This page serves as a guide to implement conversational product filtering for Vertex AI Search for commerce. This document provides data-backed best practices to ensure a successful implementation of conversational product filtering for mid-to-large retail businesses.
Vertex AI Search for commerce conversational product filtering is an AI-powered tool that transforms search into a guided experience to accompany shoppers when they interact with large product catalogs. When a site user performs a broad search (like coffee table or red dress) that returns thousands of results, conversational product filtering intelligently prompts them with follow-up questions to quickly narrow down their options.
Business use case
The conversational product filtering capability in guided search is specifically designed to address broad, ambiguous or very nuanced search queries. Applying filters to narrow the results significantly increases both revenue and user engagement.
The primary goal of conversational product filtering is to help shoppers find the right items quickly and intuitively.
Businesses use conversational filtering to:
- Accelerate product discovery: Help shoppers quickly narrow down vast product selections (such as going from 5,000 area rugs to a few hundred targeted results) by asking relevant questions.
- Refine personalization: The questions and multiple-choice options are custom for every query, based on historical filter usage data for that specific query (coffee table is historically filtered by color more often than size, so color can be asked first).
- Simplify implementation: Questions are predesignated for product attributes such as color and width, with one question per attribute.
One-way conversation user journey
Conversational product filtering operates as a one-way conversation that accompanies the shopper throughout their search journey on an ecommerce site. The AI model asks the shopper a question, and the shopper answers.
The shopper initiates a search query. Example: area rugs
The retail site returns 80+ pages of product results.
Vertex AI Search for commerce asks the shopper on the site a question to help narrow their search. Example: Which color are you looking for?
The shopper selects an answer from a list of multiple-choice or free-text options. Example: blue
The product results on the page are immediately filtered based on the shopper's selection.
Search then presents the next most relevant follow-up question. Example: What shape are you looking for?
Figure 1. Conversational filtering user journey.
Follow-up questions in search
If conversational product filtering is enabled, follow-up questions on the site drive a conversation that ensues until one of the three following scenarios occur:
- A preconfigured minimum product count is reached (a conversation is not useful when only two products show up).
- The user clicks on a product and adds it to their cart (the objective).
- Conversational product filtering runs out of AI-generated questions.
Use as an alternative to dynamic facets
Dynamic facets are associated with broad queries and resulting high search return counts, which leads to low revenue per query. End users can become overwhelmed when they see tens of thousands of results and abandon the search. Conversational search is able to refine queries and can be used with dynamic facets. Conversational product filtering offers some advantages over dynamic facets, being more human, more interactive, and using less on-page real estate.
For more information, refer to the Facets page.
Edit generative questions
Conversational product filtering encourages a human-in-the-loop interaction with the generative AI questions by allowing retailers to preliminarily edit, overwrite, or deselect AI-generated questions according to their preferences, based on the uploaded catalog. Questions can be edited or disabled individually or in bulk in the Search for commerce console or the API in order to tailor the questions they want to appear in the search.
User interaction with filters
This section describes how to configure conversational product filtering. Replacing static, hard-coded filter elements with dynamic conversational filtering to free up screen space for more targeted products is recommended. All applied filters, regardless of their origin, should globally update the product grid.
Subsequent conversational questions should intelligently adapt to the complete set of applied filters, offering both multiple-choice and free-text input options.
Unified global filters
Shoppers can interact with both conversational filters and any remaining classic filter elements. Your frontend implementation must be able to handle this scenario.
- Global application: When a user makes a selection from any filter element on the page, whether it is a conversational product filter or classic filter element, the product grid must update to show results with all global filters applied.
- Intelligent follow-up: The next conversational question the user sees should be relevant based on the complete set of applied filters applies, regardless of which element the user selects. For example, if a shopper selects a
colorfilter from the conversational element and asizefilter from the classic filter element, the subsequent conversational question should not ask the shopper what size they want.
Filter types
Conversational product filtering enables the option to use both multiple choice selections and free text user input.
Multiple choice
Vertex AI Search for commerce can present up to 20 multiple-choice options, based on the value names in the product catalog. Options appear in a sorted list of the most relevant choices. As previously noted, for long options (such as long brand names), ensure users can side-scroll rather than wrap to new lines, to maintain vertical compactness.
Free text input
The product filtering feature supports free text input, allowing shoppers to enter their own answer if the option they are looking for isn't listed. While this option exists, more customers opt for multiple choice selections.
If you implement free text, consider a button that says Type your own which, when clicked, reveals a visual display for a text input window directly on the page, or opens a separate dialog window for text input.
Replace hard-coded elements
Many commerce search site developers have prebuilt, manual category filter components in their web interface intended for top revenue-generating queries. These are typically time-consuming to produce, expensive, and not very dynamic.
Figure 2. Example of hard-coded element display.
- Recommendation: The core idea behind conversational filtering is to enable you to quickly deploy dynamic experiences like these across all you products, not just for the few top queries that the visual elements were designed for. Therefore, identify and remove elements that conversational filtering is designed to replace. Avoid having two competing sets of filter elements that perform similar functions. This frees up space on the screen to be able to show more targeted products.
Data ingestion and quality
The Vertex AI model's intelligence is built on user interaction data. The onboarding process uses a two-phased approach to data ingestion.
Phase 1: Initial start with historical events
To begin, the model can be trained on historical event data. This data is initially ingested into the Google environment, allowing the model to be effective even on new projects with no live interaction data.
Phase 2: Transition to live query data
After the capability is live and collecting data, Vertex AI uses the live query data stream to refine the serving model. The live query data is generally of higher quality than historically captured event data as historical events can sometimes be missing key information. This makes live query data more effective for ongoing optimization.