RST Software
Editorial Team
Magdalena Jackiewicz
Reviewed by a tech expert

How to build a marketplace search engine in 10 steps

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Shoppers searching for products on an online marketplace can feel like wandering through a dense jungle. Endless rows of similar products, confusing categories, and no clear path to what they actually want. Creating a stellar search experience cuts through the chaos, directing customers straight to the items they desire.

Crafting a top-notch search experience is not just about providing a search bar. It's about understanding and anticipating the needs of your users, offering them a guided tour through the dense forest of products. A stellar search engine acts as a compass, pointing customers directly to their sought-after items, simplifying their journey, and enriching their shopping experience.

In this guide, we explore what makes marketplace search engines tick and how to build one that entices and retains customers.

What is a marketplace search engine?

A marketplace search engine sifts through vast arrays of products and services, categorizing and sorting them to match user searches accurately. At its core, it employs complex algorithms and data processing techniques, like natural language processing and machine learning, to grasp and anticipate what users are looking for, even when faced with varied ways of expressing those searches, including synonyms and misspellings.

The advanced system of ecommmerce search engines intelligently adepts at personalizing results. It does not just present a list of products, but curates these lists based on individual user preferences and past behaviors, making each search experience unique.

In essence, a marketplace search engine is engineered to efficiently handle large datasets, ensuring rapid and relevant search results, even in environments with millions of items and high user traffic. Its role is crucial in facilitating efficient and satisfying user interactions in e-commerce platforms.

Why do you need a solid search experience?

It is estimated 35% of online sales occur through marketplaces. To seize a share of this lucrative trillion-dollar arena, delivering unparalleled search is an indispensable priority.

Without a reliable search engine, marketplaces resemble impassable rainforests. They are full of treasures but near-impossible for shoppers to actually find any. Oppositely, robust search functionality helps users to find what they need using text or image, and do it quickly, accurately, and in a manner that resonates with their intentions. It is vital for marketplaces aiming to achieve:

  • Organic user base growth by refining site experience,
  • Higher conversion rates by connecting consumers with better-matched products,
  • Competitiveness matching search giants like Amazon and Alibaba.

Advanced search also provides valuable user behavior insights to inform marketing campaigns and catalog decisions. Essentially, stellar search capabilities unlock marketplace growth potential.

Key features of a great marketplace search experience

To build a marketplace search engine that delivers truly seamless discoverability, it is crucial to focus on integrating advanced processing capabilities. The following traits characterize platforms adept at captivating and retaining loyal customers:

Ability to index enormous marketplace catalogs

Marketplaces boast vast product catalogs. Newegg holds over 38 million stock keeping units. Etsy has 60 million listings. Alibaba, the marketplace giant, reportedly has hundreds of millions of products.

Hence, if you want to build a marketplace search engine able to index such a massive variety of products, prepare to track perpetually fluctuating assortments and ingestion of astronomical product volumes spanning heterogeneous items. This level of complexity and scale poses unique challenges, such as managing data from multiple sellers and rapid catalog updates as stock availability perpetually shifts.

Ability to rapidly present search results at extreme scales

Blazing-fast systems are imperative to process floods of queries from high site traffic. Best ecommerce search response times are measured in milliseconds, just like in Algolia NeuralSearch. It allows smooth experiences like search-as-you-type. Performance and stability during traffic spikes also minimize revenue losses.

As an example, during major sale events like Black Friday, search engines like these are tested to their limits. Alibaba's Singles' Day sale, known for recording billions in sales, relies heavily on their search engine's ability to handle sudden surges in queries and still provide accurate, rapid results. This not only enhances customer experience but also significantly contributes to the event's success, illustrating the critical role of high-performance search engines in large-scale e-commerce operations.

Ability to provide relevant and personalized results

Entrepreneurs looking to build a marketplace search engine must prioritize personalization to enhance user experience and engagement. Connecting each shopper with products they will love requires custom relevance-ranking formulas factoring in:

  • Margins,
  • Popularity,
  • Ratings,
  • Past purchases,
  • and more.

Personalized results tuned to individual tastes also build loyalty. Platforms designed to build marketplace search engines from Doofinder or Algolia enable configuring result preferences, e.g. showcasing higher-margin items first or promoting a specific brand's listings.

