Headless commerce revolutionizes traditional E-commerce platforms by decoupling the front-end and back-end of online stores.
Discover industry-specific benchmarks and optimization best practices in our exclusive report.
Bauhaus Czechia Hits Revenue Heights with Prefixbox's AI Search
Read Case Study
Leverage cutting-edge automations that analyze patterns in search logs and clickstream data to return relevant search results.
Our state-of-the-art data mining and automation tools analyze anonymous search logs to improve search relevancy.
Automations run continuously to account for changing trends and user behavior. Search results are re-ranked dynamically and improve over time to reflect shoppers' habits, search history, and seasonality. You can also manually fine-tune settings in the Prefixbox Admin Portal.
Shoppers can type anything into the search box, which can be difficult for a search engine to decipher.
Shoppers expect to see the results they had in mind immediately, regardless of how they phrased their query.
Traditional search engines only leverage text matching instead of understanding user intent, which often leads to zero result search pages and irrelevant results.
Throughout the year, Ecommerce stores have peak times that require a fast and scalable solution that can handle load increases without slowing down or breaking.
This feature leverages historical search data to mine Autocomplete results for individual stores.
For example, Query Suggestion Mining makes it easy to see the most popular searches in your store based on weighted events such as search count, click event, and cart events.
How it works:
Related Searches are data-driven query suggestions that make it easy for shoppers to refine their search or easily continue their shopping journey. This feature predicts shoppers’ subsequent searches by analyzing query pairs based on aggregated users’ previous sessions.
For example, if shoppers have historically searched for a drill and then searched for a cordless drill, the algorithm will recommend a cordless drill to future shoppers looking for drills.
Query Understanding translates shoppers’ natural language queries into search engine expressions to return relevant results.
For example, shoppers may search for ground contact wood, construction wood, pine beams, or pressure-treated lumber when looking for construction lumber.
Spell correction fixes spelling mistakes in shoppers’ search queries. It’s trained to a specific store’s catalog and language to understand typos, provide accurate corrections, and return relevant results.
For example, if shoppers misspell adjustable wrench, the Custom-Built Speller will understand what they were looking for and return the correct results.
Synonyms are important in Ecommerce search. Manually thinking of relevant synonyms for each search query is mind-numbing, so we’ve automated this process.
For example, if a shopper searches for a tool kit, the algorithm will identify relationships between similar keywords like tool set or toolbox. With Prefixbox, you can also set up two-way and one-way synonyms and exclusionary rules.
Product Popularity Score is calculated by our ranking algorithm and relevancy to display the most accurate results first. Product Popularity Scores are re-computed daily to reflect shopper behavior and seasonal trends.
Retailers can see an individual product’s popularity score and view the breakdown of how event type and count impact the score.
Dynamic Re-Ranking improves the search engine’s ranking algorithm, so the most relevant results appear highest on the SERP.
For example, if a shopper searches for a lamp, they will automatically see results for products that have historically generated the highest click-through rate and engagement.
Data Mining and Automation is part of Prefixbox Insights. Explore our in-depth insights and experimentation platform to see how you can improve the shopping journey and move your KPIs.
Visit our pricing page to learn about our 14-day free trial and how you can optimize your Ecommerce search solution.