The growth of online communities like Facebook, Twitter and LinkedIn gives us a preview of how the Netizens of tomorrow will communicate with each other. A year ago, Twitter hit 50 million tweets per day. According to CTIA survey results released in Dec 2009, some 5 billion text messages were sent daily in 2009 and the number has only grown since then, so is the old plain telephone call becoming a dinosaur? Not really, although some Generation Z teens confess to never making a phone call, they communicate with their friends entirely through the internet. At the same time, the pace of mobile calls is also increasing. According to CTIA, 6.1 billion voice minutes were used per day in 2009. Internet based communication, unlike the phone call, can be from one to many, many to many or machine to machine (M2M). It can take place between a pre-defined group of people or can be free form. Certainly, the amount of data generated by communication services used is growing exponentially.
The challenge is how to gain insight into customer behavior and preferences in this environment. The volume of data operators need to handle is growing at a very fast pace, and some of that information, such as data related to services from a third party vendor, is available to the service providers for only a short time. Operators need the ability to capture and analyse the data in real-time in order to respond to customers and make offers that are time sensitive, relevant and meaningful to the user. This real-time analysis can also provide price elasticity and dynamic re-pricing of the service based on the value of information to the user. With the availability of real-time rating and charging engines and aligned back-end systems, operators can take
advantage of this insight to rapidly innovate and execute with a closed-loop feedback mechanism so as not to leave money on the table. In most cases just-in-time or location and context specific information has greater significance and value to the subscriber. There is also time dependent value to information. We see this today in some of the advertising- pricing models in the online world. The real- time bidding models for online ad placement are very dynamic, factoring in the user context, publisher inventory availability and the advertiser appetite to pay for showing the ad to a user of a certain profile. In context of the user, customer intelligence doesn’t get more relevant than this. The days of warehousing the data and analysing yesterday’s data is not going to provide the service providers timely insight to drive relevant offers to customers in order to increase differentiation and customer satisfaction.
Shortened response window
As the volume of data to be analysed increases and the window for responses gets shorter, very fast analytical appliances are needed to troll through petabytes of data and to bring in data from multiple heterogeneous, source systems in real-time. Data such as network usage, content accessed, customer service, location, preferences and consumption history are all required to get a full picture of the customer.
The nature of the CSP (Communications Service Provider) is also changing, it is no longer the traditional network operator. Players like Google, Apple, Skype and Facebook are also providing communication services, they do not own any network, but they do provide ways for their subscribers to communicate. Every channel surfing click, every page view and every download is a goldmine of information that provides insight into the subscriber behaviour. The business intelligence solutions of the future, as mentioned before, will comprise of very fast analytic appliances that can access data from multiple data sources and either analyse it in- memory with sophisticated mathematical models and algorithms or offload it to a data store where it will still be analysed in near real-time.
In either case, the goal would be to provide actionable insight and even automate the system response while the user is still engaged. Significant amounts of data will come from outside the enterprise; this could be from network partners, third party content providers, financial transaction settlers or other devices with which a user communicates. We can see a role for third party content syndicators, who act as service bureaus to collect, correlate and analyse the data by individual subscribers or subscriber segments and provide input to the service providers, advertisers and content creators. This information when tied-in with in-house data provides the complete end-user subscriber related intelligence to design and offer the most compelling services at price points attractive to the user.
Analytical tools must become ubiquitous Today, the availability of analytical tools and access to real-time in-memory analytical appliances is not ubiquitous at a traditional operator. Many business users neither have access to data that they need to make informed decisions nor easy to use intuitive analytical tools available to analyse the data. Both IDC and Gartner forecast a significant investment in this area in the next three to four years by the industry. In the old environment, when the majority of the data was related to plain old telephony service, this was not too critical but in the always-on, 4G environment, where only a fraction of the data going through the switch is related to a voice call, it is becoming imperative that the business users have access to business intelligence tools and solutions that are simple to use and intuitive.
Such tools must enable them to get the insight into customers that they need to drive development of innovative products and services at price points attractive to their end- user customers. Something as simple as a search-like interface that most people are familiar with today provides an idea of the functionality of the tools needed. Business users are also asking for access to best practices and packaged solutions that enable them to get value from their systems rapidly.
Customer intelligence in the emerging 4.0 era will need to be more comprehensive and feature all encompassing analysis of data from a large number of systems and data sources, not all of which are internal. It will need to analyse the data and execute or suggest an action in real-time while the end-user is still engaged. This can extend to dynamic rating and charging which will enable the service provider to innovate quickly and become more profitable. The information will need to be accessible and available to a lot more people in the organisation and support self-service for decision makers in a very easy to use and intuitive manner. The challenge for traditional operators is, therefore, clear. The good news is that tools and processes are available that will enable them to remain at the heart of the digital value chain.
The author, Rani Goel is global senior director of telecoms industry marketing for business analytics at SAP.