Test product pages on simulated shoppers

Most lost sales are not about the product. Shoppers cannot find the delivery cost, do not trust a brand they have never heard of, cannot tell which size to pick, or hit an account wall before checkout. Your analytics show where they left. They do not show which of these it was.

• Product pages: are price, delivery, returns and sizing where shoppers look for them?
• Offers and promotions: is the deal clear, and does it sound real?
• Ads and emails: would shoppers stop, open and click?
• The path to checkout: can a new shopper get from a product to the payment step?

Product page objections. Paste a product page and describe your shopper, such as "first-time buyers of trail running shoes in the UK". The objections come back ranked: no visible delivery cost, no size guide, reviews that look thin, a returns policy buried in the footer.

Offer wording. Test a promotion line as copy, or put two versions, such as "Buy two, get the third free" and "Save a third when you buy three", in front of the same simulated shoppers and compare.

Ads before spend. Drop in the ad image, or a video of up to 3 minutes, and see who would click, stop, glance or scroll past, and why.

Promotional emails. Paste a campaign email with its subject line and see who would ignore it, read it or act on it.

In a flow, simulated shoppers open your store in their own real browsers and try a task such as 'Find a gift for a runner and get to checkout'. They search, filter, choose options, add to the cart, and fill in contact and delivery forms with made-up details, for up to 40 steps.

Mimiq gives them no payment details, so nobody pays, and the task should end at the payment step. The report shows how far each shopper got, where they stopped and why: a size they could not choose, a delivery cost that appeared late, a forced account.

Checkout flow testing Testing a Shopify store

Describe shoppers the way you would target them: country, age range, what they are shopping for, how price-sensitive they are. A store that sells to both gift buyers and enthusiasts should say so, and the segments in the report will show where they split.

If you have customer data, import it from a CSV as simulated twins, with names, emails and phone numbers stripped before upload.

They cannot hold the product, try it on, or judge a fabric beyond what the photos show. Questions of fit, quality and returns need real customers and your returns data.

They do not predict conversion rate, basket size or return on ad spend. They really use your store, so on a live site they can leave abandoned carts. A CAPTCHA or bot check can stop them; a public staging store without one works.