AI-powered NBO programs for telcos

1. AI Powered Next Best Offer:
Opportunities and Challenges
www.exacaster.com
Elina Petrova
Director, Customer Experience at Vivacom
Sarunas Chomentauskas
CEO at Exacaster

2. Telecom industry has vast customer data reserves and
unique opportunities derived from leveraging this data
Opportunity #1 (700bn USD)
The upside from handling customer data
to support traditional telco product
portfolio growth is estimated to be up to
700bn.
Opportunity #2 – expanding product
portfolio (1000bn USD)
Telcos are expanding product portfolio with
digital products and customer data is one of
the key success factors to make this strategy
work well.
Opportunity #3 (55bn USD)
Selling derived data products to third
parties is already an established practice
and is expected to generate up to 5%
from total telco revenue.
Source: various analyst reports

3. Next Best Offer / NBA tackles the 700bn USD upside related to core product
portfolio on strategic, commercial and operational levels
Strategic Commercial Operational
1. Sync with customer needs and
wants
2. Enables product portfolio
expansion and digital
transformation
3. Reduces comparability to
competition via personalized
bundles
1. Improved customer experience in
digital and traditional channels
2. Incremental revenue from better
upsell/cross-sell execution
3. Reduced customer churn
1. Lower cost & higher automation in
customer base mgmt. decisions
2. Improved conversion across
multiple channels
3. Improved customer data and
product catalogue quality as a
byproduct
NBO/NBA implementations can focus on one of the three levels to deliver 9 key outcomes:

4. Exacaster is your Digital Transformation Partner specializing in
Telecom NBO and Customer Data Management
We crunch data on 40,000,000 telco subs daily spanning 3
continents
We are among the 50 fastest growing tech companies
in CEE
We are based in the
Baltic & Nordic tech
cluster that gave you
Skype, Spotify and
Revolut

5. We have travelled the Telco NBO implementation road more
than 30 times across the globe

6. We see that all NBO implementations touch on same key
themes and must overcome the same challenges
1. Be relevant to the customer
2. Be consistent across touchpoints
and time
 Who is the customer?
 Which products & services?
 Which channels?
 Who is responsible?
Key themes Key challenges to tackle

7. Challenge #1: Who is the customer?
Telecom companies have multiple service, account, contract etc.
hierarchies that obscure the real customer
House-
hold
Customer
Service
Customer 1 Customer 2
Broadband
Internet
Fixed line
service
Mobile
service
Mobile
service
Customer 3
Household
TV
service
Device
Typical telco customer identification hierarchy
!
Negative experiences for a single
customer could lead to
disconnection of all related
household services
• Allows to understand
individual service use &
status
• Allows to understand customer
engagements with the company
& identify contact person
• Allows to understand
household engagements with
the company and relationships
between customers
• Allows to identify used
devices & related network
issues
• Mobile number
• Fixed line number
• Email address
• Person
• Passport ID
• Personal code
• Household ID
• Address
• Cable_modem
• Set top box
• Mobile phone
• USB dongle
Level Context
Representative data
examples
Data
Quality
Typicallygooddata
quality
Typicallybaddata
quality

8. Self service data
processing for
business users
Identity resolution
tailored for telco
business cases
Connectors to
multiple source
and destination
systems
Automatic
aggregations
Strict data
quality checks
You need a Customer Data Platform
designed 100% for telecoms
CDP is a new category of software which brings
additional capabilities vs traditional data
warehousing & big data technologies.
The key focus is on customer identity resolution,
easy self-service data management and real time
capabilities for CRM & Marketing business
stakeholders.

9. Challenge #2: Which services and products to cover?
Telcos have a wide variety of services with complex rules
Mobile Single
Postpaid
Upsell
Cross-sell (Mobile2Home)
Device upgrade
Retain
Prepaid
Retain
Migrate to postpaid (Pre2Pos)
Cross-sell (Mobile2Home)
Home
Single
TV
Retain
Upsell
Cross-sell (TV2BBI)
Cross-sell (Home2Mobile)
BroadBand Internet
Retain
Upsell
Cross-sell (BBI2TV)
Cross-sell (BBI2Mobile)Fixed Voice
Multiple
Bundles
Retain
Upsell
Cross-sell (Home2Mobile)Non-Bundled
Converged offering
Home services Single/Multiple Retain
Upsell/Cross-sell
Prepaid + Home services Multiple
Migrate to postpaid
Postpaid + Home services Multiple Retain / Upsell
Service category Services package Offer / Product Action
Don’t underestimate:
Telecom NBO is at least 10x more
complex than digital retailers’
personalization setup.
A total of 22 major scenarios listed on the left must be
covered by NBO/NBA to achieve full scope in a
converged services portfolio provided by a typical quad-
play telecom company today.
This is often further increased by a variety of value-
added and digital services.
Compare this complexity to the one or two major
recommendation scenarios which e-commerce players
often implement.

