The main task of this talk is to see how Data Science can influence big companies to generate new revenue and more profit. Subjects that will be addressed in this talk are: • Understanding a value it brings to corporations on long-term (direct revenue generation not only cost reduction); • Data Science is important part of digital transformation. Are corporations ready? • Management dedication on investment; • Lack of Data Science managers acting as a link between Data Scientists and Business managers. Provide motivation/interesting tasks for Data Scientists while validating investments in business environment; • Lack of skillful Data scientists; • Compensation of Data Scientists among other Employees (obviously a different scales needs to be applied); • Examples of Applied Data Science as revenue generators in Telenor Serbia;

1. Dr. Saša Radovanović
Data Science & CLM team leader
TELENOR Serbia
September 2018
Why is Data Science still not a mainstream in
corporations?

2. Data Science
value in long-term
Key Enablers for
Data Science Success
Employee skills, fit &
compensation
AGENDA
2.1. 3. 4.
Practical Examples,
B2B

3. • Plan appropriately
• Act on time
• Staying relevant
• Structured approach
• Deep insights on
customer behavior
• Power of Prediction
• Rather then constantly
accepting it
• Key driver for implement.
• Find relevant info
Happy clients.
Earn & save money
• Customer satisfaction
• Revenue generation
• Cost reduction
Relevant information @
right time
Unhide insights & turn
into advantage
Challenge existing
practices
Data Science reduces intuition.
Ingredients to business success — not a guarantee of it.
1.

4. Data Science Success
Top
management
support
ENABLERS
Sources for
data
creation
governance
& quality
Beneficial to all
stakeholders
across
the organization.
KEY ENABLERS for Data Science Success in Corporations.
Constant
Learning on
Machine
Learning
Employee
experience
& fit
Decide which types
of data
can be captured
and effectively
governed & used.
Innovative
knowledge/skills
vs. Traditional
Business As
Usual.
Cooperate with:
- Start-ups
- Universities
- Best practice
share
1.

5. Traditional
services
Churn
prediction
Competit.
offers
Existing services through old & new
channels
Maximize retention success
Maximize all commercial steps to maximize Total return
Maximise a commercial return from Data Science.
New services
Marcomm
optimization
Targeted recommendations
through Machine Learning &
Predictive modeling
Physical
Channels
Actions
Right on time
Aggregated
Data
Analytics
Real-time
own
products
Effective
marcomm
invest
Econometr.
predictive
modeling
2.
1
2
3
Digital
Channels

6. Upsell & cross-sell customers with existing services
Increase engagement on digital channels
Maximize revenue from existing services through old &
new channels.
Enable long-term forecasting & early detection of
customer’s value loss.
• Based on hundreds of parameters use predictive
modeling's to forecast customer value.
• Mark possible decrease in time.
• Tailored Offer products for each segment depending
on customer needs, behavior, demographics, device
type, app usage.
• Use Digital customer journey,
• Simplify self-care, purchase, renewals online.
• App Gamification
2.
1

7. • Start with maximum number of
features from various data sources.
• Select most important one using
PCA.
• Perform feature engineering.
• Again, select important using PCA.
• Use several predictive models (RF,
nnet, C5.0..).
• Use resulting list wisely.
Smart churn prediction Secure effective communication
Prepare competitive save-desk
offers
Achieve predictive accuracy
>40% with recall >40%.
The larger the discount the
higher the predictive precision is
required.
Understanding of Data Science
advantages.
Maximize retention success.
• Prioritize churn-prediction list
based on most important criteria
(gross-profit, strategical
importance, early adopter etc).
• Work with Marketing & Sales team
to define most suitable retention
offers based on profit/loss
calculation.
• Provide proper communication to
Salesmen, Retailers, Telesales.
Stress Data Science analytics help
& importance in given selection.
• Prepare clear offer communication
to customer based on his need
and behavior.
• Take care of GDPR.
2.
2

8. Agregated
Data Analytics
as a Service
New services
Real-time
offering own
products
Real-time
offering 3rd
party products
Foot-traffic over time
Structure of devices
Apps penetration, # users
Daily/Monthly Internet add-ons
Roaming add-ons
Digital services
Targeted shopping offerings
based on location and time
Customers
• Consultancy on
data analytics
• Join products &
service outline
• Marketing/sales
• Bank Mobile App:
Penetration &
share
• Foot traffic on
ATM
• Foot-traffic on
locations in time -
h/m/d
• Gender
Market reserach Banks New retail
Offer New services based on Big Data & Data Science.
Aggregated
Data from IoT
Additional tool for steering sales
demand towards Play+ TPs with
higher monthly fee.
• How many
roamers are in
country?
• How many in city?
• Average stay?
Travel agency
Shopping alerts
Interest ARPU Location Gender Time
<50$
50-100$
>100$
Smart Cities Vehicle manager Vessel manager
Own products
• Use data-science to select eligible customers.
• Trigger offers in right moment (walking close to shop).
• Offer Digital services during usage.
2.
3Telco industry examples

9. Data Science
value in long-term
Key Enablers for
Data Science Success
Employee skills, fit &
compensation
AGENDA
2.1. 3. 4.
Practical Examples,
B2B

10. Business Management
Leader
Data Science in
Business
Data Scientists
Core-business
employees
• Motivate
• Translate
• Give challenging tasks
• Innovation
• Recognition
• Team work in corporation
Employee skills, fit & compensation.
• Lack of Data Science managers - a link between Data Scientists & Business managers.
• Provide motivation/interesting tasks for Data Scientists while validating's investments in business.
• Lack of skillful Data scientists
• Compensation of Data Scientists among other Employees (Appling different pay scales)
3.

11. Data Science
value in long-term
Key Enablers for
Data Science Success
Employee skills, fit &
compensation
AGENDA
2.1. 3. 4.
Practical Examples,
B2B

12. D
B
Next Best Offer (NBO) – best suitable product in right time.
A
C
Insights
From 3600 customer view
NBO algorithm
Uptake probability + real-
time trigger
+ business rules
Best offer
Ranked list of offers
Channels/
Front end/
Communicate to
customer at any touch point
Usage
Behavior
Billing
information
Demographic
Network
information
Offer 1
Offer 2
Offer 3
Black list check
1
4.
App

13. Churn prediction modeling: Identify critical MSISDN with
highest probability to churn using Machine Learning in „R“.
Telesales Renewal
Churn Modeling in „R“
Take 24months
data on churn
Extract important
features
Predictive model for
Churn in next
3months
List of potential
churners
• Detailed data sharing
• GDPR
• NDA
• Secure transfer
• 2 Months in advance
• Partial base
• New feature extraction
• More months in advance
• Full customer base
Pilot with
Innovative companies
Vendor
4.

14. Key accounts recommendation for B2B customers on click in
CRM system.
Customer acceptance
Based on
recommendation
Negotiation
Data base storage
CRM system
Recommendations
available during
Customer Lifecycle.
Key Account Manager
4.

15. SUMMARY
• Understanding of Data Science long-term value -transformation
• Top Management support
• Employee skills, fit & compensation
• New & existing services commercialization
1
2
3
✓
✓
✓• Revenue from Existing services through old & new channels
• Retention success
• New Service introduction & monetization
• Pilot projects with start-ups in Data Science
• Hire/gather Data Scientists with Business acumen to start business
• Cooperate with University
• Start with Open-source SW for knowledge sharing (R, Python)
• In-house/external trainings & incubators for Data Scientists
• Spread best-practise from other countries
For Data Science penetration in corporations, important gradients are:
To maximize Data Science return on investment, we need to maximize
several commercial steps:
Possible steps to take