Grain aspires to use data to identify food trends and taste profiles in order to cater the best cuisines to its customers. The first step in achieving this vision was to build a feedback collection mechanism that allows for more useful and actionable feedback for our food and operations teams. Essentially this means getting the fundamentals of operations right while collecting personal taste preferences in preparation for the development of food innovation and smart food recommendation in the next phase.
Here are some issues we identified based on a) the quality and quantity of feedback email replies, b) customers' complaints about the feedback emails:
Grain’s target customers are young professionals with spending power, digital natives with high expectations when it comes to digital products. They are more pro-active when it comes to interacting with digital brands, they identify with the brands' values and form a personal connection with the brands they choose.
Understanding the state of the email feedback system, how that fit into our customers' day-to-day life and the customers' behaviors and attitude towards giving feedback for a product was essential to the solution phase:
The two main objectives of the project became apparent to us: we needed to make our feedback system as unobtrusive to our customers as possible and at the same time collect more data for the company.
Our initiatives for this project include:
We started with identifying the most important case in which our customers would want to leave a feedback for their order. As mentioned earlier, generally the majority of customers don't leave feedback unless their experience is a peak experience (peak negative or peak positive). This happens after they have made their order and eaten their meal. They then revisit their order confirmation email or the Grain's website and apps to leave their feedback for the specific order. After speaking to a few of our customers, we came to the conclusion that the feedback form should then be available to them upon landing on the Grain's website and apps after they've received their orders.
On the other hand, we also started looking into the different rating systems. For the new feedback experience, we wanted our customers to be able to as quickly as possible rate the individual dishes in their order, this could mean having an upward of 6 items (the average order size) to rate. Additionally, given the ambiguity introduced by the 5-star rating system, we hypothesized that a more 'human' approach would reduce cognitive load and yield better results. Smiley faces were taken into consideration. As we were picking the different happy and sad faces for our feedback form, we realized that the human emotion is also very complex and customers potentially would also infer a smiley face differently from each other. We went back to the drawing board and revisited the intent of the feedback system. We agreed that a binary rating system would be efficient, accurate, simple and get us the most straight-forward data set. The thumbs up proved to metaphorically make sense to our customers as a strong approval/disapproval signal but remained playful and friendly.
Looking through our customers' feedback provided us with some insights into the most frequent complaints that our customers have for our food and delivery service. We wanted to make it easier for our customers to quickly surface these negative experiences by adding quick key words into the feedback form. The data gathered from this would then become actionables for our internal teams. We worked on a quick mobile prototype to test these assumptions along with the interaction design of the feedback form.
The final round of usability test on the feedback form brought about some findings that helped us make the last few tweaks to our designs:
Rapid prototyping and testing helped us tremendously in quickly validating our assumptions for this project.
We launched our new feedback system and measured the results, both quantitatively and qualitatively. We found that the feedback completion rate has increased significantly, in fact, doubled the original number. More importantly, Grain can now build a meal and delivery score dashboard to monitor the quality of our food and delivery service. This will enable food innovation and smart food recommendation later on.
Balancing customers' needs and business' needs has led to some interesting results in this project. Our customers' first instinct rendered the individual input field feature redundant but as the business needs prove to bring customers values, the final result was extremely positive for all stakeholders.