Shopper insights on User Intuition is an eight-step workflow: Create Study, Customize Plan, Review Goals, Setup Interview, Test Conversation, Launch, Invite Participants, and Review Insights. The product is built so a category manager, a shopper marketing lead, or a brand team can launch a shopper-interview program in an afternoon, then watch transcripts, satisfaction-rated calls, and synthesized reports show up inside the Intelligence Hub within 48-72 hours. Pricing sits at roughly $20/interview on the Pro plan, and the 4M+ research panel covers 50+ languages, which means a continuous monthly program is financially viable for mid-to-large CPG brands and retailer category teams running multi-region shopper work. This tutorial walks through each of the eight steps with the verbatim labels you will see in the product, the choices that matter most for shopper insights specifically, and the links to the underlying documentation.
For the broader methodology (how to design a shopper program, the five-stage path to purchase, what questions to ask), see the methodology guide. For the headline pricing, the proof points, and the comparison against in-store intercepts and shop-alongs, see the User Intuition shopper insights platform page. This piece stays in the workflow lane: what to click, where to click it, and why each setting matters when the cohort you care about is current category buyers at the moment of shelf decision.
Shopper insights cover the in-store moment of decision, what the buyer sees, considers, and chooses at the shelf or in-app checkout. If you’re researching brand perception, motivations, or category-level usage, see How to Run Consumer Insights with User Intuition.
What is shopper insights, briefly?
Shopper insights is the practice of interviewing recent category buyers about the moment of decision: what they saw on the shelf or the product page, what they considered, and what tipped them toward the SKU they put in the basket. It sits between two failed substitutes: POS and loyalty card data, which records what was bought but never why, and traditional shop-alongs, which run $1,500 to $3,000 per intercept, take 6 to 10 weeks, and catch shoppers mid-trip when they are too distracted to articulate the decision.
Done well, shopper insights answers four questions every category and shopper marketing team needs every season. First, what triggered the trip or the category entry? Second, what alternatives were in the consideration set when the shopper stood in front of the shelf? Third, what cue (price, pack, position, promotion) tipped the choice? Fourth, what changed since last reset?
The mechanism is depth on the shelf moment. A 30-minute conversation with a shopper who bought the category in the last 14 days, with 5 to 7 levels of structured probing, surfaces decision drivers that a 2-minute post-purchase survey will never reach. Pack visibility, shelf-block confusion, trial-size hesitation, promo-mechanic perception, private-label parity moments, these are the patterns underneath the price excuse. They show up only in conversation.
Modern AI-moderated platforms compress the timeline from weeks to 48-72 hours and the cost from $50K+ per study to $200 floor pricing. Instead of one annual shop-along wave, category teams can run 30 to 50 shopper interviews every month, feed every transcript into a searchable Intelligence Hub, and watch shopper intelligence compound study by study. The workflow below is built around that cadence.
Why doing this on User Intuition is different
Most shopper research tooling makes you choose between depth and speed. Behavioral panels are fast and shallow. Shop-alongs are deep and slow. User Intuition delivers both, and the product flow is shaped by that goal.
The AI moderator runs 30-minute laddering conversations with each shopper and probes 5 to 7 levels deep on what they saw, considered, and chose. Every conversation runs the same methodology, so a study with 20 interviews and a study with 300 interviews are directly comparable. There is no fieldworker in the aisle. Shoppers report higher candor with AI than with brand-affiliated researchers, and the 98% participant satisfaction rate reflects how the experience lands.
Recruitment is built in. The 4M+ research panel covers 50+ languages and lets you target by category buyer behavior, demographics, and geography without building a list. For retailer-led work, loyalty contacts flow in via the Contacts integration or a unique study link in the post-purchase email. The embed widget drops always-on shopper signal into cart-abandonment pages, post-purchase confirmation screens, and the customer portal.
Synthesis runs in the same product. The Reports tab generates an Executive Summary, Top Insights, Detailed Analysis, and Recommendations, grounded in verbatim shopper quotes. The Intelligence Hub layers across studies, so summer and winter waves surface compounding patterns that any single-study report would miss. Pricing at $20/interview makes the entire stack sustainable as a continuous program. See the shopper insights solution page for the full positioning, and the best shopper insights platforms breakdown for the comparison against incumbents.