Consider Netflix's recommendation system as a parallel; it analyzes user behavior to suggest shows and movies. Similarly, a marketplace search engine uses buyer history and preferences to personalize product recommendations. This not only makes the search process more efficient but also enhances the likelihood of customer satisfaction and repeat purchases.

Tools for non-tech teams to control the search results

Business users need intuitive interfaces enabling them to:

  • Assess performance – utilized to analyze the performance of the search engine. This includes tracking metrics like search query frequency, click-through rates, and conversion rates to identify trends, understand user behavior, and make data-driven decisions to enhance the search experience,
  • Run seasonal promotions and campaigns – a marketing team might use these tools to execute promotional strategies effectively. This involves setting up and managing campaigns directly from the interface, such as highlighting seasonal products or running discounts on specific categories. These tools allow for quick adjustments in response to market trends, enabling teams to capitalize on opportunities to boost sales and visibility without needing technical intervention,
  • Merchandise trending items without developer’s help – this aspect is crucial for responding swiftly to changing consumer interests and market dynamics. By highlighting trending items, marketplaces can improve customer engagement and capitalize on the popularity of these products,
  • Analyze search trends and user feedback – the tools can provide insights into search trends and aggregate user feedback related to the search experience. Understanding what users are searching for and their feedback on the search results can guide business teams in optimizing product assortments, improving product information, and refining marketing strategies.

Ability to handle large taxonomy of product categories

One of the most important steps to build marketplace search engine is understanding the unique taxonomy of the products in your marketplace. Taxonomies organizing millions of products across thousands of categories simplify browsing and filtering. Selecting optimal data schemes for variable item attributes also bolsters relevance.

In the context of a marketplace like eBay, which sells a vast array of products from collectibles to electronics, the search engine must categorize and filter items across 36 major and numerous subcategories. This not only aids in user navigation but also ensures that products are easily discoverable, enhancing both seller visibility and buyer satisfaction.

Advanced control of how results are display, sorted, and filtered

Sophisticated ecommerce search demands capabilities like infinitely scrolling results, personalized autocomplete suggestions, dynamic filtering, and customizable sort orders. Solutions must also be easily implemented across web and mobile apps.

For example, Zalando, a popular fashion e-commerce site, utilizes these features to enhance the shopping experience. Users can sort products based on:

  • Price,
  • New arrivals,
  • Popularity,

and use filters multiple filters like:

  • Size,
  • Color,
  • Brand,
  • Sustainability,
  • And more,

making it easier to find what they're looking for in a user-friendly manner.

Ease of frontend implementation

Enhancing the ease of frontend implementation is a pivotal aspect when aiming to build a marketplace search engine. This process significantly reduces the complexities and workload on developers tasked with creating seamless and interactive site search interfaces. By utilizing robust libraries that adeptly handle intricate logics for essential components such as filters, infinite scroll, and responsive design elements, the development process becomes more streamlined. These libraries are crucial in accelerating the creation of user-centric search experiences that not only meet but exceed user expectations.

In the context of building a marketplace search engine, solutions provided by companies like Elastic stand out. Elastic’s approach to building a marketplace search engine is particularly noteworthy for its user-friendly integration capabilities with a diverse range of frontend frameworks. They offer extensive code samples and detailed documentation, which play a crucial role in lowering the barriers to implementation. This comprehensive support simplifies the incorporation of complex search functionalities into various web and mobile platforms, ensuring a consistent and high-quality user experience across different devices.

How to build an ecommerce search engine?

The optimal solution to build marketplace search engine balances functional capabilities, ease-of-use and total cost of ownership. Managed services reduce technical burdens associated with open-source software at scale. However, carefully evaluate options of search engines for shopping against current and projected search complexity alongside available skills before deciding.

Step 1: Decide whether you want to opt for on-premise or cloud solution

When considering how to build marketplace search engine, choosing between cloud-based and on-premise solutions is crucial. Cloud-based 'Search as a Service' offerings significantly reduce infrastructure hassles, providing scalable and maintenance-free solutions. In contrast, on-premise deployments on owned servers offer maximum control and customization, which can be particularly beneficial for niche marketplaces with specific requirements.

For instance, eBay's internal search engine, Cassini, rolled out in 2013, is tailored to synchronize product availability among its diverse independent seller storefronts. This kind of customization and flexibility might be constrained by standardized, hosted solutions.

