When to nudge a delinquent customer for a payment — that is the question. Or rather, that is one of many — many — questions that Signals are helping telecom companies (telcos) answer. Because when to call and ask for payment can actually help determine whether to call at all, and eliminating calls can lead to significant savings. So how do Signals do this? And more important, how can telcos, specifically, take advantage of these Signals to improve customer experience and maximize bottom-line impact?
Getting Started with Signals
The first step in this process is the adoption of Big Data technologies. By now, most telcos have made the requisite investments in large-scale data processing and storage capabilities as well as visualization tools that can help them harness the power of Big Data. But the key lies in applying all this capability toward tangible business outcomes. This is where the concept of “Signals” comes in.
At the most basic level, Signals carry useful information about underlying events, customers, systems, and interactions. They are coded intelligence and can be descriptive, predictive, or prescriptive in nature and help drive tangible business outcomes. Some examples of Signals include customer behavioral patterns such as purchase frequency or data usage, operational insights such as propensity to call for a servicing issue, or even customer risk scores to name a few. Telcos must identify and incorporate this intelligence into daily operations at the minutest levels to unleash the real value of Big Data.
To incorporate Signals into an organization in a meaningful way, telcos must be perpetually creating and maintaining their Signals. Specifically, each Signal set needs to be as follows:
1) Dynamic — continually updated as new data comes in.
2) Flexible and adaptable — easily modeled and tweaked with changing parameters and targets.
3) Scalable and reusable — easily replicated and applied across a multitude of business problems, sometimes spanning different domains and use cases.
If carefully created and implemented, a telco’s repertoire of insightful Signals can help address multiple business issues across diverse domains. Take the example we touched on above about delinquent customers. Every year, telcos incur high operational costs making outbound payment-reminder calls to high-risk customers who have passed their payment due date and are delinquent. These communications are based on simple automated rules: High-risk customers get a disproportionately higher number of calls within a shorter time frame than medium-risk customers. However, a deeper analysis of the transactional patterns of high-risk customers (the Signals) reveals that not all high-risk customers are created equal. There are several sub-segments within them, such as habitually late payers vs. those who never intend to pay at all — and several categories in between. In this instance, a Signal around payment patterns of habitually late payers might push the decision to call them back a couple of days. A percentage of these customers will likely pay on their own, eliminating the need to call them altogether and resulting in a less annoyed customer and substantial savings from reduction in outbound calls.
The Multiple Uses of Signals
Throughout the telecom industry, Signals can improve everything from offer targeting to customer service to actual phone or cable service. What’s more, they’re using the same Signals in multiple ways, which is, ultimately, the power of Signals. Here are just a few examples of how telcos are currently using — and reusing — Signals to improve customer satisfaction and their bottom line:
- Signals around customer behavior are highly useful for targeting marketing offers having to do with retention, cross-sell, upgrades, and migration. Sometimes a Signal could be a classification of each customer's need state — a complete view into how the customer behaves in relation to the company. This highly personalized Signal is created by combining multiple Signals — usage Signals such as call, data, and text behavior (including volume, frequency, and duration), Signals related to historic offer uptake rates, Signals related to handset profiles and servicing issues, and others. A combined holistic Signal of this nature can help target a customer much more accurately with the right offer in the future and vastly increase uptake rates on first contact. These Signals can be continually updated as customer behavior evolves, which helps them remain relevant and reusable.
- The notes from call center reps typically capture a lot of richness about customer servicing issues. Signals can be created by identifying highly recurring words or combinations of words, such as “remote + reboot + tile,” with other variables, such as “Customer truck roll history.” When applied to each call, this newly created Signal is typically a very good indicator of whether this problem can be solved on the phone or a truck needs to be rolled. The end result is that more issues are resolved on the phone by agents, which reduces the number of unnecessary truck rolls and increases operational savings.
- Signals that state the number of days until the end of a promotion or trial period for each customer can help marketers proactively reach out to the customers who are likely to discontinue service in an effort to entice them to stay with an offer. The same Signal can simultaneously help customer service departments anticipate in-bound call volume peaks, so they can staff accordingly.
With the help of the latest advancements in data analytics, thousands of such Signals continue to be developed, each one shortening the cycle for data mining and standalone modeling efforts. Big Data technology can harness their true power by implementing them in a dynamic, scalable, and re-usable manner and helping multiple business departments leverage the same common set of intelligence for more informed business decisions. We’re seeing first-hand how Signals created around individual customers, plans, or devices can drive sales and marketing efforts in new ways. Likewise, Signals for various domains, such as customer lifecycle management, prospecting, and network operations are leading to smarter operations, increasing efficiencies and effectiveness.
Opera Solutions is deeply entrenched in creating and maintaining the thousands of Signals that are now driving some of the world’s leading telecommunications companies. Learn more about how Signals can improve operations and customer service with our white paper “Delivering Big Data Success With the Signal Hub Platform.”
Ankur Desai is a principal at Opera Solutions. With 12 years experience leading sales and delivery of analytics solutions and client services at Fortune 100 companies, he currently leads Signal Hub and client services for telecommunications and retail at Opera Solutions. Ankur holds a bachelor’s degree in economics and political science from the University of Rochester and a master’s degree in operations research from Columbia University.