IT BI
Content
2011
Business Intelligence in Telecommunication Industry
By: Debesh Majumdar 09BM8016 Kumar Bipallav Mani 09BM8068
Contents
Pages 1. Introduction 2. Gartner Predictions 3. Analysis of the telecom industry
a. Key areas to be addressed b. Advantages of using BI in the telecom industry
2–4 4 4-5
4-5 5
4. Data Sources and Users 5. Data Warehousing Models 6. Strategic Business Intelligence Architecture 7. Technological Architecture 8. Key Performance Indicators (KPIs) for Telecom 9. BI Tools available 10. Gartner Magic Quadrant 11.TCO of BI applications 12. Conclusion
5-6 6-8 9 - 10 11 12 - 15 15 - 16 17 17 - 18 19
1
Introduction
The telecommunications industry is increasingly becoming more competitive because of the entry of number of competitors eager to get their share of pie in this exponentially growing business. One of the most important metric used by the telecommunications carriers to measure their success is by the size and growth of their profit margins. Therefore, the service providers are under intense pressure to squeeze profit from the slim margins available to them.
Why is Business Intelligence needed in Telecom Industry • Revenue leakage, costing the industry $100 billion annually This is one of the biggest issues facing the telecom service providers. Smart BI solutions can go a long way in plugging the loopholes and making revenue tracking accurate.
• Inaccurate or missed inter-carrier billing This happens especially in case of roaming when network providing the calling services changes from one telecom service provider to other providers due to changing locations.
• Fraud, a $12 billion annual industry problem Fraud is a real threat not only from the revenue generation perspective for the service provider but also for the overall security of customers and country at large. Exponentially increasing customer base clubbed with the ever increasing advancement in technology poses a huge challenge for the telecom providers to handle fraud. Advanced and robust BI solutions can be very useful in mitigating and eventually doing away with this menace.
•
Churn, a multi-billion dollar problem exacerbated by the wireless number portability requirement
2
Churn rate for telecom service users has always been a problem for the industry with customers switching over to the competing providers for lower price, better connectivity or some offers. With the introduction of mobile number portability this problem has only become more serious as the most important factor for not switching from one provider to another i.e. the mobile number is no longer an issue. BI solutions can be used to keep a check on the changing preferences of the customers in order to give them the flexibility of choosing the offers or service they want without switching to a competitor.
• Inefficient network usage and least cost routing plans Many a times we have seen the signals switching between different areas of the same service providers which clearly indicates a problem in the layout of the towers. Business Intelligence can be used very effectively to reduce the operational overheads. It can be especially useful in planning the layout of towers in a way which maximizes its utilization.
Key facts and predictions for the Indian Telecom sector: • The Indian Telecommunications network with 621 million connections (as on March 2010) was the third largest in the world. Indian telecom network had become the second largest in the world with total subscriber base of over 670 million at the end of July 2010. With this overall tele-density in the country had reached over 58% mark. In last five years not only urban tele-density had risen from 26% to more than 125%, but rural tele-density had also increased phenomenally from 1.73% to over 27%. • • The sector is growing at a speed of 45% during the recent years. Expected mobile subscriber base will touch around 771 million by the year 2013.
The service providers or carriers rely heavily on the analysis of tons of data they get from call detail records
3
(CDR). Increasingly powerful data warehouses and smarter business intelligence solutions are the only tools which can handle and make sense out of the terabytes of data and help them meet profit goals. Multiple departments in the firm provide in-depth data regarding various aspects, analysing and integrating these enables carriers to reduce revenue leakage and churn, mitigate fraud, optimize network usage and improve the bottle line thereby increasing profit. Large networks and their associated switches, call centres data, billing systems data and service department’s data can generate hundreds of millions of individual CDRs daily. These data are slated to grow exponentially as new services, advanced technologies, number portability etc. become common. The everexpanding volume of important data are the ingredients which are and would be creating a huge strain on the traditional RDBMS, servers etc. thereby necessitating the adoption of smarter Business Intelligence infrastructure and solutions.
Gartner Predictions:
Through 2012, more than 35 per cent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets By 2012, business units will control at least 40 per cent of the total budget for BI. By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.
