STEP 1: Primary Research

Approach:  I structured the questions to ensure that they were relatable, mixing qualitative and quantitative questions, limiting the length of the survey, and limiting unknown jargon to ensure seamless survey experience.

Summary: Overall, those who have taken the survey have used delivery app services before, but do not regularly use it. They would rate the speed of current food delivery app services a 3.5 out of 5. Respondents typically order a variety of food, particularly selecting based on speed, cost, selection, and ease of use. In particular, respondents use the rating/reviews, prices, images, speed, and cuisine to determine what they select on delivery apps. Thus, a delivery app that is more upfront about hidden delivery charges, potentially removing a tip policy, adding an option for the user to directly contact the restaurant for custom orders, having easy to use filtering methods, having customizable options, including more promotions, and ensuring fast delivery would result in customer satisfaction based on these responses. Ultimately, this survey illustrated how the pain points included hidden prices and fees, speed, and uncertainty of the quality of their food. This includes the food temperature and even potential of their order being missed or eaten by their driver.

Pain Points: Negative experiences include not receiving their order on time, not receiving their order at all, or receiving their order partially eaten, missing, or cold. 

Impact: This research plays a fundamental role in helping the design of the food delivery app for several cases.

STEP 2: Competition Research

Approach: For this approach, I identified the top 3 direct competitors, analyzed their objectives, observed their tactics, determined their strengths and weaknesses, analyzed their unmet needs, and reviewed their product. I also created a comparison matrix to determine my findings. 

Impact: Identified and compared top 3 leading food delivery applications based on key characteristics.

STEP 3: User Persona and Empathy Map Creation

Approach: For the user personas, I create two profiles: 

Type A: Dylan is a 22-year-old business strategist working for a large entertainment company in Los Angeles county. He holds a bachelor’s degree in business management and cinematic arts and has been in the field for a few years. He is passionate about food yet struggles to find the time to cook dinner or eat outside alone. Without anyone to enjoy the food with him, he constantly seeks ways to try new foods and get quality meals through delivery applications. He prefers to eat Asian cuisines such as sushi, Korean BBQ, and pad thai. 

Type B: Sumedh is a 27-year-old software engineer who works from home. He often forgets to eat (lost in code) and uses delivery apps to find a quick bite while working. He looks for cheap and quick foods, favoring junk foods. Sometimes, he likes to treat his girlfriend to tasty dinners but does not want to go outside to pick it up. He often enjoys eating food at home while watching a show. However, he does not like the hidden service fees and tip system of current delivery apps.

For the empathy map, I created it based on the outlined user personas and their anonymous survey results. For each quadrant, I filled it with relevant information gathered from the primary research stage. 

Impact: I was able to identify pain points and narrow the optimal target audience.