Profiting from the next level of knowledge management
Pragmatic steps to deploying next-generation, search-based applications called "intelligent assistants"
I recently went to a prominent technical website’s self-help application for my smart phone. It’s a sad tale of low-quality service and unnecessary costs that started with a simple search about leveraging fee-based service for my new phone. First, I queried using Google. I got a long list of postings on different sites that I had to click through. Two pages of links did not appear to have the answer I needed, so I went to the service provider’s site for help. I executed my query on the site and received, “No search results found.” So, desperate for an answer I called the support line.
Ironically, I was offered a recording of how I could solve the problem myself online. Frustrating, wouldn’t you say? After 10 minutes, an agent answered. I repeated my problem. She told me she couldn’t solve the problem. I had to deal with the application provider … and so the story goes.
This is happening every day inside companies:
- Prospects or consumers can’t find product information.
- Existing customers/consumers fail to find answers on self-help.
- Customer support and service technicians are forced to read through long lists of documents and disparate information sources. Sometimes they can’t find answers, so they rely on tribal knowledge.
- R&D engineers invent their own answer to problems already solved. Worse yet, they waste time on unsolvable problems or ideas that have already failed.
Historical practices underlie most of these challenges:
- Document focus—Historic methods for producing content have left companies with large stores of long documents. Content is often still optimized as if we’re using paper.
- Not purpose-optimized—When the job of search solutions was viewed as a way to locate relevant documents, content structure was not optimized to allow search to locate specific content within the document.
- Islands of information—Separate social media solutions are used for content such as tips, tricks and discussion forums. Some data remains in transactional systems. That results in separate sources of separately tagged information to manage, curate and search.
- Lack of insight on use or value—Metrics used to optimize consumer website experiences are not being used to optimize the productivity of the most expensive, critical knowledge worker tasks.
So what is the result of those gaps in our knowledge management solutions? At a major electronics manufacturer, field service technicians were spending 15 percent of every week searching through disparate systems to find answers to repair challenges. That added up to $7.5 million to $10 million annually in excess costs in the system and critical downtime in its manufacturing process.
Smaller chunks of knowledge
What is the answer? A new form of Siri-inspired search applications called “intelligent assistants.” Siri (part of Apple’s iOS) and Google have opened our eyes to the potential that if we can understand more about context and more about the details of the content, we can get beyond search sets that list documents and go straight to answers.
An intelligent assistant is a search-based application that sits on top of content that is established in smaller components. Essentially, you must break down your knowledge into smaller chunks of information, typically by task, concept or idea. Each smaller component can then be tagged, making comments about each step in a diagnosis or some smaller part of a product reference available as a search result. Those smaller pieces can be easily scanned, consumed and used in real time.
For example, at a leading national channel-driven services organization, product line executives had to find a way to help their indirect channel better understand and configure a complex range of higher-end products for business customers. Training didn’t really help. The product fit was subjective, and multiple options could be presented, making it too complicated for an auto configurator.
It was far too easy for the sales reps to forget all the provisions and alternatives between sales calls. One of the greatest barriers to sales through a channel is perceived complexity. Channel organizations will avoid selling anything that is too complicated to learn. So, support costs escalated as the service organization “held the hands” of the sales force through a high-cost, deal-by-deal support model.
An intelligent assistant was created in only four months to guide configuration of the right product solution and to literally answer questions about product applicability and selection. Early results are very positive, and, with proper content curation, the organization believes it will reduce support costs by nearly 80 percent and be able to redirect call center resources to growing revenue.
How do you get started?
If you take careful steps upfront, you can quickly ramp your knowledge management team to create intelligent assistant applications throughout your organization to solve your most pressing collaboration challenges. The following are three simple techniques to take your first steps toward intelligent assistants:
- Pareto (named after the Italian economist Vilfredo Pareto) each problem to drive effort and the business case.
- Establish a task-focused component content architecture.
- Leverage DITA-based technologies.
Pareto each problem.
According to Wikipedia, “The Pareto principle states that, for many events, roughly 80 percent of the effects come from 20 percent of the causes. Business management consultant Joseph M. Juran suggested the principle and named it after Vilfredo Pareto, who had observed in 1906 that 80 percent of the land in Italy was owned by 20 percent of the population. Pareto developed the principle by observing that 20 percent of the peapods in his garden contained 80 percent of the peas.”
So, it is likely, looking at any given problem, that having proper component content over 20 percent of the content is going to lead to most of your results. The trick is: which 20 percent?