AI Browser for Ecommerce Teams: Prospect Research

This guide is for ecommerce teams that run prospect research. It names the surfaces a ecommerce team typically uses, where the friction sits, and how an AI browser like Strawberry runs the workflow without forcing the team to learn a new stack.

How ecommerce teams approach prospect research

A ecommerce team runs this work in a specific way: run direct-to-consumer or B2B online retail with a stack of Shopify (or similar), ads, fulfillment, and customer support. The current pain is concrete - margins are tight; creative quality determines CAC; competitive pricing requires constant monitoring. The reason an AI browser helps here is that ecommerce teams already touch many surfaces (Shopify or BigCommerce, Klaviyo or Mailchimp, Meta Ads, Google Ads, Recharge or similar), and the bottleneck is the human moving data and context between them.

What a good prospect research run looks like for ecommerce teams

The goal is to decide whether a prospect is worth a calendar slot and prepare a personalised first touch. Success metric: first reply rate above 8% and a meeting booked in under 14 days from first touch. In an industry context that means: ad creative iteration plus weekly competitive scan plus customer support response queue all in one place.

Buying signals prospect research should react to

The signals that should trigger prospect research for a ecommerce team include: competitor product launch, platform algorithm update, supply chain disruption. Strawberry watches the public web (LinkedIn, news, job boards, the company's own site) for these and pairs them with whatever lives in the team's existing tools.

How Strawberry runs prospect research for ecommerce teams

1. Connect the existing stack (Gmail, CRM, sheets, Slack, etc) so Strawberry can read in-place.
2. Define one sentence of what 'done' looks like for prospect research in your specific ecommerce team setup.
3. Ask Strawberry to read the relevant context, then research the gaps via the browser.
4. Strawberry produces the prospect research output in the shape your team can use immediately.
5. A human reviews before any external action (send, update, post) goes out.
6. The approved output gets logged back into your system of record so the next person sees it.

A real prospect research output for ecommerce teams

This is an example of the shape, not your literal team's output - swap the specifics for your context:

• Anna Lindqvist - VP Marketing, Voi Technology
• ICP fit: yes (Series D scooter co, EU expansion, 1500 employees)
• Talking point 1: hired 4 paid-acquisition managers in last 90 days - clear shift toward performance marketing
• Talking point 2: spoke at SuperVenture last month on scooter unit economics
• Talking point 3: company just announced Germany pull-out - retention focus is likely a priority
• Suggested first message: short, references the SuperVenture talk, asks one specific question, no calendar link

When this is right for ecommerce teams, and when it is not

This workflow is right when ecommerce teams have multiple recurring instances of prospect research to run each week, and when the existing stack is mostly online and connectable. It is the wrong fit when prospect research happens once a quarter or requires deep domain expertise the agent does not have. In that case, the ecommerce team should run it manually and capture the playbook for the next iteration.

Three mistakes to avoid

• Researching prospects who don't match ICP - the brief is wasted
• Generic talking points ("impressive growth") that don't reference any real signal
• Copying public bio text instead of synthesising fit

Caveats

Strawberry holds back on sending email, updating CRM records, or changing shared systems until a human approves the action. Treat the agent as a fast first-draft author, not an autopilot.