Realizing the Potential of Machine Learning€¦ · Analyze employee level of usage of HR Dpt...

© 2017 TM Forum | 1

Realizing the Potential

of Machine Learning

Hervé Bouvier -

Pauline Maury - linePaul

Andreas Polz - BearingPoint

© 2017 TM Forum | 2

Agenda

▪ Who we are

▪ What is Machine Learning & its benefits for Telcos

▪ What is HyperCube and Why it can help

▪ Our experience – Case study

© 2017 TM Forum | 3

Agenda Who are we ?

▪ A data science consultant team of

50 people in France and 80 in

Europe with:

▪ PhDs in machine Learning, analysts

▪ Big data (Hadoop) architects &

developers

▪ Full stack Web Developers

▪ Mastering a wide range of machine

learning methods & developing A

unique & proprietary Smart

Analytics technology- HyperCube

© 2017 TM Forum | 4

What is a Datascientist ?

What his friends think he is What his mother think he is What society think he is

What his manager think he is What he think he is What he actually is

© 2017 TM Forum | 5

« Machine

Learner »

Developers

Architects

Statisticians

Business

Experts Data Analysts

Data

Science

TraditionalResearch

ComputerScience

TraditionalSoftware

Subject MatterExpertise

Math &Statistics

MachineLearning

© 2017 TM Forum | 6

Agenda

▪ Who we are




© 2017 TM Forum | 7

What is Machine Learning ?

Computational learning using

algorithms to learn from and

make predictions on data

DatasetData

preparation

Model Validation Prediction

Test DataTraining data

TA

RG

ET

© 2017 TM Forum | 8

Customer

Analytics

Operational

Analytics

Fraud & Risk

Analytics

ACQUIRE | GROW | RETAIN MONITOR | DETECT | CONTROL MANAGE | MAINTAIN | MAXIMIZE

▪ Which of my clients are likely to

accept upsell offer planned in my

next DM campaign?

▪ What are the intervention rules

which would help to improve

customer satisfaction?

▪ How to develop sales performance

across my retail network?

▪ How do we identify, measure and

mitigate fraud, especially ones that

are hard-to-detect and low

frequency/high impact?

▪ Which of my clients are likely to

drop out of my loyalty program?

▪ How can we optimize costs to settle

and costs to serve in claims

handling?

▪ How to optimize my resource

allocation for preventive

maintenance effort?

▪ What are the reasons for journey

delays and levers for better

planning and accuracy?

▪ How to Support frictionless travel

across multiple modes of

transportation?

We address a wide range of business issues by

unleashing value from operational data

© 2017 TM Forum | 9

Fraud

detection

> Mitigate losses

via fraudsters

profiling

Customer

Targeting

> Boost

enrollment /

Upsell Program

efficiency

Network

Experience

> Anticipate

failures &

Cust. felt

experience

Churn

prediction

> Identify risky

cust., listen to

concerns & push

custom offersTelco Usage

Cust.

Interactions

Contracts

Sta

tic

info

rma

tio

n

Beh

avo

ria

l

info

rma

tio

n

Cust. profile

Payment

incident risk

> Profile & assist

unwealthy

customers

PoS

Performance

> Leverage best

practices to drive

perf. across network

Client Service

Efficiency

> Maximize

satisfaction &

reduce AHT/CTO

Network

PoS

Op

era

tio

ns Recommendation &

Personalization

> Build-up 360° view

& enhance

marketing

effectiveness

Machine Learning addressing TelCos stay awake issues

© 2017 TM Forum | 10

Agenda

▪ Who we are




© 2017 TM Forum | 11

A cutting-edge analytics platform that derives operational insights and provides amazing accuracy

and stability in predictive modeling

What is HyperCube ?

