This article will give you tips on how to analyze responses from an ecommerce shopper survey about promotions and discounts. If you're collecting this data, I'll walk you through the best tools and actionable ways to uncover insights fast.
Choosing the right tools for survey data analysis
Your approach and tooling will depend on the structure of your survey data—namely, whether it's mostly quantitative or qualitative.
Quantitative data: Numbers are easy—counting how many shoppers selected each promotion or discount is straightforward with tools like Excel or Google Sheets.
Qualitative data: Text responses to open-ended or follow-up questions are trickier. When you're looking at dozens or hundreds of replies, it's impossible to read everything yourself. This is where AI-powered tools become essential for surfacing patterns and themes quickly.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy your exported survey data into ChatGPT and chat about it. It works—just paste the answers and start prompting for trends or themes.
But managing a big batch of survey text in ChatGPT isn’t very convenient. You’ll have to chunk large datasets, manage context limits, and keep notes outside the chat. AI can still surface valuable insights, but you’ll spend more time in setup and manual effort.
All-in-one tool like Specific
Specific is built for survey creation and AI-powered response analysis, end-to-end. Not only can it collect responses (and ask smart, AI-powered follow-up questions to boost the quality of your data), it also analyzes everything automatically.
When your results are in, Specific summarizes open-ended replies, highlights key themes, and distills actionable insights—no spreadsheets, no manual reading. You can have a real conversation with the AI about your survey, just like with ChatGPT, but with bonus features for filtering and controlling the data sent for analysis.
Learn more about how AI survey response analysis works in Specific if you want a more streamlined workflow.
Useful prompts that you can use for Ecommerce Shopper promotions and discounts analysis
Whether you’re analyzing with ChatGPT, another GPT-based tool, or an all-in-one platform, the right prompts make all the difference. Here’s what I recommend for digging into Ecommerce Shopper data on promotions and discounts.
Prompt for core ideas: This is my go-to for distilling large data sets quickly. It works out-of-the-box in Specific, and you can use it in GPT-based tools as well.
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
AI always does better if you give it extra context. For example, you might open with a reminder:
Analyze these survey results from ecommerce shoppers about promotions and discounts. My main goal is to understand what drives their purchasing decisions and why they seek discounts. Please focus on motivations specific to online shopping behavior.
“Tell me more about XYZ (core idea)” is a quick follow-up to get deeper details on any insight the AI surfaces. Try this if you want to drill into “cart abandonment” or “influencers on coupon use.”
“Did anyone talk about XYZ?” This is direct and essential when you’re validating hypotheses; just replace XYZ with topics you want to check, like “loyalty programs” or “brand switching.” Add “Include quotes” for evidence in their own words.
Prompt for personas: If you want to map out distinct types of ecommerce shoppers your survey reveals, use this prompt:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: This is incredibly useful for discovering what’s making your shoppers hesitate, abandon carts, or wait for deals:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for motivations & drivers: If you want to see what fuels purchasing behavior and how promotions play a role, try:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for sentiment analysis: Are your shoppers positive, annoyed, or neutral about your discount strategy? Ask:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for suggestions & ideas: To collect actionable feedback, prompt AI for this:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: To find new product or campaign angle opportunities, use:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
The right prompts let you dig far deeper into what makes shoppers tick—and how promotions and discounts are truly influencing their decisions. And keep context at the center: For example, 82% of customers are influenced by promotions when shopping online—so it pays to find out which type of promotion matters most to your audience. [1]
How Specific analyzes qualitative data by question type
In Specific, analysis is built around the structure of your survey itself—so you get summaries that are actually meaningful for each question.
Open-ended questions (with or without follow-ups): You get AI-generated summaries for all the responses, including the additional context surfaced by probing follow-up questions. This produces a much richer theme analysis than just looking at standalone comments.
Choice questions with follow-ups: Every choice gets its own theme summary, based solely on the follow-ups tied to that particular answer. So, for example, you can see why people picked “percentage-off” discounts over “free shipping.”
NPS questions: Each group—detractors, passives, promoters—gets a unique summary of their follow-up feedback. It’s easy to dig into why someone loves your discount policy, or why another thinks it’s not enough to make them buy now. (You can instantly create an NPS survey for ecommerce shoppers about promotions and discounts in Specific.)
You can do the same with ChatGPT, but you’ll need to filter and organize each set of replies yourself—a lot of copy-paste work, especially if your data grows beyond a few dozen entries. With Specific, it happens instantly as results roll in.
If you’re looking for ideas on how to structure your questions in the first place (and why AI follow-ups matter), I recommend checking out this guide on the best questions for ecommerce shopper surveys on promotions and discounts.
How to tackle challenges with AI context size limits
Here’s a real challenge: Large language models (including GPT-4 and others) can’t process unlimited amounts of survey data in one go—they hit context size limits. If you have hundreds or thousands of responses, it simply won’t fit at once.
Specific tackles this (and you can borrow these tactics for your own workflow):
Filtering: You can filter conversations based on how users replied. Only conversations where people answered selected questions or picked specific answers are sent to the AI. This lets you target cohorts (“Shoppers who mentioned digital coupons”) and keep your analysis focused.
Cropping: You can crop down the questions sent to AI analysis. If you only want the AI to see responses to the last question (“How did promotions affect your decision?”), just send that chunk. This helps you fit more responses within the model’s limit—and ensures you don’t lose vital context on a technicality.
In Specific, both features are available by default, keeping your qualitative analysis stress-free as your survey scales.
Fun fact: Digital coupon redemptions are expected to make up nearly 85% of all coupon redemptions by 2024. [2] That’s a ton of feedback and usage signals you might want to analyze—meaning smart filtering and cropping are your best friends.
Collaborative features for analyzing ecommerce shopper survey responses
Collaboration pain points are real when analyzing survey responses around promotions and discounts. When your team is trying to parse hundreds of open-ended answers from shoppers—especially when multiple teammates want to “chat with the data” in their own way—it’s easy to get lost in slack threads, comment chains, and version chaos.
In Specific, you analyze just by chatting with AI, and every teammate gets their own threads. You can create multiple chats in the analysis interface—each with its own filters and focus, letting you slice the data by promo type, shopper region, or even sentiment. Each chat also shows who created it, so it’s easy to organize work and see which colleague is working on which angle.
Clear attribution and collaboration: Every message in AI Chat shows the sender’s avatar and name, so when you’re collaborating on insights about why 75% of online shoppers say discounts drive their decisions [3], you’ll always know whose question sparked a breakthrough or surfaced a trend.
Fewer silos, more action: With these features, teams work together (not in parallel silos) to drive change. That could mean launching better-timed flash sales, new loyalty perks, or experimenting with discount types that actually convert—based on what your shoppers told you, in their own words.
Want to start fast? Try out the AI survey generator tailored for ecommerce shopper promotions and discounts, or check the AI survey generator if you want to create a survey from scratch and customize every detail as you go.
Create your ecommerce shopper survey about promotions and discounts now
Quickly uncover what drives your customers to buy, switch brands, or wait for deals. Collect genuine feedback and turn it into insights with AI-powered analysis—so you act on what matters most, instantly. Create your own survey and start decoding shopper behavior today.