The 8-step walkthrough
The product flow is the same for every study type. The choices below are tuned for shopper insights specifically.
Step 1: Create Study
Open the New Study screen and you will see six pre-built templates: Win/Loss Analysis, Churn Analysis, NPS and CSAT, Customer Onboarding, Brand Health, and Custom Design. For shopper insights, two work depending on the angle. Pick NPS and CSAT if you are studying current category buyers and want to understand what is driving satisfaction with the SKU they chose at shelf. Pick Customer Onboarding if you are studying first-time category entrants and want to understand what they expected, what surprised them on the shelf, and what they are now doing with the product.
The selected card highlights with a dark background. Click Save & Continue. Either template pre-loads the conversation flow, screener defaults, and learning goals tuned for category-buyer interviews.
Shopper-specific tip: the same study can hold multiple shopper segments (frequent, occasional, lapsed, switchers) by adding a screener at Step 4. Mix segments whenever possible, because the contrast between frequent-buyer rationale and occasional-buyer rationale is where the highest-leverage shopper intelligence lives. See Choose Your Study Type for the full template comparison.
Step 2: Customize Plan
The Customize Plan screen opens a chat with Charles, our AI researcher. Type your context and Charles drafts the full research plan: Objective, Background, North-Star Learning Goal, Key Sub-Questions, Conversation Flow, and Interviewer Guidelines.
For shopper insights, give Charles three things. First, the category: “We are studying shoppers who bought {category, e.g. ready-to-drink coffee} in the past 14 to 30 days.” Second, the path-to-purchase moment: “We want the in-store decision moment in mass and grocery, plus in-app checkout in delivery and click-and-collect.” Third, the shopper segment: “Mix of frequent buyers, occasional buyers, and recent switchers.” Charles returns a plan that asks about trip triggers, the consideration set at shelf, the cue that tipped the choice, and post-purchase rationalization.
Shopper-specific tip: tell Charles which retailers and planograms you care about. He uses retailer context to design probes that map shelf decisions to real-world planogram differences across mass, grocery, club, and online. Read the Customize Plan docs for the full set of questions.
Step 3: Review Goals
The Review Goals screen opens the research plan in a rich text editor. Study Name sits at the top (Charles suggests one, like “Q3 Shopper Insights, Ready-to-Drink Coffee”; rename it to fit your internal naming convention). Below that, every section of the plan is editable. Highlight, retype, add, delete. Changes save automatically.
This is the step where most shopper studies fail. The default plan asks the shopper to describe the trip and the choice, but a study that only collects the choice misses the consideration set, and a study that only collects the choice without the consideration set produces “the shopper picked us because it was on sale” and nothing more useful. Add explicit sub-questions under Key Sub-Questions for all three: what they SAW (visible cues, the shelf block, the digital tile), what they CONSIDERED (alternatives evaluated, including private label and competitor brands), and why they CHOSE (the trigger, the cue, the moment).
Shopper-specific tip: keep Key Sub-Questions to 4 to 6 themes with 2 to 4 questions each. Longer plans mean longer interviews mean lower completion rates. The Review Study docs have the full editing toolbar reference. Click Save & Continue when the plan reads the way you want.
Step 4: Setup Interview
The Setup Interview screen handles the participant-facing experience. Two voice cards: Elliot (Male, American) and Clara (Female, American). Click the play button on each to preview. Below the voice cards, the Default Mode toggles control format: Chat, Audio, or Video. Audio is the recommended default for shopper insights because voice captures the shopper narrating the trip with hesitation, surprise, and emphasis on shelf cues that chat compresses out.
Below the format toggles is the screener section. Add the highest-leverage shopper screener: “When did you last buy {category}?” with a filter to last 14 to 30 days so recall is fresh. Add a second for segment fit: “How often do you buy this category?” with frequent, occasional, and lapsed buckets.
