The Impact of AI on Product Discovery: How Brands Can Adapt to the New Era of Shopping.

Table of Contents

1. Key Highlights:
2. Introduction
3. The Rise of Conversational Commerce
4. Why AI Traffic Is Invisible in Google Analytics
5. Testing the Tools: How Each AI Platform Handles Attribution
6. How to Fix It: Attribution Solutions for AI Traffic
7. What Shopify Brands Need to Do Next
8. Start Tracking Smarter with Littledata
9. FAQ

Key Highlights:

• Conversational AI tools are transforming the way consumers discover products, leading to a shift from traditional search engines to personalized AI recommendations.
• Many brands face challenges in tracking AI-driven traffic, primarily due to incomplete UTM parameters and tracking issues that result in misattributed conversions.
• Solutions like Littledata can enhance attribution for Shopify brands, ensuring they capture valuable insights from AI traffic.

Introduction

The rise of artificial intelligence (AI) has ushered in a significant evolution in the e-commerce landscape, particularly in how consumers discover and purchase products. With tools such as ChatGPT, Perplexity, and Google Gemini gaining traction, shoppers are increasingly turning to these AI agents for personalized recommendations rather than relying on conventional search engines like Google or social media platforms such as Instagram. This paradigm shift presents both exciting opportunities and complex challenges for brands navigating the new terrain of digital marketing and customer engagement.

As consumers become more comfortable with AI-driven interactions, businesses must adapt to this change. One of the most pressing issues they face is tracking and attributing traffic generated through these AI tools, as traditional analytics methods often fall short. This article delves into the disruption caused by AI in product discovery, examines the invisibility of AI traffic in analytics platforms, and offers practical solutions for Shopify brands to enhance their marketing attribution.

The Rise of Conversational Commerce

The emergence of conversational commerce has fundamentally altered the customer journey. As shoppers grow accustomed to engaging with AI, their expectations and behaviors are shifting. Recent studies reveal that a significant percentage of consumers are open to using AI for various purchasing decisions:

• 70% of consumers are willing to use AI to book flights.
• 65% would consider AI to book hotels.
• Between 50% to 60% of consumers are interested in using AI for purchasing clothing, beauty products, electronics, and medicine.

This shift from keyword-based searches to intent-based queries is notable. Instead of searching for "best slingback flats under £150," consumers are articulating more specific requests, such as "What are some stylish flats with no heel under £150 I can wear to work?" This evolution highlights the growing reliance on AI to facilitate product discovery and suggests a need for brands to rethink their marketing strategies.

Why AI Traffic Is Invisible in Google Analytics

Despite the increasing traffic from AI tools, many Shopify brands find it challenging to track this traffic effectively. Traditional attribution models rely on specific criteria:

• Clear and precise referral links.
• UTM parameters that categorize traffic sources.
• Predictable user journeys leading from discovery to conversion.

AI search disrupts these assumptions in several ways:

Incomplete UTM Parameters

AI tools like ChatGPT may provide a utm_source=chatgpt.com, but often lack additional UTM parameters, such as utm_medium. This omission can lead to misclassification in analytics tools like Google Analytics 4 (GA4), resulting in significant gaps in data.

Redirect Issues

When an AI tool links to an outdated or incorrect product page, redirects can strip crucial referrer information. This loss of data complicates attribution and makes it difficult for brands to understand where their traffic originates.

Multi-step User Journeys

AI platforms often utilize multi-step linking, which can obscure the path from the initial interaction to final conversion. For example, Perplexity may link to an internal result page before directing a user to a product page, complicating the tracking process.

Session Loss across Devices

Users frequently browse across multiple devices or browser tabs. In such cases, the original traffic source is often lost, leading to sessions being recorded as "direct" or "unassigned" in GA4. This lack of clarity hampers brands' ability to pinpoint which channels are driving valuable sessions.

Testing the Tools: How Each AI Platform Handles Attribution

To better understand the attribution challenges posed by different AI platforms, Edward Upton, CEO of Littledata, conducted an experiment testing product discovery across ChatGPT, Perplexity, and Google Gemini. The goal was to simulate how consumers might navigate their shopping journeys using these AI tools.

ChatGPT: Strong Discovery, Weak Consistency

While ChatGPT excels at generating product recommendations, it often struggles with consistency in attribution. The platform typically begins with generic search results, requiring users to refine their queries to get specific product options. While it sometimes includes direct links to product pages and utilizes utm_source=chatgpt.com, it rarely employs a complete set of UTM tags. Additionally, outdated links and redirects can result in broken attribution, limiting its effectiveness as an attribution tool.

