Solutions

Traditional market research overemphasizes rational decision-making. However, decisions aren't always logically driven. By having shoppers undertake a shopping trip, we naturally observe their typical behaviors without posing complex questions. Behavioral tracking aligns more with in-market performance than traditional concept tests (0.8 vs 0.2 in studies).
Using eCommerce for behavioral market research is cost-effective and relevant, even for categories predominantly sold through offline channels.

• Between different product ideas which one is the most likely to succeed?
• What is the cannibalization of the new product launch on the rest of the existing portfolio?
• What is the price point maximizing appeal and value for the new product for the total brand?
• What is the packaging best able to maximize appeal?
• What is the best communication asset to launch this new product?

• Monadic and behavioral, much higher correlation to in-market performance
• Realistic, it forces the team to work on content that could be really used when the product idea is launched
• Time and cost-efficient, in comparison to virtual reality type of test it can be set up in a matter of days and would be much cheaper to execute (results ready in ~2 weeks)
• Simple, no need for an app. or additional device. We track everything visible on our respondent screen on their laptop or mobile no complicated setup is needed, making the whole process very natural for them

• Test environment, quickly recreate our clients category (including competitors) with minimal input needed. Various stores available (Amazon, Walmart, WB Mason)
• Respondents, recruit the relevant target audience based on the client brief. Use the biggest suppliers existing on the market
• Field, track all the actions respondents have on the store and follow up with additional questions on their behavior
• Results, based on clear metrics (buying, vol/value share, etc.) and providing actionable next steps.

We know that content on the product page is vital for increasing sales conversions, especially when testing various options. It's essential to create a page that mirrors real eRetailer features (like secondary images, A+, etc.) and interactive mechanisms on both mobile and desktop platforms. This way, we can confidently predict that the observed results will translate to actual outcomes when the materials are displayed on the live product page.

• What is the proper set of secondary pictures we should use to improve conversion?
• Should we invest on Amazon A+ Premium content, or can we perform well with standard A+ content?
• Is the uplift this eRetailer promises us with a new feature realistic?
• What should we prioritize in terms of quality between video and secondary pictures?

• Monadic and behavioral, much higher correlation to in-market performance
• Realistic, it forces the team to work on content that could be really used when the product idea is launched
• Time and cost-efficient, in comparison to virtual reality type of test it can be set up in a matter of days and would be much cheaper to execute (results ready in ~2 weeks)
• Safe, do not use online assets that have been proved better
• Simple, no need for an app. or additional device. We track everything visible on our respondent screen on their own laptop or mobile no complicated setup is needed, making the whole process very natural for them

• Test environment, recreate a product page environment reproducing the look and feel of eRetailers: Amazon, Walmart, Carrefour, and many more
• Respondents, recruit the relevant target audience based on the client brief. Use the biggest suppliers existing on the market
• Field, respondents browse several product pages (tested product + key competitor), that are fully interactive
• Results, based on clear metrics (time spent, buying, image conversion, etc.) and providing actionable next steps.

In eCom performance, the primary image stands as the cornerstone. On most platforms, this image drives users to open the product page.
Meanwhile, on grocery platforms, a staggering 99% of purchases come directly from the results page with the simple action of "adding to cart." Given its significance, choosing the correct primary image is essential.

• What is the right primary picture to improve opening and conversion rate?
• What is the primary image efficiently communicating “Premium”?
• What is the best way to communicate the key distinctive benefit this product offers?
• What is the best way to communicate in a consistent way the different benefits of each product in the range?

• Monadic and behavioral, much higher correlation to in-market performance
• Realistic, it forces the team to work on content that could be really used when the product idea is launched
• Time and cost-efficient, in comparison to virtual reality type of test it can be set up in a matter of days and would be much cheaper to execute (results ready in ~2 weeks)
• Safe, do not use online assets that have been proved better
• Simple, no need for an app. or additional device. We track everything visible on our respondent screen on their own laptop or mobile no complicated setup is needed, making the whole process very natural for them

• Test environment, an eCom shopping experience by reproducing the look and feel of eRetailers: Amazon, WB Mason, Walmart, Carrefour, and many more
• Respondents, recruit the relevant target audience based on the client brief. Use the biggest suppliers existing on the market
• Field, respondents go for a shopping trip where the content is fully interactive. They can decide to buy 1 product, several, or none
• Results, based on clear metrics (buying, opening, vol/value share, etc.) and providing actionable next steps.

