This year’s Search and Findability survey gave us a good indication of upcoming trends on the market. The activities and technologies that organisations are planning to start working with, are all connected to improving effectiveness. By using technology to automatically perform tasks, and by understanding the users’ needs and giving them a tailored search experience, there is a lot of potential to save time and effort.
Top 5 activities organisations will focus in:
- Natural language search interface, e.g. Query aid or chatbots (29%)
- Personalisation e.g. tailored search experience (27%)
- Automatic content tagging (24%)
- Natural Language Processing, NLP (22%)
- Machine Learning (20%)
The respondents planning to start working with one of these areas are more likely to be interested in, or are already working with, the other areas in the top 5. For example, out of the respondents saying that they are planning to use a natural language search interface, 44% are planning to start with personalisation as well. If you were to add the respondents already working with personalisation to that amount, it would increase by 75%. This might not be a big surprise since the different areas are much related to one another. A natural language search interface can support a tailored search experience, in other words – lead to personalisation. Automatic content tagging can be enabled by using techniques such as NLP and Machine Learning.
A Natural Language Search interface is a way of trying to find targeted answers to user questions. Instead of search based on keywords, the goal is to understand the question and generate answers with a higher relevancy. Since a large amount of the questions asked in an organisation are similar, you could save a lot of time by clustering and/or providing answers automatically using conversational UI. Learn more about Conversational UI.
One way to improve the Natural Language Search interface is by using Natural Language Processing (NLP). The aim with NLP is to improve a computer’s speech recognition for example by interpreting synonyms and spelling mistakes. NLP started out as a rule-based technique which was manually coded, but the introduction of Machine Learning (ML) improved the technology further. By using statistical techniques, ML makes it possible to learn from data without having to manually program the computer system. Read more about improving search with NLP.
Automatic content tagging is a trend that we see within the area of Information Management. Instead of relying on user created tags (of various quality) the tags are created automatically based on different patterns. The advantage of using automatic content tagging is that the metadata will be consistent and that the data will be easier to analyse.
Personalisation e.g. tailored search experience is a way to sort out information based on the user profile. Basically, search results are adapted to the user needs, for example by not showing things that the user do not have access to and promoting search results that the user frequently looks for. Our findings in this year’s survey, show that respondents saying they are currently working with personalisation consider that users on both the internal and extern site find information easier. Users that find the information they search for easily, tend to be more satisfied with the search solution.
Results from this year’s survey indicates that organisations are working with or planning to working with, AI and Cognitive-related techniques. The percentage doing so has grown compared to previous surveys.
Do you want to learn more about cognitive search?
Author: Angelica Lahti, Findability Business Consultant