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In which areas can big data and predictive analytics provide the most value (revenue increase, savings, efficiencies...) to European telecom companies/carriers? specially in the capex and opex areas
Hello! Thanks for your question about the areas in which big data analytics can help increase value to telecom providers in Europe.
We found several case studies that mention a drastic increase of revenue, in the range of 1.5 million EUR/ year total, as well as significant cost savings both in OPEX and CAPEX (up to 40% in OPEX and 20% in CAPEX). Below you will find a deep dive of these findings.
METHODOLOGY
We first looked at case studies from European telecom providers that specifically mention the use of big data and predictive analytics to boost their profit in terms of revenue. Then we looked further into the details of how these methods help reduce cost and increase efficiency, specifically considering CAPEX and OPEX areas.
We sorted the findings according to presumed value size, as requested.
INCREASED REVENUE BY CUSTOMER ACQUISITION AND RETENTION
There are several flows of profit that are successfully being influenced by both big data and predictive analysis. Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) are the biggest cost points in telecommunication business, and these losses can be partially mitigated by those methods. According to a business report by TIBCO Software, European telecom carriers successfully profit from predictive analysis to develop business models that attract and retain customers. One of the most important areas has been found to be mobile data usage and other mobile services, as opposed to the use of phone calls, which have been in decline for a while. The report explains that predictive analysis helps find price points for data usage, which today's customers are increasingly interested in.
As portrayed in an article found on Presidion, the analysis of big data generated by customer behavior, likewise, is an important tool to address customer acquisition and retention. The principle here is to deliver improved quality of service, that includes location-based services, tailored marketing campaigns, next best actions for sales and services, network intelligence, social media insights and high-velocity fraud detection.
In a case study about the European telecom provider Si.mobil, both big data analysis and predictive analysis have successfully been used to identify customer behavior with respect to both "pay-as-you-go" customers, and long-term contract customers.
For "pay-as-you-go" customers, annual revenue could be improved by EUR 400,000, using targeted marketing. Long-term-customers revenue could be increased by EUR 1.1 million by specifically targeting customers at risk to change providers.
SAVINGS AND IMPROVED EFFICIENCY: OPEX
With respect to operational cost, a white paper found by Cenx states that significant savings can be achieved by using big data analysis to detect and avoid problems before they occur. Network operators can be provided with vital information on utilization and performance in such a way that resources can be allocated in a more targeted way. This provides an improved service quality while at the same time saving on operational expenses.
An article by Ey provides figures. By providing predictive analysis to benefit the allocation of resources (technicians), regular monitoring of key performance indicators, a better tracking of spare parts, as well as the reduction of energy consumption, OPEX cost can be brought down to up to 40%.
In the business case of one of the European market leaders in the big data area, Infosys, it is mentioned that intelligent networks help to address issues like network downtime by automating fault-finding, fixing and bypassing, which enables a higher efficiency of operations.
SAVINGS AND IMPROVED EFFICIENCY: CAPEX
With respect to capital expenditure, Omnitele states that by using big data driven methods to sharpen the precision of mobile networks and improving return on investment. It is mentioned that CAPEX savings of 20% or more are realistic. They go on to explain how 45 mobile operators have been analyzed and that the best return on investment has been achieved by the Swedish company Tele2. They successfully have used predictive models based on simulations and usage patterns. With these methods, a precision of CAPEX improved the return on investment, for example in terms of where to invest in hardware and where to invest in the geographical network extension.
To wrap it up, the European market for telecom operators continues to greatly benefit from predictive analysis using big data. While customer acquisition and retention are being addressed in order to maintain a continued increase in revenue, operating and capital cost can be cut down due to significantly improved predictions, while providing a better customer experience at the same time.
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