17 Steps to Turn Open-Ends into Gold with Qualitative Coding 🎨
I LOVE really thorough and nuanced open-ended survey responses. There’s nothing like the pure, raw, words of your target market telling you, in their very own words, exactly what is good, bad, right, or wrong about your product.
But if I have, let’s say… 2,000 open-ended responses to read through… well, now I’m in a pickle.
Not only would that take forever to read, but then I’ll also become subject to various biases as I try to extract insights, things like recency bias, confirmation bias, availability heuristic and so on.
That’s where qualitative coding comes in! In survey research, coding refers to the process of analyzing qualitative data—such as open-text responses to questions—and categorizing them into structured, quantitative formats. This is done to make the data easier to interpret, analyze, and act on. Here’s how it typically works:
Respondents may provide detailed text answers to questions like “What do you like most about this product?” Then, these text responses are reviewed and assigned to predefined categories or themes (e.g., “quality,” “price,” “design”). If themes aren’t predefined, they may be created during the coding process based on patterns observed in the responses. Finally, the results of the coding process are quantified without manually sifting through large volumes of text.
While I’m on the topic, I should mention that Pollfish is building out some AI capabilities that help improve this process, nevertheless, best practices for qualitative coding will remain critical. So, let’s jump into our 17 expert-level tips.
1. Define Your Codebook with Precision ✍
Your codebook is the Rosetta Stone of your analysis. It needs clarity, consistency, and relevance. Suppose you’re working for Celsius Energy Drinks. You’ve conducted interviews with their audiences: RedBull drinkers and fitness enthusiasts. Create a code for each recurring theme, like “Energy Boost” or “Taste Preference.” If 73% of respondents fall into a bucket called “too sweet,” it’s time to refine those flavor profiles.
Here’s an example of codes for a coffee startup trying to distinguish itself from packaged bean roasts like Peet’s and Starbucks.
2. Distinguish Literal from Interpretive Answers ⚖️
At the onset of qualitative coding, it’s vital to separate literal responses (“I prefer more fruity flavors”) from interpretive sentiments (“It tastes like a tropical vacation in a can, but it’s too sweet”). This helps ensure you capture direct references (like brand names) alongside deeper, more emotional undercurrents of feedback. For example, if Nike tested a new sports drink line targeting “Fans of Gatorade, BodyArmor, and Powerade,” open-ends on “favorite sports drink traits” might literally mention “freshness” 47% of the time but interpretive sub-themes such as “reminds me of fun training days” 21%. By splitting these two types of statements, your coding structure gains both concreteness and deeper insight, setting the stage for a thorough analysis.
3. Test on a Pilot Group First 🧪
Rather than diving head-first into coding all open-ends from thousands of respondents, pilot your approach with a smaller sample. For instance, if Nestlé ran a short open-end question on “What’s your ideal coffee experience?” among just 50 coffee enthusiasts, you might find that 60% referred to “bold flavor,” while 40% specifically mentioned “low sugar content,” giving an initial direction on potential codes. This pilot run would highlight potential pitfalls (like repeated mention of “cheap pricing” in 15% of answers) and help refine the codebook for the larger study.
4. Embrace Thematic Bins for Coding 📂
Start with broad buckets—like taste, brand association, or packaging—before zooming in on more granular codes. This helps you keep track of major categories in open-ends, such as “memorable marketing campaigns” or “bad aftertaste.” If Starbucks ran a study, they might create top-level bins like “Flavor Innovations,” “Brand Design,” and “Competitive Mentions,” then break them down further (e.g., “tropical,” “spicy,” “eco-friendly packaging”). The result: a structured system that grows in detail without turning into a labyrinth of unmanageable codes.
5. Consider Cross-Category Overlaps 🔎
In the real world, an open-end response often spans multiple themes. “I love the new lime flavor, but the branding is way too flashy” touches on both taste and design. A single mention of “too sweet” can slide into health concerns and negative brand perception if respondents elaborate about sugar intake. If Pepsi found that 30% of open-ends discussing “too sweet” also tied it to health anxieties, it would enrich their insights and guide more precise product or messaging tweaks.
