Cognitive Analytics for Customer and Network Insights - Mark Barnett Head of End to End Solutions, NOKIA, Central Europe and Central Asia - ITU
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Cognitive Analytics for Customer and Network Insights Mark Barnett Head of End to End Solutions, NOKIA, Central Europe and Central Asia 1 © 2018 Nokia
2
What have big data and apples in common?
Data is generated… …collected… …stored…
…and this is what we base
our big data analytics on…
2 © 2018 Nokia
End-customer expectations change
Dependent Empowered
Retail only Buying online
Seller’s terms Buy on own terms
Wait for delivery Instant gratification
Mass offers Personalized
Static plans Digital services
Multi-channel Omni-channel
Buy products Rent services
3 © 2018 Nokia
Today’s typical operator customer challenges
Retain high Find new Move to Improve agility
value revenue customer through right
customers streams faster centric network data at the
management right time
4 © 2018 Nokia
Questions we help address across the operator organization
For Operations For Marketing
• Who is impacted? Are they high value subscribers in • How are my customers using our services?
mobile or fixed? • Whom to target for marketing promotions / campaigns?
• Where are they impacted? And how many? • To whom should I upsell my services?
• What are the top problems? • Who is likely to churn? Whom to retain?
• How do I prioritize what & where to fix first? • Which device portfolio to optimize? Which to upsell?
For Care For Management
• What is the issue being faced by this customer? • Are my subscribers satisfied? What is their satisfaction
• What is this customer’s profile? Is he is a high value level?
subscriber? What is his satisfaction score? • Which locations / regions are performing well / worst?
• What should I do next? (next best action) • How are my high value subscribers using our services?
What are their top issues?
• Can I avoid recurrence of this problem?
5 © Nokia 2018
C o r e a n d b a c k g ro u n d c o lo r s :
R 18 R 0 R 104 R 168 R 216
G 65 G 201 G 113 G 187 G 217
B 145 B 255 B 122 B 192 B 218Nokia vision of how to put it all together 6 © Nokia 2018
Customer Insight
Connected set of analytics apps, based on telco optimized Big Data and Analytics architecture
Operations Engineering Care Marketing Business analysts Data scientists
Insight applications across touch points Purpose built use cases
To address business needs of
Analytics different teams –
use case operationalized with CEM
Customer Customer Customer OTT & Network library Office
Experience Care Quality Service Operations
Index Insights Insights Experience Insights
Big Data analytics
to compute holistic and deep
User interface Toolkits & SDKs API view of each customer in
operator’s network
Analytics/Machine Learning
Big Data
Data
from different network &
Data integration – Near real-time & batch data ingestion business touch points
Network data (fixed, radio, core, application), IT/CRM/BSS, Devices, Customer Care, Surveys between customer and
operator
7Customer Experience Index evolution
CEI is a composite score between 0-100 providing a near real-time measurement of satisfaction levels of
78 any customer in the operator network.
Before: Delivered experience 2017: Perceived experience
in 2018: Cognitive experience
100 100 100
…
…
…
0 0 0
CEI score (0-100) calculated based on CEI score calculated based on new non- CEI score calculated based on machine
linear model linear model learning algorithm
• Experience as delivered by network • Closer to experience as perceived by • Faster time to market with auto-tuning
human • Improved accuracy & personalization
• Computed for every customer and
calibrated against survey data • Sensitivity per segment/region/device
8 © 2018 NokiaCognitive Experience: Powered by Machine learning and Deep learning in CEI
Automated CEI calibration Predictive CEI
Calculates optimized weights for KPIs Uses KPI data with optimized weights and customer
satisfaction surveys to calculate scoring
Deeplearning ML chooses the
CEI best algorithm
Randomforest family for the
most accurate
KPIs GBM CEI at the given
time
Auto-tuning of CEI The most accurate scoring
9 © 2018 NokiaCell Site Degradation Prediction
Prevent service degradation and network outages
Average precision
Nokia
AVA
80 %
Network data, operational Predictive analytics, pattern Automated alerts, faster root Predict cell site degradation up
data weather patterns, social recognition. Statistical and machine cause analysis and prioritized to 7 days in advance
media sentiment learning methods recommended actions
10 © 2017 NokiaSimilar Ticket Recognition WIN
NER
AI to decrease time spent resolving trouble tickets
Increase troubleshooting
effectiveness up to 40%
AI analyzes previous New entry to the
tickets learning model
Nokia MIKA Faster resolution of netw ork
issues through AI
Support case Nokia MIKA guides engineer to
opened fastest resolution path
11 © 2017 Nokia• Indoor Building Products
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