This case study guides you through market research by following Ana, a fictional startup founder, as she conducts research for her early-stage venture.
Scenario: Ana lives in Bucharest and has an idea for a meal kit delivery service tailored to Romanian cuisine and local produce. She’s noticed many of her busy colleagues resort to takeout and wants to offer a healthier, home-cooked alternative that saves time. We’ll see how Ana uses market research to validate and refine her concept.
Ana’s primary objective is to validate that there is demand for a meal kit service in Bucharest and understand her potential customers. She outlines a few key questions: Who would use this? Why would they want it (or why not)? How much would they pay? What dishes or features would appeal to them?
From these, she drafts hypotheses:
Ana prioritizes the riskiest assumptions: Is the problem real (demand)? Will they pay ~150+ RON? These two she labels as must-validate. The others are important but secondary (features, channels can be adjusted if core demand exists).
To tackle these hypotheses, Ana plans a mixed-method approach:
She sketches a timeline: 2 weeks for interviews (including recruitment), overlapping with survey design and launch, then a week to analyze and a decision point by end of month. The landing page she can set up in a day and run ads to during week 2–3 as well.
Ana refines her target persona: “Mihai, 30, a software developer in Bucharest”. Mihai works ~10 hours a day, often stays late at the office or gets home tired. He values good food but isn’t an experienced cook. He and his partner try to eat somewhat healthy but end up ordering pizza or local takeout a few times a week. He has decent income (so can spend on convenience). Pain points: grocery shopping is a chore due to crowds and time, deciding what to cook is mentally taxing, and cleaning up is the worst part after cooking. Goals: wants quick, tasty, not-too-junkfood dinners.
She also considers a second persona: “Andreea, 34, a lawyer and mother of a 4-year-old”. Andreea is extremely busy juggling work and family. She cares about her child eating healthy home-cooked meals, but often doesn’t have the energy to cook from scratch every day. However, she is more price-sensitive (family budget) and worried whether her child will eat certain foods. This persona might be more challenging to satisfy, but could have high need.
Ana decides to focus recruitment primarily on people like “Mihai” (young professionals, either single or couples without kids) as her early adopter segment, since they have more disposable income and flexibility. She will include a couple of “Andreea”-type working parents in interviews to see if they express interest, but she’s aware they might have additional concerns.
Screener criteria: Age 25–40, living in Bucharest (or immediate area). Works full-time (40+ hours). Cooks dinner at home at least occasionally (not someone who never cooks, because if they truly never cook, a meal kit might be too high a behavior change). Ideally, people who do feel pain around dinner: she includes a screener question “How much do you agree: ‘I struggle to find time to cook on weeknights’ (1–5)?” – she’ll prioritize those who answer 4 or 5 (agree). Also a question “Have you ever tried a meal kit or meal delivery service?” – if yes, she definitely wants to talk to them for comparative insight; if no, that’s fine (most in RO haven’t, since not common).
For the survey targeting, she will use Facebook ads with filters: age 23–45, Bucharest radius, interests like “food delivery, fitness, busy lifestyle, technology” (rough proxies). Also, she plans to share the survey link in a local Facebook community for young professionals and ask coworkers to pass it along.
She lines up 12 potential interviewees: some through friends (a friend group of engineers), two through LinkedIn cold messages (young professionals who posted about busy work), and two working parents via a moms’ Facebook group who volunteered. She’ll schedule about 10 of them, expecting maybe 8–10 to actually happen.
Ana posts in the “Girls Gone International – Bucharest” Facebook group (popular among expats and locals) briefly describing she’s looking to chat with busy professionals who struggle with cooking – she gets a few interested comments and messages. She also asks a friend at a big tech company in Bucharest to share an internal Slack message recruiting interviewees (offering a 50 RON food voucher as thanks). This yields a handful of volunteers. She uses Google Forms for a quick screener as planned. From 20 responses, she filters down to 12 that fit well (and a few that were outside criteria, which she politely thanks and notes for possibly later – e.g. someone older who was very keen, etc.).
She reaches out and schedules 30-minute Zoom interviews in the evenings (since they work daytime). For scheduling, she uses Calendly to make it easy – blocking 7–9pm on a few weeknights and letting participants pick slots. She also schedules two interviews on a Saturday morning for those who prefer weekend.