Here’s what an overlap of codes might look like:
6. Use a Hybrid Approach of Manual & Automated Tools 🤖
While AI-driven text analysis can spot recurring words like “refreshing” or “fake sugar” in a split second, human coders bring the nuance needed to interpret sarcasm, humor, or cultural references. Combining technology with a human touch ensures both efficiency and accuracy. If Coca-Cola leveraged an automated text analytics tool to scan 1,000 open-ends about a new “Fiery Citrus” flavor concept, it might reveal 38% describe it as “intriguingly spicy.” However, manual coding could further reveal that 12% use language implying confusion with hot sauce—something an algorithm alone might not pick up.
7. Rely on Keyword Frequency but Don’t Stop There 🔢
Seeing that “mango” (or “citrus”) gets mentioned 45% of the time is helpful, but it doesn’t tell you if people actually liked it. Qualitative coding means probing deeper: is the context positive, negative, or neutral? For instance, 20% of those flavor references could be complaints about it tasting too artificial. If Target introduced a “Spicy Mango” flavor concept and found “mango” repeated often, they’d still need to decode whether it’s a beloved highlight or a disliked gimmick. Balancing frequency data with context-based codes prevents misreading simple word counts as unequivocal endorsement.
8. Explore Sentiment Beyond Positive/Negative 🤷
Everyone likes to boil down sentiment to a neat “people loved it” vs. “they hated it.” Real-world feedback is rarely that tidy, often falling into categories like “cautious optimism,” “mild disappointment,” or “confusion.” If Monster Energy tested an open-ended question—“What emotions come to mind when you see our upcoming Spicy Lime can design?”—they might find 47% responding with “intrigue” or “curiosity,” while 15% say they feel “overwhelmed” by the bright color palette. Accounting for these nuanced sentiments provides a more layered perspective on how people actually feel.
For instance, prior to the 2024 election, I ran a survey on Trump vs. Kamala, primarily trying to isolate the key swing issues that either party could have leveraged to sway the vote in their particular direction.
I also had an open-end question asking “why are you voting for the candidate you’re voting for?” Of course, these open ends are of greater value when first segmented into the verified behavioral subgroups (Registered Democrats, Republicans & Independents) that I easily could target within the Pollfish platform. All this to say, if you looked at these with a simple positive/negative lense, you’d miss out on all of the critical insights hiding in the nuanced language of the respondents.
As an interesting real-world example – “two evils” came up a ton in my survey, indicating a fair amount of dislike for both parties’ candidates, but the need to support the one they figured was less bad for the country.
By the way, you can access this survey’s results here.
9. Incorporate Emotional Lexicons 🧠
Emotional lexicons, which map keywords to specific feelings like excitement, nostalgia, or annoyance, can give your codes more texture. For instance, “craving that tang” might imply positive anticipation, whereas “reminds me of cough syrup” is a negative association cloaked in nostalgia or medical undertones. If Red Bull discovered that 27% of respondents mention it “smells like childhood candy,” that’s a distinct emotional anchor labeled as “sweet nostalgia.” Doing so helps quantify intangible reactions and articulate them more convincingly to stakeholders.
10. Use Iterative Refinement of Codes 🎯
Nobody gets their codebook perfect on the first try—especially when participants mention off-the-wall responses like “it tastes like a flaming rubber duck.” Start with best-guess categories, then refine them as new themes emerge during analysis. If Kellogg’s discovered a recurring mention of “mid-day pick-me-up” (19% of open-ends) that didn’t fit neatly into existing bins, they might add a new code under “functional benefits.” An iterative approach ensures your coding framework evolves alongside real human language and creativity.
11. Build Intercoder Reliability 🏆
Having more than one person code the same open-ended responses can feel like watching two chefs argue over salt, but it’s crucial for consistency. You want to ensure that what one coder labels “fruity fragrance” isn’t translated by another coder as “flowery aroma.” If General Mills had multiple coders working on the same dataset, they’d likely measure intercoder agreement—often with something like Cohen’s kappa—to confirm that their categories and definitions are robust and replicable. The better your reliability, the more confidently you can communicate findings to the higher-ups (who usually just want the bullet points anyway).
