segments, energy commodities, geographic locations, and functional reach.
Prepare for your interview with these top 20 SAP SRM interview questions. For more IT Profiles, Sample Resumes, Practice exams, Interview Questions, Live Training and more…visit ITLearnMore – Most Trusted Website for all Learning Needs by Students, Graduates and Working Professionals.
Looking to add weight to your resume? Check out for ITLearnmore for varied online IT courses at affordable prices intended for career boost. There is so much in store for both fresh graduates and professionals here. Hurry up..! Get updated with the current IT job market requirements and related courses. For more information visit http://www.ITLearnMore.com.
Infosys - Telecom OEM Solutions | Quote to Cash White PaperInfosys
Telecom OEMs can create value in quote to cash cycle operations by implementing solutions to reduce order cycle time, cost per order and revenue leakage
The secondary market research challenge in Pharma - An inside view on how to overcome the perceived market monopoly
Data, Analytics and Consulting services are an integral part of a pharmaceutical company’s pre and post drug launch activities. These organizations spend anywhere between USD 30 million to USD 50 million on a single service provider while sourcing various secondary market research services. These services provide them with indispensable insights about patients, payers, prescribers, drug positioning and so much more.
However, the number of global service providers in the market has not grown in tandem with the increasing demand for quality pharmaceutical data, analytics and consulting. This, in turn, has resulted in not only the lack of choice of service providers at a global scale, but also single supplier dominance in the secondary market research space.
Watch Beroe’s Senior Research Analysts, Angad Singh as he shares insights on how to negate the stiff challenges that pharmaceutical organizations face in order to effectively source secondary pharmaceutical market research.
About the speaker:
Angad Singh - Angad Singh is a Marketing Services - Mass Communications expert at Beroe Inc. He specializes in providing primary & secondary market research related procurement intelligence to Fortune500 companies and identifying key trends with respect to several end-use industries of primary & secondary market research. In his 3 years at Beroe, Angad has built extensive knowledge and expertise in categories including printing and fulfilment, market research, in-store promotions and merchandizing. He has written and published several thought leadership papers. Some of the topics he has covered in his papers include “The secondary market research challenge in pharma - An inside view” and “Market Research - Emerging Markets and The Never Ending Role of Technology”.
Motorola Reinvents its Supplier Negotiation Process Using Emptoris and Saves ...Emptoris, Inc
Learn about a Fortune 500 company that reinvented its supplier negotiations and saved $600 million.
For more information, please visit:
Emptoris website: http://www.emptoris.com/
Emptoris blog: http://emptorisinc.blogspot.com/
YouTube channel : http://www.youtube.com/emptoris
Revenue Assurance Industry Update - Webinar by Dr. Gadi Solotorevsky, cVidya'...cVidya Networks
The Revenue Assurance arena is going through significant changes. To learn more, see this webinar presentation by Dr. Gadi Solotorevsky, cVidya’s CTO and Chair of the Revenue Assurance Modeling Team of the TM Forum. Find out more about those changes, the reasons behind them, and how they come into play in the daily activities of Revenue Assurance departments.
For more information on revenue assurance: http://www.cvidya.com/
Pinnacle digital advisors -How U.S.Telecoms Can More Effectively Convert Data...sangeetk072
Pinnacle Digital Products ,Pinnacle digital advisors,,Pinnacle digital is the leading provider of next generation network and customer analytics solutions
http://pinnacledigital.in/index.html
With these changing business dynamics, leading companies are rethinking their approach to the after sales business, as the same cannot be taken for granted any more.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings - Efrat Nissimov, Director of Revenue Assurance Product Management, cVidya, in the Telecom Analytics 2013 Conference in Atlanta, January 30-31, 2013
Customer churn classification using machine learning techniquesSindhujanDhayalan
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
C5.0, Decision tree, KNN, ANN, and SVM. CRISP-DM approach was followed for developing the project. Accuracy rate, Error rate, Precision, Recall, F1 and ROC curve was generated using R programming and the efficient model was found comparing these values.
Data Mining on Customer Churn ClassificationKaushik Rajan
Implemented multiple classifiers to classify if a customer will leave or stay with the company based on multiple independent variables.
Tools used:
> RStudio for Exploratory data analysis, Data Pre-processing and building the models
> Tableau and RStudio for Visualization
> LATEX for documentation
Machine learning models used:
> Random Forest
> C5.0
> Decision tree
> Neural Network
> K-Nearest Neighbour
> Naive Bayes
> Support Vector Machine
Methodology: CRISP-DM
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
Telecom Billing's evolving role in post pc eraEhtisham Rao
With OTT proliferation in the mobile business, telecom operators are struggling to redefine the value of their services for operators. Business models abound, billing remains a key opportunity area for telecoms. this talk covers high level telecom interventions related to billing and their evaluation as source of sustainable competitive advantage.
Reincarnating traditional infrastructure outsourcingNIIT Technologies
Ever since Traditional Outsourcing has gone almost extinct, enterprises are focusing on investing more in next-generation service providers that can provide them flexibility and agility to match the ever changing dynamics of business. This paper highlights how and why traditional infrastructure outsourcing market is shrinking dramatically. It also explains how the new age vendors can adapt to new technology to provide benefits to Gen 2.0 clients.
Capital market firms are making decisions on which business lines, asset classes and services to keep and operate and which ones to exit. Regulatory reform and the
clearing mandate are driving the firms to consolidate their traditional exchangetraded derivatives (Futures and Options) and OTC derivatives into a single clearing
business, even while bi-lateral, uncleared derivatives will continue to co-exist with cleared products.
L&T Outthink Challenge - National Finalist & Campus WinnersNaveen Kumar
A consulting solution for a manufacturing giant which has been facing problems from government ban on imports of steel and local resistance to new projects on lands used by villagers.
An integrated gamification strategy based on behavioral analytics and international trade arenas.
Renewable Energy - Diversification strategy in India - ConsultingNaveen Kumar
We propose an Integrated gamification proposal for the diversification into the renewable industry of India. Our Model is based on the proposed indigenous Arena Selection Model to identify, impact and move pitch.
Barclays - Case Study Competition | ISB | National FinalistNaveen Kumar
We were the National Finalist in the case study competition organized by ISB partnered with Barclays.
Our solution to Barclay's bank to increase its foray into the consumer lending bank using customer segmentation using clustering techniques - Multi factor cluster analysis on 1.5 Million credit profile datasets.
We identified profitable pools from our ML model which were then coupled with upcoming banking trends such as open banking to increase market share.
Hedge Fund case study solution - Credit default swaps execution system and Gr...Naveen Kumar
I designed the entire end-to-end trading architecture of a hedge fund.
The execution system for integrating a fund with Credit default swap capabilities and also solved Hedge fund's liquidity constraint in moving funds across the countries.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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