Analytics is one of the most valuable resources for today’s communication service providers. Those who know how to properly leverage and transform subscriber data into new revenue opportunities will be well positioned to overcome traditional utility pricing challenges and emerge as true digital lifestyle providers.
Using big data analytics, operators are able to improve subscriber experience and make better use of their networks. By capturing a wide variety of application, subscriber, device and QoE data from networks, operators can gain a deeper understanding of customer behavior and activity on their networks. They can then use analytics to segment their customer’s digital lifestyles and capitalize on the value given to connectivity by introducing personalized service plans. This not only builds better customer relationships, but also allows operators to generate new streams of revenue, increase average revenue per user (ARPU) and reduce churn.
In order for a big data analytics to be truly effective, the data must be highly granular. The more the detail on application use and subscriber behavior available, the greater precision there will be when it comes to planning new services. An analytics platform must also be able to identify as many over-the-top applications (OTT) as possible, so operators can identify and stay ahead of the latest trends.
To demonstrate the value that granular analytics offers, consider the following scenario:
Meet Customer A and Customer B. They have the same breakdown of mobile volume usage- video, web browsing, file sharing, Facebook usage, etc. However, does this mean they belong in the same segment? Should they be targeted in the same way?
Using granular analytics, we will see that volume usage alone is not enough to make strategic business decisions. Let’s take a closer look at each customer’s behavior and identify several practical use cases operators may consider for specific patterns.
Video Consumption Patterns: Customer A tends to watch HD Netflix videos at night, while Customer B accesses video throughout the entire day. Additionally, Customer A stops watching after the first buffer stall of 10 seconds, while Customer B watches until the end, even with stalls. In this scenario, the CSP can offer Customer A a video priority delivery plan free for a month, with an extra charge to continue.
Facebook Consumptions Patterns: Customer A has one Facebook session per day lasting approximately 32 minutes, during peak hours. Customer B has 10 sessions a day each lasting an average of 3.2 minutes, at all hours of the day. Here, the CSP can offer Customer A a “happy hour” plan that will move online activity to off-peak hours.
Device Usage Patterns: Customer A watches videos on an iPad, while customer B watches on a PC tethered to his iPhone. The CSP can put Customer B on a tethering plan that is better suited to his mobile video streaming habits.
Messaging Patterns (SMS vs. IM): Customer A sends 100+ SMS messages per month and uses WhatsApp only for incoming messages. Customer B uses WhatsApp exclusively and extensively. The CSP can offer Customer A a lower price SMS package to keep them texting.
Smartphone Calling Patterns: Customer A uses voice calls only, while customer B uses Viber for outgoing calls. The CSP should therefore consider offering Customer B premium VoIP delivery that guarantees QoE and generates additional ARPU.
Social Sharing Patterns: Customer A shares photos in the evening with auto backup from iPhone to iCloud. Customer B prefers live video broadcast with Bambuster. In this case, the CSP can target customer B with a plan that guarantees fast video upload
Now that we have a better sense of their behavior, let’s take a closer look at the demographics. Who are Customers A and B?
Customer A is a 47 year old female who lives in a remote suburb and has been a customer for 10 years.
Customer B is a 23 years old male who lives in the city and has been a customer for two years.
This example clearly demonstrates that although the usage patterns appeared similar on the surface, the two customers are quite different and must be targeted differently.
Of course, this breakdown of information is only attainable with the right analytics tool, and these use cases are only possible with the right business processes in place. It is therefore crucial to implement a holistic approach that allows information to be shared and acted upon across departments, including marketing, finance, operations and customer service.
I encourage all operators to consider how a strategic approach to analytics can impact their bottom line. A big data analytics platform can help identify and sort tens of thousands of records each day, allowing operators to focus on the important task of transforming their business intelligence into profitable action.
Andrei Elefant has more than 10 years of experience in product management. He joined Allot in 2000 as a product manager, and served as director of product management before being promoted to his current position.