A site’s search engine is a key tool for improving conversions of both ecommerce and lead generation sites. Just think of the central role it plays in realities as different from each other as Amazon or Netflix.
One of the reasons why a site’s search engine is vital is because of its ability to substantially shorten the time spent by the user to find what he or she wants, thus going on to improve the overall browsing experience.On sites with particularly large catalogs this becomes a vital function because the real risk is that, no matter how well the information architecture is developed, a user will not be able to find what he or she is looking for within the unit of time and attention he or she had decided to invest in this purchase.
The search engine is one of the tools that, when integrated with profiling and personalization systems, can allow us to gather perfect information to segment users. In fact, what can be more accurate about a user’s purchase intentions than his or her explicit search for a product?
Native in-house search engines are, in several respects, often inadequate and require refinement and maintenance work that cannot be tackled manually. They are also totally lacking in one key aspect, namely, being able to work with different product selection algorithms for different customer segments, so as to increase the relevance of the results shown
Are all users searching for the same keyword the same?
Certainly not, segmenting them and being able to propose different types of results to them can make a huge difference.It is at this stage that it becomes crucial to be able to integrate search with artificial intelligence-based profiling systems, which will be able to recognize our users and learn from their behavior, improving search results over time.Let’s look together at three concrete examples of algorithms that can be used with different types of users:
- Is the searcher an anonymous user on the first visit? We could use an algorithm tied to the most popular products, possibly conditioning the result according to our product rotation management or commercial policies (e.g. showing popular products with high stock first, or not showing products of which we have particularly low stock).
- Is the searcher a user, again anonymous, who has a browsing history or comes from specific product campaigns?In this case I can use an algorithm that gives prevalence to results based on his browsing history, for example, showing as first results those based on his previous browsing.
- Who search is a customer, perhaps VIP, who always buys high-end products? The correct choice in this case might be not to show low-end product results so as not to lower his or her perception of our store while increasing the likelihood that his or her average shopping cart will continue to be high.
As you can see, in-house search is for all intents and purposes an integral and paramount part of building a consistent user experience.However, it is critical, in order to properly integrate this tool within our personalized marketing strategy, to have technology that is able to recognize users and apply different strategies in real time in displaying results.
If you would like to learn more about these issues, contact us to speak with one of our experts with no obligation!
