Information Can Help You Get the Right Cell Phone Plan

Research examines whether "rational" consumers always chose the best plan for themselves.

November 01, 2007

| by Alice LaPlante

Choosing a cell phone plan has become one of the great rites of passage in modern life. There are literally thousands of such plans being offered by mobile telecom service providers worldwide. And even after you’ve narrowed down your choices to those being offered by a single telecom vendor, the options can be bewildering. What’s a rational consumer to do?

This question intrigued Sridhar Narayanan, an assistant professor of marketing at Stanford GSB. With colleagues Pradeep Chintagunta from the University of Chicago, and Eugenio Miravete from the University of Texas at Austin, he embarked on a research study to examine how the inherent uncertainty of guessing future telephone use impacted consumers’ ability to choose the best plan to meet their needs. As part of this, the researchers examined how quickly consumers were able to learn from their mistakes and switch to a more optimal plan when appropriate.

“One of the questions we really wanted to answer was whether ‘rational’ consumers always chose the best plan for themselves,” said Narayanan, who is the GSB’s Coulter Family Scholar for 2007-2008. “A second, and related, question was whether—and how fast—consumers learn from their mistakes.”

His findings were instructive. As it turns out, consumers frequently make mistakes when choosing their plans. This is due to the inherent uncertainty involved. Not knowing what their phone usage will be, consumers have to guess, and not surprisingly, these guesses often turn out to be wrong. “What matters is that they learn from those mistakes as quickly as possible, and switch plans as necessary to fix those mistakes,” says Narayanan. Indeed, one of the major conclusions of this study is that the more information consumers have, the faster they can correct any mistakes that they make.

Specifically, people on fixed telecom plans tend to learn a lot more slowly than those on measured priced plans due, in large part, to the itemized bills that accompany measured rate plans. Because of the details on those bills, consumers realize right away if they’re not on the optimal plan.

Although the modern-day cell phone plan market provided the impetus for the study, the complexity of the marketplace, and the lack of a comprehensive set of consistent data made it difficult to build a viable analytical model based on the current cell phone environment. Narayanan and his colleagues thus used data from a 1986 local telephone service pricing experiment conducted by South Central Bell.

In that trial, consumers in a number of Kentucky cities were given two choices: to stay with the traditional plan that offered them unlimited local calls for a fixed fee, or move to a measured pricing scheme in which they were charged a per-call fee on top of a smaller base fee. Under the first plan, usage did not matter: They paid the same every month no matter how many local calls they made. With the second plan, the monthly cost increased proportionally with their use of local telephone services.

The basic problem consumers faced in 1986 in Kentucky is the same one faced today: how to choose the plan that minimized costs while meeting specific needs. But this was complicated by the fact that customers were uncertain about what their future phone usage would be at the time they had to make their decision. They simply had to take their best guess.

In Kentucky, not surprisingly, consumers who made a lot of local calls chose the fixed plan, while those who dialed less chose the measured plan. But measured plan users learned much faster whether they had made the right choice, largely due to easier access to information. They were given better usage details through their itemized telephone bills. Since the fixed bill was the same regardless of usage, consumers took longer to figure out when they had made a mistake.

“Consumers on fixed plans are much less inclined to look through the bill and see what they did over the past month,” said Narayanan. “On a measured plan, you are very much conscious of your usage, and what it is costing you.”

Ultimately even consumers on the fixed rate plans did learn from their mistakes and switched when it made economic sense, said Narayanan. “People do eventually learn in such cases, but they learn very slowly,” he said.

In today’s cell phone market, there’s the added complication that consumers are frequently forced to commit to a contract that binds them for months or even years to a particular plan. Thus, even if they learn that they have made a mistake, they are unable to correct it without paying a financial penalty.

Another major point Narayanan’s model proved was that there are ample opportunities for telecoms to offer a range of plan choices that both benefited consumers and contributed to their own bottom lines. “This is not a case where the consumer has to benefit at the expense of the vendor, or vice versa,” he said. “There is room for plans that give consumers what they really want while maximizing provider revenues as well.”

Finally, a third thing that the model measured was the value of information. “Why do people make suboptimal decisions? They don’t have enough information. So how much is that information worth to them?” asks Narayanan. As it turns out, the value of information about a telecom bill is “relatively modest,” he says, “but it’s still worth something.” This could have ramifications for policy decisions. For instance, some countries mandate what information needs to be provided on a bill, and some even require telecom providers to notify consumers when their bill would have been lower if they’d chosen an alternative plan.

One of the reasons that information about cell phone bills is deemed not to have great value could be that phone bills represent just a small percentage of consumers’ budgets. “But the conceptual question is valid in a different context—say, a medical choice where you would pay a lot more if you make the wrong decision,” says Narayanan. “In such cases, the price consumers would pay for information could be quite high.”

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