Do You Know Your Product Attribution from Your Marketing Attribution?
Don’t worry if not; this A-Z glossary has you covered! This glossary is jam-packed and organized into four sections:
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Product Attributes and Data Enrichment
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AI Search and Discovery
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Measurement and Analytics
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Agentic AI and Machine Learning
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Product Attributes and Data Enrichment
AI-Powered Product Attribution:
An AI-driven process, as opposed to the common human-powered merchant review, of automating the validation of existing product data as well as generating a complete set of additional product attributes. The AI involved typically includes, but is not limited to, Computer Vision and Natural Language Processing.
Dynamic Attribute Taxonomy:
A living library of consumer-oriented attributes and product details that often uses AI to analyze search data to extract up-to-the-minute natural language terms.
Micro-Trend:
A rapidly emerging, short-lived style or phenomenon that gains quick popularity within a niche audience, primarily through platforms like TikTok and Instagram, and may or may not result in a new aesthetic with a hashtag and even a “core” attached, like “#SirenCore”, #MermaidCore’s darker cousin.
Natural Language Product Attributes:
The rich vocabulary that consumers use when describing the product details and characteristics of both past purchases and how they, in their own words, evaluate new items.
Product Attribution:
The process of analyzing product information and images in order to validate information as well as identify missing characteristics and mico-details in order to apply this data to enrich product records, online product listings, and product display pages, as well as a foundational data source of truth to generate additional product content, such as descriptions, highlights, styles, etc.
Product Attribute:
A specific characteristic or feature of a product that describes its properties, functionality, or style.
Product Data Enrichment:
Product data enrichment refers to the process of improving and expanding product information, such as adding more details, specifications, descriptions, highlights, and other data, to a catalog to make it more complete, accurate, and compelling for customers.
Product Data Validation:
The identification and rectification of inaccurate information in datasets, such as an item category classification for attribute detail.
Product Tag / Tagging:
The application of descriptive labels to online merchandise, including keywords and categories, to boost discoverability and improve search results online.
Product Taxonomy:
A hierarchical classification and descriptor normalization framework for organizing and standardizing merchandise based on characteristics and relationships.
Special Occasion:
There are the traditional occasions for when an item is needed, such as a “black-tie event,” and there are a myriad of special occasions that are less common yet still have distinct and nuanced dress codes, such as a “Coachella Concert Outfit.”
Taxonomy Alignment:
The process of mapping and harmonizing two different sets of product data, such as between a brand and a retailer or a marketplace.
Zillennials:
A “hybrid” micro-generation born between 1994 and 2000, wedged between Millennials and Gen Z with unique cultural and technological experiences. They are just one example of how shopping behaviors and preferences can vary from audience segment to segment and from generation to micro-generation.
AI Search and Discovery
Ad Quality Score:
In Google Ads, this is a 1-10 rating that measures the effectiveness and relevance of keywords, ads, and landing pages in Google Ads campaigns. The score is calculated based on three key components: expected click-through rate (CTR), ad relevance, and landing page experience, which are evaluated by comparing performance against competitors bidding on the same keyword. A higher Quality Score leads to lower cost-per-click (CPC), better ad positioning, and improved overall campaign performance, making it a critical metric for advertisers to optimize their digital advertising efforts.
Answer Engine Optimization (AEO):
A cutting-edge content strategy that optimizes digital content to provide direct, concise answers to user queries across AI-powered platforms like voice assistants, chatbots, and search engines. Unlike traditional SEO, AEO focuses on structuring content to deliver precise, zero-click answers that can be immediately extracted by AI technologies, prioritizing user intent and conversational language.
Faceted Search:
Also known as Filters and Facets, this is an e-commerce filtering method using characteristics to progressively narrow down large selections that optimize product discovery and rely on product data that is aligned with that e-commerce site’s taxonomy.
Generative Engine Optimization (GEO):
An emerging new discipline of enhancing digital content to be found and favored by AI-powered search engines, with the retailer’s goal being, similar to Search Engine Optimization, organically increasing product visibility and driving more traffic to an e-commerce website.
