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8/12/2019 North Stream White Paper
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Operationalefficiency
Network
Sales
Analytics
Subscriberlifecycle
Financialperformance
0
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2008 2009 2010 2011 2012
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Financial performanceUse cases Description
Revenue
Channel optimization Product portfolio optimization Pricing optimization
predict the best channels for each product and optimize distributor margins analyze product portfolio to identify unserved customer segments etc.
predict customer price sensitivity for complex plans (roaming, voice and data etc.)
Variable cost
Acquisition cost optimization Retention cost optimization predict customers most likely to respond positively to new offers focus resources on at-risk high value customers and identify best retention offerFixed cost
Customer care cost reduction Marketing analysis/optimization reduce care calls, tickets and truck rolls through identifying problem commonalities improve efficiency and execution of campaignsCAPEX
Infrastructure planning Traffic optimization plan infrastructure investments based on network and data usage analysis route traffic to efficiently load networksAccounting/Forecasting
Wholesale reconciliation Revenue leakage Customer lifetime value
identify sources of discrepancy and reconcile interconnect charges identify revenue leakage due to system misconfiguration or failed components
predict customer lifetime value through behavioral and service usage analysis
Subscriber lifecycleUse cases Description
Attraction
Customer insight and targeting Sales and channel analysis create target profiles based on analytics of product usage, customer behavior identify the most suitable channels and sales strategy for each productAcquisition Value segment prediction New customer analysis predict the future value segment of a new customer based on initial data analyze new customers to assess success of marketing campaignsService Delivery Contextual offers Service quality improvement High value service upsell
tailor offers based on context such as customers location
configure network to optimize service quality through performance data
target subscribers most likely to acquire additional service
Billing
Fraud detection Bad debt forecasting detect sources of fraud such as cloned SIMs, device theft, top-up vouchers misuse forecast bad debt based on analysis of subscriber payment historyRetention Churn prediction Churn prevention Competitor destination
prediction
identify the most likely churners based on predictive analytics
tailor personalized offer to potential churners
predict which service provider customers are churning to
Operational efficiencyUse cases Description
Network Capacity management Performance management identify and prevent network congestion based on service usage analytics monitor and ensure consistent service quality regardless of location, device etc.Customer care Customer problem case analysis Priority customers service Customer sentiment
analyze customer problems, speed of resolution etc. to improve customer care
identify priority customers and ensure their customer service satisfaction
detect customer sentiment through social media analysis
Products, Sales and Marketing Customer profiling/segmentation Top-up optimization Product analysis
360 customer insight based on demographics, product, digital usage, billing etc.
create promotions, tiered pricing etc. based on individual subscriber behavior
analyze product performance, margins, cannibalization, price changes etc.
Regulation/Governance
Contract/SLA enforcement Roaming analytics Regulatory reporting
track network performance to ensure vendors compliance with contracts
analyze national and international roaming patterns and usage monitor QoS to ensure compliance with spectrum license requirements
Management
Continuous businessoptimization
Predictive planning Internal staffing
optimize business processes based on identifying organizational bottlenecks etc.
plan allocation of resources for future needs analyze, predict and plan internal staffing needs
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Background and market context
! An African CSP is the leading operator in its country andhas managed, through a successful strategy focused on
low cost handsets and underserviced areas, to increase
its prepaid customer base
! However, the above strategy, together with competitivepricing from other players, has decreased prepaid ARPU
and pushed down on margins
! The CSP faces the challenge of increasing stickinessamong prepaid segment and top-up revenues
Top-up optimization solution
! The top-up optimization solution identifies the customerslikely to respond positively and tailors a personalized
offer with a top-up reward (e.g. Top-up $10 now, get $3
extra)
! The CSP deployed the top-up optimization solution. Forthe analysis they used data sources such as CDRs,
credit balance etc. in order to select customers to targetand identify a personalized offer
ResultsThe results were compared between a series of monthly top-
up stimulation campaigns executed by the CSP without using
any analytics and a series of campaigns using the top-up
optimization analytics. The target group for both campaigns
was 40%. The resulting impact was put in the context of the
CSPs overall business performance.
By extracting and analyzing raw data (CDRs, CRM customer
profile, top-up server data, service usage etc.), the top-up
optimization solution provided a 63% increase in campaign
net revenue. The solution can be implemented in near real-
time with 'closed loop' features, i.e. selecting the right action
for continued campaigning.
The data analytics vendor was Comptel.
Top-up optimization analytics increased the campaign net revenue in prepaid segment by 63%
Use case mappingOperational
efficiency
Subscriber
lifecycle
Financial
performance
Products, Sales and
MarketingService Delivery Revenue
Old CampaignCampaign using
analytics
Increase in campaign net revenue
from analytics solution63%
Increase in operators total prepaid
Revenue0.6% 1.0%
Background and market context
! A South-East Asian CSP observed a slow uptake ofmobile TV service after its launch
! The CSPs marketing department had the objective tounderstand mobile TV usage, accelerate its adoption
among subscribers and increase the overall usage for
the current viewers
Mobile TV Upsell Solution
! The CSP conducted a SMS/MMS marketing campaignpromoting a premier league football mobile TV channel
! The campaign used analytics to target subscribers basedon demographics, device type (subscribers with the
devices that were best suited for mobile TV) and content
history (content interest, past viewing habits etc.)
The subscribers who received messages showed an initial
fivefold increase in uptake of the service (which stabilized at
twofold after a month) compared to subscribers who were not
targeted in the campaign. The campaign tracked a control
group and included untargeted segments in order tobenchmark performance and learn best practices. Among the
subscribers who were targeted by the campaign and saw the
promoted football match, 60% returned for viewing of next
match. The overall viewing time per subscriber increased by
16%, creating deeper service loyalty.
