Auto-buyer Personas

Remote User Research

Agency: Nash Interactive

Skills: User Research, Survey, Visual Design, Customer Journey, Persona, HTML, CSS

Software: Amazon Mechanical Turk, Illustrator CC, Atom, Github

Summary

With the use of crowd-sourced technology like Amazon Mechanical Turk it’s become possible to distribute surveys cheaply and simply. This breakthrough enables organizations to conduct what once cost thousands of dollars for pennies, help agile product teams iterate quickly towards an informed solution. In other words, remote user research can make your life easier.

Discovery

Sometimes, agencies want to be user-centered and execute good user experiences, but don’t always have the facilities to do so. Customers aren’t always accessible, and facilities are expensive to rent. While comparing market research platforms I came across Amazon Mechanical Turk, a crowd-sourced platform online work. With some digging I found that academics have flocked to Mechanical Turk because it allows them to get fast, accurate and representative results from the general population. Thanks to the low cost I was able to get sign-off on a budget for user research.

In addition to utilizing Amazon Mechanical Turk, I also used data from Google Analytics and secondary research. The analytics data helped me pinpoint our most active demographics, what devices they used and what their interests were. I learned that (based on our data) car buyers were likely to be from ages 25-34 or 35-44 and were more likely to be men than women. These statistics were affirmed by recent studies that covered global and domestic trends. You can find a list of studies and articles that influenced this project here: list of sources.

Definition

One of the most difficult parts of creating personas is defining the scope. What elements would best serve our use case? Our goal is to help customers buy cars through informational videos online. We can track what happens on our products, but not before or after. These questions have the potential to impact the car buying process of the consumer as a whole, because we can create a shift from a digital touchpoint to a service.

For this persona the elements chosen are as follows:

Design

Using a simple screener I asked the respondents to state in their own words if they had purchased an automobile in the last twenty-four months. After the screener I gather some basic demographic data such as gender, education level, job and salary. These data points provide the setting to create a narrative for our personas to tell. Moving on I sought to understand their mental model:

  1. Did you buy new or used?
  2. What attributes were you certain of before your search (body style, year, make, model, trim)?
  3. Which online tools do you find helpful?
  4. Which devices do you use while shopping for a car?
  5. What prompted this purchase?
  6. What do you dislike about shopping for cars?
  7. How do you learn about new cars?
  8. Where are you when shopping for a car? (Describe your surroundings.)
  9. How could shopping for new cars be easier?

As the data rolled in for each demographic I documented it on my whiteboard and created a rough draft of the layout for the digital poster. Having the physical documentation mapped out in an easy to understand way helped me format and structure things much more easily, once I moved into illustrator.

Persona Process from Nathan Nash

Now that all the data had been collected all that was left was the fun part, deciding who the personas would be and look like.

Results

In the end, the final product is a digital representation of our customers based on a composite of data from both surveys distributed through Amazon Mechanical Turk and Google Analytics. This document serves as an artifact to help remind us of our customers and is left as a reference for all our future digital video related projects.

User Personas from Nathan Nash