Seek and you shall find: Onsite Search with FactFinder and Medienwerft

Customers take the search function of online stores for granted. It is rarely the focus of attention. Companies are thus missing out on opportunities to generate more sales. This is because the search function is a key component for e-commerce companies and online shoppers alike and influences many areas of the customer journey: a good search function recognizes the customer’s search intention, matches the results to it, and thus increases the likelihood of a purchase because the customer quickly finds what they are looking for. Research shows that site visitors who use search are twice as likely to complete a purchase. Good search results also encourage customer loyalty – the customer comes back.

Search with FactFinder and “Authentic Intelligence”

E-commerce platforms such as SAP Commerce come with an internal search function – in this case SOLR. Medienwerft experts clarify whether this covers all customer requirements in the Search Health Check – an auditing process that analyzes the performance of the search and identifies weak points. If advanced functionalities such as personalization of the search, ranking rules, A/B testing or the use of artificial intelligence are desired in order to tailor the results even more precisely, SOLR, for example, comes up against its limits. Although it would be possible to integrate the functions, this would involve a great deal of effort and corresponding costs. In this case, it makes sense to integrate an external tool such as FactFinder’s, which specializes in precisely these functionalities.

Through “Authentic Intelligence” – a combination of AI and manual control – it automatically delivers relevant, personalized results. That’s because it allows store operators to leverage all the benefits of AI – including automation, optimization and personalization – while bringing the experience, skills and creativity of their teams to search results. For example, Authentic Intelligence can help companies sell off inventory of products that are overstocked, or prioritize a specific brand for sale.

For this purpose, the search function of FactFinder brings the following attributes:

1. Filtering and faceting: Users should be able to narrow down their search results based on various attributes such as price, category, brand or availability. The search should also be customizable by category, as the same aspects are not relevant for every product type: Dimensions are important for a TV, for example, but not for a video game. The use of filters should also not lead to zero results.

2. Autocomplete and suggestions: These features make real-time suggestions to users and complete search queries. They help reduce the number of clicks before a product is added to the cart and can make the overall user experience more intuitive. Suggestions can also be enriched with product images, categories that may be relevant to the search query, prices, and other creative content that helps or inspires shoppers.

3. Personalization and recommendations: Search should take into account the user’s preferences, purchase history, and browsing behavior to offer personalized recommendations tailored to individual preferences. Even for the same search term, personalization can take into account different aspects such as brand preference or price sensitivity. Self-learning algorithms are used for this purpose, which include similar users or product associations in addition to user interactions.