Assisted eCommerce: Is this the future of retail?

I recently spoke with a customer who is scaling a human-assisted shopping model. This isn’t the “chat with a support agent” version, but a full personal shopping experience via phone, video calls, and even home visits.

Think of the Nordstrom personal stylist model, but extended to online and offline channels.

They’re able to justify the cost because of their high AOV (average order value), but even then, achieving high conversion rates in each session is critical to keeping unit economics viable.

Achieving that high conversion rate hinges on two things:

1. Precision prospect scoring

You can’t route every visitor to a 1:1 shopping session. You route only the probable buyers.

To do this, our customer built an LTV and propensity-to-purchase model based on lots of features, such as:

• Device characteristics (e.g., iPhone vs. Android)
• Geo and demographic indicators
• Historical purchases categories
• Browsing behavior categories
• Add-to-cart and drop-off patterns
• Engagement with marketing touchpoints

The goal of building this is model to answer the question: Does this shopper justify the cost of an assisted session?

2. Personalization during the session

Once a shopper is selected for assisted commerce, the interaction must accelerate decision-making, so making the best use of that session to deliver the most personalized experience is paramount.

Powering that is an AI-based recommendation engine that looks at a lot of signals, including:

• What the shopper has been browsing recently
• Past purchases
• Price sensitivity and discount behavior
• Fit/style preferences
• Recently abandoned products
• Complementary items

The output is a set of product recommendations that must be covered during the assisted session. It is also an online learning system that evolves as the user likes or dislikes certain products.

How customer data infrastructure powers scoring and personalization

RudderStack forms the core data foundation for both.

The lead-scoring models are built using event data collected through RudderStack. This includes everything from device type (iPhone vs. Android) to physical location to past browsing and purchase history, all of which feeds into the model.

Similarly, personalization is delivered via an AI/LLM agent that is powered by the customer’s historical behavior data.

Attributes like device type, geolocation, browsing depth, product interactions, and purchase history are streamed into models without additional instrumentation work.

The business outcome is simple: consistently high conversion rates, even with high-cost sessions.

Can AI make assisted shopping viable for all retail?

Could the latest voice and video AI technology make it viable to offer assisted shopping even for low-value, everyday purchases? People are already using voice and video AI for interviews, KYC verification, and customer support. Why not go shopping?

The workload that currently requires humans might soon be offloaded to:

• AI stylists
• AI sales advisors
• Hybrid human + AI co-pilot assistants

If that happens, assisted shopping might expand from high-AOV luxury items to mainstream retail.

Although we can’t know for certain where the future will land, one thing seems certain:

Tools like RudderStack will power the personalization engine, whether the “assistant” is a human or an AI agent.

So, what should this category be called? Customer Data Platform or Agent Data Platform?

FAQs about assisted eCommerce

What is assisted eCommerce?

Assisted eCommerce is a shopping model where customers receive real-time help choosing products. It can involve human stylists, sales advisors, or AI agents that guide decisions through chat, video, or phone. The goal is to increase conversion and order value by making each session more personalized.

How is assisted eCommerce different from regular online shopping?

Regular online shopping is largely self-service. Assisted eCommerce adds a human or AI “co-pilot” that helps customers discover, compare, and decide on products in real time. It relies on behavioral data, past purchases, and recommendation models to make each interaction more relevant.

Why does assisted eCommerce require strong customer data infrastructure?

Assisted eCommerce depends on accurate, real-time signals like browsing behavior, purchase history, price sensitivity, and marketing engagement. Customer data infrastructure collects, transforms, and delivers this data to LTV and recommendation models so teams can score visitors, trigger assisted sessions, and personalize conversations without building fragile, one-off pipelines.

How do AI agents fit into assisted eCommerce?

AI agents can act as stylists or sales advisors that understand a shopper’s preferences, constraints, and history. They use machine learning models and real-time event data to surface the best products and offers during each session. Over time, they learn from likes, dislikes, and outcomes to improve their recommendations.

Can assisted eCommerce work for everyday retail, not just luxury?

Yes, as voice and video AI become cheaper and more capable, assisted eCommerce can move beyond high-ticket items. If the cost of the “assistant” drops, retailers can justify offering guided shopping even for lower-value purchases, as long as the underlying data and models keep conversion and retention strong.