For the survey, she creates a Google Form (or Typeform for nicer UX) with ~15 questions (multiple-choice and Likert mostly). She then sets up a Facebook Ad campaign with a small budget (say $30 over a week) targeting her demographic, with ad copy like: “Busy after work? No time to cook? Help us with a 3-minute survey about weeknight dinners and get a chance to win a 100 RON Uber Eats voucher.” This incentive and the targeting help drive clicks. She also posts the survey in the “Bucharest Tech Professionals” LinkedIn group and asks people to share.
Within a week, she gets about 120 responses. She closes the survey after reaching a nice round number and randomly picks a winner for the voucher to keep her promise (and announces it to participants).
Meanwhile, she set up a quick landing page using a tool like Launchrock or Carrd. It explains: “BucateAcasă – We deliver fresh ingredients and traditional recipes to your door. Cook a healthy dinner in 30 minutes, no planning needed!” with a nice food image. It has a sign-up form: “Get early access and a discount when we launch – enter email.” She drives a separate small ad campaign to this page to gauge conversion. Additionally, she includes the link at the end of the survey (“Would you like to sign up for early access?”). Over two weeks, 50 people enter their emails (some from survey, some from ads). That’s an encouraging sign of interest.
All interviewees showed up except one no-show (which she replaced with another person from the waitlist). She ended up with 9 interviews (30–40 min each). She gave each a 50 RON supermarket e-gift card as a thank you via email afterward (they were pleasantly surprised, building goodwill).
During interviews, Ana follows her discussion guide. She starts with “Tell me about your typical evening on workdays – what do you do for dinner?” This opens them up. She listens as they describe their routines. She asks follow-ups like “How do you feel about cooking after a long day?” and “What are the biggest challenges for you with weeknight meals?” Without fail, time and fatigue come up. One quote: “By the time I’m home, it’s 8pm… honestly, I just want something quick or I’ll skip dinner or snack on bread.”
When introducing her concept, she is careful not to “sell” it. She phrases it neutrally: “There are services where you get a box with recipes and ingredients to cook at home. Have you heard of that? (some have via US or hearsay, many haven’t). How do you think something like that might fit into your life, if at all?” She gets a variety of responses: Some say that sounds great (“I like cooking but hate the prep and shopping, so that could be ideal”). Others are hesitant (“Depends on price… if it’s too expensive, I might as well order ready food”). She notes down objections like cost, portion size (a single guy worried about kits usually being for 2 people), and whether recipes might be too hard.
She also does a mini concept test in the interview: showing a sample menu or recipe card design on screen to gauge reactions. One participant lights up at seeing a favorite Romanian dish (sarmale) offered as a 30-min version: “Oh, if I could make sarmale that fast, I’d love it!” Another says they actually prefer simpler grilled chicken-type meals on weekdays, nothing fancy.
Throughout, Ana practices the Mom Test principles – she doesn’t lead with “Would you buy my service for X?”, she instead asks “What do you usually spend on dinner when you cook vs. order?” and later “If a service like this delivered 3 dinners a week for, say, 150 RON per week – what’s your reaction to that price?” Some people compare it to their grocery spend (“I spend maybe 100 RON a week on groceries for dinners, so 150 is higher but maybe for convenience… hmm”), others compare to eating out (“If it’s 50 RON per meal, that’s about what I pay for takeout, so not bad if it’s good quality”).
She diligently takes notes, flagging exact phrases. She notices multiple interviewees mention lack of planning: “The hardest part is deciding what to cook and having all the stuff – I often realize I’m missing an ingredient.” This is validation for the value of meal kits (they remove planning/shopping).
After interviews, she has a good feeling – the problem is real for almost all of them, interest level varied but mostly positive if price/quality are right.
The survey data rolls in and she monitors key results:
The landing page test: out of roughly 300 ad clicks, 40 signed up (a ~13% conversion). Given not everyone who clicked is target (ads can misfire), it’s not bad. It shows some real interest – 40 potential early customers. She notices many sign-ups came after she added a line “Introductory price ~ from 120 RON/week” on the page – possibly addressing the price question helped conversion.
Ana now has a wealth of information. She uses an affinity mapping approach for the qualitative interview data:
She writes down key quotes and observations on sticky notes (in Miro). For instance:
She clusters these into themes:
She tallies quant data and integrates with these themes:
She uses a competitive matrix to place herself vs alternatives: e.g. columns for “Home-cooking (DIY)”, “Takeout/Delivery”, “Meal Kit (us)”, and rows like “Time required”, “Healthiness”, “Cost per meal”, “Variety”, “Effort”, etc. From research: Home-cooking = healthy, variety as you want, but high time/effort. Takeout = low effort, but can be unhealthy/expensive. Meal Kit aims to sit in the middle: moderate effort, healthy, moderate cost, convenience of not shopping. This helps her articulate her positioning clearly.
