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W A C

Carla Cardoso, WeDo Technologies

2 WeDo Technologies 2017 | All rights reserved & strictly confidential

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


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.


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


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.


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


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


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


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


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


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


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


[email protected]

[email protected]


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)



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


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


Decision Tree

Business

Challenge

Required

Information

POC Strategy

Approach

POC

Deliverables

Analytics

Techniques

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