Perplexity: Smarter Queries, but Attribution Falls Short

Perplexity stands out for its ability to interpret user intent effectively, such as when a user asks for "elegant shoes under £150." However, it often relies on multi-step links that can hinder referral tracking. Furthermore, it rarely includes UTM parameters, and its recommendations may direct users to physical stores, complicating digital attribution efforts. Despite delivering accurate product results, Perplexity's limited traceability poses challenges for brands seeking to analyze their traffic sources.

Google Gemini: Helpful Guide, Lacks Purchase Intent

Google Gemini serves as a helpful guide for shoppers, providing valuable commentary and insights. However, it lacks a strong focus on specific products, often requiring explicit prompts to surface shopping links. Typically, links generated by Gemini lead to homepages or marketplaces like Amazon, with no UTM tagging to facilitate proper attribution. As a result, brands often find themselves losing track of potential conversions from this platform.

How to Fix It: Attribution Solutions for AI Traffic

Given the growing reliance on AI for product discovery, Shopify merchants must take proactive steps to enhance their tracking and attribution practices. Littledata offers solutions that can help brands address these challenges effectively.

Littledata's Approach to Attribution

Littledata provides Shopify brands with tools designed to enrich traffic data and improve tracking for AI-generated traffic. Some key features include:

• Automatic UTM Parameter Enrichment: Littledata enhances traffic data by automatically adding missing UTM parameters, ensuring accurate categorization in analytics platforms.
• Preservation of Source and Medium Data: The tool maintains referral information even in the case of redirects, helping brands understand the origin of their traffic.
• Streaming Server-Side Events: By capturing server-side events, Littledata ensures that critical data points, such as product views and add-to-cart actions, are recorded accurately, even if the user journey began with an AI tool.
• Insightful Data Visualization: With enhanced data tracking, brands can gain a clearer picture of their traffic sources, enabling informed decisions about marketing strategies and budget allocation.

These solutions empower Shopify brands to better understand their customer journeys, allowing them to optimize their marketing efforts and protect their ad spend.

What Shopify Brands Need to Do Next

To thrive in the rapidly evolving landscape of AI-driven commerce, Shopify brands must take decisive actions:

1. Enhance Product Discoverability: Ensuring that products are discoverable through AI platforms is crucial. Brands should optimize their product listings and descriptions to align with the conversational search queries used by consumers.
2. Upgrade Analytics Capabilities: Brands should consider upgrading their analytics infrastructure to account for the new traffic sources introduced by AI tools. This includes employing solutions like Littledata to enhance attribution accuracy.
3. Track AI Traffic Effectively: Accurately tracking AI-generated traffic is essential for informing marketing budgets and content strategies. Brands must implement robust tracking solutions to understand which AI platforms are driving conversions.

Simply appearing in AI search results is insufficient; brands need to analyze what is working and why. This data-driven approach will enable them to make more informed marketing decisions and maximize their ROI.

Start Tracking Smarter with Littledata

As AI continues to shape the future of shopping, brands cannot afford to let valuable traffic slip through their fingers. Tools like Littledata provide a necessary safeguard for Shopify merchants, enabling them to track traffic from AI platforms like ChatGPT, Perplexity, and Google Gemini effectively. By harnessing these insights, brands can make informed decisions, optimize their ad spending, and ultimately grow confidently in an AI-powered marketplace.

For those interested in seeing AI product searches in action, a detailed walkthrough is available on YouTube, showcasing real-world applications and examples of how brands can better navigate the complexities of AI-driven product discovery.

FAQ

What is conversational commerce?
Conversational commerce refers to the use of messaging apps, chatbots, and AI tools to facilitate shopping experiences and product discovery. It emphasizes personalized interactions over traditional search methods.

Why is AI traffic often misattributed in analytics?
AI traffic is often misattributed due to incomplete UTM parameters, redirect issues, multi-step user journeys, and session loss across devices, which complicate traditional tracking methods.

How can brands improve their tracking for AI-generated traffic?
Brands can improve their tracking by implementing solutions like Littledata, which enrich traffic data with missing UTM parameters, preserve source and medium information, and capture server-side events for accurate attribution.

Is it necessary for Shopify brands to upgrade their analytics tools?
Yes, upgrading analytics capabilities is crucial for Shopify brands to keep pace with the shifting landscape of product discovery and ensure they accurately capture and analyze traffic from AI platforms.

What role does AI play in the future of e-commerce?
AI is set to play a transformative role in e-commerce by enhancing product discovery, personalizing customer interactions, and changing how consumers engage with brands, making it essential for businesses to adapt to these changes.