Analysis of the telecom industry
Key areas to be addressed 1. Operations and budget analysis. 2. Capacity management. 3. Network operations management. 4. Customer churn management 5. Customer and market segmentation 6. Fraud management
4
7. Call centre management 8. Subscribers’ usage pattern management.
Advantages of using BI in the telecom industry: 1. Improving the customer experience which would also result in finding different torrents and avenues for revenue generation. 2. Individual call detail records (CDR) of customers could be recorded with unmatched scalability which would enable thorough analysis and provide incisive insights. 3. Advanced analytical functions for monitoring financial data. 4. Creating and analysing models to develop loyalty among customers. 5. Ensuring a full proof security of customer’s financial and personal data. 6. Continuously finding ways for providing customized offers and benefits to the customers their activities which would take care of their changing preferences. 7. Getting all the relevant information relating to the network status and service levels from all the circles and areas. 8. Keeping a tab on overhead expenses as well as finding ways to maximize individual’s efficiency by monitoring workforce expenses on customer services like call centres etc. based on
Data Sources and Users
Data Sources Call Detail (Switch) Billing Data (Billing Centre) Customer Care (Call Centre) Subscriber Data (Operational CRM) Network Performance (OSS) Data Users Marketing Sales Service Operation and Maintenance Network Planning
Table 1: Depicts the sources of data and the departments using the data.
5
The table shown above illustrates the various sources from where the data gets generated like Call centers and other point of customer service generates data which are used by the customer service department, similarly billing data are sources of data which are used by the sales department for gearing up their sales efforts based on the bill details. Like if the bill details contain major portion related to overseas calls, then the offers related to ISD calls can be targeted towards such customers.
Data Warehousing Models
Fig 1: Data Warehousing Model
6
Fig 2: Data Warehousing Model
The above two models are few of the many data warehousing models used by telecom companies. The model basically consists of Fact tables and dimension tables. Dimension tables contain attributes that describe fact records in the fact table. The attributes provide descriptive information in some cases while in other cases they are used to specify how fact table data should be summarized to provide useful information to the analyst. Dimension tables contain hierarchies of attributes that aid in summarization. For example, a dimension containing customer information would contain descriptions related to the customer like customer name, customer ID, customer’s circle, customer’s state, customer’s city of residence, zip code, house number and may be further subdivided so as to reach a level where identification of each customer becomes possible. Dimensional modelling produces dimension tables in which each table contains fact attributes that are independent of those in other dimensions. For example, a customer dimension table contains data about
7
customers, a tariff table contains information about the various tariff plans available, the pricing and duration related to each tariff plan etc. and a distance dimension table contains information about countries, circles, states, the originating point of the call and the terminating point of the call. Queries use dimension table’s attributes to specify a view into the fact information. For example, a query might use the customer, distance, date and tariff dimensions to ask the question "What was the cost of the last call made the XYZ customer on 10th April, 2011 at 11 pm?" Other queries can similarly be used to drill down to an even more complex or detailed data.
8
Strategic Business Intelligence Architecture
Fig 3: Strategic BI architecture.
A strategic BI architecture would ensure that real time data is captured from all the different sources possible and be robust enough to take care of expanding needs in the long run. Such a BI architecture is deployed keeping a longer term perspective and is flexible enough to take care of the increasing complexity.
9
A standalone CRM application in spite of the continuous improvements is not geared to match up to a fullfledged BI system’s analytical capabilities. CRM softwares can provide answers to relatively simpler queries but find little use in actual scenario and may not be very helpful in answering tough questions which will have actual business impact. There needs to be the ability to carry out what if analysis and other such comprehensive situational analysis in a highly dynamic market conditions, so as to come up with solutions to lingering problems or to the problems that may occur in future. Also almost all the departments are affected by any business decision and the level of complexity that needs to be handled can be handled only by a BI application. For example, activating a new customer affects many departments from customer and service support to accounting, credit approval, billing, network planning, network support, inventory management etc. There is very little visibility of the bottlenecks and disconnects in such a scenario. The activation of new customers has revenue implications and, therefore, affects the share price and market evaluation of the carrier and hence is a key business performance parameter. The same lack of visibility happens in many other areas as well: network operations and management, interconnect billing, service creation, and introduction of new services and offers, determining the profitability of various products, services, and call rate plans etc. The systems at most service providers have been built for financial and accounting purposes and do not provide adequate information across processes and functional areas to support business users in making business decisions based on the power of information. The business intelligence applications extract and connect disjointed systems and not-so-easily available data from disparate sources and enable the business user and decision maker to make an informed decision.