CONNECT

DATA

VIZUALIZE

DATA

EXPLORE

DATA

MODEL

DATA

interact with your

data quickly and

intuitively

gain insights on key

drivers and related

relationships

generate predictive models

and measure performance in

a few simple steps

Connect easily to

existing sql/NoSql

database

Along with its proprietary algorithm, it provides a selection of open source state-of-the-art

algorithms and a framework to develop and deploy customized business apps tailored to

clients needs

© 2017 TM Forum | 12

Visualisation

Vac25 – Logistique

Regression 50_var

Vac25 – Gradient

Boosting 50_var

Random

Vac25 – HyperCube

– 50_var

Vac25 – Random

Forest 50_var

Vac25 – Log Reg 50_var

Vac25 – Gd Boosting

50_var

Random

Vac25 – HyperCube – 50_var

Vac25 – Rand Forest 50_var

Var25 - Logistique

Regression 50_var

Vac25 – HyperCube –

50_var

Vac25 – Gradient

Boosting 50_var

Vac25 – Random Forest 50_var

?

Prédiction

AnalysePrescription

Data

management

© 2017 TM Forum | 13

Explain & Predicty at the heart of HyperCube value proposition

Why

Who

Understand drop

out rationales

Anticipate and

target customers

Critical Business Issues Purpose

my customers

are willing to

leave?

are the most

likely to

leave?

EXPLAIN

PREDICT

Outcomes

… are 3.5x riskier

Age < 35

Owns Product A

Contract Tenure [2;5]

Analytics insights

▪ Features selection

▪ Critical threshold

▪ Business rules

▪ Data Vizualisation

Client 123

Client 232

Client 133

…

Client 211

Client 121

Scoring & Local drivers

Clie

nts

with

1

0,91

0,8

…

0,15

0,1

Billing / Age / Usage PdtA

Tech Issue / HMoving / Age

Usage Pdt B / Billing / Gender

…

Usage PdtA / Tenure / Age

Usage PdtA / Tenure / Billing

Illustration with loyalty management

© 2017 TM Forum | 14

BearingPoint helped Telco operators to successfully optimize their operations

Network preventive

maintenance

Customer satisfaction /

Inbound call prediction

Business Challenges What we did Our Cients

▪ Find out patterns in core and access network to enhance customer experience & increase cost efficiency

▪ Prepare framework to establish preventive maintenance in a continuously learning organisation

▪ Understand root causes of incoming calls from high value customers

▪ Predict customer base propensity to contact pro actively Client Service

Employee Satisfaction

▪ Analyze employee level of usage of HR Dpt service portfolio

▪ Define employee segmentation (clustering) related to HR service usage

▪ Build-up predictive models to anticipate HR needs per employee and enhance relevance of HR

push notification

Fraud▪ Profiles fraudsters and key drivers for non-payment behaviors

▪ Build-up predictive models at activation and after first 4 weeks of activity to assess level of

fraud risk

Churn prediction ▪ Build-up predictive models to anticipate level of churn risk across B2C customer database

PoS Network performance ▪ Identify key drivers for Point of Sales performance defined as tNPS, Opex intensity & Market share

▪ Build-up specific action plans for both existing store concepts

© 2017 TM Forum | 15

Agenda

▪ Who we are




© 2017 TM Forum | 16

Use case 3Use case 2Use case 1

Telecom

Fraud

Reduce non-payment incidents for a Telecom Operator

Our results

Context & Challenges

➢ Actions plan &

Quick

wins identified

➢ Fraud predictive

model ready for

industrialization

Est. ROI :

