Product search is a key factor in digital commerce. Customers expect fast and precise results, similar to Google. An optimised search can make all the difference for retailers: from an improved customer experience to fewer no-result pages. Find out how to optimise the search function in your shop system with modern AI technologies.
What constitutes a good product search function?
In today’s world of e-commerce, a powerful product search is crucial for your online shop’s success. Customers expect to find the product they are looking for quickly and want an intuitive search experience that is forgiving of typos. They browse via large search engines such as Google, which offer impressive relevance and speed. On the other hand, an outdated or inaccurate search system can quickly lead to frustration and abandoned purchases.
Customer search expectations
Customers want one thing above all: precise results, found quickly. Whether they search for a specific product name or enter a general category, the best results should be displayed immediately. It shouldn’t matter if they mistyped a word or used alternative terms. Along with the ability to search synonyms and other suggestions, a forgiving auto-correct function is essential for answering search queries successfully. For a product search to fulfil these requirements, it must be based on state-of-the-art technology. In addition to having multilingual capabilities, it should be able to recognise synonyms, automatically correct spelling mistakes and take alternative spellings into account.
The challenges of manual search optimisation
For large online shops that offer millions of items, manual product search optimisation quickly becomes almost impossible. Traditional search solutions are stretched to their limits here, as maintaining a list of synonyms or incorrect spellings is incredibly time-consuming. An effective product search function must be able to overcome these challenges in an automated and intelligent way to offer customers a seamless shopping experience.
Time-consuming maintenance of synonyms and word lists
One of the biggest challenges in manually optimising the search function is maintaining synonyms and word lists. Many items are searched for under various terms: one customer might type “notebook,” while another types “laptop.” Without a continuous adjustment of the search parameters, such terms will be ignored. Manually recording all these synonyms and alternative spellings for each product is not only extremely time-consuming but also becomes nigh on impossible as the number of products increases. What is called for here is a search technology that does this work automatically.
Country-specific customisation and multilingual capabilities
E-commerce is global, and product search solutions become increasingly complex as online shops internationalise. The ability to search for products in multiple languages is crucial for achieving success in new markets and for customer satisfaction. Country-specific adaptations, such as alternative spellings or terms used in different dialects, must be taken into account. For example, a product sold in German-speaking countries may be labelled differently for Germany, Austria and Switzerland respectively. Manually managing and translating such terms is tedious and prone to errors, which is why modern search systems should automate this task.
Long-tail searches and the challenge of infrequent search queries
Another issue with traditional search solutions is dealing with long-tail searches – that is, very specific or rare search queries. These search terms often have a low search volume, but they are no less important for an online shop’s sales turnover. Manually optimising is not enough here, as it is impossible to predict each and every possible combination of search terms. AI-based search systems, however, can intelligently cluster search terms and display meaningful results even for less frequent queries. This increases the likelihood that customers will find what they are looking for, even when specific or unusual search terms are used.
AI-based search solutions: how they work and why they matter
Artificial intelligence has revolutionised the way e-commerce searches work. By using AI, not only can search queries be answered more precisely and quickly, but intelligent links can also be created that significantly improve the user experience. AI helps to deliver relevant results and increase the conversion rate, especially for complex long-tail searches. The technology analyses customer behaviour in real-time and continuously optimises the search results.
AI-supported auto-correction and synonym recognition
One of the most powerful features of an AI-supported search solution is auto-correction. Spelling mistakes and typos are common in search queries, especially for complex or technical terms. AI can recognise these errors and correct them automatically so that customers still get the desired results. In addition, the AI automatically recognises synonyms and alternative terms without having to enter them manually. For example, a search for a “notebook” will also include products that are referred to as a “laptop” or “ultrabook” without the customer having to start a new search.
Real-time optimisation of search results
Another important feature of AI-supported search solutions is the real-time optimisation of search results. The AI learns from the user’s search and purchasing behaviour and dynamically adapts the results. Popular or particularly successful products are placed higher in the search results, while less relevant products are moved to the bottom. This real-time optimisation ensures that customers are always shown the best and most relevant products for them, thereby significantly increasing the likelihood of a purchase.
Intelligent search query redirect function
Another key feature is the intelligent search query redirect function, particularly useful for complex or long-tail searches. One good example is a search for “e-bikes”. Instead of simply displaying a list of e-bikes, the AI can redirect the search query to a page with an embedded product advisor to help users select the right model. This significantly improves the user experience, as it enables customers to find products more quickly and provides personalised advice.
Three expert tips for better search results
1. Use redirects
An effective way to guide users to the best results is by using redirects. A redirect takes customers directly to topic-specific landing pages or product categories, which is especially useful for generic or less precise search queries. Instead of being directed to a page that is not especially relevant because the search is unclear or too broad, users are taken straight to more appropriate content. For example, when searching for “smartphones”, the user could be forwarded directly to a page displaying the best-selling or best-rated smartphones. This saves time, ensures clarity, and increases the conversion rate.
2. Combine the search with personalised product recommendations
In order to further personalise the search experience, it is worth integrating product recommendations directly into the search results. These recommendations are based on the customer’s previous behaviour, such as previous purchases or products viewed. A personalised product recommendation during the search increases the relevance of the results displayed and makes it more likely that customers will find and buy a suitable product. By using AI and machine learning, these recommendations can be adapted in real time in order to optimally serve customers’ individual needs.
3. Use search queries for targeted merchandising strategies
Search queries provide valuable data that retailers can use to manage merchandising strategies in a targeted manner. Frequently searched products or categories should be placed prominently, and seasonal or campaign-related items should appear high up in the search results. Furthermore, placements such as retail media ads can highlight products from brands advertised in a specific campaign. This not only increases sales but also boosts the visibility of specifically marketed products.
Conclusion: optimised product searches lead to greater success
An optimised product search not only improves user satisfaction but also the conversion rate. If customers can find what they are looking for quickly, the likelihood of them making a purchase increases significantly. Furthermore, intelligent search technology reduces no-result pages, which leads to an improved shopping experience. For retailers, this ultimately means more sales, greater customer satisfaction, and fewer abandoned purchases.
Improve your product search now!
novomind iSHOP offers an out-of-the-box integrated search solution with numerous features such as fuzzy search, synonyms, and search exclusions. If something more is required, we recommend the intelligent AI add-on from our partner searchHub, which optimises every current on-site search.