10
Technological Architecture:
Fig 4: Technological Architecture of BI suite
The generic technological architecture of a BI application is shown above. The data from various sources like Call Centres, Network Operations and Performance Centres etc. are extracted, transformed and then loaded into the Data Warehouse. Often there is a Data Mart which interfaces between the client side terminal and the Data Warehouse and basically acts as a cache and speeds up the retrieval of relevant data. Data mining tools are invariably present in a BI suite and form the backbone of the BI application. The data retrieved as a result of the query is used across the organization.
11
Key Performance Indicators (KPIs) for Telecom:
Call Centre • Wait times • Average speed of answer • Call volume • Number of complaints received • Revenue per call • Average quality of calls • Number of call transfers • Average call length • Number of one call resolutions • Abandon rates • Customer satisfaction • Number of calls answered within ten seconds • Agent efficiency
Systems and Network Performance Analysis/Capacity Planning • Availability • Grade of service • Service life of equipment • Bit error ratio (data, bits & elements transfer) • Downtime/Time out of service • Call completion ratio • Cost of support systems • Cost of operational systems • Average call length • Analysis of ASR routes • Network traffic, congestion • Idle time on network • Dropped calls
Revenue/Financial Analysis • Average revenue per user (ARPU) • Prepaid ARPU • ARPU from contracts • Revenue per voice-minute • % of non-voice revenue • Average revenue realization (ARR) • Minutes of usage (MoU) per subscriber • Average revenue per subscriber (ARPS) • Periodical Revenue Analysis • Analysis of company overhead • Profit and loss analysis • Recovery analysis
12
Customer Satisfaction •Average score from external surveys •Average score from internal surveys •Average score from call monitoring •Total number of complaints •Total number of unresolved issues •Number of responses generated
Quality/Usage Analysis of volume of successful calls •Mean opinion score •Service •Duration of calls •Billed amount of each calls
Coverage •% of land covered with services •% of population covered with services •Average land unavailable to services •Average population unavailable to services •Access to customer service
Customer Analysis •Customer segmentation •Analysis of subscriptions •Top N churns •Churn
Marketing •Effect of promo campaign on subscriptions •Trend analysis •Segment analysis •Call behaviour analysis
Faults and complaints •% of open and level of escalation priority required •% closed •Mean time to resolved •Work in progress •Customer service level statistics
Compliance/Service Analysis •Service connection •Timeframe for repairs and installations •Reliability •New service connections •Activations, deactivations, reactivations •Miscellaneous services •Waiting time •Waiting period before grant of service •% order error rates and reasons
Fraud Analysis •Normal Traffic •Identity deviations from normal traffic patterns •Normal usage per customer per area of country •Identify phone numbers of customers with high deviation
Fig 5: Key Performance Indicators and their usage according to the section:
13
Key Performance Indicators based on the type of Office:
Fig 6: KPIs based on the Front Office or Back office requirements.
Key Performance Indicators are a great way to track the parameters against which the effectiveness and performance of the organization can be gauged. KPIs help organizations prioritize among competing tasks by linking it to some visible and critical result like bottom-line, top line, reduction in number of complaints etc. In this way they act as reflect the organization’s key business drivers (KBDs) also known as critical success factors(CSFs)
Key Performance Indicators should be defined based on the following: • • • • Specific – The KPIs should be very focused and specific leaving no room for ambiguity. Measureable – They should be easily measurable. Achievable – They should be set at a level which can be achieved. Relevant or results based – KPIs should be framed keeping the organization’s strategies, goals, objectives etc. in mind. • Time bound – They should be time bound so as to create a sense of urgency and motivate the employees to achieve them.
14
Once the KPIs are selected they can be used based on the importance and urgency by creating facts and dimension table in the Data Warehousing model which can collect the relevant data and aid in further analysis as well as keep a check on the same.
BI Tools available
Fig 7: List showing major commercially available BI tools and current version available.
The market for Business Intelligence tools is increasingly rapidly not just in telecommunication sector in all major industries. Many commercial players are already in the market with complete suites to cater to the different needs of the customers in various industries. The BI tools market is mainly dominated by Oracle, SAP, Microsoft, IBM and Microstrategy.
