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Can AI really improve website and app search?
April 26, 2021
It should come as no surprise that users now demand and expect more from their digital experiences. User expectations have shifted considerably over the last decade, and excellent digital experiences are now a must-have rather than a nice-to-have. And yet, many companies struggle to meet these expectations. One area that is often significantly lacking is the search on-site search function. In the digital age, every website or app must serve the needs of a wide variety of visitors, but getting this right can be a challenge. For companies facing this problem, artificial intelligence and machine learning could be the answer.
Artificial Intelligence and machine learning now make it significantly easier to serve fast and relevant results to meet both the user and business needs. Let's take a look at how a robust AI-driven solution can benefit your business.
Scalable is a word loved and frequently overused in the tech community. To keep things simple, in this context, we mean the search capability needs to work for your business and users now and in the future. Manually optimising keywords is not going to work for large data sets and varying requirements. And as your company grows and matures, increasingly large data sets become the norm.
Machine learning software can work through a vast amount of information in a matter of milliseconds and continually learn and refine over time. So if your product data is planned to increase in terms of breadth or depth, or you are creating a content hub and continually pushing content, the more you can automate with machine learning algorithms, the better.
Machine learning solutions can dynamically rank content to surface the most relevant information. The algorithm processes and sorts through all available information and metadata to predict what the user is looking for first. And it doesn't stop there - the algorithm also feeds back responses so that it learns to improve the results over time.
For an example of this tech in action, consider the following. When users search for "dresses" in the summer months, the algorithm will prioritise lightweight summer wear over heavier winter jumper dresses. Through relevance feedback loops, the algorithm has learnt that there is more demand for certain items in particular categories at that point in the year.
Thanks to natural language processing (NLP) (a branch of AI that studies how machines understand human language), systems no longer only process words but aim to understand the real meaning. Users both consciously and unconsciously express their purchase intent, and AI helps read between the lines to improve the user experience.
In addition to returning relevant results, AI can also surface highly personalised content for a given user. By analysing user activity, we can also present information that we understand to be useful to the user even if they are not explicitly searching for it. One technique to achieve this is called “segmentation modelling”. This is where users are profiled over time and communicated with according to what we know about them. This can include data like location, previous search history, device type, age, marital status, and much more - if the data exists, there's a pattern to be found.
By optimising for scalability, relevance, accuracy, and personalisation, users get the content they want and need faster. For businesses, this results in higher conversions and richer digital experiences that promote brand advocacy.
Blending both automation with the ability to tweak and override manually is where the search sweet spot can occur. A balance between both the user needs and the business objectives is what fundamentally leads to successful outcomes.
If you need help navigating your search journey, then get in touch.