PerformanceMax Campaigns:
Also known as PMax, this is Google’s AI-powered campaign type that automatically manages and optimizes advertising campaigns and media dollars across multiple Google channels (or “Surfaces”) as well as ad units.
Product Discovery:
A multifaceted process that helps people find and explore new products, typically online, that meet their needs or desires. It encompasses various digital touchpoints and strategies designed to guide shoppers toward items they’re looking for or might be interested in, even if they weren’t explicitly searching for them.
Product Listings (PLs) vs Product Listing Ads (PLAs):
While Google’s Product Listing Ads (PLAs) are now known as Shopping Ads, many in retail still refer to these ad units and organic placements as PLAs and PLs. On Google, the same product information is synced with Google Merchant Center (GMC) to power both PLs and PLAs.
Retail Media:
Also known as Commerce Media, this refers to advertising placed within retailers’ websites, apps, and physical stores, allowing brands to promote their products at or near the point of purchase. The most popular retail media ad unit is the Sponsored Product Listing due to its effectiveness in driving highly profitable, bottom-of-the-funnel sales by appearing prominently in search results and category pages.
Search Engine Optimization (SEO):
The strategic process of optimizing digital content to improve website visibility and ranking on search engine results pages, ultimately driving organic traffic. In the context of retail, it is the process of enriching catalog data and digital product content in order to enhance discoverability and conversion across traditional search engines as well as emerging AI-powered platforms. See AEO and GEO for more specifics on this emerging channel.
Semantic Search:
A technique aimed at understanding the intent and contextual meaning behind a query rather than just matching keywords in e-commerce platforms. While semantic search produces superior results as compared to keyword-only matching, it still requires product data enrichment is still required for optimal semantic search performance.
Shopping Ads (FKA Product Listings):
An ad unit on Google that is a highly visible, visual product advertisement that displays relevant products at the top of search engine results. These rely on detailed merchandise information managed in Google Merchant Center in order to optimize ad performance and relevance.
Sponsored Listings:
An ad unit similar to Google Shopping Ads and found in Retail Media that displays sponsored product ads within on-site search results and product listing pages. Like Google Shopping Ads, Sponsored Listings also rely on detailed merchandise information in order to optimize ad performance and relevance.
Transformer Search:
An AI-driven approach that leverages deep learning models to enhance product discovery and search experiences in e-commerce. This technology utilizes transformer architectures to analyze and understand complex relationships between user queries, product attributes, and consumer behavior. While transformer models can work with minimal product data, enriched product attributes significantly enhance model performance.
Vector Search:
A method representing items and queries as mathematical vectors, enabling more nuanced and context-aware results in online shopping environments. Like semantic and transformer search, it produces superior results as compared to keyword-only matching, yet it still requires product data enrichment, given that the quality of vector search results depends on the richness of the product data used to generate the vectors.
Zero-Click Search:
When a user finds the answer to their search query directly, without needing to click through to a website. This trend challenges traditional e-commerce traffic models by reducing website visits while simultaneously offering retailers an opportunity to optimize product visibility through enhanced data presentation and rich search results. Successful adaptation requires retailers to focus on advanced search technologies, semantic search optimization, and creating compelling, information-rich product listings that can capture consumer attention directly within search engine interfaces.
Measurement and Analytics
A/B Testing:
Also known as split testing, is a method of comparing two versions to determine which performs better in achieving specific goals. In this process, one version (the control) might be shown to one segment while the other version (the variant) is displayed to another segment, allowing for statistical analysis of user behavior to identify the more effective option based on predetermined metrics.
Average Revenue Per Visitor (ARPV):
A metric calculating the average income generated by each website visitor, useful for assessing overall performance in digital retail.
Cold Start Forecast:
A model of predicting sales for a new product when there is little to no historical sales data available, creating a “cold start” challenge for accurate prediction. This is quite common when launching new items in retail and typically relies on analyzing attribute similarities with existing products.
Contrafactual DiD:
Also known as Counterfactual in DiD, it is an advanced version of the difference-in-difference method incorporating alternative scenarios to estimate causal effects more accurately in e-commerce.