There are many open source solutions available and the smaller to medium sized firms especially prefer these as against the commercially available solutions mainly due to the lesser cost incurred upfront. Also this provides them with more flexibility in checking the suitability of the BI solutions in their organization
15
as these open source solutions provide a minimum of a month long free trial. The total cost of ownership for these solutions is lower compared to the commercially available solutions.
Fig 8: Opinions about the total cost of ownership of open source solutions.
The major open source BI tools vendors are shown below:
Fig 9: Few of the major open source BI tools vendors.
16
Gartner Magic Quadrant
Fig 10: Gartner’s Magic quadrant showing the relative positions of market players in BI tools.
TCO of BI applications
Fig 11: Pie chart depicting the share of Total Cost of Ownership in an organization.
17
The total cost of ownership (TCO) of Business Intelligence applications in a typical organization is split mainly into the following:
Staffing: accounts to close to 50% Software: accounts for about 20% Hardware: accounts for about 8% Training: accounts for about 8% Downtime: accounts for 5% Others: accounts for 9%
Fig 12: Comparison between the traditional reporting system and BI solutions in terms of TCO
18
Conclusion The telecommunication industry is one of the first industries to adopt the Business Intelligence solutions mainly due to the ever increasing data that they have to handle. Most of the telecommunication companies have moved to scalable and smarter BI solutions which have helped them in making significant reduction in their bottomline and increase their topline by customizing the services to be offered to their expanding customer base. Anticipating customer needs and providing them with the services they want at cheaper cost has vastly improved the loyalty of the customers towards their service providers. This has become a possibility largely due to the advanced and robust BI solutions which can provide access to real time and relevant data in various formats which enables the management to make strategic decisions.
BI solutions have not only helped the telecom companies in increasing the customer loyalty, it has helped every department of the organization to achieve a level of efficiency which can hardly be imagined without a successful implementation of Business Intelligence solution. It has helped in improving operational, marketing, customer service and financial efficiency to name a few. The growth of the BI applications is only slated to grow in the future as the competition few stiffer. One of the major challenges facing any organization using the BI solution is getting the most out of it by effective implementation of the solution which can only take place in a true sense when the whole organization participates enthusiastically.
19
Business Intelligence in Telecommunication Industry
By: Debesh Majumdar 09BM8016 Kumar Bipallav Mani 09BM8068
Contents
Pages 1. Introduction 2. Gartner Predictions 3. Analysis of the telecom industry
a. Key areas to be addressed b. Advantages of using BI in the telecom industry
2–4 4 4-5
4-5 5
4. Data Sources and Users 5. Data Warehousing Models 6. Strategic Business Intelligence Architecture 7. Technological Architecture 8. Key Performance Indicators (KPIs) for Telecom 9. BI Tools available 10. Gartner Magic Quadrant 11.TCO of BI applications 12. Conclusion
5-6 6-8 9 - 10 11 12 - 15 15 - 16 17 17 - 18 19
1
Introduction
The telecommunications industry is increasingly becoming more competitive because of the entry of number of competitors eager to get their share of pie in this exponentially growing business. One of the most important metric used by the telecommunications carriers to measure their success is by the size and growth of their profit margins. Therefore, the service providers are under intense pressure to squeeze profit from the slim margins available to them.
Why is Business Intelligence needed in Telecom Industry • Revenue leakage, costing the industry $100 billion annually This is one of the biggest issues facing the telecom service providers. Smart BI solutions can go a long way in plugging the loopholes and making revenue tracking accurate.
• Inaccurate or missed inter-carrier billing This happens especially in case of roaming when network providing the calling services changes from one telecom service provider to other providers due to changing locations.
• Fraud, a $12 billion annual industry problem Fraud is a real threat not only from the revenue generation perspective for the service provider but also for the overall security of customers and country at large. Exponentially increasing customer base clubbed with the ever increasing advancement in technology poses a huge challenge for the telecom providers to handle fraud. Advanced and robust BI solutions can be very useful in mitigating and eventually doing away with this menace.
•
Churn, a multi-billion dollar problem exacerbated by the wireless number portability requirement
2
Churn rate for telecom service users has always been a problem for the industry with customers switching over to the competing providers for lower price, better connectivity or some offers. With the introduction of mobile number portability this problem has only become more serious as the most important factor for not switching from one provider to another i.e. the mobile number is no longer an issue. BI solutions can be used to keep a check on the changing preferences of the customers in order to give them the flexibility of choosing the offers or service they want without switching to a competitor.