The data analytics vendor was Guavus.
A targeted upsell campaign using subscriber analytics led to a 5-fold
increase in Mobile TV uptake and usage
Use case mapping
Campaign benefits
Increase in uptake for mobile
TV channel
Immediate 5x for targeted
subs, stabilizing at 2x
Increase in avg. viewing time
(1 month)16%
Effectiveness of targeting
segments
2-4x more uptake than off
segment
Results
Supported by analytics, the CSP was able to conduct asuccessful marketing campaign that raised awareness for the
football channel and, by targeting the most likely viewers,
increased adoption of the service.
Operational
efficiency
Subscriber
lifecycle
Financial
performance
Products, Sales andMarketing
Service Delivery Revenue
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Background and market context
! An East European CSP is the countrys second largestoperator by revenue and subscriber base
! ARPU has been relatively stable the past few years butas the market has matured and mobile penetration has
increased, the new subscriber growth rate has dropped
! The CSP faces the challenge of retaining existingcustomers, while attracting new ones from a limited pool
Churn prevention solution
! The churn prevention solution is an extension beyondprediction as it not only identifies potential churners likely
to respond positively but also tailors a personalized offer
! It allows CSPs to increase the success rate of retentioncampaigns as the better personalized offers are more
likely to be accepted by potential churners
and a series of campaigns using the vendors churn
prevention analytics. The target group for both campaigns
was 12% of the prepaid customers. The resulting impact was
put in the context of the CSPs overall business performance.
By extracting and analyzing raw data (CDRs, CRM customer
profile, service usage etc.), the churn prevention solution
provided a 259% increase in campaign revenue gain. The
solution can be implemented in near real-time with 'closed
loop' features, i.e. selecting right action for continued
campaigning.
The data analytics vendor was Comptel.
Churn prevention analytics increased the campaign revenue gain in prepaid
segment by 259%
Use case mapping
Results
The results were compared between a series of monthly
campaigns executed by the CSP without using any analytics
Operationalefficiency
Subscriberlifecycle
Financialperformance
Products, Sales andMarketing
Retention Revenue
Campaign benefits
Increase in retained prepaid customersfrom analytics solution
3.6 times
Increase in campaign revenue
gain from vendors analytics solution259%
Background and market context
! A North American CSP had a lack of timely, in-depthinsight into the drivers behind customer care interactions
! The CSP was interested in improving their understandingof the drivers of customer care costs, but were having a
hard time overcoming the difficulty of correlating data
from numerous, disparate sources
! The CSP needed the information to be available quicklyto CSP employees from a variety of groups
Customer Care Solution
! The application collects and analyzes data fromnumerous disparate sources and provides actionableinsights
! The solution identifies which attributes are common oroutside of the norm regarding calls, tickets and truck rollsby using advanced analytics techniques
! Examples include device interoperability issues andunexpected impacts from scheduled maintenance
ResultsEstimates of processing requirements are more than 1m data
records daily, coming from more than 12 different systems, in
near real time.
A decrease in care events resulted from a reduction in mean
time to understand issues and more accurate, targeted call
deflections and the decrease in churn would come with better
customer experience.
Initial estimates put expected future savings to the CSP at
about $11 million in calls, tickets, truck rolls and operational
man hours. Additionally, an estimated 0.1% reduction in churn
will be achieved; churn today costs the CSP about $816
million.
The data analytics vendor was Guavus.
Analysis of customer care drivers is estimated to reduce interaction costs by $11m
and the churn rate by 0.1%
Use case mapping
Campaign benefits
Decreased call center, trouble ticket, andtruck role costs
$11m over lifetime
Decrease in churn rate through moreeffective customer care
0.1%
Operationalefficiency
Subscriberlifecycle
Financialperformance
Customer Care Retention Fixed Costs
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Background and market context
! A Latin American CSP suspected a local interconnectpartner of fraud based on large and systematic
differences in usage reporting. The CSP did not have the
expertise to reconcile the differing sets of records to
identify the correct wholesale cost and identify the cause
of the discrepancies
Wholesale reconciliation solution
! The wholesale reconciliation solution was used toanalyze the CDRs of both CSPs. The system collected
large quantities of CDRs and the records were filtered
down to those of interconnected calls during the periods
in question. The records were then transformed to the
same format for direct comparability and matched basedon a variety of call meta data fitting within certain
tolerances
! The application was able to resolve the CDRs of the twoCSPs and guide network engineers towards the common
point of failure in the interconnect records keeping
Results
Based on the analysis performed, it was found that incorrect
core network configuration was the reason for the
records discrepancy. While revenue was lost, it was not a
case of fraud.
Within two months, the CSP was able to reduce the mismatch
for the incoming minutes reported by 93% and the difference
for outgoing minutes by 80%. The application provided
information that aided in the root cause analysis of the
records discrepancy and let to its correction.
The data analytics vendor was Salamanca Solutions
International.
Wholesale reconciliation analytics helped CSP reduce discrepancy in interconnect charges by decreasing
mismatch in incoming minutes reported from 15% to less than 1%
Use case mapping
Operationalefficiency
Subscriberlifecycle
Financialperformance
Network Service DeliveryForecasting/accounting
Analytics benefits
Reduce the mismatch for the incoming
minutes reported
from an average of 15%
to less than 1%Reduce the mismatch for outgoing minutesreported
from an average of 5%to less than 1%
8/12/2019 North Stream White Paper
9/12
9
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