north stream white paper

8/12/2019 North Stream White Paper

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Operationalefficiency

Network

Sales

Analytics

Subscriberlifecycle

Financialperformance

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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%


! 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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! 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


Subscriberlifecycle


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%


! 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%


Subscriberlifecycle


Customer Care Retention Fixed Costs


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! 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


Subscriberlifecycle


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%


9/12

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