Implementing an intelligent search engine (think Coveo, Algolia, Constructor, Searchspring) on your eCommerce site is a complex project that requires significant back-end and front-end programming efforts, and consequently time and resources. Yet a well thought-out search can transform the customer experience, increase conversions and optimize inventory management. So how do you ensure successful integration?

Here are the 6 key steps to take to successfully complete this strategic project:

1. Data quality
2. User experience
3. Back-end
4. Preserving SEO during implementation
5. Continuous optimization of search results
6. Artificial intelligence

1. Data quality: the foundation of any search engine

A search engine can only function effectively if the data it exploits is of high quality. In other words, without clean, well-structured data, results will be disappointing.

Before launching development, it’s crucial to ensure that your product catalog and all its associated data are up-to-date and well-organized. If your product attributes are poorly populated, your engine won’t be able to deliver relevant results to users.

“Imagine a pair of blue shoes available in stock. If the attributes “color” and “size” are not correctly entered in your database, a customer searching for “blue shoes size 10” won’t find any results, even if the product exists and can be purchased online”. – Tristan Daoust-Patrao, Product Owner at Novatize

To address this issue, it’s essential to cleanse and enrich data through a structured process that includes extraction, organization, analysis and regular updating. Leverage artificial intelligence tools to automate certain tasks, but be prepared to invest in an initial phase of “manual cleansing”.

2. User experience: thinking front-end

Implementing an intelligent search solution affects much more than your website’s search bar. It often redefines the entire user interface, including the search bar with query suggestions and results previews, results pages with filters and facets (price, color, etc.), category pages that can be entirely replaced by dynamic results, product recommendations (cross-sell/up-sell).

“It’s essential to target the content you want to display on the front-end and to design realistic mock-ups from the outset. Search solutions often have limitations that can curb design creativity.” – Annie-Pier Coulombe, Full Stack Developer

Guarantee effective front-end analytical monitoring

To improve the search experience, take full advantage of intelligent algorithms and gain business intelligence on your customer base, it’s crucial to collect front-end data, specifically data on the behavior of your eCommerce visitors.

Key points to remember:

• Tracking tags: Add data attributes to your key elements: search field, results, add-to-cart buttons, etc. These tags inform the search platform about products consulted and actions taken (clicks, purchases, wishlist, etc.);
• Visibility on performance: Events reported in this way are aggregated in an analytics tool (search engine admin, Google Analytics, etc.). You can track exactly what your customers are looking for, which products they consult or buy, and where they may abandon their journey;
• Personalization and relevance: Intelligent search engines use this data to automatically adjust results: most popular products, more relevant suggestions, etc. The result? A seamless search experience, capable of continuously adapting to your customers’ preferences.

To sum up, front-end data collection gives you a clear view of buying behavior, while feeding AI that personalizes the search experience. Properly designed and integrated, this approach boosts engagement and conversion, while remaining simple to maintain with a little technical support.

Before implementing an intelligent search engine, start by identifying all the interfaces impacted, so that you can design appropriate mock-ups, validated by your development team. Pay particular attention to facet configuration, which is based on your product attributes, as incorrect configuration can distort results. Then evaluate the costs and benefits to ensure that the engine’s added value justifies any lost functionality. Finally, implementing a “data layer” will enable you to efficiently collect usage data on the pages served by the engine.

3. Back-end: synchronization and integrations

The back-end is like the kitchen of a restaurant: if the set-up is not optimal, the whole efficiency of the service suffers. A high-performance search engine rests on two pillars: the quality of the data and the way it is retrieved and displayed. These elements must work in harmony to guarantee accurate results and a smooth user experience.

Data, a central resource

Well-structured data is as crucial as the way it is used. Search engines rely on a centralized, regularly updated index to ensure that the information displayed to users is relevant. However, it’s important to understand that:

• 80% of standard requirements can often be covered thanks to the native integrations offered by intelligent search solutions (e.g. Coveo, Constructor);
• The remaining 20% require customized programming to meet your company’s unique business rules. These customizations make it possible, for example, to manage complex relationships between data, or to implement logic specific to your business objectives (prioritization, segmentation, etc.).

Data synchronization and updating

To ensure that the search engine reflects the reality of your product catalog, it is essential to implement robust synchronization processes:

• Automation: Synchronize data on a regular basis (prices, inventory, availability);
• Adaptability: Connect multiple systems such as PIM, ERP or external platforms (e.g. YouTube for videos), taking care to define a source of truth for each type of data;
• Enrichment: Leverage the index to enrich relationships between data (products, categories, attributes), enabling more accurate and useful front-end display.