In our world, everybody uses a mobile phone to learn more about a product or service. We want to help brands transform a text concept into a realistic website.
It does not feel natural for “normal” people to project themselves into a product or service described only in text. Browsing a mobile website does not require much; they do that for more than 3 hours daily!

• What is the right way to talk about a new R&D innovation to make it appealing to shoppers?
• How do different services complement each other, and do they attract additional / incremental buyers?
• What is the right brand to launch a new product?
• How does a 15% price increase for the service/product impact customer purchasing behavior?
• Is there any adverse effect on our brand`s perception when integrating a "Buy now" button?

• Monadic and behavioral, much higher correlation to in-market performance
• Realistic, it forces the team to work on content that could be really used when the product idea is launched
• Time and cost-efficient, in comparison to virtual reality type of test it can be set up in a matter of days and would be much cheaper to execute (results ready in ~2 weeks)
• Simple, no need for an app. or additional device. We track everything visible on our respondent screen on their laptop or mobile no complicated setup is needed, making the whole process very natural for them

• Test environment, replicate various mobile websites to test elements in your concept/product idea. You can specify the website details for testing or seek our assistance in creating them
• Respondents, recruit the relevant target audience based on the client brief. Use the biggest suppliers existing on the market
• Field, respondents browse and either add products to their cart or simply visit without making a purchase. We track all their actions and ask follow-up questions once the shopping trip ends
• Results, based on clear metrics (time spent, buying, image conversion etc.) and providing actionable next steps.

Self-order kiosks are now a major ordering channel for quick-service restaurants and retailers. Every screen, button and default option shapes what ends up in the basket. Yet customers cannot tell you why they skipped a dessert prompt or abandoned their order at the payment step, because these decisions are made in seconds, often on autopilot.
So we don't ask them. We rebuild your kiosk interface and let respondents place a real order, then track every tap, hesitation and drop-off. You see the impact of a new menu layout, promotion or upsell prompt on order completion and basket value before it reaches a single restaurant.

• Which menu layout helps customers find what they want fastest and order more?
• Which upsell or combo prompt raises the average ticket without slowing the order or annoying the customer?
• Where in the flow do customers abandon their order, and why?
• Which promotion placement and wording drives the strongest uptake?
• How should a new product be featured to win trial without cannibalizing best-sellers?
• How do price display and price changes affect what customers choose?

• Monadic and behavioral, much higher correlation to in-market performance
• Realistic, respondents complete a full order on a working replica of your interface, not a static screenshot
• Safe, test new layouts and offers without disrupting live restaurants or running costly in-store pilots
• Granular, every tap is timestamped, so you can pinpoint the exact screen where customers hesitate or drop off
• Time and cost-efficient, set up in a matter of days, with results ready in ~2 weeks

• Test environment, recreate your kiosk interface (menu, categories, product customization, upsell prompts, cart and payment) from your current screens or new designs
• Respondents, recruit the relevant target audience based on the client brief. Use the biggest suppliers existing on the market
• Field, respondents place an order in a realistic scenario. We track every action, then follow up with short questions on their choices
• Results, based on clear metrics (order completion, basket value, upsell attach rate, time to order, drop-off by step) and providing actionable next steps

Shoppers cannot describe their own journey. They remember the product they bought, not the eleven they considered and abandoned, the three weeks it took, or the platform that actually changed their mind. So we don't ask them. We meter a panel of real category shoppers, log their genuine browsing, and reconstruct one purchase journey per person from the raw clickstream.
No tasks, no scenarios, no simulated store: real shoppers, real devices, real timelines. The result maps every journey across the four Moments of Truth — search, product page, cart and post-purchase — so you can count exactly how many shoppers survive each stage, and where you lose the rest.

• What triggers a shopper to enter my category, and how long is the window between that trigger and the purchase?
• Which platforms, retailers and communities do shoppers actually use to decide, and in what order?
• At which stage does my funnel leak, and how does my drop-off compare with my competitors'?
• Who am I genuinely cross-shopped against, and does that competitive set narrow as shoppers commit?
• How much research does a purchase in my category really take, in pages, minutes and days?
• Where do AI assistants sit in the journey, and are they discovering products or validating a decision already made?