Shopper-specific tip: pick Clara for categories that skew female-buyer (beauty, home care, kids) and Elliot for categories that skew male-buyer (auto, men’s grooming, beverages). The match between moderator voice and shopper demographic measurably improves recall on shelf cues. Read the Setup Interviewer docs for the full mode-selection guide.
Step 5: Test Conversation
The Test Conversation screen prompts you with “Ready to test your study?” Click Start Test Conversation and you experience the full participant flow: greeting, study introduction, research questions, AI probing, and wrap-up. Test conversations do not count against usage limits. After the test, a feedback screen asks “How was the conversation?” with Good or Needs Work options.
For shopper insights, run two test conversations before launch. Run the first as a frequent buyer who picked the category leader on autopilot, and listen for whether the AI probes hard enough on what they saw versus the rehearsed loyalty story. Run the second as a recent switcher who chose private label, and listen for whether the AI probes past the price excuse into pack parity, shelf-block visibility, and trial-size moments.
Shopper-specific tip: if the AI accepts a surface answer, go back to Step 3 and tighten the Interviewer Guidelines with explicit instructions like “Probe at least three levels deep when shoppers cite price.” Then re-test. The Test Conversation docs cover what to look for.
Step 6: Launch
The Launch screen shows three summary cards (Study Type, Interviewer, Interview Type), the Study Name, and the full Research Plan Preview. Final review sits in three accordions: Research Plan, Interviewer Settings, Study Configuration. When everything looks right, click Save and Launch (the one with the rocket icon). Your study goes live immediately. You are redirected to the Study Dashboard.
The detail teams miss: launching does not automatically send invitations. The study is live and ready to receive participants, but recruitment happens in the next step. Most teams want to review the config with the cross-functional team (category, brand, shopper marketing) before triggering recruitment.
Shopper-specific tip: launch within days of the shopping moment so recall is fresh. Mid-week mornings (Tuesday through Thursday, 9am to 11am local time) see the highest completion rates among shoppers. The Launch docs cover everything that can and cannot be changed post-launch.
Step 7: Invite Participants
This step opens after launch. From the Study Dashboard, the Invites tab plus the Actions dropdown gives you four invitation methods that map to shopper recruitment patterns.
Research Panel opens recruitment from the 4M+ panel filtered by category buyer behavior, with country and language selection here for multi-region shopper work. This is the default for category teams without a retailer relationship, and the only practical way to reach competitor-only and lapsed buyers at scale. Invite Your Customers imports contacts manually, via CSV, or from a synced loyalty CRM (Salesforce, HubSpot); retailer category teams use this for loyalty program lists. Share Link generates a unique URL for post-purchase emails or the loyalty newsletter for high-recency, high-recall shoppers. Embed Widget drops always-on shopper signal into cart-abandonment pages, post-purchase screens, and the customer portal.
Shopper-specific tip: combine methods. Panel recruitment for competitor-buyer cohorts where you do not have shopper contact info, retailer loyalty for fresh-recall recruitment. Set country here for any multi-region work. The Recruiting overview covers all four side by side.
Step 8: Review Insights
Once interviews complete (first ones inside hours, full cohorts within 48-72 hours), three tabs hold the output. The Calls tab lists every interview with email, status, date, duration, quality rating (High, Medium, Poor), and end reason. Click any row to expand the full transcript with audio playback. The Reports tab holds the AI-synthesized analysis: Executive Summary, Total Participants, Top Insights at a Glance, Detailed Analysis with verbatim quotes, and Recommendations. Click Generate Report once you have at least 2 completed interviews; click Regenerate Report as more land.
The third surface is the Intelligence Hub, accessed from the left navigation. Each Hub session is a workspace where you sync studies, upload supplementary files (planogram PDFs, POS CSVs, syndicated panel exports), and ask natural-language questions across everything in the room. For a continuous shopper program, this is the compounding layer. It clusters shopper journeys across category and segment, surfacing patterns single-shopper studies miss, like why occasional buyers convert at price points frequent buyers do not notice.