Integrations: between standard and customized

While native integrations can provide a solid foundation, they often need to be extended to cover the specifics of your project:

• Standard base (80%): Native connectors between popular platforms such as SAP or Shopify offer ready-to-use solutions for generic needs;
• Customization (20%): As each company has its own priorities, it’s common to go beyond native integrations. For example, creating specific feeds for marketing campaigns or adjusting the way certain content is indexed.

Anticipating and managing complexity

Back-end architecture can quickly become complex, requiring thorough planning to avoid inefficiencies:

• Preliminary analysis: Identify the needs of different eCommerce team members, critical data flows and possible impacts on front-end, SEO or performance;
• Relationship optimization: Structure the relationships between different elements (products, categories, content) to ensure perfect consistency;
• Cost control: Anticipate storage costs and resource requirements to avoid unpleasant surprises.

Long-term impact

A well thought-out back-end ensures operational peace of mind:

• Reliability: Smooth synchronizations and well-designed integrations ensure that searches are always up to date;
• Scalability: A robust architecture can adapt to your future needs, whether integrating new data sources or adopting advanced functionalities;
• Performance: Efficient implementation prevents slowdowns, reduces costs and improves the overall user experience.

4. Preserving SEO during implementation

Intelligent search often generates new dynamic URLs to display results and different front-end pages. This can complicate your SEO if you don’t anticipate this problem.

For your SEO, this can lead to duplicate URLs, especially when category pages are generated by both the CMS and the search engine, as well as a possible loss of organic traffic in the event of poorly implemented redirects. To avoid these problems, collaborate upstream with your SEO teams to establish a clear redirection strategy, and ensure that new URLs respect the best practices of search engines like Google.

If you’d like to find out how an SEO specialist can help you optimize your eCommerce, read our article “How can an SEO specialist improve your eCommerce?“.

5. Continuous optimization of search results

Once the search engine is in place, the work doesn’t stop there. Performance must be continuously monitored and optimized to meet user needs.

Indicators to monitor :

• Click-through rate (CTR): Shows whether results are relevant;
• Add-to-cart rate: Indicates the proportion of users who add a product to their cart after viewing it;
• Conversion rate: Measures the direct impact on sales (for eCommerce);
• Pogo-sticking: If users frequently return to the search page, this may indicate irrelevant results.

Essential search engine features to validate :

• Boost/Bury Rules: Prioritize or remove certain products from results;
• Synonyms (Thesaurus): Connect terms to broaden searches (e.g.: “computer” = “PC”);
• Fuzzy Matching: Correct typos and imprecise queries;
• Stop Words: Ignore common words for more precise searches;
• Stemming/Lemmatization: automatically manage word variants (e.g. “courir” = “course”);
• Natural Language Processing (NLP): Interpret complex queries in natural language;
• Relevance Tuning: Adjust results according to criteria such as popularity or recency;
• Geolocation (Geo-Search): Display localized results based on users’ location;
• Faceted search: Add dynamic filters (brand, price, category, etc.);
• Hierarchical search: Allow navigation by categories and sub-categories;
• Pinning/Slotting: Manually or dynamically fix the position of certain products;
• A/B Testing: Test different configurations to maximize performance;
• Understanding results: Understand why a product appears in the results;
• Dashboards: Track KPIs such as click-through rates and searches without results.

6. Artificial intelligence: an essential ally

Modern search solutions leverage artificial intelligence to deliver results that are more relevant, personalized and tailored to users’ needs:

• Natural Language Processing (NLP): Understanding complex queries and interpreting natural language;
• Personalization: Results adjusted according to user preferences, history or behavior;
• Automatic optimization: Dynamic adjustment of results thanks to machine learning and data analysis;
• Semantic search: Identification of relationships between terms to provide richer, more contextual answers;
• Learning to Rank: Ranking of results according to business and user priorities, based on machine learning models.

AI can greatly improve your search engine’s performance by:

• Better understanding users’ search intentions;
• Personalizing results based on past behavior;
• Automatically optimizing product rankings;

“AI offers huge opportunities, but it requires human supervision to avoid drift and ensure relevant results.” – Pierre-William Plante, Account Director

By combining algorithmic power and human control, AI is transforming search engines into strategic levers for an enriched user experience.

Don’t forget the organizational impact of this transition. Train your marketing, IT and customer service teams to use and optimize the new system. Adapt your internal processes to make the most of it, and communicate the changes clearly to your employees and customers.

A well-implemented intelligent search can transform your business into a powerful search engine, making it much more than just a tool. When properly integrated, it improves the customer experience, boosts conversions and optimizes product management. To succeed, clean up your data, anticipate front-end impacts, manage your technical integrations and monitor your post-deployment results. Finally, take advantage of artificial intelligence while maintaining control over its results.