Telecommunications companies usually aren’t the most popular among consumers, anecdotally speaking. It’s pretty common to hear people express frustration with some aspect of their communications provider — be it convoluted billing, unwanted marketing emails, hard-to-navigate customer service, high plan prices, the list goes on and on and on.
As a result, it may be unsurprising to learn telecommunications companies experience high rates of customer churn — also known as the rate at which customers cease doing business with a company. As Investopedia notes, we most commonly see churn expressed as a percentage of subscribers who leave within a given timeframe. In the case of telecoms specifically, churn usually takes the form of a customer ending their contract or canceling their subscription service.
Let’s take a closer look at the prevalence of churn in the telecommunications industry, how costly it is for companies and why customers tend to cancel their plans at higher rates in this arena.
How Customers Feel About Telecom Providers
Would you be surprised to learn that data paints a similar picture of customers’ perceptions of telecoms? The American Customer Satisfaction Index, which measures the overall customer satisfaction by sector according to a formula, reported these top 10 benchmarks for 2019:
As you can see, telecommunications is far from one of the most highly regarded sectors — an ongoing challenge companies are aiming to address in the interest of profitability and competitiveness.
How Expensive Is Telecom Customer Churn?
It’s tough to put an exact price tag on customer churn, as it depends on many factors. However, it’s safe to assume a large portion of companies’ budgets currently go toward trying to gain new customers, while holding on to existing ones. Based on a churn rate just under two percent for top companies, one source estimates carriers lose $65 million per month from churn.
As Computer Weekly cites, mobile operators spend approximately 15 percent of their revenues on network infrastructure and IT — but a whopping 15 to 20 percent of revenues on the acquisition and retention of customers.
It’s long been known retention of existing customers is less expensive than acquisition of new ones. In fact, a Canadian study found it costs nearly 50 times less to retain than acquire. Ergo, companies aiming to minimize the cost of churn are focusing increasingly on retention strategies and alleviating customer pain points that may otherwise cause patrons to cancel their subscriptions and head for greener pastures — usually into a competitor’s waiting arms.
Reducing Customer Churn in the Telecom Industry
Getting to the bottom of why telecom customers are so apt to churn can help companies make targeted decisions aimed at keeping them. This is especially true in our era of advanced data analytics in the telecoms industry, with the availability of self-service tools capable of helping companies understand performance from various angles so they can then take action to drive better business outcomes.
In particular, AI-driven analytics tools can mine massive datasets for performance insights relevant to customer churn, then push them to the attention of marketers, customer service managers and executives so they can factor these findings into subsequent decisions.
It’s also important to identify which customers are most at risk of churning based on their behaviors and account history. This way, companies can act to keep these customers before they bail out with a compelling offer or loyalty reward. Understanding patterns driving the most valuable groups of customers can help companies refine their acquisition efforts as well, helping them target people most likely to sign up and stick around for the long haul.
Customer churn is likely costlier in telecoms than in other industries based on customers’ attitudes toward the sector, but companies aware of this fact can use data analytics in an effort to specifically bring down churn rates by identifying and relieving customer pain points, as well as understanding customer behavior on a deeper level.
