During my time as a Digital Platforms intern at Servco Pacific Inc., I was able to participate in a two-day long hackathon with other members of the Digital Strategy and Marketing teams. Our goal was to create a personalized car-buying experience.
"How can we optimize the current car-research experience on the Toyota Hawaii site?"
After synthesizing the behavioral analytics, my group and I identified a core issue with the current car-research experience.
"The current SRP assumes that customers already know what they want and what they're looking for."
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We had roughly 30 participants over 2 rounds of testing. Participants wanted to have enough information to understand the different pricing between order types in a clear, concise and visible format.
Once Toyota Hawaii customers land on the Toyota homepage, we could include an ingress into our proposed Car Match survey. After customers indicate take the survey, they would be shown a customized SRP with relevant vehicles and ways of continuing their research process offline.
The goal of our first feature, the Car Match Survey, was to educate and tailor the customer's vehicle suggestions according to their needs and preferences.
My team and I developed six different categories of questions, which were inspired by the most popular filters on the SRP as well as more personalized filters that we would've liked to see. The order of the questions were informed by user behavior preferences.
We broke up the questions into individual pages so customers could focus on one thing at a time. The idea was to prevent customers from feeling overwhelmed by all the potential preferences, which was a pain point we saw with the filters on the SRP. As a part of Toyota Hawaii, we wanted our branding to be as consistent as possible as to not confuse customers who were already familiar with the brand.
Our goal here was to make car-buying information more visible and accessible, especially for new customers. By hovering over the information icon, customers could easily view the definitions for certain terms like cash, lease or finance.
Although participants gravitated towards the pop-up modal in Prototype D, we ultimately went with Prototype B since it had the clearest language and did not disrupt the ordering flow compared to the other options. This option also required the least amount of tech effort, which would allow us to roll-out the differentiated pricing information to our users faster. The copy required some additional refinements to be more short, informative and concise.
Although my team and I had access to customer behavior analytics, it would've been optimal to gauge customer pain points and needs based on feedback from actual customers.
If we had more time outside of the time constraints of the hackathon, my team and I would have liked to create more iterations of our proposed solution.
Although we had structured the order of the questions based on the most interacted with customer preferences, we would've liked to present the questions in different orders to gauge which structure yielded the most accurate car match results.
