Target’s ChatGPT Shopping: A Playbook for Retailers

A big shift is happening in retail: the “front door” to your shop isn’t always your website or your app anymore. It’s the chat window.

Target’s new beta experience inside ChatGPT puts that reality on full display. Shoppers can ask for help (“plan a holiday family movie night”), get curated product suggestions, add multiple items to a basket, and check out with delivery, pickup, or drive-up. That’s not a chatbot bolted onto customer service. It’s shopping as a conversation, with purchase baked in.

For Irish retailers and e-commerce teams, this matters for one reason: customers are forming buying intent inside AI tools—and if you’re not present when that intent forms, you’re competing only after the decision is halfway made.

What Target actually launched (and why it’s different)

Target’s ChatGPT “app experience” is a new kind of commerce surface: a conversational interface that can recommend, bundle, and transact.

Here’s what stands out:

• Curated discovery inside ChatGPT: A user describes a scenario, not a SKU. The experience responds with a themed set of products.
• Multi-item baskets and one transaction: It’s not “here’s a link.” It’s a path to checkout.
• Fulfilment choices built in: Shipping, pickup, and drive-up are part of the flow.
• A roadmap to personalisation: Target signals that personalised recommendations and basket-building will deepen over time.

Snippet-worthy reality: If your AI experience can’t build a basket and complete an order, it’s not commerce. It’s content.

This is also not happening in a vacuum. Retailers are rushing to improve AI-powered shopping because it reduces friction in product discovery—especially on mobile, where browsing large catalogues is still painful.

Why “shopping in ChatGPT” is really an omnichannel play

The obvious headline is “Target is selling inside ChatGPT.” The more important story is omnichannel.

A conversational shopping interface only works if the back end is solid:

• Accurate inventory signals (what’s actually available, where)
• Real fulfilment logic (cut-off times, substitutions, cold chain for fresh)
• Clean product data (attributes, dietary tags, sizes, compatibility)
• Consistent pricing and promotions (so the customer trusts the result)

Target is effectively turning conversation into the top of the funnel, while relying on mature omnichannel operations to finish the job. That’s the blueprint.

What Irish retailers can take from this

You don’t need Target-scale budgets to apply the lesson. You do need to accept one uncomfortable truth: AI UX exposes operational weaknesses immediately. If your stock file is messy or your substitutions are inconsistent, the AI experience will feel “wrong” faster than a traditional site.

For Irish grocers, pharmacies, DIY, and fashion retailers, the early wins are often:

• “Build me a basket” missions (weekly shop, kids’ party, Christmas hosting)
• Store-specific fulfilment (click-and-collect windows that actually match staffing)
• Post-purchase support (returns, order changes, substitutions)

The business case: where conversational commerce pays back

Conversational commerce isn’t a novelty feature. It targets three expensive retail problems: search fatigue, low conversion, and poor basket building.

1) Better discovery means higher intent

Most on-site search is brittle. Customers type “nice candles” and get a thousand results. Conversation lets them describe context: “cosy winter scent, not too sweet, under €20.”

That matters because it produces intent you can act on:

• constraints (budget, allergies, size)
• occasion (gift, dinner party, new baby)
• preferences (style, colour, brand avoidance)

2) Basket-building increases average order value (without feeling pushy)

Target’s example is basically a bundle: blanket + candles + snacks + slippers. That’s classic merchandising, but it feels like help rather than upselling.

A well-designed AI shopping assistant can increase basket size by:

• prompting for missing essentials (“Do you need plates or bin bags for that party?”)
• offering smart complements (“pair this pasta with a sauce and parmesan”)
• suggesting substitutions that preserve the mission (“similar fit, available in your size”)

3) Fewer dead ends reduces customer service cost

When the AI experience is connected to real policies and order data, it can answer questions that normally hit your contact centre:

• “Can I collect today?”
• “Will this arrive before Christmas?”
• “What’s the return policy on opened electronics?”

The stance I’ll take: AI that doesn’t reduce service load is usually AI that isn’t connected to the business.

The hard part: personalisation without creeping people out

Target says personalisation is coming. That’s where most retailers get it wrong.

