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Upping Your Findability Game

We often think of search results as a list of links to documents or webpages.  The more precise and relevant the better.  But that’s not really what most users want or need.  Rather, what they need is an answer to a question.  This is where the findability game is going. 

Do you use Apple's Siri application?

When you ask “Any good burger joints around here?” Siri will reply “I found a number of burger restaurants near you.” Then you can say “Hmm. How about tacos?” Siri remembers that you just asked about restaurants, so it will look for Mexican restaurants in the neighborhood.”

Ok, now envision using something like Siri for your customer self-service, knowledge management, or enterprise search applications.   “Will you pre-approve my loan application?”  “Sorry, we can’t because your credit rating is below our threshold;” Or, “Which form do I use to change my medical insurance plan?”  “Click on this link to access the form you need to change your insurance plan.”

READ: Intelligent Virtual Assistants Are Search-Based Applications

The technologies are now available to make search a more effective conversation.   In a way, search has always been a conversation – but one in which neither the searcher nor the search system are very eloquent:  The searcher enters a vague search term and the system responds like a self-centered bore at a party rambling on about all sorts of irrelevant information.

Searchers are always going to enter very little information about their query.  It is the goal of the search system to ask the searcher what they mean, to probe about their intent or to present possible choices both before they search and throughout the process of reviewing results.

Upping your findability game is going to require three major elements.  First, you will need to do a better job understanding the intent of user questions.  Second, you will need to be able to retrieve precise answers to questions – rather than force users to search for their answer within the body of a document.  Finally, as the consumer comes to expect natural language transactions, you will need to integrate search with speech.

CASE STUDY: Allstate Business Insurance Agents Speed Up Quoting with Context Sensitive Help System

If you are ready up your game, it’s time to begin the transition to providing answers not search results.

For a look at how we use information architecture to design and build an Intelligent Virtual Assistant download our white paper: Making Intelligent Virtual Assistants a Reality. In this paper we show how to deploy intelligent applications in your enterprise to achieve real business value.

 

Seth Earley
Seth Earley
Seth Earley is the Founder & CEO of Earley Information Science and the author of the award winning book The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable. An expert with 20+ years experience in Knowledge Strategy, Data and Information Architecture, Search-based Applications and Information Findability solutions. He has worked with a diverse roster of Fortune 1000 companies helping them to achieve higher levels of operating performance.

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How to Design Omni-Channel IVAs That Humans Love To Use

During our webinar, "Omni-channel Virtual Assistants - The Next Frontier in Voice and Text for Customer Service guest speaker Chris Featherstone, who leads business development for AI and speech recognition services at Amazon Web Services (AWS) and Seth Earley, CEO of Earley Information Systems (EIS) discussed ways to design omni-channel virtual assistants to optimize their use across voice and text. When supported by an appropriate information architecture and designed with a deep understanding of the customer, virtual assistants can access enterprise knowledge efficiently, saving time and money. The key to success is to structure the underlying information so it can be retrieved and used by any channel, including humans, to deliver the responses that customers need. Here's a recap of the conversation.