By Rob Chimsky, VP Insights at Guavus

The last few years have seen wireless phone services move from luxury items to fundamental communications tools. 
The ITU estimates that in 2013, penetration rates in developed markets will reach 128 percent, while in emerging markets they will hit 89 percent.
The rate in developing markets becomes even more significant when factoring in economic capability, which means that among people who have the economic means in these emerging areas, penetration is likely getting very close to developed markets. 
But the success gained from getting wireless devices into consumers’ hands doesn’t have a beneficial corollary for operators that have seen a significant slowing in their aggregate subscriptions and revenues.
The GSMA reported a worldwide annual growth rate for subscribers of over eight percent from 2008 to 2012 but from 2012 to 2017 this is expected to drop to about half this rate. 
Meanwhile, total operator revenues between 2008 and 2012 grew by 4.2 percent but during the following five years to 2017 this is projected to be 2.3 percent, according to the industry body. 
In Europe, revenue growth has turned negative with a two percent decline that is expected to continue for the next several years. 
These statistics point to an evolving marketplace for mobile operators over the next few years that will be dramatically different to the past. Mobile operators will have to adopt new tactics to effectively combat competition and fight the trends on revenue deceleration.  
A key weapon in this new battleground will be the effective use of big data. One of the main assets that operators have to leverage in this environment is their relationship with subscribers and the knowledge operators have about these customers. 
Changes in market share for operators will increasingly come from taking customers from another operator. Both sides know this, so managing churn will continue to take a greater amount of operator focus. 
Effectively managing the user experience across the entire customer lifecycle is imperative to minimising churn and attracting customers from other networks. 
Big data is an important facet in optimising the customer’s experience by allowing the operator to synthesise multiple data sources into a comprehensive picture of the customer’s reality. 
Understanding this reality permits the operator to proactively address potential service issues as well as assure that the customer is on the right plan that best suits their usage. 
This level of customer intimacy creates a closer link with the subscriber and should enhance the overall relationship with resulting minimisation of churn. 
Additionally, gaining a reputation for a high degree of customer service will be a valuable weapon in encouraging customers to change carriers in an environment where pricing differences between operators are nominal and differences in product offerings are minimal and largely hard to sustain.
A second aspect where customer intimacy created by effective use of big data will come into play is around the services offered by an operator. The differentiating factor for the operator is in their ability to tailor the specific portfolio of services and capabilities that are uniquely delivered to the subscriber. 
For example, customers who have significant interest in social networking can be offered packages that highlight this type of usage.
In order to stimulate usage and resulting revenue, operators can also use customer knowledge to present offers and promotions that will be the most relevant for individual customers.  This relevancy maximises the likelihood of a subscriber accepting an offer and strengthens the carrier’s relationship with that customer.
Google has already demonstrated the significant value that is inherent in customer knowledge when trying to target advertising and create a higher probability of sales success. 
In the future, as issues around customer privacy become more settled, this same customer knowledge can be used with customer permission to help third parties better tailor their services for the end user. Monetising this customer information will then become another revenue stream for the operator.
A further evolution will be to combine customer profile and policy requirements with data analytics to automatically and dynamically create new solutions. 
Based on parameters that have been previously defined by the customer, data analytics can determine the current context of the subscriber and then either allow or deny various capabilities, customer access, and applications to be employed. 
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