12. Visualize Trends for Stakeholders 📊
A giant spreadsheet of coded data can be about as exciting as reading the phone book, so consider turning your codes into graphs or infographics. If Kraft Heinz noticed that 44% of respondents bemoan a “lack of carbonation,” they could show it in a pie chart or bar chart for immediate clarity. The same goes for trending new flavors: if 35% highlight “tangerine” as the next big thing, that’s prime real estate on a simple visualization. Visual cues not only streamline strategic decision-making but also help internal teams and external clients see patterns at a glance.
13. Weave in Real-Life Context 🌐
A response like “I’d totally buy this if I saw it while gaming” reveals lifestyle or situational context worth coding. If Danone realized that 23% of their yogurt consumers are primarily snacking “during gaming sessions,” they’d uncover an untapped promotional angle. Layering context codes—like “gaming environment,” “social gatherings,” or “on-the-go”—helps paint a complete picture of when, where, and why consumers engage with a product. It’s the difference between marketing a “great-tasting snack” and a “fuel for your epic gaming marathon.”
14. Tracking Changes Over Time 🕰️
Qualitative coding isn’t a one-and-done scenario if you plan to iterate your product or brand. If Hershey ran monthly open-ends to see how a new chocolate bar formula is received, they might find that in the first month, 40% of comments focus on “lack of creaminess,” but by the third month—after tweaking the recipe—only 8% mention it. Monitoring how themes rise or fall month-over-month can identify if changes are resonating or if a brand-new wave of complaints is brewing.
15. Merge Qualitative with Quantitative Indications ⚖️
Survey data often mixes open-ends with closed questions like rating scales or MaxDiff. For instance, if Unilever introduced a new “Tropical Chill” flavor and 65% rate it a 4 or 5 on a 5-point scale, while 35% rate it 3 or below, overlaying that with open-end codes clarifies why certain people adore it (“tastes exotic,” 22%) and why others are less keen (“too reminiscent of chili sauce,” 13%). This synergy between coded text and numeric metrics gives stakeholders a fuller, more actionable story.
16. Turn Insights into a Next-Level Storytelling Device 📜
Once you’ve coded all the open-ends, tie it back to real business actions and strategy. If Mars were to code thousands of responses, they might show how a good chunk (38%) of people prefer “lighter carbonation,” leading to potential R&D changes, or how 26% ask for a “healthier energy blend,” guiding a marketing pivot. As Troy Harrington of Pollfish puts it, “Qualitative coding unlocks the ‘why’ behind the ‘what,’ equipping brands with narrative depth that resonates.” Include additional capabilities, like video highlight reels, to let stakeholders experience actual respondent quotes or a montage of consumer emotions—nothing sells a story like seeing it firsthand.
17. Know When to Stop Coding—Don’t Overthink It 🔒
Sometimes, enough is enough. If Dunkin’ is analyzing “morning routine habits,” and themes stabilize at “coffee,” “breakfast,” and “time-saving,” stop there. Coding forever doesn’t lead to better insights.
Qualitative coding is both an art and a science, offering unparalleled depth to market research when approached with care. If you don’t anticipate tracking drivers or running surveys to samples large enough to justify a dedicated qualitative coding effort, then Pollfish is perfect for you. For busy professionals who have the resources, leveraging the full coding services that Prodege offers can help the journey from raw data to actionable insight become less daunting and more empowering.
Written by Troy Harrington
Troy serves as the product evangelist for Pollfish. Leveraging 13 years of marketing leadership experience, he drives awareness of Pollfish's valuable insights capabilities, enabling brands to make better decisions.

Ready to Try Pollfish?
Create your survey with AI, target high-quality respondents starting at $0.95 per complete, and start getting results in just minutes in real-time. From running a simple product concept survey to managing a constant stream of trackers for dozens of clients in dozens of countries, we’ve got you.