Posted: February 24th, 2014 | Author: Matti Aksela | Filed under: Behind the Scenes, Events | Tags: big data, business applications, contextual intelligence, machine-learning, Mobile World Congress, predictive analytics, value | 1 Comment »
You probably have not been able to avoid hearing the term, “Big Data”, nor about the expectations of its limitless possibilities for communications service providers (CSPs). CSPs have a unique opportunity to delve into the spectrum of network, customer, service and other information at their fingertips and flowing through their OSS/BSS, eventually using it to improve both internal operations and customer-facing processes.
But Big Data sadly often means Big Projects. It is not just management of the three core “Vs” – volume, velocity and variety – that contributes to this, so can the setup of the technology to store and collect the data. But often, the biggest challenge stems from the fourth “V”, or value. What do operators need to do in order to drive true value from Big Data? I believe that there are some key requirements for being successful here:
- Have a strategic business objective to focus on. Do not just collect data for the sake of collecting data, but have a goal in mind and a roadmap of what to do to drive more value when you reach that goal. (Buy-in from the boardroom, of course, helps, too, especially with issues like breaking down organisational silos.)
- Don’t start with a blank slate. It’s important to have a set of proven, productised applications to address your business pains, whether it be customer experience-driven like smart throttling or network-focused for proactive service management, for instance.
- Collect experience and learning in your organisation if you see information as your key asset, but don’t wait until you have built an experienced team to do so – have that as your plan, but start generating value from operational applications from the get-go.
And that’s where Comptel comes in. We’ve been developing our Big Data offering to help CSPs give their initiatives a running start, and also supply them with the tools to support information-based decision-making and derive the true value they’ve been looking for – quickly and in a future-proof and extendable way – to solve acute business pains and build on that success.
Comptel provides a true Big Data solution, addressing all key components of the Big Data process:
1) Data Ingestion: Integration, importing and formatting of historical and real-time data from CSPs’ own data sources, combined with external data for a truly holistic view of the business. This is powered by Comptel’s proven technology used in our mediation solutions.
2) Data Management: Transformation, correlation, enrichment and manipulation of data to ensure optimal usability, and using the most appropriate methods to store data—whether it be Hadoop for unstructured data, massively parallel processing databases or in-memory data grids.
3) Data Analysis: Highly accurate, real-time predictive analysis, modelling and reporting, powered by machine learning.
4) Business Analytics Applications: Productised solutions to solve acute business pains, utilising the whole Big Data solution to drive immediate value.
One important aspect of Comptel approach’s is the utilisation of both historical and real-time data to drive true value—we do not see these as separate discrete steps of a process, i.e. building a predictive model and then applying triggers based on the model’s predictions, but instead having the predictive model applied in the real-time data stream to reap and benefit from contextual intelligence.
Clearly, Big Data analytics is reshaping the telco landscape. According to our recent research supported by Vanson Bourne, about two-thirds of telco executives (64 percent) say they are already in the process of leveraging Big Data to improve customer service, for instance. This is the time for Big Data to show that it is not just a hyped concept but a true generator of value—and we believe that the way to do that is through scalable, Big Data solutions designed to achieve and build on CSPs’ business objectives from day one.
To discuss how contextual and operational intelligence can augment CSPs’ efforts, come to our booth (Hall 5, Stand 5F41) at Mobile World Congress 2014. Look forward to seeing you in Barcelona this week!
Want to learn more about telco in 2014? Download our new eBook, “What Telco CMOs and CTOs/CIOs Are Thinking in 2014.”
In this eBook, we share exclusive, global executive research that highlights:
– Executive strategies for 2014
– Barriers to integration
– Technology priorities
– Attitudes toward data & planning
Posted: June 13th, 2013 | Author: Matti Aksela | Filed under: Industry Insights | Tags: analytics, big data, predictive analytics | 1 Comment »
You can’t go far in the telecommunications industry – or nearly any other industry, for that matter – without hearing about the importance of big data. As we’ve mentioned before, with voice and text revenues declining, the art of applying analytics to customer data is moving into the spotlight. By monitoring how people are using data, communications service providers (CSPs) are hoping to discover new revenue streams and create more personailised marketing offers.
Paolo Sidoti, global managing director of Accenture’s Network Business Services group, recently explained to Light Reading that operators need to “reconnect with customers,” and big data analytics is the tool they’re using to do it. All of that sounds good in theory, but the reality isn’t as clear cut.
Accenture reported that just 22 percent of operators are “very satisfied” with their analytics tools. More than a third said they are “dissatisfied.” Another recent finding from the company indicated why that may be: CSPs are gathering a lot of data, but 61 percent said that it isn’t relevant for their business strategies.
Big Data Can Be a Big Mess
The emphasis on big data has inspired a lot of CSPs to pay closer to attention to what their customers are actually doing, but without a way to automate interactions and create an operational analytics program that automatically learns and adapts to use cases over time, big data can create more work than it’s worth. No CMO wants to sit in front of the computer sorting through 100,000 different customer profiles to try and manually predict who would top-up to watch a video on their phone, and then try to figure out what campaign would work best.
