Download full infographic now

This holiday season is shaping up to be a record breaker, despite its short timeframe! Even with only 27 days between Thanksgiving and Christmas, consumers are expected to spend a whopping $241 billion online this year – that's an 8.4% increase from 2023!

The reality is, search abandonment is a significant problem for many online retailers. In fact, a staggering $2 trillion is lost to search abandonment worldwide every year!

Why does this happen?

• Frustrated Shoppers: Only 1 in 10 consumers find exactly what they're looking for on a retail website.
• Site Abandonment: After an unsuccessful search, 4 out of 5 consumers are likely to leave the site and purchase the item elsewhere.

What's changed?

Gone are the days of endless scrolling and filtering. Today's shoppers expect a more intuitive search experience. They want search engines to understand their needs, offer tailored suggestions, and guide them to the right products.

Many eCommerce retailers are missing out on potential revenue because their legacy search engines struggle to keep up with today's complex search queries.

Legacy search solutions rely on decades-old keyword-matching technology, often requiring manual intervention to deliver quality results. This leads to a poor user experience, as highlighted by recent benchmarks: 41% of eCommerce sites perform below an acceptable rating for search UX.

1. Exact Match Queries

• Natural Language Processing (NLP) capabilities: Traditional search engines struggle with accurately identifying and matching exact phrases due to outdated natural language processing abilities.
• Query handling issues: Legacy systems may aggressively apply stemming and stop word removal, and can be case-sensitive, potentially stripping important terms or leading to inaccurate results.

2. Product Type Queries

• Product-specific metadata: Legacy systems may not have been designed to store and index detailed product metadata, making it difficult to accurately identify and retrieve products based on their type.
• Synonyms and variations: Product types can have many synonyms and variations, which can be challenging for legacy search engines to understand.

3. Feature Search Queries

• Feature extraction: Legacy systems may not have the ability to extract and index specific features from product descriptions or metadata.
• Feature relationships: Traditional search engines may struggle to understand complex relationships between features, such as how they interact or are dependent on each other.

4. Use Case Search Queries

• Contextual understanding: Legacy search systems may not be able to understand the context of a search query, such as the user’s intention or purpose.
• Abstract of subjective terms: Use case queries often involve abstract or subjective terms that can be difficult for traditional search engines to interpret.

5. Abbreviation & Symbol Search Queries

• Index limitations: The indexing process may not be optimized to handle abbreviations effectively. For instance, if an abbreviation isn’t explicitly indexed, the search engine might not recognize it as a valid term.
• Character limitations: Some traditional search engines might have character limitations that prevent them from accurately processing queries containing symbols or special characters.

6. Compatibility Search Queries

• Keyword-based matching: These engines primarily rely on matching exact keywords or phrases. Terms like “compatibility” are broad and can have many different meanings depending on the context. This makes it difficult for the engine to accurately interpret the user’s intent.
• Entity recognition: These engines may not be able to effectively recognize entities (e.g. products, devices) within the query. This is crucial for understanding the context of terms like “compatibility” and providing relevant results.

7. Symptom Search Queries

• Semantic understanding: Traditional search engines often lack the semantic understanding to interpret the underlying meaning of terms like “symptom”. This is particularly challenging when the query involves multiple concepts or entities.
• Contextual understanding: Traditional search engines may not be able to effectively understand the context of the query, especially when it involves multiple concepts or relationships.

8. Non-Product Search Queries

• Ambiguity and complexity: Non-product queries often lack clear intent and require nuanced understanding, making it difficult for traditional search engines to accurately interpret and rank relevant content.
• Diverse formats and personalization: These queries encompass various content types and benefit from personalized responses, which traditional search algorithms may struggle to effectively handle.

9. Conversational Queries

• Complex and nuanced: Conversational queries mimic the way people speak, often using questions or complete sentences. This can make it challenging for traditional search engines to capture the nuances of context and provide relevant results.

10. Long-Tail Search Queries

• Specific and detailed: Long-tail queries often include multiple words or phrases and provide a more precise search intent. Many eCommerce sites using legacy search engines struggle with these query types due to their reliance on traditional keyword matching.

If your eCommerce site is relying on a legacy search engine, you're likely missing out on potential sales. It's time to upgrade to a sophisticated AI-first search solution that can handle complex search queries and identify shopper intent.

Why AI-First Search is the Key

How GroupBy Outperforms Legacy Systems

GroupBy's eCommerce search and product discovery platform is the first of its kind to offer retailers and wholesalers access to Google Cloud’s next-generation search engine that utilizes state-of-the-art trained ML and AI algorithms.

Here’s 6 ways our platform is unique:

1. State-of-the-Art AI: GroupBy’s platform, powered by Google Cloud Vertex AI Search for Commerce, harnesses machine learning trained on 1.8 trillion events and 85 billion daily interactions, ensuring unmatched precision and continuous improvement.
2. Revenue-Optimized Search: GroupBy's platform integrates buyability and personalization, going beyond relevance to prioritize results that drive revenue. By analyzing user behavior and context, it maximizes conversion and profitability.
3. Advanced Query Understanding: Leveraging NLP and machine learning, GroupBy deciphers user intent from behavior patterns and product attributes, delivering highly relevant, personalized and revenue-optimized search and recommendations results.
4. Outcome-Driven Optimization: GroupBy allows businesses to align search and recommendation strategies with key goals, such as AOV or revenue, for measurable business outcomes.
5. Rule-Free Merchandising: GroupBy’s AI reduces business rule creation by 80%, freeing teams to focus on revenue strategies and boosting productivity by 50%+, while adapting to market changes more quickly.
6. Engineered for Complex Queries: GroupBy's platform is built for multi-faceted queries, interprets nuanced language, industry terms, long-tail, conversational and even specialized B2B searches. It supports 72 languages for global scalability.

See how Rebag increased search revenue by over 50%

Rebag embarked on a mission to recreate the personalized service synonymous with a traditional luxury boutique within the digital realm. However, their previous search solution posed a significant obstacle. Unlike traditional retailers with standardized inventory, the inherent uniqueness of their inventory – each pre-owned item boasts a distinct SKU – presented a formidable challenge for the existing platform. This resulted in inconsistent and often irrelevant search results, frustrating customers in their quest to discover the perfect piece. Furthermore, managing their extensive inventory along with gaining a deeper understanding of buyer intent remained ongoing hurdles for Rebag.

To elevate their online customer experience, Rebag implemented GroupBy's cutting-edge eCommerce Search and Product Discovery platform. Since implementing GroupBy's platform, they’ve seen significant improvements including search revenue that has skyrocketed by over 50%.

To learn more about what Rebag has accomplished with the help of GroupBy’s platform, read the full case study here.

Download full infographic now

Ready to Transform Your eCommerce Search?

Don't let outdated search technology hold you back this holiday season.

Contact GroupBy today to learn more about our AI-first search solution and start driving more sales.