Step 2: Dimension, provision, deploy, and scale your infrastructure

The architecture of your search engine and the anticipated workload are key determinants of the necessary resources, such as compute power, memory, network capacity, and storage. Accurately predicting indexing and querying needs is vital for optimal infrastructure provisioning. This foresight allows for scaling plans that accommodate evolving catalog sizes, ensuring that your search engine remains efficient and responsive as your marketplace grows.

Step 3: Understanding your users’ search behavior and intent

To effectively build a marketplace search engine, understanding user search behavior and intent is crucial. This involves analyzing:

  1. How users interact with your search platform. This involves observing and analyzing the ways in which users engage with the search feature. Do they use specific keywords or long-tail phrases? How often do they refine their search queries? Understanding these patterns helps in optimizing the search algorithm to be more responsive to the common ways users interact with it.
  2. The types of queries they make. Different users might have varied approaches to searching for products. Some may use very precise, product-specific terms, while others might use broader, category-level queries. Analyzing the range and nature of these queries can provide insights into user expectations and preferences, allowing for adjustments in how the search engine interprets and responds to different types of queries.
  3. Their navigation patterns. Understanding user navigation patterns post-search is crucial. Do users often click on the top results, or do they scroll through several pages? Do they frequently modify their search after viewing the results? This information can be used to refine the search engine's ranking algorithm and the layout of the search results page to better align with user browsing behaviors.

Gaining insights into user behavior helps tailor the search experience to better meet their needs, enhancing overall satisfaction and engagement. This not only improves the user experience but also encourages longer session times and potentially higher conversion rates, as users find what they are looking for more efficiently.

Step 4: Prepare your data, and create relevant taxonomies

The initial step in preparing your data involves aggregating information from various sources, such as product information management systems, into a unified schema that is actionable by the search engine. Smart text analyzers process this data for indexing, while custom classification tags enhance future discoverability.

For example, products might be tagged by category, brand, color, or inventory status.

Elasticsearch’s ecosystem contains extensive native connectors expediting aggregating data from external databases like MongoDB or CRMs like Salesforce into indexed search corpora.

Step 5: Utilize natural language processing (NLP) for semantic understanding

Implementing natural language processing in your search engine is key to achieving semantic understanding of user queries. NLP allows the system to comprehend the context and nuances of search terms, moving beyond literal keyword matching to more accurately reflect user intent. This technology is especially important in today's e-commerce environment, where users expect intuitive and responsive search experiences, as it adds awareness to both queries and products, as well as empowers product recommendations and semantic search.

Natural language processing endows the search engine with the 'awareness' to understand not just the user queries but also the products themselves in an e-commerce application. This dual awareness means the search engine can interpret the meaning behind a user's search and match it with the most relevant products, considering factors like context, synonyms, and user intent​.

AI-empowered product recommendations and semantic search capabilities, enhanced by NLP, contribute significantly to business growth in e-commerce. Semantic search understands the meaning behind a search query, allowing for more nuanced and relevant product recommendations. This approach can transform how users find and interact with products, leading to more meaningful connections between shoppers and items​.

Step 6: Handle synonyms, misspellings, and typos

Accommodating human error during search avoids over-strictness frustrating users while preserving result accuracy. Excessively loose query interpretations alternatively risk cluttering relevant hits with irrelevant products sharing vague similarities.

For instance, Algolia search engine maintains a balance between accuracy and user-friendliness by offering features such as auto-suggestions for corrected spellings, guiding users to their intended search results rather than just literal matches. 

Phonetic matching further enhances this capability by accounting for inaccurate pronunciations and homonyms, ensuring that even imprecisely formulated queries lead to relevant results.

Step 7: Provide clear search suggestions and auto-completion

Incorporating clear search suggestions and auto-completion into your marketplace search engine significantly enhances user experience. This feature aids users in refining their search queries, offering relevant suggestions based on their input and previous search history. It not only speeds up the search process but also helps users discover products they might not have initially considered.

For example, A DIY enthusiast enters “LED li” on a home improvement site, and the search suggests a list containing “LED lights”, “LED light fixtures”, and “LED light bar” simplifying their hunt for specific home lighting solutions.

Step 8: Configure results personalization

Personalizing search results is another critical aspect of building an effective marketplace search engine. This involves using data on user preferences, past searches, and purchase history to tailor the search results to each individual. Personalization increases the relevance of search results, thereby improving user satisfaction and potentially driving higher conversion rates.

