It's the difference between a map and directions, between a recipe and a meal, between sheet music and a song. That's the difference between search results and a real answer to a question, and that difference matters a great deal to customer support teams.When you're supporting a customer (and especially when your goal is first call resolution), getting answers quickly and correctly is of critical importance. That's when customer support professionals turn to their internal knowledge base and its built-in search tools.
Most search features run on simple, primitive keyword matching. If a support rep searches for "Outlook bug" they'll get results for every single article or document in your knowledge base that includes the phrase "Outlook bug" followed by every item that contains separate instances of both "Outlook" and "bug," followed by every item that contains either but not both "Outlook" or "bug."
If the search tool is really sophisticated, it might prioritize documents that have "Outlook bug" in the filename, or even use that phrase in titles or section headings. But regardless of how the search software generates a list of links to articles related to "Outlook bug," a list of articles is still a long way away from the answer to the question, "How do I fix this customer's Outlook bug?"
Now, most seasoned support reps develop "tribal knowledge" around how to enter better search queries, or simply recognize common issues by how customers describe them, but that's not an easily scalable or repeatable process, and it's not useful for new support reps, or when dealing with customers that describe their problem in unusual ways. Search skills help them get a better list of links, such that the first entry is probably the right one, or at least is wrong less often, but better links are still not real answers.
There's a better way for search to behave.
You may have noticed that Google, the undisputed king of search, has started offering excerpts of Wikipedia articles when you search for certain highly trafficked and highly specific items. For example, a Google search for Queen Elizabeth will return a summary of her Wikipedia biography, some vital stats, and a handful of recent images of the British monarch above and to the right of the usual list of links.
Google isn't pointing you to information you're looking for. It's giving you the info you want, with the option to go deeper. And it's that level of summarization and context analysis that is required to empower internal search tools for effective customer support.
When search gives you the answer even one click sooner, that’s one less moment on a call, and one step closer to consistent first call resolution.
To achieve this level of search sophistication, you need to adopt a knowledge-centered support model, and you need to employ a knowledge base that includes this level of search sophistication as a core feature.
Talla is developing a search-centric, artificially intelligent knowledge designed specifically to help customer service and support representatives. If you want to make search a real partner in your Knowledge-Centered Service environment, contact Talla today.