Conclusions:
She also addresses unanswered questions: For instance, she’s curious whether offering a 2 servings vs 4 servings option would change perceived value (some singles might prefer 2, but others liked leftovers). She might experiment in the pilot. Another question: best channels to scale acquisition? Her research suggests Facebook/Instagram ads worked for awareness. She might also try refer-a-friend incentives in pilot to see if users will recruit others (since word-of-mouth is powerful in such products).
Next Steps Action Plan:
After the pilot, Ana will do another round of analysis. Suppose the pilot reveals, say, that users loved the convenience but maybe portion sizes were indeed a bit small for some – she’ll tweak recipes. Or maybe everyone keeps complaining about the cooking time still being 40 minutes – then she knows she needs to simplify recipes further or pre-chop ingredients.
She’ll also keep an eye on market trends: if a big international player announces entry to the market, she might pivot to emphasize local cuisine even more. Or if user research later shows environmental concerns (all that packaging in kits), she might adapt with a recycling program – but these are future iterations.
Crucially, Ana establishes a habit of continuous customer engagement. She sets up a feedback channel (perhaps a Facebook group for her early customers or a feedback form every delivery) to constantly learn. She plans periodic check-ins, like quarterly surveys to all active customers to gauge satisfaction and solicit ideas for new recipes or improvements.
Over time, as her startup grows, she’ll conduct new research for new questions: e.g., exploring expansion to other cities (do people in Cluj or Sofia have similar needs?), or researching the family segment when she’s ready to broaden (maybe running a focus group with parents to design a family-friendly meal kit offering).
In essence, Ana’s startup journey is now intertwined with ongoing research. By systematically conducting market research, she moved from a gut-feel idea to a validated concept with a clear target audience, honed value proposition, and actionable plan. This rigor not only increases her chances of product–market fit but also gives her a compelling, evidence-based story to tell investors: she can show the data behind her decisions and that she’s deeply in tune with her customers.
The case of Ana’s “BucateAcasă” illustrates how each research step informs the next. Your own startup’s research will be different, but the process of hypothesize → test → learn → iterate remains the same. By following these steps, you ensure that your venture is continually guided by the market and customers – which is the surest path to building something people truly want.
This case study guides you through market research by following Ana, a fictional startup founder, as she conducts research for her early-stage venture.
Scenario: Ana lives in Bucharest and has an idea for a meal kit delivery service tailored to Romanian cuisine and local produce. She’s noticed many of her busy colleagues resort to takeout and wants to offer a healthier, home-cooked alternative that saves time. We’ll see how Ana uses market research to validate and refine her concept.
Ana’s primary objective is to validate that there is demand for a meal kit service in Bucharest and understand her potential customers. She outlines a few key questions: Who would use this? Why would they want it (or why not)? How much would they pay? What dishes or features would appeal to them?
From these, she drafts hypotheses:
Ana prioritizes the riskiest assumptions: Is the problem real (demand)? Will they pay ~150+ RON? These two she labels as must-validate. The others are important but secondary (features, channels can be adjusted if core demand exists).
To tackle these hypotheses, Ana plans a mixed-method approach:
She sketches a timeline: 2 weeks for interviews (including recruitment), overlapping with survey design and launch, then a week to analyze and a decision point by end of month. The landing page she can set up in a day and run ads to during week 2–3 as well.
Ana refines her target persona: “Mihai, 30, a software developer in Bucharest”. Mihai works ~10 hours a day, often stays late at the office or gets home tired. He values good food but isn’t an experienced cook. He and his partner try to eat somewhat healthy but end up ordering pizza or local takeout a few times a week. He has decent income (so can spend on convenience). Pain points: grocery shopping is a chore due to crowds and time, deciding what to cook is mentally taxing, and cleaning up is the worst part after cooking. Goals: wants quick, tasty, not-too-junkfood dinners.
She also considers a second persona: “Andreea, 34, a lawyer and mother of a 4-year-old”. Andreea is extremely busy juggling work and family. She cares about her child eating healthy home-cooked meals, but often doesn’t have the energy to cook from scratch every day. However, she is more price-sensitive (family budget) and worried whether her child will eat certain foods. This persona might be more challenging to satisfy, but could have high need.
Ana decides to focus recruitment primarily on people like “Mihai” (young professionals, either single or couples without kids) as her early adopter segment, since they have more disposable income and flexibility. She will include a couple of “Andreea”-type working parents in interviews to see if they express interest, but she’s aware they might have additional concerns.
