1. Business Analytics Applications in Budget Modelling
To Improve Network Performance
Eleni Rozaki
Institute of Technology Tallaght, Dublin Ireland

2. Key Principles of the Project
Integrate all Network Data Sources
Mobile Network Performance Management
Cost Considerations and Customer Segmentation
End to End Optimisation using Business Analytics
Budget Planning Classification Rules using Predictive Analytics

3. Data Sources in Mobile Networks
Rate of Revenue
TECHNICAL
Customer churn
Revenue
Cost
Texts
Calls
Networking
Optimise
Manage
Customer
Profiles
CRM
Billing
Predict
Business Analytics
Rate of cost of optimisation services

4. Automate the Network Optimisation process

5. Cost Considerations and Network Performance
Estimate network optimisation costs , overheads and estimated revenue
Build a data mining model and estimate customer willingness to pay
For each customers deal, estimate the number of users
Using the estimates, determine the profit for mobile services
Choose the most profitable deals, prioritise customer profiles based on their needs

6. End to End Optimisation Using Business Analytics

7. End to End Optimisation Plan to Track Expenditures
Dynamic profiling & enhanced customer segmentation
Personalization of customer deals based on their needs
Identify newly emerging customer issues
Customer churn prediction
Budget planning-track expenditures

8. Review an efficient budget process and improve expenditure prioritisation
Network Data
Budget planning-Track expenditures
Reporting / Analytics
Revenue Data
Enterprise BI and reporting
Applications
Identify future revenue
and expenditure trends
Call Details
Enterprise Analytics Applications
Web and
Mobile
Text Data
Customer ERP/CRM Data
Customer web
And mobile Apps
App Data
Mobile App Server
Internet Data
Accuracy of
Forecasts
Channel Campaigns
Optimisation Plan
Marketing Campaign Mgt
Mail
Costs
SMS
Network Optimisation Cost
Print
POS
Marketing Cost
Budget Planning

9. Clustering Techniques
Research Methodology - Data Mining Models
Cost
Sensitive
Classifiers
Neural
Networks
Clustering Techniques
Deals
Customer profiles
Location
Demographics
Offers
Costs

10. An End to End Optimisation Model Using Clustering Techniques
Dynamic profiling
Personalisation of customer deals
Identify newly customer issues

11. A Budget Model Using Neural Networks
Budget planning-forecasting
Identify future revenue and expenditure trends
Cost efficiency in mobile networks
Financial and operational planning

12. Data Mining Models Cost Sensitive Classification Clustering
Neural Networks
Classification
Clustering
Association
Rules
Relations & Patterns
Prediction
Unsupervised Model
Sequence
Discovery
Backpropagation algorithm

13. Costs in Machine Learning
The learning algorithms of our cost model:
Minimising classification errors
Different types of misclassification have different costs based on the customer deals
Provide more informative output of performance assessment under highly imbalanced data
Make the prediction that minimises the expected cost

14. Results Clustering Neural Networks
Pros: Summarising information about customers profiles and customers deals. 77.5% accuracy
Cons: Numerical variables
Neural Networks
Pros:  Hidden relations between the different mobile deals and users profiles. 87.5% accuracy
Cons: Risk of overfitting the model

15. Conclusions 1
The strategic use of business analytics could bring major benefits to mobile service providers 2
Mobile providers need network optimisation solutions that are embedded in business analytics to correlate information from different sources 3
Data such as services, device portfolios, cost and billing and network service quality can be used for efficient customer segmentation 4
Business analytics helps with financial planning and ensures that network optimisation costs and budgets are accurate