Agent Spotlight: Ireland Rodriguez, a Remote-Work Loving Leader From Colombia
February 17, 2023
March 17, 2023
4
min to Read
Pricing Intelligence
As the eCommerce industry continues to grow, the competition for online visibility and customer engagement becomes fiercer. One key factor that can significantly impact your eCommerce success is product feed management.
A well-structured product feed can increase your store's visibility on search engines and shopping channels, leading to higher click-through rates, conversions, and ultimately, sales. In this article, we will discuss the essential dos and don'ts of product feed management in 2023 to help you optimize your online store and dominate the digital shelf.
Before diving into the best practices, it's crucial to understand what product feed management entails. A product feed is a structured data file that contains detailed information about the products available in your online store.
This data can include product titles, descriptions, images, prices, and more. Product feed management involves optimizing and maintaining this data to ensure accurate and up-to-date information is displayed across various online channels such as search engines, shopping platforms, and social media.
Effective product feed management can make or break your eCommerce success. It plays a vital role in:
- Enhancing your store's online visibility
- Boosting customer engagement and click-through rates
- Ensuring accurate and consistent product information
- Improving search engine rankings and organic traffic
- Streamlining your marketing and advertising efforts
With these benefits in mind, let's explore the dos and don'ts of product feed management.
Product titles are among the first things potential customers see when browsing online. Make sure your titles are clear, concise, and informative, including essential details such as brand, product type, size, and color.
Additionally, incorporate relevant keywords to improve search engine rankings.
High-quality images are crucial in capturing the attention of potential customers. Ensure your product images are well-lit, clear, and accurately represent the product.
Using multiple images from different angles can also help customers get a better understanding of your products.
Your product descriptions should be informative and engaging, providing all the necessary details customers need to make an informed decision. Utilize bullet points to break down complex information and include relevant keywords to boost search engine visibility.
Outdated or incorrect pricing and inventory information can lead to dissatisfied customers and abandoned carts. Regularly update your product feed with accurate pricing and stock information to ensure a smooth shopping experience for your customers.
Proper categorization and the use of relevant product attributes (such as size, color, material, etc.) can improve your products' visibility on shopping platforms and search engines. Ensure you assign your products to the appropriate categories and use specific attributes that customers are likely to search for.
Different shopping platforms and search engines have unique requirements for product feeds. Failing to meet these requirements can result in your products not showing up on these channels. Familiarize yourself with each platform's specifications and ensure your product feed complies with their guidelines.
Using generic or inaccurate product information can negatively impact your store's credibility and lead to customer dissatisfaction. Make sure your product feed contains accurate and detailed information, tailored to your specific products, to build trust and ensure a positive shopping experience.
While it's essential to include relevant keywords in your product titles and descriptions, avoid keyword stuffing. Overusing keywords can make your content appear unnatural and spammy, which can hurt your search engine rankings and deter potential customers.
With more and more customers shopping on mobile devices, it's crucial to optimize your product feed for mobile viewing. Ensure your product images, titles, and descriptions are properly displayed and easy to read on smaller screens to cater to mobile shoppers.
While using automated tools can help streamline your product feed management process, it's essential not to rely solely on them. Regularly review and update your product feed manually to ensure accuracy and compliance with platform-specific requirements.
Invisible has unique scale capabilities when it comes to product feed management. We’ve done it before.
A major eCommerce player gave us 50,000 dormant SKUs and 16 days to enrich and optimize for a massive online marketplace’s search engine. In that time, we 10x’d their investment by increasing search engine visibility by 49% for enriched product listings and generated $900k in new revenue from over 3,000 unique SKUs.
To unlock the full potential of your eCommerce inventory, learn how Invisible combines competitive pricing intelligence with product feed management.
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As the eCommerce industry continues to grow, the competition for online visibility and customer engagement becomes fiercer. One key factor that can significantly impact your eCommerce success is product feed management.
A well-structured product feed can increase your store's visibility on search engines and shopping channels, leading to higher click-through rates, conversions, and ultimately, sales. In this article, we will discuss the essential dos and don'ts of product feed management in 2023 to help you optimize your online store and dominate the digital shelf.
Before diving into the best practices, it's crucial to understand what product feed management entails. A product feed is a structured data file that contains detailed information about the products available in your online store.
This data can include product titles, descriptions, images, prices, and more. Product feed management involves optimizing and maintaining this data to ensure accurate and up-to-date information is displayed across various online channels such as search engines, shopping platforms, and social media.
Effective product feed management can make or break your eCommerce success. It plays a vital role in:
- Enhancing your store's online visibility
- Boosting customer engagement and click-through rates
- Ensuring accurate and consistent product information
- Improving search engine rankings and organic traffic
- Streamlining your marketing and advertising efforts
With these benefits in mind, let's explore the dos and don'ts of product feed management.
Product titles are among the first things potential customers see when browsing online. Make sure your titles are clear, concise, and informative, including essential details such as brand, product type, size, and color.
Additionally, incorporate relevant keywords to improve search engine rankings.
High-quality images are crucial in capturing the attention of potential customers. Ensure your product images are well-lit, clear, and accurately represent the product.
Using multiple images from different angles can also help customers get a better understanding of your products.
Your product descriptions should be informative and engaging, providing all the necessary details customers need to make an informed decision. Utilize bullet points to break down complex information and include relevant keywords to boost search engine visibility.
Outdated or incorrect pricing and inventory information can lead to dissatisfied customers and abandoned carts. Regularly update your product feed with accurate pricing and stock information to ensure a smooth shopping experience for your customers.
Proper categorization and the use of relevant product attributes (such as size, color, material, etc.) can improve your products' visibility on shopping platforms and search engines. Ensure you assign your products to the appropriate categories and use specific attributes that customers are likely to search for.
Different shopping platforms and search engines have unique requirements for product feeds. Failing to meet these requirements can result in your products not showing up on these channels. Familiarize yourself with each platform's specifications and ensure your product feed complies with their guidelines.
Using generic or inaccurate product information can negatively impact your store's credibility and lead to customer dissatisfaction. Make sure your product feed contains accurate and detailed information, tailored to your specific products, to build trust and ensure a positive shopping experience.
While it's essential to include relevant keywords in your product titles and descriptions, avoid keyword stuffing. Overusing keywords can make your content appear unnatural and spammy, which can hurt your search engine rankings and deter potential customers.
With more and more customers shopping on mobile devices, it's crucial to optimize your product feed for mobile viewing. Ensure your product images, titles, and descriptions are properly displayed and easy to read on smaller screens to cater to mobile shoppers.
While using automated tools can help streamline your product feed management process, it's essential not to rely solely on them. Regularly review and update your product feed manually to ensure accuracy and compliance with platform-specific requirements.
Invisible has unique scale capabilities when it comes to product feed management. We’ve done it before.
A major eCommerce player gave us 50,000 dormant SKUs and 16 days to enrich and optimize for a massive online marketplace’s search engine. In that time, we 10x’d their investment by increasing search engine visibility by 49% for enriched product listings and generated $900k in new revenue from over 3,000 unique SKUs.
To unlock the full potential of your eCommerce inventory, learn how Invisible combines competitive pricing intelligence with product feed management.
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.