Shopper-specific tip: create a single Intelligence Hub session called “Shopper Insights” at the start of the year (or one per category), sync every monthly study into it, and keep adding sources. Then ask questions like “How has the way occasional buyers describe pack parity changed across the last three planogram resets?” The Intelligence Hub docs cover the full output surface.
Mini case study
A mid-size CPG brand in better-for-you snacking ran the workflow above on a cohort of 45 shoppers across three retailers (mass, grocery, online delivery) who had bought the category in the past 14 days. The team set up the study in a single afternoon: NPS and CSAT template at Step 1, category and three retailer environments briefed to Charles at Step 2, learning goals tightened at Step 3 to cover what shoppers SAW (the shelf block), CONSIDERED (private label and two competitor brands), and CHOSE (the cue), Audio mode and the 14-day-recency screener at Step 4, two test conversations at Step 5, study launched on a Wednesday morning at Step 6.
Recruitment used the Research Panel filtered to recent category buyers across the three target markets. All 45 interviews completed within 48 hours.
The Reports tab and the Intelligence Hub surfaced a finding the brand team did not expect. Price came up as the primary driver in only 11 of 45 conversations. The dominant theme, present in 28 of 45 transcripts, was pack parity at shelf: shoppers reported the private-label pack had quietly closed the visual gap and the two now read as substitutable in a 3-second shelf scan. The team rebuilt the front-of-pack hierarchy and added a trial-size SKU. Within two planogram cycles, share recovery on the target retailer reached low single digits.
“We were losing share and blaming price. User Intuition interviewed 45 shoppers in 48 hours. Price came up in only 11 conversations. The real issue was pack parity, our shelf block had quietly become indistinguishable from private label.”
Category Insights Lead, mid-market CPG snacking brand
What gets the best results from shopper insights on User Intuition?
Tips for getting the most out of the workflow, gathered from the patterns that consistently produce shelf-strategy movement.
Keep the research plan tight. Aim for 4 to 6 main themes with 2 to 4 sub-questions each at Step 3. Plans with 8+ themes produce interviews longer than 25 minutes and completion rates drop sharply. Twelve total questions or fewer keeps completion above 70% on shopper cohorts.
Always cover all three: SAW, CONSIDERED, CHOSE. A study that only collects the choice produces “I bought {brand} because it was on sale” and nothing more. The shelf block, consideration set, and cue need explicit sub-questions.
Run two test conversations every time. One as a frequent loyal buyer, one as a recent switcher to private label. The single highest-leverage 20 minutes you can spend before launch is catching shallow probing on the price excuse while it is still cheap to fix.
Recruit close to the shopping moment. Set the screener to 14 to 30 days. Recall on shelf cues degrades fast. A shopper interviewed 60 days post-purchase gives you brand image, not shopper insights.
Pick voice over chat for shopper work. Voice captures the shopper narrating the trip in real time. Hesitation, surprise, and emphasis on shelf cues are exactly the signals you cannot recover from a chat transcript. Reserve chat for international cohorts where the participant’s language fluency makes typing a better experience.
Run continuous, not seasonal. 30 to 50 shopper interviews per month at $20/interview on the Pro plan compounds into a much sharper picture than one 100-interview annual wave. The Intelligence Hub gets meaningfully more useful every month you feed it, especially across planogram resets and seasonal promotional windows.
Drop the embed widget on cart-abandonment and post-purchase pages. This is the highest-leverage continuous shopper signal on the platform. It captures the moment of decision while the shopper is still in the journey, not 30 days later when memory has rationalized the choice.
Sync the Intelligence Hub monthly. Every completed study should land in the same Shopper Insights session. Cross-season pattern recognition is the highest-leverage output, and it only works if every study lands in the same session. See shopper insights versus consumer insights for the program-design distinction that drives which findings land where.
Route insights inside 7 days. Shopper findings degrade fast across resets. Route specific findings to specific owners (category management, shopper marketing, brand) with a defined SLA. The studies that move shelf strategy are the ones whose findings reach a planogram or brief the same week.
FAQ
The frontmatter FAQ block above answers the most common questions. The full canonical answers also surface inline in the workflow above.