10. Years of data science work can be cut by
reusing existing Telco NBO frameworks like
ours
Exacaster NBO
engine
Deep Learning
neural
networks
ML classifiers
ML clustering
Collaborative
filtering
Association
rules & graphs
Elementary AI / ML
building blocks
Customer scoring
Product
recommenders
Price plan & bundle
recommenders
Compatibility &
eligibility rule
engines
Churn risk
Credit risk
Probability to save
Value after migration
Devices
Content
Offers
Price plans
Bundles
Technical
Commercial
A pre-built telco industry best practice algorithms framework
Your unique NBO Set-up
Or Build from scratch in house?
Consulting Staffing
Learning
agile
Learning the
industry
Realizing
the NBO
scope
Searching
for right
data…
…
Configuration
Go Live and
AB test forever

11. Challenge #3: Which channel to use? Telecoms have a broad array of
channels including digital, retail, call center etc each with own quirks
Approach 1. All products in all channels? Approach 2. Some channels sell specific products?
POS
Call center and
telemarketing
Apps/ Web
Product 1 Product 2 Product 3
Product 1 Product 2
Product 3
Smart TV
Product 3Product 1 Product 2
Product 4
Product 4
Product 4
or
POS
Call center and
telemarketing
Apps/ Web
Smart TV

12. NBO must be embedded in a seamless way between Big Data & existing
BSS, channel and reporting platforms to support any future channel/product
Data Storage – Data
lake
Information from
multiple sources is
stored in a data lake
Data integration -
ETL
Map, identify and
extract additional
required data sets
Data Sources
Usage of multiple data
sources from different
locations
ML / AI engines
Leveraging machine
learning algorithms to
empower better
decisions
Reporting &
Visualization
Reporting and tracking
results in order to
improve performance
Customer 360 profile
The information is used
to construct > 700
metrics about
individuals
Supporting channel execution
Decisioning is executed and integrated with existing
channel systems
1 2 3 4 5 6 7
Tables
Extraction
Clean, Reconcile,
Enrich & Transform
Data
On prem or
AWS Data Lake
Operational
masterdata
Billing
Datasources(analytics,BSS)
BSS
CRM
Customer 360
Next best offer
engines
Churn and Next
best Action
algorithms
Micro
segmentation
Long term learning
feedback loop
Real time
E-mail
SMS, WhatsApp,
etc
POS
Self-service
(WEB)
Call center,
Telemarketing
Precalculated
API
Scoring and
Recommendations
Customer
Expose
recommendations to
end user
Track results of
campaign
Live learning feedback
loop
NBO/NBA

13. Challenge #4: Who is responsible? NBA/NBO by its nature cuts across
departments and needs an agile approach with top management support
IT
Commercial
Sales/Channel
operations
Digital
Products …
Segments …
Customer Care
Retail
BI
Me!
Me!
Me!
Me!
Me!
Me!
We!

14. After the project: typical commercial impacts delivered by NBO
Most projects achieve ARPU gain between 5% and 10%
28%
14%
42%
14%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
25% and up 10-25% 5-10% Below 5%
33%
15%
22%
30%
0%
5%
10%
15%
20%
25%
30%
35%
25% and up 10-25% 5-10% Below 5%
Most projects achieve Churn reduction of 25% or higher
Improving channel focus, reach & conversion - main driver of impact when NBO initiative matures
Typical unfocused channel performance (monthly)
• 23% channel reach
• 10% channel conversion
Typical focused channel performance (monthly)
• 10% channel reach
• 46% channel conversion
2% 4,6%
ARPU uplift Churn reduction

15. Implementing NBO at
Vivacom
15

16. Bulgarian Telecom Market
▪ 140% penetration
▪ 3 play market with almost equal subscriber share
▪ Shift from volume to value
▪ From minutes to data and content
▪ 60% Internet penetration
▪ 85% Pay TV penetration
▪ More than 600 players
▪ 2 convergent operators: Vivacom & A1

17. ▪ Incumbent operator
▪ ~5500 employees
▪ ~2 mln customers
▪ Launched Mobile in 2005, market leader in revenue in six
consecutive years
▪ # 1 in IPTV and FTTx, fastest 4G mobile network
▪ Fully converged services portfolio
▪ Digital transformation programme started in 2017
About Vivacom

18. Offering Process Before
Data extraction Analysis Offers assignment
Campaigns
organization
Results
tracking/Reporting
Monthly data extraction
Data export in MS excel
KPIs calculation in MS
excel
Manual offer
calculation
Automatic offer in POS
Customer database
upload for external call
centers
Difficult to execute in
the details needed
1 2 3 54
▪ Very broad services & products portfolio (hundreds of options)
▪ Complex technical and commercial eligibility rules
▪ Different sales channels
▪ Emerging digital channels (e-commerce)

19. Our Journey
▪ Exacaster and Vivacom formed a joint delivery team to implement NBO
▪ Roll out of Exacaster Customer Profile, Exacaster Customer Journeys and
Exacaster NBO solutions
▪ Customer data quality improvements: 6 months
▪ Segmentation & NBO implementation: 4 months

20. Offering Process After
Daily 360 profile
recalculation with 800+ kpis
Automatic lifecycle
and usage
segmentation
Automatic offer
assignment
Automatic offer in
POS, e-shop and
ext. call centers
Daily dashboard
Data extraction Analysis Offers assignment
Campaigns
Organization
Results Reporting
1 2 3 54
▪ The team role changed from executor to supervisor
▪ Much faster reaction and flexibility with automation
▪ Fast, easy and detailed results tracking
▪ A/B testing capability
▪ Implementation of Customer 360 profile improved existing BI data quality

21. Main Learnings
▪ Daily data means we are faster than everyone else now
▪ We pioneered Agile because Waterfall here is not efficient
▪ Managing expectations - scope can become very large
▪ Big shift from just doing to strategic segment management
▪ Data quality – solving this needs time
▪ Offer catalogue – another crucial prerequisite (developed
internally)

22. asasas
Thank you – visit our stand for more!
Elina Petrova
Director Customer B2C
Elina.petrova@vivacom.bg
Sarunas Chomentauskas
CEO
sarunas@exacaster.com