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Read on for details on how Openroad’s Finegold uses FB ads and landing page conversions to drive user research on products in development.
During early development of Openroad, Rafi immediately set out to determine their target audience and begin market research. To do this, he started by creating a minimum viable landing page and a series of Facebook ads.
(Side note: Their app just launched, and you can find it here!)
To start, Rafi created a landing page as if the final product already existed (in reality, the product was in early stages of development). The landing page itself was quite bare, with the initial page containing only a series of three bullet points. The bullet points included a description of the product, a call to action, and a hook: “get help instantly in a crash.”
From there, viewers could scroll down the page (which only totalled about five screens) and see the testing and science behind the product itself. In Rafi’s words, “We wanted to give people some level of confidence, while explaining how the technology works.”
Rafi then supplemented these descriptions and explanations with screenshots and excerpts of the user interface — in essence, “bringing the product to life.”
Alongside these scrolling screens (and featured at the top of the landing page), Rafi positioned a call to action and a “download here” button. When clicked, these buttons redirected the viewer to another page to be placed on a waiting list for the final product.
From there, Rafi used Hotjar analysis to determine where participants focused the majority of their time on the page as well as analyze basic screen replay and hotspot mapping. Using Hotjar and Google Analytics, he could see where viewers focused and exactly where they clicked.
From those results, Rafi learned that participants were more likely to click the download and call-to-action (CTA) buttons at the top of the page rather than the bottom. He also found that some viewers didn’t even scroll to the bottom to learn about the science behind the product, they just wanted it.
Of course, to analyze how people react to a landing page, you need to get them there in the first place. Rather than zooming in on a target market based on educated guesses, Rafi created several different Facebook ads for the general population and ran them in two separate rounds to test marketing messages.
In the first round, all of the ads were skewed toward fear by using darker colors, sirens, and occasional emotional language. In the second round, the ads were more neutral: they contained the same language but were designed with a neutral color palette and without the same loud sirens and jarring sounds.
The first ad stated, “16,000 people in the US get in a crash everyday, get the app that protects you.” The second read, “‘I’m going to get into a crash today!’... said no one ever.” And the third ad’s language varied, but said something to the effect of, “This app could save your daughter/son/mother’s life.”
Effectively, the ads were an easy form of idea-screening. Out of all the ads, the last one resulted in the most conversions. Since the product wasn’t technically ready for release, Rafi defined a conversion as “when someone actually gets to our website and hits the ‘download the app’ button.”
In both the first and second rounds, the results were surprising: the data skewed toward men each time, even though Rafi and his team had predicted the opposite. In addition, while Rafi correctly predicted that Openroad would be popular with parents of teenagers, he was surprised to see that he also got responses from across the age spectrum, regardless of whether they had kids.
By taking a broad approach with social media, Rafi was able to gather a large data set and see who the product really resonated with. It showed him demographics he could test further when they’re ready to seriously target customers. And, what’s more, “we now have a base of users who have actually tried to download our product,” he explained.
If viewers clicked on the “download the app” button, rather than being taken to the app store, participants were asked if they would like to be placed on a waiting list (a good initial litmus test for market testing). From there — if the wait-list participants were willing — they were directed to a survey focused on two main areas:
1. “What is one benefit you hoped to derive from the app?”
2. “Who do you think this app is for?”
As to question one, Rafi expected participants to identify parents of teenagers as the dominant audience for the app, but he actually found that most people believed the app would be helpful for anyone who drives. Participants liked the fact that the app provides guidance once you’ve been in a crash and that it also notifies designated loved ones.
From the survey, Rafi was able to shift the advertising and marketing language going forward to fit participant feedback. For example, while marketing originally classified the product as an “emergency response” app, survey participants used the term “crash detection” more; from this feedback, Rafi was able to reduce cognitive load on the consumer going forward by using easy-to-understand terminology and concepts.
After surveying waitlist participants, Rafi then interviewed “anyone that was interested in talking to him.” The profile of interview candidates was a lot more varied than expected, ranging from old to young, parent to adult child, and everything in between.
Drawing from the ever-growing waitlist, Rafi was able to connect with a broad pool of participants rather than just local respondents to identify key customer needs.
“This was the first time I'd used remote user testing. I've always done in person testing. It was really cool because it meant that rather than targeting a local population, I spoke to someone in San Diego, someone in North Carolina, someone in the D.C area, et cetera. So you're getting a very rich base of people,” he said.
Rafi asked participants four main questions:
A lot of interviewees said the product would be for their relatives (parents or children), while others wanted the app for themselves so they could feel safe while driving. Thankfully, most participants had an accurate expectation of the product and its capabilities; but where there was confusion, Rafi was able to adjust the marketing language accordingly.
During one interview, when he asked if the participant would recommend this product to other people, Rafi was delighted to find she already had. The respondent had “told her husband, coworkers, parents, and even her neighbors” all before the first prototype had even been released!
An important insight that came out of these interviews was the importance of a “test run” and occasional assurances that the product was working. Similar to a smoke alarm, the Openroad app is one that (ideally) won’t be used frequently.
To assure users that the product is working, Rafi and his team enriched the product by adding a “test run” of the software during installation to show users how the product works without actually alerting emergency responders of an accident. Another idea he hopes to use includes a notification of how many miles the user has been protected for over a given period of time.
After receiving feedback from interview participants, Rafi was able to alter the product according to their suggestions and get it ready for prototype testing. Reaching back to the same list of survey respondents and waitlist participants, Rafi encouraged both groups to test out the first version of the product and provide further feedback the development team could implement.
While normally Rafi uses User Interviews for user testing products, here — where the ideal user group is still malleable and undefined — Rafi found the waitlist to be a sufficient pool of participants, especially where there has been “a very rich base of people” interested in the product and a “rich, virtuous cycle” of feedback, he explained.
To recap, Rafi’s application of the lean startup theory to Openroad followed this map:
Their app just launched, and you can find it here!
Rafi plans to do further research so Openroad can continue to develop a product that continually fits the user’s desires and expectations.
Note: Looking for a specific audience to participate in your UX research? User Interviews offers a complete platform for finding and managing participants. Tell us who you want, and we’ll get them on your calendar. Find your first three participants for free.
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