• Inefficient network usage and least cost routing plans Many a times we have seen the signals switching between different areas of the same service providers which clearly indicates a problem in the layout of the towers. Business Intelligence can be used very effectively to reduce the operational overheads. It can be especially useful in planning the layout of towers in a way which maximizes its utilization.
Key facts and predictions for the Indian Telecom sector: • The Indian Telecommunications network with 621 million connections (as on March 2010) was the third largest in the world. Indian telecom network had become the second largest in the world with total subscriber base of over 670 million at the end of July 2010. With this overall tele-density in the country had reached over 58% mark. In last five years not only urban tele-density had risen from 26% to more than 125%, but rural tele-density had also increased phenomenally from 1.73% to over 27%. • • The sector is growing at a speed of 45% during the recent years. Expected mobile subscriber base will touch around 771 million by the year 2013.
The service providers or carriers rely heavily on the analysis of tons of data they get from call detail records
3
(CDR). Increasingly powerful data warehouses and smarter business intelligence solutions are the only tools which can handle and make sense out of the terabytes of data and help them meet profit goals. Multiple departments in the firm provide in-depth data regarding various aspects, analysing and integrating these enables carriers to reduce revenue leakage and churn, mitigate fraud, optimize network usage and improve the bottle line thereby increasing profit. Large networks and their associated switches, call centres data, billing systems data and service department’s data can generate hundreds of millions of individual CDRs daily. These data are slated to grow exponentially as new services, advanced technologies, number portability etc. become common. The everexpanding volume of important data are the ingredients which are and would be creating a huge strain on the traditional RDBMS, servers etc. thereby necessitating the adoption of smarter Business Intelligence infrastructure and solutions.
Gartner Predictions:
Through 2012, more than 35 per cent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets By 2012, business units will control at least 40 per cent of the total budget for BI. By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.
Analysis of the telecom industry
Key areas to be addressed 1. Operations and budget analysis. 2. Capacity management. 3. Network operations management. 4. Customer churn management 5. Customer and market segmentation 6. Fraud management
4
7. Call centre management 8. Subscribers’ usage pattern management.
Advantages of using BI in the telecom industry: 1. Improving the customer experience which would also result in finding different torrents and avenues for revenue generation. 2. Individual call detail records (CDR) of customers could be recorded with unmatched scalability which would enable thorough analysis and provide incisive insights. 3. Advanced analytical functions for monitoring financial data. 4. Creating and analysing models to develop loyalty among customers. 5. Ensuring a full proof security of customer’s financial and personal data. 6. Continuously finding ways for providing customized offers and benefits to the customers their activities which would take care of their changing preferences. 7. Getting all the relevant information relating to the network status and service levels from all the circles and areas. 8. Keeping a tab on overhead expenses as well as finding ways to maximize individual’s efficiency by monitoring workforce expenses on customer services like call centres etc. based on
Data Sources and Users
Data Sources Call Detail (Switch) Billing Data (Billing Centre) Customer Care (Call Centre) Subscriber Data (Operational CRM) Network Performance (OSS) Data Users Marketing Sales Service Operation and Maintenance Network Planning
Table 1: Depicts the sources of data and the departments using the data.
5
The table shown above illustrates the various sources from where the data gets generated like Call centers and other point of customer service generates data which are used by the customer service department, similarly billing data are sources of data which are used by the sales department for gearing up their sales efforts based on the bill details. Like if the bill details contain major portion related to overseas calls, then the offers related to ISD calls can be targeted towards such customers.
Data Warehousing Models
Fig 1: Data Warehousing Model
6
Fig 2: Data Warehousing Model
The above two models are few of the many data warehousing models used by telecom companies. The model basically consists of Fact tables and dimension tables. Dimension tables contain attributes that describe fact records in the fact table. The attributes provide descriptive information in some cases while in other cases they are used to specify how fact table data should be summarized to provide useful information to the analyst. Dimension tables contain hierarchies of attributes that aid in summarization. For example, a dimension containing customer information would contain descriptions related to the customer like customer name, customer ID, customer’s circle, customer’s state, customer’s city of residence, zip code, house number and may be further subdivided so as to reach a level where identification of each customer becomes possible. Dimensional modelling produces dimension tables in which each table contains fact attributes that are independent of those in other dimensions. For example, a customer dimension table contains data about
7
customers, a tariff table contains information about the various tariff plans available, the pricing and duration related to each tariff plan etc. and a distance dimension table contains information about countries, circles, states, the originating point of the call and the terminating point of the call. Queries use dimension table’s attributes to specify a view into the fact information. For example, a query might use the customer, distance, date and tariff dimensions to ask the question "What was the cost of the last call made the XYZ customer on 10th April, 2011 at 11 pm?" Other queries can similarly be used to drill down to an even more complex or detailed data.
