How shoe brands can actually use AI shopping assistants

We have been working with e-commerce shoe stores for a while now, and the same problem seems to pop up, consumers abandon their carts because they can't find what they're looking for, or they're unsure about fit.

Traditional website filters aren't the same any more, specially with the increasing demand from consumers.

Here's what studies show about AI shopping assistants that actually work for shoe retailers—and the specific steps you can take starting today.

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The real problem AI shopping assistants solve

Let's be honest about what consumers actually struggle with when purchasing shoes online:

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-They don't know their size in your business

-They can't describe exactly what they want ("comfortable shoes for office")

-They're overwhelmed by too many options

-They need quick answers about materials, care, or return policies

A good AI shopping agent handles these conversations naturally, just like a knowledgeable salesperson would in-store.

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What actually makes a difference

After testing various solutions, here's what separates effective AI assistants from glorified website chatbots:

-Size and fit intelligence that works

-The best systems that we have come across are seen to combine features such as your brand's sizing data with consumer feedback. For example, if 80% of buyers say your running shoes "run small," the AI over time learns to recommend sizing up.

-Conversational search that understands context

Your assistant should handle queries like "waterproof boots for construction under $150" and return relevant results, not just keyword matches.

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Integration with your existing retailer data

It needs to access real inventory levels, shipping information, and knowledge on your buyer's questions—not just basic product specs.

Here is what you can do!

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First, start by cleaning up your product data

Before adding any AI tool, audit your product information. I've seen too many brands rush into AI with messy data and wonder why it doesn't make sense.

Create a spreadsheet with these columns for every product:

• Product name and SKU
• Category and subcategory
• Available sizes and current stock

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Key features (waterproof, steel toe, etc.)

• Materials used
• Care instructions
• Common fit feedback from reviews

Pro tip: Export your client's questions & emails from the last six months.

Look for recurring questions about specific products—this becomes training data for your AI.

Then move towards choosing the platform you want to build on

It is typically recommended to use these options based on budget and technical capacity:

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For Shopify stores (easiest):

Tidio AI (begins at $39/month, good for basic queries)

Gorgias AI (integrates with client service, around $60/month)

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For custom solutions:

OpenAI's Assistant API (requires developer, but highly customizable)

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Dialogflow (Google's platform, good middle ground)

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For larger operations:

Salesforce Einstein (enterprise-level, full integration)

Dynamic Yield (includes AI recommendations)

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Then set up your first use cases

Don't try to solve everything at once.

Here are ideas that can be a great initial point for you

• Recommending Sizes: "What size should I get in these boots?"
• Product discovery: "I need comfortable shoes for standing all day"
• Basic product info: "Are these shoes vegan?"

Write out 10-15 example conversations for each scenario.

This provides you with a testing framework.

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Following which, it is super important to test and then re-build.

Run internal tests with your team first. Have everyone try to "break" the system with unusual questions.

Track these metrics from the start:

• Percentage of questions the AI answers correctly
• Conversion rate for users who interact with the AI
• Average session duration after AI interaction
• Most common unanswered questions (these show you what to improve)

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Three brands doing this right

• Allbirds uses their AI to handle sustainability questions and recommend care products.
• Smart move since these are common concerns for their environment friendly customers.
• Rothy's trained their assistant on their unique sizing (they only make whole sizes) and common questions about their washable materials.
• Thursday Boot Company focuses their AI on helping customers choose between similar boot styles—a major decision point for their customers.

Notice none of these brands are trying to be everything to everyone. They picked specific problems their customers face and solved those well.

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The practical stuff nobody talks about

• Budget realistically: Plan for $500-2000/month for a decent solution, plus setup costs.
• Cheaper tools are out there, but what happens is that they often create more problems than they solve.
• Staff training matters: Your customer service team needs to understand how the AI functions so they can help when it fails.
• Privacy compliance: Make sure your chosen platform complies with GDPR, CCPA, and other relevant regulations. This isn't optional.
• Mobile optimization: Over 60% of shoe shopping happens on mobile. Test your AI assistant thoroughly on phones, not just desktop.

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Common mistakes that are holding e-commerce companies back

• Trying to get rid of human customer service entirely. AI should handle the basic questions, so your team has the time and energy to focus on more focus on complex issues.
• Not updating the training information. Product lines change, seasonal preferences shift, and customer questions evolve. Plan monthly reviews.
• Ignoring failed interactions. When the AI can't help someone, that's providing you with valuable information about what to improve.
• Over-promising capabilities. Be clear about what your AI can and can't do. Disappointed customers are worse than no AI at all.

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Getting the ball rolling

Pick one specific customer pain point your AI will solve. Maybe it's size confusion, or maybe customers can't find waterproof options quickly enough.

Set up a simple solution focused on that one problem. Test it with 10 real customers. Fix what's broken. Then gradually expand.

The brands, seeing real results from AI shopping agents, didn't implement everything at once. They started small, measured carefully, and built on what worked.

Your customers don't care about your Artificial Intelligence, they care about finding and purchasing the right shoes quickly and easily. Keep that in mind, and you'll build something useful instead of just following trends.

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