300k€+/year/fraud rate point

400k+clients

7%+fraud rate

500+variables

▪ Mobile handset

subsidization at risk

due do fraud rate

level increase

▪ Need to revamp

current targeting

methods

▪ Willingness to

understand & profile

fraudsters vs good

payers

share of customers covered

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 20% 40% 60% 80% 100%

Shar

e o

f n

on

-pay

ers

cove

red

Activation model

Activation +Activity model

Client currentscoring

Wizard

© 2017 TM Forum | 17

Fraud

Revenue

Contract

Cust. Profile

▪ Age

▪ Gender

▪ Localization

▪ Correspondance

vs billing

▪ COGS

▪ Price

▪ Offer type

▪ Subsidy level

▪ Billing cycle

▪ Total revenues

▪ Revenues from roaming

▪ Revenues from data

Usage

▪ Onnet/offnet calls

▪ Ratio calls in/out

▪ Sms/mms in/out

▪ Data volume

Nb of inbound calls

Nb sms sent

Customer Revenue

Nb of outbound calls

MFU Handset

Duration data usage

Variable Set Variables ranking

▪ Activation

Channel

▪ Activation date

▪ Salesman code

▪ Agent

Context Device

▪ MFU handset vs

subsidized

▪ Smartphone Y/N

▪ Model brand

▪ Model price

sta

tic

be

ha

vio

ral


Telecom

Fraud


© 2017 TM Forum | 18

HyperCube helped to find out influencial factors and specific local profiles

standing for a high level of risk

Customers that match the following conditions …

This rules concerns:

5% of Fraudsters

2% of total customers

… are 9 times more likely to stay loyal

Activation Channel TELESALES

OUTBOUND

2,6

between 17

and 40

5,1

9

ABC

Is

Is

Is

Offer

Age

0

50000

100000

150000

200000

250000

300000

0

0.5

1

1.5

2

2.5

3

Customers recruited via Telesales outbound are 2.6 times more

likely to be non-payers


Telecom

Fraud


© 2017 TM Forum | 19

01,0002,0003,0004,0005,000

Alarm_1

Alarm_2

Alarm_3

4300

2500

800 1000

0

2,000

4,000

6,000

Boardtype 1

Boardtype 2

Boardtype 3

Boardtype 4

# alamrs


Telecom

Preventive

maintenanceAlarms’ preventive maintenance: Increase customer experience

Context & Challenges➢ Short term solution: Implement an Early Warning Dashboard

(E.W.D. - Operations cockpit with daily reports)

Improve response time and avoid “blind spot” outages:

preventive maintenance

➢ Long term solution: the data structure and quality was not

sufficient for most analyzed data sources

Establish a structured and comprehensive data warehouse

(DWH)

Introduce Data Mining technologies and methodologies to

improve the data quality and enable detailed analytics

Raise network quality

• Higher network stability

• Upfront identification of incident root causes

• Faster reaction to incidents

Increase cost efficiency

• Reduction of costs of operations

Enhance customer experience

• Enhance customer communication

by better knowing the network and

network event

© 2017 TM Forum | 20


Telecom

Preventive

maintenance

We analyzed different alarm types from January to May according to their

impact on the network

Alarms’ preventive maintenance: Increase customer experience

N0= 43,634,389

Filter for German alarms

N1= 2,912,658

Filter for relevant alarms

N2 = 1,761,884

ALARM_1 ALARM_2 ALARM_3 ALARM_4 ALARM_5 ALARM_6 ALARM_7 ALARM_8 ALARM_9

Alarm dataInventory

dataDevice data

Geographic

al data

© 2017 TM Forum | 21


Telecom

Preventive

maintenance

We identified regional alarm concentrations and local problem spots which

covered the majority of alarms in the areas in different cities in Germany

Alarms’ preventive maintenance: Increase customer experience

• Regional alarm concentrations and rules were identified where under specific hardware and software setups alarm concentrations occurred

• Local spots in large and small cities could be due to single incidents or single problem boards which caused a majority of the identified alarms

• The geographical location and the socio-economic factors did not have a significant influence on the number of alarms on the network elements

*Anonymised data

Berlin* Pattern – Rule on the average number of alarms

Under the following conditions …

✓ Board Type is Board A

✓ Hardware Version is B

✓ Software NE Version is NEv2.02

✓ Software Board Version is v1.01

✓ Ort is Berlin*

This rules concerns:

▪ 87,2% of all occuring alarms in Berlin*

▪ 30,3% of the total number of alarms matching:

➢ « Board type »

➢ « Hardware » and

➢ « Software »

The average number of alarms per board is 36,2

times higher than the average of all locations!