8
Strategic Business Intelligence Architecture
Fig 3: Strategic BI architecture.
A strategic BI architecture would ensure that real time data is captured from all the different sources possible and be robust enough to take care of expanding needs in the long run. Such a BI architecture is deployed keeping a longer term perspective and is flexible enough to take care of the increasing complexity.
9
A standalone CRM application in spite of the continuous improvements is not geared to match up to a fullfledged BI system’s analytical capabilities. CRM softwares can provide answers to relatively simpler queries but find little use in actual scenario and may not be very helpful in answering tough questions which will have actual business impact. There needs to be the ability to carry out what if analysis and other such comprehensive situational analysis in a highly dynamic market conditions, so as to come up with solutions to lingering problems or to the problems that may occur in future. Also almost all the departments are affected by any business decision and the level of complexity that needs to be handled can be handled only by a BI application. For example, activating a new customer affects many departments from customer and service support to accounting, credit approval, billing, network planning, network support, inventory management etc. There is very little visibility of the bottlenecks and disconnects in such a scenario. The activation of new customers has revenue implications and, therefore, affects the share price and market evaluation of the carrier and hence is a key business performance parameter. The same lack of visibility happens in many other areas as well: network operations and management, interconnect billing, service creation, and introduction of new services and offers, determining the profitability of various products, services, and call rate plans etc. The systems at most service providers have been built for financial and accounting purposes and do not provide adequate information across processes and functional areas to support business users in making business decisions based on the power of information. The business intelligence applications extract and connect disjointed systems and not-so-easily available data from disparate sources and enable the business user and decision maker to make an informed decision.
10
Technological Architecture:
Fig 4: Technological Architecture of BI suite
The generic technological architecture of a BI application is shown above. The data from various sources like Call Centres, Network Operations and Performance Centres etc. are extracted, transformed and then loaded into the Data Warehouse. Often there is a Data Mart which interfaces between the client side terminal and the Data Warehouse and basically acts as a cache and speeds up the retrieval of relevant data. Data mining tools are invariably present in a BI suite and form the backbone of the BI application. The data retrieved as a result of the query is used across the organization.
11
Key Performance Indicators (KPIs) for Telecom:
Call Centre • Wait times • Average speed of answer • Call volume • Number of complaints received • Revenue per call • Average quality of calls • Number of call transfers • Average call length • Number of one call resolutions • Abandon rates • Customer satisfaction • Number of calls answered within ten seconds • Agent efficiency
Systems and Network Performance Analysis/Capacity Planning • Availability • Grade of service • Service life of equipment • Bit error ratio (data, bits & elements transfer) • Downtime/Time out of service • Call completion ratio • Cost of support systems • Cost of operational systems • Average call length • Analysis of ASR routes • Network traffic, congestion • Idle time on network • Dropped calls
Revenue/Financial Analysis • Average revenue per user (ARPU) • Prepaid ARPU • ARPU from contracts • Revenue per voice-minute • % of non-voice revenue • Average revenue realization (ARR) • Minutes of usage (MoU) per subscriber • Average revenue per subscriber (ARPS) • Periodical Revenue Analysis • Analysis of company overhead • Profit and loss analysis • Recovery analysis
12
Customer Satisfaction •Average score from external surveys •Average score from internal surveys •Average score from call monitoring •Total number of complaints •Total number of unresolved issues •Number of responses generated
Quality/Usage Analysis of volume of successful calls •Mean opinion score •Service •Duration of calls •Billed amount of each calls
Coverage •% of land covered with services •% of population covered with services •Average land unavailable to services •Average population unavailable to services •Access to customer service
Customer Analysis •Customer segmentation •Analysis of subscriptions •Top N churns •Churn
Marketing •Effect of promo campaign on subscriptions •Trend analysis •Segment analysis •Call behaviour analysis
Faults and complaints •% of open and level of escalation priority required •% closed •Mean time to resolved •Work in progress •Customer service level statistics
Compliance/Service Analysis •Service connection •Timeframe for repairs and installations •Reliability •New service connections •Activations, deactivations, reactivations •Miscellaneous services •Waiting time •Waiting period before grant of service •% order error rates and reasons
Fraud Analysis •Normal Traffic •Identity deviations from normal traffic patterns •Normal usage per customer per area of country •Identify phone numbers of customers with high deviation
Fig 5: Key Performance Indicators and their usage according to the section:
13
Key Performance Indicators based on the type of Office:
Fig 6: KPIs based on the Front Office or Back office requirements.
