Studying the Relationship Between Phone Price and Income
Recently, economists from the University of Chicago and the National Bureau of Economic Research looked to measure trends in what they termed “cultural distance” — the ability to correctly identify a person’s demographic group, financial status, gender, and other attributes by examining their purchasing behavior, media consumption, social attitudes, and how they spend their time during the day.
While the entire study provides fascinating insight into the different indicators of social and economic status, what really caught my attention was the fact that researchers found ownership of an iPhone was the best indicator of a person’s income quartile. In fact, they wrote, “Knowing whether someone owns an iPhone allows us to guess correctly whether the person is in the top or bottom income quartile 69% of the time.”
This study immediately caught our attention at Sabio because it dovetailed closely with research that we’ve conducted over the past several years. When it comes to mobile advertising, being able to accurately identify those who are high earners — or have more disposable income to spare — is an incredibly valuable ability to have, especially when it comes to finding buyers for big-ticket purchases, such as cars or homes.
Taking the University of Chicago analysis as our starting point, we wanted to see whether knowing how much a person’s phone cost could give us a clue about their income. After all, iPhones are expensive, but there are other smartphones that retail at a similar price. For the purposes of the study, we assumed that most people paid the fair retail price for the phone they used (as opposed to it being gifted or bought secondhand), as that is by far the most common scenario.
While it’s fairly simple to find out what the average retail price of a phone is across the United States, being able to determine someone’s financial status is a good deal trickier. For one thing, it depends on which metric you focus on, such as annual income or financial assets owned. We opted to focus on income, using data on median incomes by zip code gathered from the United States census as our proxy. Not only was this information publicly available and reliable, but because we also knew which zip codes the phones we were analyzing were located in, it made it much easier to compare results.
Armed with information from Sabio’s database, we were able to map out the average price of a cell phone against the zip codes that the phones we were studying were located in. From there, we also plotted the reported median income (based on census data) in order to determine whether any trends could be seen.
We found that there was a positive correlation between the average price for a cell phone in a zip code and the median income of people in that zip code. While the findings of our study are necessarily limited by its narrow scope and the fact that there was some statistical noise, our regression results indicate that the variable we identified (phone price) is in fact statistically significant when determining median income.
To put it more plainly, the results of our study show that the more expensive your phone is, the more likely you are to come from a higher income bracket. Our model predicts that, for every dollar that the average price for a cell phone in a given zip code increases, the median income for that zip code will also increase by $122.70 — in other words, by a fairly significant amount.
With these insights, it is possible to better identify higher income customers by knowing which mobile phone they own, which in turn allows advertisers to upgrade their consumer profiles, and improve their targeting, preference modeling, and more. For brands, this means minimizing the guessing involved when it comes to identifying prospective customers. Such information can also be used to run further studies that determine the product and creative affinities that people of various income brackets have, which in turn can help boost the accuracy of marketing campaigns.
While more research in this area would be immensely valuable, this study is an important starting point when it comes to better understanding the relationship between people’s mobile phones and their consumption preferences.
Joao Machado is SVP of Product Marketing at Sabio, the media and technology company behind App Science®, a proprietary machine learning platform that pairs observations of consumer behavior to corresponding data to inform marketing decisions. He is responsible for developing compelling go-to-market initiatives and sales enablement programs and productizing Sabio’s solutions. Prior to joining Sabio,Machado was US Director of Mobile at OMD, where he built the company’s mobile practice, AirWave, and was responsible for all dedicated mobile efforts within OMD USA. Machado has held previous positions at MindShare and 10th Degree. Machado is on the speaking circuit and has recently spoken at events including, thinkLA, Digiday, iMedia, MMS and more. Machado was also responsible for the first mobile upfront in the industry.