Posted: August 14th, 2012 | Author: Matti Aksela | Filed under: Industry Insights | Tags: Advanced Analytics, analytics, CEM, contextual intelligence, CSPs, Customer Experience Management, Heavy Reading, Marketing, mobile, SNA, Social Network Analytics, viral marketing | Comments Off on The Benefits of Social Network Analytics for Marketing
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?
Posted: July 11th, 2012 | Author: Matti Aksela | Filed under: Industry Insights | Tags: Advanced Analytics, analytics, BI, business intelligence, churn, mobile, Mobile BI, predictive analytics, SQL | 1 Comment »
There’s an interesting intersection between the popularity of mobile devices and the appetite for business intelligence (BI). Inevitably, the demand to display and interact with BI on mobile devices is growing and will continue to do so as more mobile technology supports this function. Already, we have tablets and smartphones with high-quality displays and interactive capabilities – but this is just the beginning, if you take into account the full potential for mobile BI.
Mobile BI is mostly relevant in the consumption of information, which is reflected in the need for simpler interactions in BI infrastructures. After all, nobody wants to be writing complex code, like SQL, on their smartphones. Rather, one of the key benefits of a successful BI system is the ability to show the same information to all users. For instance, dashboard reporting with drill down functionality and reports that scale easily across devices will be vital for success. And the more access points there are, the more important this standardization is.
Let me also say, however, that mobile BI will not – and should not – replace existing BI systems. Instead, mobile BI should complement existing systems by providing organisations with added speed and flexibility for consuming the available information. I also believe the move to mobile will give even more importance to more advanced analytical methods. For example, the ability to easily, effectively and accurately segment data based on certain attributes or to combine relevant information, such as churn predictions, into the revenue forecasts, as well as data visualisation approaches will come to the forefront. This allows for the information to be accessed – in a relatively refined form – across the entire organisation.
In other words, I see the advances in mobile BI being very much complementary to the other movements we are seeing in BI and analytics as a whole – bringing easier and more operational access, through complementary methods, such as predictive and advanced analytics. This, in turn, provides more refined data in a form that is easy to utilise across the organisation to maximise effectiveness of — not only the BI and analytical tools — but the people using the information generated. The latter point, being able to flexibly but securely access the information when and where it is needed to minimise “information lag”, is certainly a strong value proposition and will help mobile BI gain its foothold.
Posted: July 3rd, 2012 | Author: Leila Heijola | Filed under: News | Tags: Advanced Analytics, analytics, CEM, CIQ4T, Comptel, CUG, customer experience, Denmark, User Group | Comments Off on The Results Are In: Analytics Play a Key Role in Customer Retention
This year’s annual Comptel User Group will be one to beat – with visits to the Carlsberg Brewery and the famous amusement park, Tivoli, where many attendees rode the world’s oldest wooden rollercoaster. In addition to the fun had around Denmark, there were also many memorable conversations at the conference, most of which revolved around the customer experience.
Like at past user groups, Comptel held an interactive voting session where we polled our audience of customers and partners to gain deeper insights on the topic of analytics. The survey focused on customer retention strategies, including when and how to engage with subscribers, and what techniques telecom professionals are employing to keep them happy.
Sixty-seven percent of respondents said they believe inconsistent service quality and poor customer service are among the biggest contributors to churn. To help manage this, 64% of participants said anticipating subscribers needs with proactive care is one of the best strategies for handling service issues, like dropped calls, low bandwidth or sluggish file loading.
Some audience members mentioned that, now, the simple reality is many operators wait for customers to complain before addressing an issue. But as Stratecast analyst Jeff Cotrupe commented, it’s always better to be proactive – communications service providers (CSPs) must take on that active role to provide better service overall. And ultimately to reduce churn, it takes predictive and contextual analytics and personalized customer interaction capabilities.
For instance, following a service issue, 46% of respondents said that they would issue an apology, opportunity to upgrade or special discount to subscribers to boost loyalty. But when it comes to keeping customers satisfied, especially after a service issue, Telesperience analyst Teresa Cottam noted that CSPs’ response should depend on what is appropriate for each individual customer and situation. While an apology might be right for some, it might irk others. This is why the actions that CSPs take following an event should be rooted in analysis of subscriber data. Just as you want to personalise services, personalising a response to an outage or fail is equally as important.
Supporting this and signaling how pervasive analytics are becoming in the industry, three out offour attendees reported using analytics daily, weekly or monthly. This isn’t surprising given that almost half also believe targeted services are critical in mitigating turnover – an area where analytical insights play a crucial role.
Timing and context were deemed among the most important aspects for realising improvements in customer interaction and business performance. Building on this, participants saw a variety of attractive applications for advanced analytics to support business needs.
We at Comptel are thrilled for the industry to embrace analytics and contextual intelligence, and to see the new opportunities that will emerge from this for churn prevention, targeted marketing and other business opportunities. Did any of the results stand out to you? What do you think will have the most powerful effect on reducing churn?
To download the full presentation, click here.