Key Performance Indicators are a great way to track the parameters against which the effectiveness and performance of the organization can be gauged. KPIs help organizations prioritize among competing tasks by linking it to some visible and critical result like bottom-line, top line, reduction in number of complaints etc. In this way they act as reflect the organization’s key business drivers (KBDs) also known as critical success factors(CSFs)
Key Performance Indicators should be defined based on the following: • • • • Specific – The KPIs should be very focused and specific leaving no room for ambiguity. Measureable – They should be easily measurable. Achievable – They should be set at a level which can be achieved. Relevant or results based – KPIs should be framed keeping the organization’s strategies, goals, objectives etc. in mind. • Time bound – They should be time bound so as to create a sense of urgency and motivate the employees to achieve them.
14
Once the KPIs are selected they can be used based on the importance and urgency by creating facts and dimension table in the Data Warehousing model which can collect the relevant data and aid in further analysis as well as keep a check on the same.
BI Tools available
Fig 7: List showing major commercially available BI tools and current version available.
The market for Business Intelligence tools is increasingly rapidly not just in telecommunication sector in all major industries. Many commercial players are already in the market with complete suites to cater to the different needs of the customers in various industries. The BI tools market is mainly dominated by Oracle, SAP, Microsoft, IBM and Microstrategy.
There are many open source solutions available and the smaller to medium sized firms especially prefer these as against the commercially available solutions mainly due to the lesser cost incurred upfront. Also this provides them with more flexibility in checking the suitability of the BI solutions in their organization
15
as these open source solutions provide a minimum of a month long free trial. The total cost of ownership for these solutions is lower compared to the commercially available solutions.
Fig 8: Opinions about the total cost of ownership of open source solutions.
The major open source BI tools vendors are shown below:
Fig 9: Few of the major open source BI tools vendors.
16
Gartner Magic Quadrant
Fig 10: Gartner’s Magic quadrant showing the relative positions of market players in BI tools.
TCO of BI applications
Fig 11: Pie chart depicting the share of Total Cost of Ownership in an organization.
17
The total cost of ownership (TCO) of Business Intelligence applications in a typical organization is split mainly into the following:
Staffing: accounts to close to 50% Software: accounts for about 20% Hardware: accounts for about 8% Training: accounts for about 8% Downtime: accounts for 5% Others: accounts for 9%
Fig 12: Comparison between the traditional reporting system and BI solutions in terms of TCO
18
Conclusion The telecommunication industry is one of the first industries to adopt the Business Intelligence solutions mainly due to the ever increasing data that they have to handle. Most of the telecommunication companies have moved to scalable and smarter BI solutions which have helped them in making significant reduction in their bottomline and increase their topline by customizing the services to be offered to their expanding customer base. Anticipating customer needs and providing them with the services they want at cheaper cost has vastly improved the loyalty of the customers towards their service providers. This has become a possibility largely due to the advanced and robust BI solutions which can provide access to real time and relevant data in various formats which enables the management to make strategic decisions.
BI solutions have not only helped the telecom companies in increasing the customer loyalty, it has helped every department of the organization to achieve a level of efficiency which can hardly be imagined without a successful implementation of Business Intelligence solution. It has helped in improving operational, marketing, customer service and financial efficiency to name a few. The growth of the BI applications is only slated to grow in the future as the competition few stiffer. One of the major challenges facing any organization using the BI solution is getting the most out of it by effective implementation of the solution which can only take place in a true sense when the whole organization participates enthusiastically.
19
