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In case you missed the previous blog posts in this series, read about the new age of digital business problem solving in part 1 or how to tackle functional fixedness in part 2.
Increasingly, many of the applications we use day to day seem to automatically know what we want. This applies to search as well; the human generated query is being replaced by simply a situation and/or a context. This intuitive and so far still conceptual solution is being referred to as “Proactive Search”.
Traditional search is reactive, as we’ve already discussed; it involves answering a query conducted by a human. It could be likened to the waiter that hangs out at the bar, waiting to be called over. He’s not doing a bad job, and he’s always attentive when he arrives. For a great dining experience though, you want him to be offering to take your coat as you come in the door, filling up your wine glass without having to be asked, and catching your eye from across the room so you never have to say, “Can you bring me the bill please?” He has to be in tune to the needs of his customers.
Personalized Versus Proactive
At its simplest, the idea of proactive search is by no means new, in the form of search-driven content. For example, Middlesex University London dynamically curates the content for their school sub-sites by using the search platform to produce home pages that bring together relevant news, staff profiles, departments, courses, events, and groups. In this sense, relevance can mean topic relevance, but it can also include factors such as recency and importance. What was traditionally the role of the CMS has been replaced by the search engine because search has the ability to make intelligent decisions about what content best serves the information need of a user and context.
Although search-driven content does not require a question to be asked, it is not possible to assert that it is ‘automatic’ or ‘intuitive’, as the user is still required to show interest in a particular area.
This idea of proactive search gets more interesting when coupled with personalization. The Middlesex University example could be taken a step further by drawing together content that not only relates to the School of Art and Design but also biases the selection of content based on the location of the user, whether the user is a student or a lecturer, or whether the user comes from a bank, a government agency, or another university. Using a search engine to intelligently curate content into topics that take into account what we know about the user makes user experience immediate, relevant, and contextualized without the need for high end personalization tools.
Search + VDA = Proactive?
Personalization could be extended further to include the concept of a search-driven virtual digital assistant (VDA). This involves automatically conducting an endless stream of behind-the-scenes searches that query a broad range of ever-changing data sources within an ever-changing user context. The digital assistant can then alert the user to situations of interest without them even being aware that this is happening, taking search to a truly automated solution.
We’ve considered how we might apply this to a sector we know quite well; the university sector. Universities contend with a vast number of business problems, but without a doubt, the most vital area of their business lies with students.
How might a university app that continuously searches student information such as course databases, attendance records, events, library records and social media prove to be an integral part of guiding students to fully engage in university life?
What about library database searches for overdue books that could prompt students with reminder notifications to pack the books in their bag before leaving their room? Or, searching across attendance records could highlight students who are habitually absent from their classes, and could notify them of their proximity to student counselling services based on social media check ins, perhaps assisting in lowering dropout rates?
This is just one example of how a search platform could, with some additional complementary technology, drive truly proactive search. We’re on the lookout for more exciting ideas for the evolution of search technology, but we are also focusing on bringing you more engaging and intuitive search solutions that we can deliver right now.
Interested to read our take on intelligent question answering? Head to part 4 of this blog series!
