Telcos capture trillions of bytes of unstructured information about their customers, suppliers, and operations. This data comes from the millions of networked sensors that are being embedded in the physical world in devices such as mobile phones and automobiles; sensing, creating, and communicating data at very high speeds. The activities of the many individuals with smart phones, who are seemingly almost constantly active on social network sites, are further fuelling the exponential growth in data.
This large pool of information can be captured, aggregated, stored, and analysed as part of every sector and function of the global economy. However, until now the typically monolithic construct of traditional IT infrastructure and solutions has been incapable of handling this large set of unstructured, high speed, complex and yet valuable data streams.
What’s changing all that is Big Data, which has the potential to provide a platform that can help an operator extract valuable business insights from this haystack of data for future growth.
Big Data and analytics can help service providers assess customer sentiment and tailor their marketing strategies to target valuable customers and improve customer experience. Operators can also tap into volumes of data generated during network usage to understand customer demographics and psychographics.
They can also leverage sentiment analysis data from social media feeds to improve as well as defend their brand image and reputation. Importantly, they can gauge social media sentiment on newly released products, offers, and campaigns in a cost effective manner and proactively create service requests to improve brand perception.
For example, analysing Twitter feeds and Facebook posts can deliver a better understanding of the service provider’s customer service performance and if there are concerns pertaining to quality of service in specific regions or customer groups. Customer care and operations teams can leverage this information to determine the next best action (treatment, remedy, etc.) to then assuage customer concerns.
Cross channel insights
Big Data offers many opportunities to gain cross channel insight. A customer’s perception of service provider’s performance and value is increasingly defined by how well the provider manages interactions across channels.
Customers often start the search for a particular product or service on the web site, then talk with a call center agent for more information, and finally complete the purchase in a retail store. The provider today has to ensure that each of these interactions is a seamless experience for the customer. By analysing customer data at each level of interaction (including mobile, web, call centers, vendors, dealers, and retail outlets) the service provider can determine if the brand promise was fulfilled and whether the customer became a promoter or a detractor.
In a live case scenario, if a customer started a purchase online adding items to his/her shopping cart but in the end abandoned the purchase, a customer service agent who is aware of this can during a subsequent call, help address the customers’ doubts and improve the chance of ensuring the sale.
Big Data and analytics solutions also offer telecom operators the technology to capture, aggregate and analyse customer experience data from each channel and correlate it with other key performance index values (KPI) like Net Promoter Scores to develop insights into customer churn predictors, life time value, and brand improvement strategies.
Real-time, context sensitive advertising
Today, when a customer logs into a telecom website, the ads that are served up have little correlation to a particular customer’s service usage, content purchases, social media activity, or site browsing history. Capturing all of this information would allow the service provider to feature ads and offers that reflect recently consumed services and applications more relevant to their current interests and likelihood to spend.
With a context-sensitive, 360 degree profile of the customer, telecom operators can recommend services or products in the context of each interaction and prior history. The adaptive logic can be integrated across multiple channels including the web, mobile, call centre, retail associate, in-store kiosk, etc. to reflect a customer preference about how and where they want to interact with the service provider.
Ad response, service usage and location data can also be collected and analysed in real-time using complex event processing Business intelligence solutions to determine target segments and product profitability margins, prior to offer conceptualisation. This would help improve marketing and advertising spend.
Location based marketing
Telecom operators can capture a customer’s location when entering a certain area (“geo-fencing”) and correlate that demographic usage to create targeted offers and promotions for communications service providers (CSP’s) and other industry partners. CSPs can also analyse a subscriber’s mobile network location data over a certain period to look for patterns or relationships that would be valuable to advertisers and partners.
For example, based on these results one may identify that a particular subscriber always drives a certain route to work each day. Therefore one knows that he tends to pass by a specific coffee shop at 7:15 am each day. So the café can choose to send him an offer to stop by for a flat white or whatever his preference is on his way to work.
Optimising the network
Big Data can also be used to deliver real-time analytics to detect when a network is down, overloaded, under-utilised or reaching capacity. This information can be analysed alongside marketing offers and promotions, seasonal trends, and customer usage (e.g., mobile applications, online gaming, or “over the top” services like Netfix) to identify network hotspots and determine where to make capital investments to support value added services and content offerings.
Today, Service Providers manage network bandwidth with either data caps or tiered pricing models. Using Big Data, it’s possible to create personalised network usage policies by combining network data with unstructured data (captured off deep packet inspection probes and other sources) and analysing it to detect customer behavioural patterns. These subscriber-specific usage policies would increase customer satisfaction for the vast majority of users (they’re not subsidising the heavy consumers) and enable service providers to maximise data consumption revenue streams.
The ability to perform real-time network analysis (including service outages, interruptions, or slow-downs) using structured and unstructured data can also be used to impact operations performance in areas outside of core network engineering like sales, support, and call centre agent operations to help determine:
Product Marketing: What services, products and bundles impacted by the network fault and how should this influence marketing and promotions. They can also run campaigns targeted to individual customers based on network events like first time user for a certain service or download of specific applications.
Customer Management: Which customers were impacted when this network fault occurred and should service requests or direct communication take place to acknowledge the issue and offer treatment?
Customer Care: Prepare the Call Centres with appropriate knowledge of the issue, customers, and services affected to better prepare agents and scale up staffing volumes as necessary.
Revenue and Churn Forecasting: What is the potential revenue, profitability, and churn impact from the outage? What is the cost or impact to revenue, brand, and other KPIs of different actions?
The Australian telecommunications market is going through a period of rapid change. As traditional revenue lines such as voice services continue to erode, new areas of business like VoIP, web-based content (IPTV) and mobile apps are emerging as new profit centres.
The arrival of the NBN will also have a substantial impact on the competitive landscape, empowering many smaller industry players to challenge the larger incumbents. The time for business innovation and the adoption of a customer-centric approach to service and marketing within the telecoms sector truly is now.
Scott Newman, senior director Enterprise Architecture and Technology Consulting, Oracle ANZ