AI shopper research for retail and e-commerce.
Your analytics show where shoppers drop off. Jean asks them why, the same night, and lets you test the fix on their digital twins before it ships.
Where they stopped, and why
- 1Shipping fee showed up at the last step“It said $122, then $131 on the payment page. I closed the tab.”38
- 2Not sure the size would fit“I'm between sizes and the chart is in centimeters.”24
- 3Asked to make an account“I just wanted to pay, not get another password.”14
- 4Found it cheaper elsewhere“Same trousers were $46 with a code on the brand site.”13
Which fix would have kept the order? Asked of 1,000 twins
- Free shipping over $10046%
- Show the fee on the product page31%
- $9 shipping, free returns23%
Next: the top two go to 100 real shoppers tonight.
Why shoppers leave carts, return orders and switch stores.
What teams run
Each study starts with a question like one of these.
Cart and checkout abandonment
“Why do shoppers leave a full cart at the shipping step?”
Returns reasons
“Why do so many dresses come back as "didn't fit", and what would have prevented it?”
Delivery and pickup options
“Would shoppers wait three days for free shipping, or pay $6 for tomorrow?”
Own brand and private label
“Would our regular shoppers switch to our own brand at $2 less?”
Assortment and range
“Which items can we cut without sending shoppers to a competitor?”
Lapsed shoppers and loyalty
“Why did last year's holiday shoppers not come back in spring?”
Interview the shoppers your data cannot explain.
Invite people who left a full cart, sent an order back or stopped buying, while the moment is fresh. The interviewer asks what they compared, what the site or store got wrong and where they bought instead.
Get past the reason code.
Return forms and exit surveys offer a short list of reasons. In a real conversation, shoppers tell you about the sizing chart, the surprise fee or the competitor with free returns. Each reason comes back counted and quoted.
Test the fix on twins before the season.
Twins built from your shoppers react to a free shipping threshold, a new return window or an own brand price in minutes. Take the two best options to real shoppers overnight, then ship one.
How you run it
- 1Minutes
Screen
A thousand digital twins answer first, so the weak options drop out.
- 2Overnight
Interview
The AI interviewer talks to real people and follows up on what they say.
- 3By morning
Decide
The number, the reasons behind it and the quotes that carry it.
Questions
How do you find out why shoppers abandon their carts?
Invite shoppers who left a cart or a checkout to a short interview the same day. The AI interviewer asks what stopped them and what they did next. By morning the reasons are counted and quoted.
Can Jean interview our own customers from an order list?
Yes. Upload a list from your CRM or order system of people who agreed to be contacted, and Jean sends the invitations. Jean can also recruit people who shop with a competitor.
Can Jean help us reduce returns?
Jean interviews shoppers who sent items back and finds the reasons a return form misses, such as sizing, photos or a cheaper price elsewhere. Twins can then react to a new size guide or return policy before you change it.
How do digital twins help retailers?
Twins are built from real shopper interviews. Use them to compare many delivery fees, promotions or own brand prices in minutes, then confirm the best two with real shoppers.
Can we run shopper research in several countries?
Yes. The interviewer runs by text or voice in the shopper's own language, so one study can cover several markets.
Can our agency offer this to retail clients?
Yes. Research agencies and panel companies resell Jean's AI interviewer and digital twins under their own brand. Jean runs the technology; you keep the client, the method and the margin.