Leveraging mobile analytics to optimize business processes

Mobile messaging traffic continues to experience explosive growth and for this reason the wireless sector faces the specific challenge of staying on top of business imperatives in the face of a massive data volume. It is not only vital for mobile operators to gain insight into mobile messaging traffic to improve service quality, but also for companies looking to optimize their customer relationships. This article will explore the challenges mobile operators are facing and discuss how new analytics solutions work to help solve those issues.


Mobile messaging traffic continues to experience explosive growth and for this reason the wireless sector faces the specific challenge of staying on top of business imperatives in the face of a massive data volume. It is not only vital for mobile operators to gain insight into mobile messaging traffic to improve service quality, but also for companies looking to optimize their customer relationships. This article will explore the challenges mobile operators are facing and discuss how new analytics solutions work to help solve those issues.

Analytics goes mobile
Traditional business intelligence asks, “what happened?” Most BI systems collect data and review it after the fact. Data is only as recent as the last bulk load into the data warehouse, commonly a daily occurrence. The increasing ubiquity of mobile devices is forcing a paradigm shift in our approach to analytics and reporting. In order to optimize our business processes, we need our historical data delivered in real time. We also need to be able to answer the question, “what is going to happen?”

Smartphones and tablets are changing the way we live. In 2010, smartphone shipments grew 87 percent year over year, surpassing sales of PCs for the first time. More importantly, The Gartner Group predicts that 85% of new mobile handsets shipped worldwide this year will have some form of data access. The stampede toward mobile device usage is accompanied by a shift in the way that people communicate and how they use applications and online services. The mobile model is at once immediate, limited in scope and minutely targeted. Users jump on and jump off, switching rapidly between various communication channels and applications, accessing and creating content that is often defined by time and location as well as more ephemeral variables. Consider the broad but limited intimacy of Twitter, the location-specific focus of foursquare or the impact of SMS and MMS communications in the recent uprising in the middle east.

The new mobile paradigm creates both challenges and opportunities for enterprises and operators. The challenge therein lies with both businesses’ and operators’ abilities to harness, predict and act on data more quickly. As users become accustomed to fast and intensive mobile device usage, their expectations of service quality and application usefulness rise accordingly. This means that operators and enterprises need to know how people are using the network, who they are and who their friends are (understanding psychographic, geographic, demographics) in order to not only have the ability to help improve quality of service, decrease network downtime and increase customer satisfaction, but also to effectively communicate with customers anytime, anywhere.

Mobile analytics in action
Mobile analytics requires a multichannel strategy. That is because on a single device an individual can have many profiles or personas. Most people use the same device for both work and personal communications. They will be using many applications and different communication methods such as SMS, MMS, WAP, email, mobile applications and mobile browser services. As noted previously, users also jump rapidly between these various channels and do so while moving around geographically. This is much different from the existing PC-centric web analytics paradigm which assumed that users were working at machines in fixed locations (or at least tied to regular wireless hotspots if on a portable laptop) using a relatively well-defined set of interfaces and applications.

Mobile analytics therefore presents a significant technical challenge in the collection and analysis of data; however, it also provides a huge new opportunity to better understand user behavior and offer compelling and immediately relevant services and promotions. The industry has been talking about location-based services (LBS) for more than a decade, but it is mobile analytics which will finally allow such services to become reality.

Mobile presents a huge opportunity for consumer product goods companies. People don’t carry around a PC, but they are attached to their mobile devices. For example, a major CPG company wants to create a coupon or loyalty program that specifically targets mobile customers. Since consumers have different phones, networks, behaviors, etc., you need to develop a marketing campaign that targets each consumer in a way that will drive individual engagement. With paper coupons costing CPG firms an estimated $7 billion per year with a redemption rate that hovers around 1 percent, the window is wide open for a cheaper, more effective solution based on mobile distribution methods. With the help of mobile analytics and m-commerce technologies available today, the CPG company can develop a mobile campaign or loyalty program that can identify best customers, execute and measure campaign performance, and achieve better ROI. Early adopters have discovered that redemption rates on mobile coupons can reach 30%, creating a very attractive investment opportunity.

Operator analytics
For the past couple decades, operators have faced a significant operational challenge—no matter how much investment they put into their own network and operations, they are part of a larger network of operators. Customers expect the same quality of service no matter what carrier the person they are contacting happens to use. An operator’s most critical business processes rely on partners who are also competitors. In the mobile world, the ecosystem is even larger, encompassing device manufacturers, application developers, web service providers and so forth.

The real challenge is that operators have little visibility into or understanding of these core business processes when they occur outside the carrier’s own firewall. Operator analytics monitors these off-network processes and provides valuable insight into off-network traffic and services. This allows operators to better troubleshoot and resolve network problems that impact message traffic.

In the case of inter-carrier messaging, network operations managers can also use operator analytics to proactively monitor nodes or binds in their network with a real-time view of network traffic activity, including load on key system components. For example, when a critical system component is over capacity, the analytics solution sends an automated alert to the designated NOC personnel notifying them of potential network problems. In response to the alert, the network manager can review the system monitoring report and the performance of the bind in real‐time. Reports indicate when a node’s performance is outside normal operating parameters, so that administrators can redirect traffic away from the bind to reduce strain on the system. This immediate network information allows the NOC team to respond proactively to network activity so they can avoid network outages and undelivered messages.

These two scenarios illustrate how operator analytics can help mobile operators better support SMS and MMS traffic on their networks. By using a combination of real-time and historical data, operators have the resources they need to make intelligent decisions, which can reduce network downtime and help the NOC team troubleshoot problems more quickly.

Telecom operators are looking for solutions that provide tangible financial results. Improving quality of service (QoS) has a direct impact on an operator’s bottom line by reducing the number of customer service inquiries and decreasing customer dissatisfaction and ultimately, churn. By enabling operators to quickly spot and diagnose network issues, they can significantly improve response times by helping network administrators address—and resolve— problems quickly. Fast response means fewer message delivery problems, increased efficiency and fewer customer service calls—all of which significantly reduce operating costs. In addition, the real-time and historical data provides network operations managers with the ability to streamline and optimize network operations and perform resource planning. Line-of-business managers benefit from the ability to better understand customer usage patterns, more quickly comprehend the results of pricing and promotions, and segment customers to provide targeted new services.

Dan Auker is a mobile analytics expert with more than 14 years experience building and commercializing mobile software and services. Dan currently directs analytics solutions offerings for Sybase 365, a division of SAP, Inc. A graduate of the Wharton School of Business, the Paul H. Nitze School of Advanced International Studies (SAIS) and UC Berkeley, Dan has worked for some of the most innovative and pioneering mobile technology firms in Silicon Valley, including Javasoft (Sun Microsystems), Pumatech (Intellisync, Inc.), and Critical Path, as well as various leadership roles at Sybase.