W A C
Carla Cardoso, WeDo Technologies
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DID YOU KNOW…?
That insights-driven businesses - firms that use data,
analytics, and software in closed, continuously optimized
loops - will take $1.8 trillion from their competitors
that are still running their companies by data rather than by
insights?
Most companies only use 27% of their semi structured data and 31% of their unstructured data for business insights and decision-making.
44% of North-American decision-makers state “I don’t have the right set of analytics tools to help me produce and execute insights”.
The Forrester Wave™: Customer Analytics Solutions, Q1 2016
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Available
Data
WeDo
Existing
Customers
WAC
Proactive
Advanced
Analytics …
Prediction
Forecast
Outlier Detection
Segmentation
Prescription
What-If Analysis
Hypothesis Testing
Impact Analysis
Correlations
…
APIs
ARPU
Commissions
Collections
CDRs
Network
Billing
CRM
TAP
WeDo Analytics Center (WAC) is a team of data scientists with global telecom expertise that uses advanced analytics techniques on top of data you already have, and transforms that data into recommendations that you can actually leverage.
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Data Lake
• Data Analytics
• Data Mining Modeling
• Business Insights
• Optimization
Enable insight-driven
decisions
• Optimization and
Efficiency
• Sell data
• Provide data related
services
• Value creation for final
customers
• Fraud Analytics
• Marketing
Analytics
• Network
Analytics
• Business
Optimization
Now that we know…
What happened? When?
Who? Where? How often?
…let’s go one step further.
Why did it happened? Is the impact relevant?
Will it happened again? How to prevent?
Data as an
Asset
Analytical
Knowledge
The value behind your data
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1 Identify opportunities
Preliminary analysis with client
to identify specific challenges
for analytic-driven
improvement.
2 Explore data
Analysis of available data
already collected by the RAID
ecosystem.
3 Build models
Make use of advanced analytics
techniques, such as segmentation,
simulation, prediction, hypothesis
testing, etc, and apply them to the
specific client challenge.
5 Transform processes
Embed the models within clients’
platforms to set alerts, dashboards,
etc., and foster an analytic-driven
approach to business optimization.
4 Extract value
Understand signals, patterns and
trends, and translate them into
meaningful insights that drive real-
world action strategies.
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RISK ANALYTICS
• Evaluate customers’ credit risk and bad debt
potential
• Predict disputes and amount of credits
• Understand network performance and prevent
outages
• Monitor devices’ inventory (prevent stock out,
detection of lost/stolen devices, …)
REVENUE ANALYTICS• Evaluate dealer risk and fraud potential
• Optimize dealer incentive strategies and channel
performance
• Mitigate revenue volatility
• Maximize revenue streams by understanding
drivers such as service type, location, tariff plan,
etc.
PRODUCT/SERVICE PERFORMANCE
• Improve campaign response rates
• Optimize margin of each product/service
• Evaluate current and future customer
expectations to anticipate future product demand
CUSTOMER ANALYTICS
• Understand drivers behind customer churn
• Identify next-best offer for every individual
customer
• Improve customer service (detect customer
sentiment and anticipate customer needs)
• Forecast and reduce cost of service by
understanding customers’ preferred method of
communication
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Use Case Business Case Customer CountrySupport
ProductAnalytical Approach
Agent RatingDevelop a new method to measure Agent Productivity that will provide
new insights on agent’s behaviour, to better understand the need of new
incentives strategies
MobileAmerica
(Latin)n.a.
Development of a Rating Algorithm
from scratch
SIM Card Migration Prediction
Predict movements between SIM Card stage within 30 days to optimize
the operators revenue recognition.Mobile Europe
RAID
RASupervised Models :Decision Trees
Event Log Analysis
Identify events responsible for EPG (Electronic Programming Guide)
bugs / client claims to reduce claims rate and understand possible
errors in box update processes.
Triple Play Europe n.a.Process Mining
Log Analytics
360º customer
view
Customer Behaviour Segmentation to build personalized marketing
campaigns according to customer consumption preferences.
Churn and Un-Payment Prediction to prioritize retention strategies
MobileAmerica
(North)
RAID
Optimize
ACPs
Un-Supervised Models: k-means
Supervised Models: Logistic
Regression + Decision Tree + NN
Subscription
Fraud
Identify activation profiles similar to blacklist profiles +
Identify multiple activations to take preventive actions that reduce
fraudsters entry probability and avoid future profit loss
Mobile EuropeRAID
FMS
Algorithm based on Levenshtein
Distance (supported by FMS
Subscription Fraud Module)
Subscription Fraud
Identify Agents more likely to accept fraudsters, as well as score of a
new activation of becoming fraudsters in order to avoid future profit loss
due to this unwanted behaviour
MobileAmerica
(Latin)
RAID
FMS
Un-Supervised Models: k-means
Supervised Models: Logistic
Regression
Agent Compensation Profile
Customer and Agent segmentations to better understand how
compensations given to clients are related to agents and products in
order to create consistent compensation policies over operator’s
departments.