© 2017 TM Forum | 22


Telecom

CRMUnderstand & Predict Customer service inbound calls

Targeting enhanced generating short term

business impacts

Ex : ~500k€ contact cost savings / mktg campaign > Telecom

Robustness over the time that limit models

updating effort

Ex : <4% loss of prediction accuracy after 3 months > Telecom

Potential synergies with others tools & methods

Ex : Up to 40% of additional targets list with standard tools

Build classifier to predict future Client

Service caller

1

Compare qualitatively analysis outcomes to already

existing analysis performed by marketing teams

▪ Ability to map and confirm proven facts & figures

▪ Capacity to increase current understanding with new

insights

Determine root cause of Client

Services inbound calls

2

© 2017 TM Forum | 23


Telecom

CRM

HyperCube has ingested and analyzed a large volume of information to

ensure results completeness and accuracy

Understand & Predict Customer service inbound calls

Client

Age

Contacts

Appels en CC

# campagnes MD reçues

#interventions tech terrain

# visites magasins

# total interactions client (+hist.)

selfcare

# sms sortants

Revenus

ARPU

Evolution ARPU

#incidents impayés

# & durée

suspensions abo

Contrats

Ancienneté Orange

Options activées

Actuelle

Ligne de marché

Précedente

Usage

VoixData

TV

Surappels

Sexe

Localisation

CSPAncienneté offre

Offre

Pay. Grat.

# migrations

Nature Dates

Pay. Grat.Actuelle Moy. Hist.

…> fixe > mobile

browsing sms mms

Internet…

voix

data

Roaming

Alertes

service

roaming …

# visites #modifs contrat# clicks

M-xN-x

…

volume motifsM1

M2…

Forf.Hors forf.

Internet

#déménagements

1MCustomers

10 kVariables

56 kCallers

1,7Call/caller

© 2017 TM Forum | 24


Telecom

CRM

Among full set of customer data, few are correlated to significant rate

of Client Service inbound calls


22.520.017.515.012.5 25.0 32.530.027.5 55.010.0

38

36

34

26

24

22

20

18

16

14

12

10

8

6

4

2

0

37.535.0

Shar

e o

f ca

llers

Acq/Ter insurance option Feb

Unlocking FebMobile Change Program Feb

Acq/Ter paying option Feb

Migration Feb

Handset renewal Feb

Dunning Process Feb

Caller rate

Gesture of Goodwill MarAt least 1 connection to coordinates webpage Feb

Extra Call Pack > 11 euros Jan

Extra Call Pack > 88 euros Jan

Segment Value 5+ Feb

At least 2 bills unpaied Feb

At least 1 bill unpaied Feb

OS Change Feb

Claims

Billing

Segment

Payments

Technical after-sales

Sales

Dunning

Selfcare

© 2017 TM Forum | 25


Telecom

CRM

Migration is the most significant reason for contacting Customer Service but

combined with handset renewal, value segment or invoice issues.


Customers matching the following conditions:

Are 15x more likely to contact CS

Union of those customers stands for 7% of incoming managed by CS (i.e. 76k calls) > very low segments intersection

Handset renewal effect

Customers willing to change their mobile Offer are 5,2x more likely to get in touch with Customer Service


✓ Has migrated

✓ Belongs to 5+ value segment

✓ Has already had a non-paying

incident

✓ Has migrated

✓ Has renewed handset


Value segment & non-paying

incident historic effects

✓ Has migrated

✓ Has consulted recently online

billing

✓ Has reduced invoice amount by

> 11€ (1month later)

Invoice issue effect

© 2017 TM Forum | 27

HyperCube key features

Geospatial

Analyze

Text

Mining

Ad.

Vizualisations

Open source ML

Algorithms

Ad. Univariate

Analysis

Unique ML

Algorithms

Embedded

notebook

Rules set

mining

Realizing the Potential of Machine Learning€¦ · Analyze employee level of usage of HR Dpt...

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