Technology is rapidly transforming how customers deal with businesses—and how businesses cater to customers. Advancements in technology have enabled customers to share their experience, both good and bad, of using a product or a service they received.
Though all this information is used by businesses to measure customer satisfaction, how can they be certain that they’re personalizing offers that are relevant to customers and tapping into the real opportunities?
For businesses to maintain a competitive edge, organizations must be prepared to constantly innovate and move forward. That means adopting progressive approaches such as machine learning and artificial intelligence. Let’s look at how predictive analytics work in the telecom sector: key activities can be optimized and automated, customer satisfaction can be given a boost, and potential complaints can be anticipated and dealt with.
How can telecom companies benefit from Predictive Analytics?
1. Never fail to meet SLAs
Meeting SLAs are critical in the telco industry. They define the level of service expected from the organization, and honoring them is a mark of reliability and commitment. In fact, breaches could result in penalties, complaints, and even customer churn. In a year alone, poor customer service costs businesses an upwards of $75 billion.
The key is to better manage incident priority. But how can one know which incidents require attention? With classification machine learning algorithms, identify breach patterns and determine tickets that are prone to SLA violations. Escalate these high-risk tickets based on severity to stop breaches from even happening.
With foresight into potential SLA breach, telco organizations can reduce customer complaints, avoid business losses, and protect business reputation.
2. Say goodbye to unplanned downtimes
Whether it’s because of malfunctions, human error, or a natural disaster, unplanned downtime is bad news. Its effect ripples across the organization—from operations to customer service. In fact, 82% of companies lose about $2M for an average of 4 hours of downtime.
With predictive maintenance analytics, anticipate and proactively address problems timely. By leveraging the classification model, one can forecast probable downtimes and determine when equipment is due for maintenance, upgrade, or replacement.
Having visibility into future outcomes will result in higher asset utilization, a boost in efficiency and productivity, and reliable customer service and offerings.
3. Bad calls will be a thing of the past
In a call center setting, predictive modeling can be used to improve productivity by optimizing staffing and scheduling decisions. This is integral in the telecommunications space, and research shows: 62% of companies view customer experience delivered by contact centers as a competitive differentiator.
Through predictive interaction analytics, call center managers can identify specific time periods when a higher inbound call volume is expected. This allows one to better optimize staffing and scheduling in order to better handle call volume fluctuations, provide an acceptable level of service, and protect brand reputation.
Additionally, predictive insights based on customer mood can be used to better re-route calls in seconds to agents with the appropriate knowledge and skill level. This will, in turn, improve call duration period and reduce repeat calls and call abandonment rates by unhappy callers.
With these proactive measures, organizations can delight customers, ensure operational efficiency, and even boost agent satisfaction.
4. Better handle complex customers
Similar to call dropping, telcos can also benefit from customer churn prediction. Attrition is inevitable for any organization, but that doesn’t mean it’s unavoidable. Churn rate is often linked with negative experiences—67% of consumers considers customer experience a primary reason for churning. And it’s important to nip this in the bud, since 13% of unhappy customers will share their negative experience with more than 15 people.
Using machine learning and clustering techniques, customers at risk to churn can be identified using data from past interactions. This segmentation allows agents to manage priorities and focus on high-risk customers, implementing strategies to keep them satisfied and discourage them from switching to other providers.
By addressing churn at an early stage, organizations can proactively meet customer needs, maintain reputation, and reduce acquisition and associated costs.
Shaping the future of telecom
These four predictive analytics use cases in telecom are just some of the many scenarios where machine learning and artificial intelligence can boost customer satisfaction, but the benefits are crystal clear.
Advanced analytics provides actionable insights to improve overall service, provide reliable services or products, and in the long run: retain and delight customers.
Beyond that, the telecommunications sector is an industry that capitalizes on the transfer and exchange of data. This makes it even more important to deal with the ever-growing volume of data. To illustrate: the data in AT&T’s wireless network has increased 470,000% since 2007. It’s critical for organizations to address this influx with advanced analytics tools.
