Product Related FAQs 💡

How does the Review summarization work?

Review summaries is an AI service offered to Bazaarvoice clients, which provides product summaries using consumer reviews. The summaries are generated natural language processing (NLP) techniques and large language models (LLMs), to generate concise summaries of customer reviews. Instead of reading through numerous individual reviews of a given product, users can read a short summary that captures key themes, opinions, and sentiments from a larger set of reviews.

Determine Eligibility for use?

It is available only for clients on Advanced and Enterprise packages having Display in their package.

What Product qualifies for a review summary?

The minimum number of reviews required to generate a summary is 30 reviews in English language. This is to ensure a breadth of consumer feedback has been provided, when generating the review summary.

What is the expected Summary length?

The summaries are up to 100 words in length. There are two formats available to chose from: a paragraph formatted summary; and a bullet point formatted summary. Please refer to the API reference for more details.

Does review summarization preserve the sentiment of the original reviews?

Yes, the review summarization model has been evaluated against relevancy and representativeness against the original product reviews. The summarization process aims to capture the overall tone of the reviews, such as positive, negative, or neutral feedback. As summaries are concise representations of many reviews, not every topic in every review can be covered, particularly for products with very large volumes of reviews. However, we evaluate our process using large evaluations sets and several evaluation metrics to ensure the summary reflects the range of opinions of the product’s consumers.

Who owns the output from the review summarization process?

The generated output (review summary) is a service provided by Bazaarvoice. Ownership of the output will reside with Bazaarvoice, with the service being discontinued at the end of the client contract.

Can review summarization be used for multiple products or services?

Review summaries are generated on an individual product basis, so yes summaries are available for multiple products as unique summaries. Clients are welcome to analyse or compare and contrast product review summaries, across products, brands or even categories and sub-categories.

Can negative reviews be eliminated while generating the summaries?

Bazaarvoice's first principle is Authenticity and Trust. Therefore, the model takes into account all reviews of a product, including positive, negative, and neutral ones. The summaries generated are based on all reviews and are completely authentic.

AI Model, Data Security, Privacy and Governance 🕸️

How safe is your data?

Input data is hosted and stored in a secure AWS environment. We prioritize data security and privacy. All processing is done within our secure infrastructure, and we do not share client data with third-party providers outside of our secure cloud platform.

Model Limitiations and Hallucinations?

Currently, the Model only supports the English Language. Additional languages will be considered for future releases. Also, only Non-EU locations will be supported. Currently data from EU geographies are not covered.

We evaluate the text summarization process with a suite of automated and human-in-the-loop both offline and online to ensure hallucinations are avoided.

What privacy mechanisms are in place to ensure PI used in connection with the AI System adheres to data privacy/protection laws?

Review summaries are solely based on moderated reviews, following BazaarVoice's extensive automated and human-moderation process. This ensures the summaries are based on approved reviews free of personal information or other non-acceptable content. These mechanisms ensure the aggregate summaries adhere to BazaarVoice’s data privacy and protection policy.

Can the model accuracy improve over time? Can the models be retrained post accuracy testing on live data?

Our Review Summaries feature is built on a robust and evolving AI framework. We continuously monitor the performance and quality of our summaries to ensure they meet high standards of accuracy and relevance. Our team of AI experts and machine learning engineers regularly evaluate opportunities to enhance our summarization process, which may include refining our algorithms, updating or fine-tuning our language models, or incorporating new AI technologies as they become available.

While we're always working to improve our service, any updates or enhancements are implemented systematically across our platform to maintain consistency and quality for all our clients. We do not currently offer customized model training or client-specific alterations to our summarization process.