Social network analytics (SNA) is becoming increasingly popular as communications service providers (CSPs) look to better understand their customers and secure a competitive edge in the market. As part of an advanced analytics approach, SNA enables CSPs to dive into the billions of daily transactions on their networks and utilise the calling patterns to identify influencers and better segment their subscribers – and ultimately realise more value.
For instance, call detail records provide CSPs with a unique insight into social interactions through the daily communication of their subscribers. This network may be even more important for CPSs than most online social networks, for example, which are just snapshots of a person’s interactions, many of which may not be very relevant, especially with regards to the activities of CSPs.
However, SNA alone is not the ultimate end-all solution and is, instead, one very valuable aspect of the larger scope of analytics. And when combined with predictive analytics, SNA truly offers a distinct advantage for CSPs. For instance, they can use SNA to power their predictive capabilities and generate insight regarding data that is otherwise unavailable on the single subscriber level. On top of this, SNA and predictive analytics can help CSPs benefit from the interactions between subscribers, help with overall customer experience management and automate operational actions to increase productivity. And let’s not forget, perhaps the best known application of SNA, viral marketing – an approach that remains one of the strongest, most effective marketing techniques. But again, it’s crucial to take into account that understanding the social network alone is not enough. Rather, when combined with the right product, predictive analytics powered by SNA can really make a difference.
Take, for instance, a teenager who texts frequently. If he or she receives an appealing SMS rate reduction that’s just right for them – perhaps one that predictive models have indicated would be suitable – this subscriber will be more likely to spread the word to those in his or her network, causing a positive ripple effect. As a result, many of these connections may pursue that same SMS rate, providing an increase in revenue for the CSP.
Ultimately, recommendations from family and friends can be far more effective than traditional advertising. In this way, combining predictive analytics and SNA can play a key role in any CSP’s arsenal. And of course, a well-executed SNA strategy balances providing personalised offers without infringing on subscriber privacy.
If you’d like to read more about combining predictive analytics and SNA— and taking this even one step further to understand and act upon the context of each interaction — download our recent whitepaper with Heavy Reading on Contextual Intelligence. What are your thoughts on SNA? Is it of value? Are there actual network influencers whose recommendation you follow regardless of the topic? Or would you say you’re more swayed by having CSPs make the right offer, and it holds more weight when the offer is recommended by those whose opinion you trust in the context of what is being offered?