Triple PlayMiddle
Eastn.a.
Un-Supervised Models: k-means +
SOM
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Improve accuracy of current migration forecast
model based on business rules in order to
reduce revenue loss due to unexpected
transfer behaviors between SIM cards status
CHALLENGE
The migration forecast allows our client to:
• Identify customers who are constantly
migration between status;
• Propose a review of current inter-state
transfer rules
• Optimization / Licensing Charging System
Portuguese 4P Operator.CLIENT
• Revenue Assurance
• Accounting (accruals)
• Product
• Operations / Engineering
IMPACTED AREAS
RESULTS
Analytical Approach has
demonstrated benefits from the
current approach in predicting card
migration.
Overall error decreased 87%
New Model
Error
1.3%
Current Model
Error
10.2%
RISK ANALYTICS USE CASE
GOALS
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Mostly
BALANCE
info available
in RAID
RA
Data
v
DEVELOPMENT
USING
R*
Analytical Tool
v
Predictive Algorithms Accuracy Measures
DECISIÓN
TREE
MISSCLASIFI-
CATION
ERROR
Model 1
Initial Status
Final Status (30 days)
Model 2
Model 3
Model 4
Status A
Status B
Status C
Status D
Status A
Status B
Status C
Status D
Churn
The PoC took a total duration of 7
weeks - 4 models were
developed, one for each initial status.
Use Case ApproachRISK ANALYTICS USE CASE
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TELCO AND OTHER INDUSTRIES
Impact Analysis
• Campaign Impact Analysis -Canada
HEALTH
Customer Analytics
• Churn Prediction - LATAM Tier1
• Behavior Segmentation - LATAM Tier1
• Xsell and Upsell Propensity - LATAM Tier1
Fraud Analytics
• Outliers Analysis for Fraud Detection -LATAM Tier1
TELCO
Customer Analytics
• Customer Future Value - Sonae(Portugal)
• Share-of-Wallet Analysis - Sonae(Portugal)
Forecast
• Out-of-Stock Analysis - UK
RETAIL
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Team Experience
Team Background
RAID Products
Success Stories
…in more than 9 top tier
institutions (telco and non-telco)
across the globe
… + 15 years of
experience in telco, retail,
finance and healthcare
… in Statistics,
Mathematics, Machine
Learning and Computer
Science
… in Customizing analytics
functionalities into existing
WeDo’s products
… in extract additional value from existing data sets
…synergies with RAID cost savings by leveraging
existing investment and
reduced project times
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ARE YOU READY TO START?
WE ARE READY!
S TA R T S M A L L
Define a small PoCscope
H O W C A N W E D O H E L P ?
Identify a present
challenge you have
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Development of a prediction model to determine likelihood of SIM’s status in 30 days
Correlations PredictionStatistical analysis
Business
Challenge
Required
Information
Reduce revenue loss due to unexpected transfer behaviors between SIM cards status
POC Strategy
Approach
POC
Deliverables
Analytics
Techniques
• Snapshot of SIM’s status (e.g.: in the end of month and within a 6 months period)
• Monthly customer historical usage behavior and balance (e.g.: 6 months period)
• Daily customer historical usage behavior and balance per MSISDN (e.g.: last 2 months)
• Rate Plan/ Service class changes
• Distribution of migration between statuses with the information of the total number of MSISDNs and Balance
• The highest likely status after 30 days for each MSISDN
• Evaluation of this model’s performance against the existing one (if applicable)
RISK ANALYTICS USE CASE
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Development of an algorithm to rate dealers based on consistent and continuously positive evolution over time.
RankingStatistical analysis
Improve management of the dealers’ channel team using performance insights
Monthly sales historical data per dealer (e.g.: 12 months period)
(Algorithm will be more elaborate according to additional variables the CSP will be able to provide)
• Dealer’s rate attribution
• Identification of the top performers
REVENUE ANALYTICS USE CASE
Business
Challenge
Required
Information
POC Strategy
Approach
POC
Deliverables
Analytics
Techniques
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Development of a mixed models to combine fraud at agent and transaction levels.
PredictionClustering
Reduce number of approved subscription transactions that turned out to be fraudulent
• Historical monthly sales per dealer (e.g.: 12 months period)
• Characteristics of the approved transactions, as well as fraudulent classification
• Customer characteristics associated with each transaction
• Segmentation of fraudulent dealers
• Aggregation of characteristics for the top 5% approved transactions with highest fraud propensity
RISK ANALYTICS USE CASE
Decision Tree
Business
Challenge
Required
Information
POC Strategy
Approach
POC
Deliverables
Analytics
Techniques