Agent Spotlight: Emmanuel Sam, An Avid Learner With a Heart Of Gold
January 27, 2023
March 17, 2023
4
min to Read
Pricing Intelligence
In the ever-growing world of eCommerce, it's crucial to stay ahead of the competition and ensure your products are easily discoverable by potential customers.
One way to do this is through effective Product Feed Management. In this comprehensive guide, we'll explore what product feed management entails, its importance for online businesses, and best practices to optimize your product feeds.
Product Feed Management is the process of organizing, optimizing, and distributing product data to various online channels such as marketplaces, search engines, and comparison shopping engines. A product feed, also known as a data feed or product catalog, is a file that contains essential information about your products, including product titles, descriptions, images, prices, and more.
Effective product feed management involves continuously updating and optimizing this data to ensure it is accurate, consistent, and meets the requirements of each marketplace you sell on. By doing so, you can improve the visibility and discoverability of your products on the digital shelf, leading to increased sales and better return on investment (ROI).
Product feed management is essential for eCommerce businesses for several reasons:
- Properly optimized product feeds help your products rank higher in search results, making it easier for potential customers to find them.
- Regularly updating product feeds ensures that customers have the most up-to-date information, reducing the likelihood of returns or customer dissatisfaction.
- Each online marketplace has its unique set of requirements for product feeds. Effective product feed management ensures your feeds comply with these requirements, allowing you to reach a wider audience.
The Different Types of Product Feeds
There are several types of product feeds, each catering to a different online channel:
Marketplace Feeds: These feeds are for platforms like Amazon, eBay, and Walmart, where you list your products for sale.
Shopping Comparison Feeds: Feeds for comparison shopping engines like Google Shopping, Bing Shopping, and Shopzilla allow customers to compare products and prices across multiple retailers.
Affiliate Network Feeds: These feeds cater to affiliate marketing networks like Commission Junction, ShareASale, and Rakuten, where affiliates promote your products in exchange for a commission.
Social Media Feeds: Social media platforms like Facebook and Pinterest also support product feeds, allowing businesses to showcase their products through social commerce.
An effective product feed should include the following components:
Invisible manages product feeds at scale with a blend of automation technology and seasoned operators. For a major eCommerce player, we boosted search engine visibility across thousands of unique SKUs by 49%, leading to $900k in new revenue on dead stock in just 16 days – 10xing the client’s investment.
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In the ever-growing world of eCommerce, it's crucial to stay ahead of the competition and ensure your products are easily discoverable by potential customers.
One way to do this is through effective Product Feed Management. In this comprehensive guide, we'll explore what product feed management entails, its importance for online businesses, and best practices to optimize your product feeds.
Product Feed Management is the process of organizing, optimizing, and distributing product data to various online channels such as marketplaces, search engines, and comparison shopping engines. A product feed, also known as a data feed or product catalog, is a file that contains essential information about your products, including product titles, descriptions, images, prices, and more.
Effective product feed management involves continuously updating and optimizing this data to ensure it is accurate, consistent, and meets the requirements of each marketplace you sell on. By doing so, you can improve the visibility and discoverability of your products on the digital shelf, leading to increased sales and better return on investment (ROI).
Product feed management is essential for eCommerce businesses for several reasons:
- Properly optimized product feeds help your products rank higher in search results, making it easier for potential customers to find them.
- Regularly updating product feeds ensures that customers have the most up-to-date information, reducing the likelihood of returns or customer dissatisfaction.
- Each online marketplace has its unique set of requirements for product feeds. Effective product feed management ensures your feeds comply with these requirements, allowing you to reach a wider audience.
The Different Types of Product Feeds
There are several types of product feeds, each catering to a different online channel:
Marketplace Feeds: These feeds are for platforms like Amazon, eBay, and Walmart, where you list your products for sale.
Shopping Comparison Feeds: Feeds for comparison shopping engines like Google Shopping, Bing Shopping, and Shopzilla allow customers to compare products and prices across multiple retailers.
Affiliate Network Feeds: These feeds cater to affiliate marketing networks like Commission Junction, ShareASale, and Rakuten, where affiliates promote your products in exchange for a commission.
Social Media Feeds: Social media platforms like Facebook and Pinterest also support product feeds, allowing businesses to showcase their products through social commerce.
An effective product feed should include the following components:
Invisible manages product feeds at scale with a blend of automation technology and seasoned operators. For a major eCommerce player, we boosted search engine visibility across thousands of unique SKUs by 49%, leading to $900k in new revenue on dead stock in just 16 days – 10xing the client’s investment.
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