Screener criteria: Age 25–40, living in Bucharest (or immediate area). Works full-time (40+ hours). Cooks dinner at home at least occasionally (not someone who never cooks, because if they truly never cook, a meal kit might be too high a behavior change). Ideally, people who do feel pain around dinner: she includes a screener question “How much do you agree: ‘I struggle to find time to cook on weeknights’ (1–5)?” – she’ll prioritize those who answer 4 or 5 (agree). Also a question “Have you ever tried a meal kit or meal delivery service?” – if yes, she definitely wants to talk to them for comparative insight; if no, that’s fine (most in RO haven’t, since not common).
For the survey targeting, she will use Facebook ads with filters: age 23–45, Bucharest radius, interests like “food delivery, fitness, busy lifestyle, technology” (rough proxies). Also, she plans to share the survey link in a local Facebook community for young professionals and ask coworkers to pass it along.
She lines up 12 potential interviewees: some through friends (a friend group of engineers), two through LinkedIn cold messages (young professionals who posted about busy work), and two working parents via a moms’ Facebook group who volunteered. She’ll schedule about 10 of them, expecting maybe 8–10 to actually happen.
Ana posts in the “Girls Gone International – Bucharest” Facebook group (popular among expats and locals) briefly describing she’s looking to chat with busy professionals who struggle with cooking – she gets a few interested comments and messages. She also asks a friend at a big tech company in Bucharest to share an internal Slack message recruiting interviewees (offering a 50 RON food voucher as thanks). This yields a handful of volunteers. She uses Google Forms for a quick screener as planned. From 20 responses, she filters down to 12 that fit well (and a few that were outside criteria, which she politely thanks and notes for possibly later – e.g. someone older who was very keen, etc.).
She reaches out and schedules 30-minute Zoom interviews in the evenings (since they work daytime). For scheduling, she uses Calendly to make it easy – blocking 7–9pm on a few weeknights and letting participants pick slots. She also schedules two interviews on a Saturday morning for those who prefer weekend.
For the survey, she creates a Google Form (or Typeform for nicer UX) with ~15 questions (multiple-choice and Likert mostly). She then sets up a Facebook Ad campaign with a small budget (say $30 over a week) targeting her demographic, with ad copy like: “Busy after work? No time to cook? Help us with a 3-minute survey about weeknight dinners and get a chance to win a 100 RON Uber Eats voucher.” This incentive and the targeting help drive clicks. She also posts the survey in the “Bucharest Tech Professionals” LinkedIn group and asks people to share.
Within a week, she gets about 120 responses. She closes the survey after reaching a nice round number and randomly picks a winner for the voucher to keep her promise (and announces it to participants).
Meanwhile, she set up a quick landing page using a tool like Launchrock or Carrd. It explains: “BucateAcasă – We deliver fresh ingredients and traditional recipes to your door. Cook a healthy dinner in 30 minutes, no planning needed!” with a nice food image. It has a sign-up form: “Get early access and a discount when we launch – enter email.” She drives a separate small ad campaign to this page to gauge conversion. Additionally, she includes the link at the end of the survey (“Would you like to sign up for early access?”). Over two weeks, 50 people enter their emails (some from survey, some from ads). That’s an encouraging sign of interest.
All interviewees showed up except one no-show (which she replaced with another person from the waitlist). She ended up with 9 interviews (30–40 min each). She gave each a 50 RON supermarket e-gift card as a thank you via email afterward (they were pleasantly surprised, building goodwill).
During interviews, Ana follows her discussion guide. She starts with “Tell me about your typical evening on workdays – what do you do for dinner?” This opens them up. She listens as they describe their routines. She asks follow-ups like “How do you feel about cooking after a long day?” and “What are the biggest challenges for you with weeknight meals?” Without fail, time and fatigue come up. One quote: “By the time I’m home, it’s 8pm… honestly, I just want something quick or I’ll skip dinner or snack on bread.”
When introducing her concept, she is careful not to “sell” it. She phrases it neutrally: “There are services where you get a box with recipes and ingredients to cook at home. Have you heard of that? (some have via US or hearsay, many haven’t). How do you think something like that might fit into your life, if at all?” She gets a variety of responses: Some say that sounds great (“I like cooking but hate the prep and shopping, so that could be ideal”). Others are hesitant (“Depends on price… if it’s too expensive, I might as well order ready food”). She notes down objections like cost, portion size (a single guy worried about kits usually being for 2 people), and whether recipes might be too hard.