• Unprompted, passive metering of real browsing, with no task, scenario or recall to bias what shoppers do
• Whole-journey, covers everything before and after your product page — search engines, marketplaces, brand sites, communities, social, AI assistants — not just the shelf
• Competitive by default, the same logs measure every brand a shopper touched, so your funnel is benchmarked against rivals rather than against itself
• Timed, journeys keep their real timestamps, giving recruitment windows in days and research effort in minutes and pages
• Verbatim triggers, real search strings show what starts a category search in the shopper's own words, and where the addressable moments sit

• Scope, agree the market, the product clusters and the brand set to be tracked, together with the category search strings that qualify a shopper as in-market
• Panel, recruit metered category shoppers who bought, or plan to buy, within the last or next 12 months, then validate every log against the qualifying actions
• Reconstruction, classify each page by type, brand and product cluster, then stitch the pages back into one ordered, timestamped journey per shopper
• Results, nine KPIs spanning engagement (platform reach, time share, entry points), conversion (stage drop-off, purchase lift, recruitment window) and influence (AI involvement, advocacy, cross-shopping) — with base sizes stated and thin cuts flagged as directional

In social commerce the video is the shelf. A creator's first three seconds decide whether a product is ever seen, and the tap that follows happens inside the same app — there is no search, no results page and no considered comparison between the content and the cart.
That content can normally only be read once it is live, by which point the media is committed and the weak pieces have already spent their impressions. A confidential or pre-launch product cannot be shown at all. Our answer is to rebuild the storefront itself. Each video under evaluation is placed inside a working replica of the shop and surrounded by a curated feed of genuine influencer and affiliate posts from rival and category brands, so nothing marks out the piece being measured. Respondents scroll on their own phones, at their own pace, and can tap through to a product page and buy — so what we record is shopping behaviour, not an opinion about an advert.

• Which of my creator videos drives the strongest watch time, click-through and conversion?
• Is it the influencer, the message or the content style that explains the difference between them?
• Does seeing several videos together lift or dilute performance versus a single exposure?
• At which stage — video view, product page, add to cart, checkout — does each video lose the most viewers?
• Did the brief I gave my creators translate into content that actually performs?
• Which affiliate content deserves the budget, before any of it goes live?

• Confidential, pre-launch products and unreleased creator content are tested weeks before anything reaches the market
• Blind, tested videos sit among curated real content from category and competitive brands, so nothing flags the test
• Monadic and combination, a clean, independent read per video, plus a separate leg where the same feed carries all of them, to measure multi-exposure
• Behavioral, add-to-cart and completed checkout are used as intent signals, not claimed purchase intent
• Video-weighted, measurement leans on watch time, completion and on-video engagement, then follows the shopper down the funnel
• Mobile-native, built for the phone, because that is the only way a shoppable feed is ever shopped

• Test environment, a replica of the shoppable feed with its core mechanics intact — products tagged on videos via the shopping-bag icon, product detail pages in classic ecommerce format, storefront and seller profile pages, and a full cart / address / payment checkout
• Respondents, recruit the category target from the biggest suppliers existing on the market and randomly assign each one to a cell on entry
• Field, track watch time and completion, tap-through from the video tag, add-to-cart and checkout, then follow up with short questions on message take-out, influencer appeal and content style
• Results, a video-level scorecard plus a funnel read per cell and the combination-leg lift, roughly four weeks from kick-off to report.

More and more shoppers start a purchase by asking an AI assistant instead of typing keywords into a search bar or a retailer. The assistant answers with a short list of products, and the shopper often picks from that list without ever visiting a results page.
That conversation is invisible to brands. Search rankings and retail analytics do not show which prompts shoppers use, what the assistant replied, or why a competitor was named first. We open the curtain: respondents run their own research with an AI assistant and every exchange is captured, then coded for brand mentions, rank in the answer, reasons given and the entry points that triggered the request.

• How often does AI recommend my brand in my category, and in which position?
• Which competitors are named instead of me, and for which needs?
• Which shopper needs and phrasings (dry skin, under $35, sensitive skin…) lead to my products, and which don't?
• What reasons does the assistant give for recommending or dismissing my product?
• Does the shopper follow the AI recommendation through to a final choice?
• Which claims and product content should I change to be picked more often?

• Shopper-led, prompts are written by real category shoppers in their own words, not by an analyst guessing what people type
• Full conversation, follow-up questions are captured, so you see how the shortlist narrows to a final pick
• Coded at scale, every prompt, answer and brand mention is tagged and comparable across respondents
• Verbatim-backed, every figure links back to the original conversation
• Actionable, results point to the entry points and claims where your brand is missing

• Brief, agree the category, the shopper target and the brands to track
• Respondents, recruit category shoppers who research a real purchase with an AI assistant, at their own pace
• Field, record every prompt and answer, then ask a few short questions on the final choice and trust in the recommendation
• Results, a brand visibility dashboard by prompt, answer, brand and entry point, with the coded verbatims behind each number.