That’s why, in its raw form, it’s hard to make big data relevant for any business strategy. The information is simply too diverse and disorganised to use, and not every solution fits the bill. Accenture’s findings showed that 93 percent of the operators surveyed said they needed new or improved products to help with analytics. With so many CSPs saying they need better tools, it’s no wonder they’re disappointed with big data. If you can’t properly sort and operationalise the data, then it will never be useful for building better relationships with customers.
One key aspect to utilising the data is to make sure it is available in a timely manner, which is something Comptel has been doing for our customers by online mediation solutions. While data does not really have an expiration data, waiting months and expecting to get the same value is just not very realistic. But purely focusing on the event at hand isn’t the solution, either.
We believe that creating an individual profile from customer data and combining it with the true context of the subscriber is the key. We also work in-depth with our customers on slower moving data and can provide excellent results there, but if I were sitting on the other side of the table, I would want a solution that takes advantage of streaming data.
Comptel’s predictive and automated analytics solves the problems of big data with powerful, machine-learning capabilities that ensure automated actions are taken at the right time to the right audience, with the right context. It’s in this way that CSPs can leverage big data analytics to customise their campaigns to each individual customer’s preferences and unique needs. More importantly, the insights that are delivered are helpful to the overall business strategy, too, making it easier to integrate analytics into customer loyalty and marketing programmes.
The use of big data for CPSs is not limited to marketing functions, either. Comptel provides predictive analytics solutions to operationalise use cases that will help show the value of data across networks and the impact and cost of different solutions on the technical environment. Additionally, predictive analytics can be linked to the subscriber level and help optimise policy-throttling activities.
Big data is also about breaking down the siloes. Rather than treating your network as one pillar of your business and the customer as the other, analytics is applied to data across the company. As we’ve said before, the future of marketing is networks and the future of networks is marketing. By bridging silos across the organisation, CSPs create a better end-to-end user experience – and with the vast volumes and huge dimensionality of this data, the way to do this efficiently is through machine learning and predictive modeling instead of trying to work within the scope of humanly manageable data.
Overcoming the Second Hurdle
In a lot of ways, the disappointment CSPs are feeling is a good sign, because it shows there’s an awareness of big data’s potential. While solutions are in use to address some parts of the problem, most operators are still experimenting with what works best for their businesses. But it’s reassuring that they’re so aggressively trying to figure that out and appreciate the value that big data can bring.
Comptel strives to stay beside CSPs every step of the way. A business strategy needs the right tools to work, and our array of solutions for big data analytics has helped businesses achieve an accuracy rate of 80 percent and outperform the competition 90 percent of the time. We’re determined to help transform big data across all of an operator’s units and siloes into actionable insights, and augment it with additional data sources from outside of the operator environment, such as social media. That way, data can become an organised and operational asset that can be used to build business and better customer relationships.
Posted: October 19th, 2012 | Author: Matti Aksela | Filed under: Industry Insights | Tags: big data, communication service providers, contextual predictive analytics, CSP, median and fulfillment, policy management, predictive analytics, value, variety, velocity, volume | 1 Comment »
There’s no denying that one of the biggest trends in IT right now is Big Data. While there are many different ways to describe it, perhaps the most commonly agreed upon, and my personal favorite, is that it must encompass the three “Vs”: volume, velocity and variety. How organisations understand and embrace these concepts varies—but I think we can all agree on one thing – there is a lot of data being generated quickly from various sources. I’ve found that one of the biggest questions organisations are asking though (which adds a fourth “V” to the equation) is: How do we derive value from Big Data?
Real-time (or near real-time) predictive analytics are gaining in popularity, and may hold the key to realising Big Data’s true value. In his keynote presentation at OpenWorld, Joe Tucci, CEO of EMC Corporation, stated that: “Real-time predictive analytics will be the killer app for this cloud era.” Personally, I could not agree more and think that this points in precisely the right direction, not just for cloud but for all businesses dealing with data.
One of the main benefits is gaining a strategic understanding of customers and the overall business ecosystem. But the key is going beyond simply collecting information, or even the ability to store and process it. The way organisations can truly realise Big Data’s potential is by leveraging it to predict behaviours and market changes, and make smarter business decisions based on that knowledge.
What exactly those actions are will depends a bit on the case—for communications service providers, it may be policy management activities or real-time, location-based marketing campaigns. And as many are already noticing, decision-making with predictive modeling can have huge benefits.
In short, I believe we must indeed look at Big Data not as a thing that happens, but as a process we act upon – through contextual predictive analytics-driven actions. Enabling these insights is important to Comptel and something we’re continually working towards by combining analytics with our high-performance mediation and fulfillment platform. If I must confess, I am really excited about what we are seeing and doing here, and the benefits we can offer to our customers!
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.