Posted on: October 09, 2015, by: Semaphore
For many organizations the call center is an important “public face.” Corporate reputations can be easily enhanced or quickly damaged by a client’s single interaction with a call center. Even in the very best of call centers the organization’s management continually strive for improvements such as, a better quality of service or delivering the same (or better) level of service with fewer people.
Three things to remember about call centers:
Successful call center operations have:
A quick Internet search will reveal many Contact Center Solutions in the marketplace which claim to improve the performance of client interactions. However when you dig into the details, you find that many of them are not much more than IP telephony routing systems providing a platform to integrate other relevant internal systems. So technology, by itself, is not the answer.
We can assume however, that having accurate information ((a) above) can be addressed with technology. Clients’ names, addresses, account details and balances, can be presented to call center staff with the accuracy of the system(s) of record in which the data is stored.
The solution to having knowledgeable staff ((c) above) is really in the hands of the HR or recruitment function, identifying and finding individuals that have the traits, skills and motivation to become knowledgeable falls within their domain. Yet when turnover is high and training is expensive and time consuming it’s difficult to develop knowledge over time – so you need a way to maximize the available information.
Which brings us to (b), relevant information. Your knowledge management strategy is likely to have tried to address this challenge by building a dynamic ‘knowledgebase’ of as much of the key information needed by your call center staff as possible. The knowledge base (and other corporate systems for anything not in the knowledgebase) can be searched by call center staff for answers to customer questions.
And with that there are two challenges. The first is linguistic, will the customer, the call center operator and the author of the source of the answer all describe the problem in the same way, using the same language and words – unlikely. A knowledgeable operator, who has heard the question many times before, will translate the question into the vocabulary of the enterprise. On the other hand, a novice operator will search for answers based on what he/she knows – which may result in a lack of information or worse, incorrect information.
The second challenge, not unlike the first, is what exactly is searchable. Search works by indexing documents and using the index to find things quickly. Indexes are created by listing the appearance of common words within a document. The problem here is that the search capability is not knowledgeable, it’s looking for the appearance of a word in a document and when it finds a document that contains that word it will return it in a list of documents in which the frequency of the searched word is the greatest. But there is something missing - context. The returned documents are not about the search topic, they simply include the searched word. In a high pressure call center environment, browsing through hundreds of documents quickly, to find the answer to the problem at hand is unwieldy. How many times have you heard “Can I just put you on hold for a moment?” and then waited two or three minutes? This is part of the reason.
What’s needed in this search scenario is a list of documents that contain what the document is about. For example, in a high tech company it is likely that you’d want a search for the word Apple to return information about Apple Computers and Steve Jobs not Haralson, Honey Crisp or Golden Delicious which are varieties of fruits. What’s needed is intelligence about the content – of the knowledgebase, of all corporate content.
Smartlogic’s Semaphore can help call centers close the enterprise search gap. Where basic search relies on the author of a document to manually add accurate metadata and isn’t able to derive the context in which a word or phrase is used, using Semaphore Ontology Manager to create an ontology allows you to define the concepts relevant to your organization and the relationships between concepts.
From the ontology, rules are published and used by Semaphore Classification Server to automatically classify the documents; the result, precise and accurate meta-data tagged content which can then be leveraged by an enterprise search engine to enhance search results.
This enhanced metadata enables call center staff ask more specific questions and then delivers precise results.
Let’s be honest, basic search is just a starting point for finding the information. Even with a comprehensive knowledge base you need a platform like Semaphore to apply intelligence to your content.
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