This personalized approach significantly enhances the user experience (UX) in several ways:

  • User preferences – tailoring search results based on individual likes, dislikes, and interests. This makes the search experience feel more intuitive and aligned with the user's tastes,
  • Past searches – analyzing previous search queries to understand user behavior and preferences, leading to more accurate suggestions in future searches,
  • Purchase history – utilizing data from past purchases to recommend similar or complementary products, increasing the likelihood of repeat purchases,
  • Geographical location – adjusting search results to reflect regional preferences or availability, ensuring that users see products that are relevant and accessible to them,
  • Device and platform usage customizing the search experience based on the device or platform (mobile, desktop, app) to optimize layout and functionality for each user's preferred mode of access,
  • Browsing behavior – considering the user's browsing history on the marketplace to recommend products that they have shown interest in but have not purchased yet,
  • Time and seasonality – modifying search results based on the time of day, week, or season, aligning suggestions with likely current needs or interests.

Step 9: Ensure high availability, maintenance, and security

Maximizing search uptime requires comprehensive planning encompassing redundancies, backups alongside proven disaster recovery procedures. Upgrades must not create noticeable outages. Robust, defense-in-depth security solutions repel external data theft attempts.

Fully hosted solutions that help e-commerce marketplaces to build marketplace search engines minimize these burdens. For example, Elasticsearch Service on Elastic Cloud furnishes multi-zone redundancy to guarantee availability alongside security measures like TLS encryption.

Step 10: Implement and iterate

To build a marketplace search engine that remains effective and relevant over time, it's crucial to regularly implement new functionalities and iterate based on user feedback. This ongoing process of refinement ensures that your search engine adapts to perpetually evolving customer needs illuminated through ceaseless feedback analysis. Such data should perpetually refine algorithms, infrastructure plans and personalized relevance strategies.

Doofinder’s search relevance configuration dashboard enables instant tweaking of result ranking parameters in response to shifting shopper behaviors observed through its real-time analytics, streamlining constant incremental enhancements.

Best ecommerce search engines

The top marketplace search solutions, Algolia, Elasticsearch, and Doofinder, each offer unique value propositions and cater to different needs within the e-commerce search engine space.

Algolia stands out with its fully hosted search API platform, which is tailored for rapid integration across multiple channels. Its primary strength lies in its ability to deliver fast, relevant search experiences at scale, which is crucial for country-level marketplaces or global e-commerce platforms. Its key features include:

  • Front-end libraries designed for omni-channel integration, highly adaptable to various customer touchpoints,
  • Intuitive drag-and-drop dashboards allow business teams to customize and optimize search functionalities without any coding requirement,
  • Algolia's proprietary infrastructure is optimized for speed and relevance, ensuring quick search results and high accuracy.

Elasticsearch is a veteran open-source search engine with deep customizability including extensive plugins. Better suited to complex niche use cases given hands-on infrastructure setup and administration.

The fully-managed Elasticsearch Service option provides relief from server provisioning and infrastructure management, making it a versatile choice for businesses with varying technical capabilities.

It is particularly favored by businesses needing a highly customizable search engine that can be tailored to specific and complex search needs.

Doofinder, the smaller brother of the previous two, focuses on simplifying the search experience, especially for mid-market e-commerce platforms. Codeless setup rapidly incorporates AI search into sites without development resources.

Advanced relevance functionality, powered by deep learning algorithms like word2vec, provides sophisticated search capabilities that enhance product discovery and user experience.

TL;DR, while Algolia excels in providing a fast and scalable search experience with an easy-to-use interface, Elasticsearch offers depth and customization for complex search needs, and Doofinder focuses on simplifying and enhancing search for mid-market e-commerce platforms with AI technology.

Need help building and customizing your marketplace search engine?

While open-source engines provide extreme customizability, truly mastering marketplace search involves extensive expertise across data pipelines, infrastructure, and search optimization. It serves as the GPS guiding customers to their desired purchases amidst a dense jungle of listings.

Managed software-as-a-service solutions reduce resource overheads while still enabling tailored relevance strategies via no-code tools. Reach out to us and let's talk about how RST can help in powering your marketplace discoveries, enabling shoppers to easily locate relevant items amongst extensive catalogs spanning millions of products.

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