For ecommerce brands
Shoppers ask AI what to buy. Make sure you're the answer.
NextNet AI shows you which product lines AI shopping answers actually recommend, which ones they only mention in passing, and what to fix first.
The problem
AI can mention your brand and still send the sale somewhere else
A shopper asks ChatGPT for the best running shoes for flat feet. The answer names your brand once, in passing, then recommends a different brand to buy. Technically you showed up. Practically you lost the sale.
Most tracking can’t see the difference. Mention counters log an appearance and call it a win. Rank trackers only watch results pages, and this decision never touched one. Neither answers the question that actually matters: when a shopper asks, are you the recommendation?
"Brands like yours and several others make cushioned options worth considering."
"For flat feet, go with your brand. It's the best-supported pick at this price."
How it works
How ecommerce teams use it
Score by product line
Setup starts with discovery: NextNet AI learns your product lines from your domain and public signals, then builds the shopping questions worth tracking for each one. Visibility gets measured the way your merchandising team already thinks: by category, not one blended domain score.
Diagnose the gaps
NextNet AI flags the structured data and entity signals your product pages are missing, the ones engines pull from when they assemble comparison and "best of" answers, so you know exactly why you're being passed over, not just that you are.
Fix in revenue order
Nobody needs to touch every SKU. Start with your highest-revenue lines and close the content and schema gaps blocking recommendation there first. And if your team doesn't have the bandwidth, fulfillment is available: we can execute the fixes for you.
Track your share of the shortlist
Market Intel monitors how often you're the recommendation versus the brands winning instead, category by category, month over month.
See the answers, not just the counts
Market Intel, built into NextNet AI, reads the actual answers and separates recommendations from passing mentions. You see which categories you genuinely own, which ones a competitor owns, and which are still up for grabs.
The outcome
Catch the loss while it's still fixable
Time to respond
You find out you're missing from a shortlist while there's still time to do something about it, not after the category underperforms and everyone's guessing why.
Fixes ranked by revenue
Your team stops debating what to work on, because fixes come ranked by the revenue at stake, not by gut feel or the loudest voice in the room.
Proof for leadership
And when the work lands, recommendation share moves on a chart you can put in front of leadership.