Difference-in-Difference (DiD):
A statistical technique comparing outcome differences between treatment and control groups before and after an intervention in digital retail.
Fractional Factorial:
An experimental design testing a subset of all possible factor combinations, reducing required tests while providing valuable insights for online retailers.
Full Factorial, Double-Blind:
A comprehensive experimental design with both researchers and participants unaware of group assignments to prevent bias in e-commerce studies.
Interrupted Time Series:
An analytical method evaluating the impact of an intervention by comparing data trends before and after a specific point in time for online retail.
Linear Extrapolation:
A prediction technique extending a linear trend beyond the original observation range in data analysis.
Marketing Attribution:
Measurement model that is channel-based in orientation and analyzes multiple touchpoints across campaigns and advertising media. The goal is to identify the impact of each touchpoint by assigning a value to each one that contributed to a desired outcome, typically a conversion or sale. Examples of attribution models include include multi-touch attribution or closed-loop attribution.
P-Value:
Also known as probability value, this is a statistical measure that quantifies the likelihood of obtaining observed results if the null hypothesis is true.
PDP View Rate:
An e-commerce metric that measures the percentage of users who view a product detail page (PDP), providing insights into user engagement and interest in specific products, which are crucial for optimizing conversion rates and improving overall sales performance. High PDP view rates often indicate effective marketing and product discovery, while low rates may signal issues with product visibility or demand.
Type I & Type II Errors:
Statistical mistakes where Type I is an incorrect positive and Type II is an incorrect negative in e-commerce data analysis.
Zero-Result Search Rate:
Also known as the null result rate, it is a metric that measures the percentage of searches that return 0 results on a website or search engine.
AI Agents and Machine Learning
Agentic AI:
Artificial intelligence (AI) systems designed to autonomously pursue complex goals and execute workflows with limited direct human supervision. It is designed to handle complex, multi-step processes across dynamic environments.
Classification Models:
Machine learning algorithms that sort data points into predefined groups or classes based on their features. These models learn to identify and assign new, unseen data to specific categories by analyzing characteristics from training datasets.
Computer Vision (CV):
An AI field enabling machines to interpret and understand visual information, such as product images and videos.
Deep Learning (DL):
An advanced machine learning subset capable of processing vast amounts of unstructured data for e-commerce applications.
F-1 Score:
A crucial evaluation metric in machine learning, particularly for classification tasks. It combines the concepts of precision and recall into a single score, providing a balanced measure of a model’s performance.
Human-in-the-Loop:
An AI approach incorporating human feedback in the machine learning process, improving accuracy and domain expertise depth.
Labeled Dataset:
This term specifically refers to a collection of data points that have been annotated with meaningful labels or tags that indicate the correct output or category for each data point. Labeled datasets are essential for supervised machine learning tasks, where models learn to make predictions or classifications based on the examples provided in the dataset.
Large Language Models (LLMs):
AI models trained on extensive text data, capable of understanding and generating human-like text for various tasks.
Machine Learning (ML)
A subset of AI that enables computers to learn from data and improve their performance on tasks without being explicitly programmed.
Natural Language Processing (NLP):
Technology enabling machines to comprehend, interpret, and generate human language, facilitating natural interactions in e-commerce platforms.
Neural Networks:
Computing systems inspired by biological brain structures, designed to recognize patterns and learn from large datasets in online retail applications.
Precision:
A critical metric used to evaluate the performance of classification models, particularly in scenarios involving imbalanced datasets. It measures the accuracy of positive predictions made by the model. This metric is essential when the cost of false positives is high, as it indicates how many of the predicted positive cases are actually relevant.
Recall:
A performance metric in AI that measures the model’s ability to identify all relevant instances within a dataset. Specifically, recall quantifies the fraction of relevant instances that were successfully retrieved by the model. Recall is most effective when used in conjunction with precision, typically balanced by the F1 score, which provides a harmonized evaluation of a model’s performance.
Training Data:
Specialized datasets used to train AI models. In retail, retail-specific training data is crucial for developing machine learning algorithms that can accurately generate product attributes and related product data.