She also does a mini concept test in the interview: showing a sample menu or recipe card design on screen to gauge reactions. One participant lights up at seeing a favorite Romanian dish (sarmale) offered as a 30-min version: “Oh, if I could make sarmale that fast, I’d love it!” Another says they actually prefer simpler grilled chicken-type meals on weekdays, nothing fancy.
Throughout, Ana practices the Mom Test principles – she doesn’t lead with “Would you buy my service for X?”, she instead asks “What do you usually spend on dinner when you cook vs. order?” and later “If a service like this delivered 3 dinners a week for, say, 150 RON per week – what’s your reaction to that price?” Some people compare it to their grocery spend (“I spend maybe 100 RON a week on groceries for dinners, so 150 is higher but maybe for convenience… hmm”), others compare to eating out (“If it’s 50 RON per meal, that’s about what I pay for takeout, so not bad if it’s good quality”).
She diligently takes notes, flagging exact phrases. She notices multiple interviewees mention lack of planning: “The hardest part is deciding what to cook and having all the stuff – I often realize I’m missing an ingredient.” This is validation for the value of meal kits (they remove planning/shopping).
After interviews, she has a good feeling – the problem is real for almost all of them, interest level varied but mostly positive if price/quality are right.
The survey data rolls in and she monitors key results:
The landing page test: out of roughly 300 ad clicks, 40 signed up (a ~13% conversion). Given not everyone who clicked is target (ads can misfire), it’s not bad. It shows some real interest – 40 potential early customers. She notices many sign-ups came after she added a line “Introductory price ~ from 120 RON/week” on the page – possibly addressing the price question helped conversion.
Ana now has a wealth of information. She uses an affinity mapping approach for the qualitative interview data:
She writes down key quotes and observations on sticky notes (in Miro). For instance:
She clusters these into themes:
She tallies quant data and integrates with these themes:
She uses a competitive matrix to place herself vs alternatives: e.g. columns for “Home-cooking (DIY)”, “Takeout/Delivery”, “Meal Kit (us)”, and rows like “Time required”, “Healthiness”, “Cost per meal”, “Variety”, “Effort”, etc. From research: Home-cooking = healthy, variety as you want, but high time/effort. Takeout = low effort, but can be unhealthy/expensive. Meal Kit aims to sit in the middle: moderate effort, healthy, moderate cost, convenience of not shopping. This helps her articulate her positioning clearly.
Conclusions:
She also addresses unanswered questions: For instance, she’s curious whether offering a 2 servings vs 4 servings option would change perceived value (some singles might prefer 2, but others liked leftovers). She might experiment in the pilot. Another question: best channels to scale acquisition? Her research suggests Facebook/Instagram ads worked for awareness. She might also try refer-a-friend incentives in pilot to see if users will recruit others (since word-of-mouth is powerful in such products).
Next Steps Action Plan:
After the pilot, Ana will do another round of analysis. Suppose the pilot reveals, say, that users loved the convenience but maybe portion sizes were indeed a bit small for some – she’ll tweak recipes. Or maybe everyone keeps complaining about the cooking time still being 40 minutes – then she knows she needs to simplify recipes further or pre-chop ingredients.
She’ll also keep an eye on market trends: if a big international player announces entry to the market, she might pivot to emphasize local cuisine even more. Or if user research later shows environmental concerns (all that packaging in kits), she might adapt with a recycling program – but these are future iterations.
Crucially, Ana establishes a habit of continuous customer engagement. She sets up a feedback channel (perhaps a Facebook group for her early customers or a feedback form every delivery) to constantly learn. She plans periodic check-ins, like quarterly surveys to all active customers to gauge satisfaction and solicit ideas for new recipes or improvements.
Over time, as her startup grows, she’ll conduct new research for new questions: e.g., exploring expansion to other cities (do people in Cluj or Sofia have similar needs?), or researching the family segment when she’s ready to broaden (maybe running a focus group with parents to design a family-friendly meal kit offering).
In essence, Ana’s startup journey is now intertwined with ongoing research. By systematically conducting market research, she moved from a gut-feel idea to a validated concept with a clear target audience, honed value proposition, and actionable plan. This rigor not only increases her chances of product–market fit but also gives her a compelling, evidence-based story to tell investors: she can show the data behind her decisions and that she’s deeply in tune with her customers.
The case of Ana’s “BucateAcasă” illustrates how each research step informs the next. Your own startup’s research will be different, but the process of hypothesize → test → learn → iterate remains the same. By following these steps, you ensure that your venture is continually guided by the market and customers – which is the surest path to building something people truly want.