Customers like relevance. They don’t like surprises.

Practical rules for personalisation that works

1. Ask for preferences explicitly
   • “Any allergies?” “What size?” “Do you prefer fragrance-free?”
2. Show why a product was suggested
   • “Picked because it’s machine-washable and rated warm.”
3. Let shoppers steer the style
   • “More minimalist, more colourful, or more premium?”
4. Remember with permission
   • “Want me to save these preferences for next time?”

In Ireland (and across the EU), this also intersects with privacy expectations. Even when you’re compliant, you still need to be trustworthy. Trust is a conversion tactic now.

Snippet-worthy line: Personalisation is only helpful when the customer can see the steering wheel.

What to build first: a phased approach for Irish e-commerce teams

Target can go big because it has the data, operations, and talent. Most Irish retailers need a sequence that protects margin and reduces risk.

Phase 1: “Conversation as assisted search” (2–6 weeks)

Start with a tightly scoped assistant that:

• guides product discovery for a few categories
• uses structured product attributes (price, size, dietary tags)
• links to product pages or baskets

Best categories to pilot:

• gifting
• health & beauty (shade matching can come later—start simple)
• home basics
• seasonal bundles (Christmas hosting, January fitness)

Phase 2: “Basket building + fulfilment checks” (6–12 weeks)

This is the step Target highlights: multi-item orders with fulfilment options.

To do it well, you need:

• inventory availability by location
• fulfilment cut-offs and capacity rules
• substitution logic (especially for grocery)

A strong KPI set here includes:

• conversion rate from AI sessions
• average basket size from AI sessions
• % of AI sessions that end in “no results”
• refund/return rate for AI-assisted orders

Phase 3: “Personalised recommendations with guardrails” (ongoing)

Once the basics work, add controlled personalisation:

• preference profiles (explicit)
• “repeat my last order” flows
• replenishment reminders (opt-in)

Don’t start here. If your catalogue and fulfilment are shaky, personalisation will only amplify the issues.

Common pitfalls (and how to avoid them)

Most companies get this wrong in predictable ways.

Pitfall 1: Treating the AI experience like a marketing campaign

If the assistant is scripted, it won’t handle real customer language. The fix is to design around missions and constraints, not slogans.

Pitfall 2: Forgetting that product data is the experience

If your product titles are messy, attributes incomplete, or imagery inconsistent, AI will recommend poorly.

A practical checklist:

• consistent sizes/units (ml vs oz, cm vs inches)
• clean variant structure (colour/size)
• rich attributes (materials, allergens, compatibility)
• reliable availability and lead times

Pitfall 3: No escalation path

When the AI can’t answer, it should hand off gracefully:

• to a live agent
• to a store phone number or callback
• to a simple form with context preserved

This is how you protect customer experience while still scaling.

What this means for the “AI in Retail and E-Commerce” series

Target’s move is the clearest signal yet that AI is becoming a mainstream commerce channel, not just a back-office tool. In this series, we’ve talked about customer behaviour analysis, personalised recommendations, pricing optimisation, and omnichannel experiences. This story connects all four.

• Customer behaviour analysis: conversation data reveals intent (occasion, constraints, preferences)
• Personalised recommendations: relevance improves when the shopper sets context
• Omnichannel: pickup, drive-up, shipping only work if ops are integrated
• E-commerce innovation: checkout inside new interfaces is where the market is heading

If you run retail or e-commerce in Ireland, the opportunity isn’t “build something in ChatGPT because Target did.” It’s more specific: build an AI shopping assistant that earns trust, grows baskets, and respects operational reality.

The next question worth asking is simple: if a customer described a mission to an AI—“I need a last-minute gift under €30, deliverable by Friday”—would your systems be able to answer confidently?

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Want help scoping your AI shopping assistant?

If you’re considering conversational commerce, start with a short discovery sprint: pick one category, define 20–30 real customer missions, map the data you need, and decide what “success” means (conversion, AOV, service deflection, or all three). The teams that win here are the ones that treat AI as product + operations, not a side project.