Electronic knowledge management for customer service has been a hot topic for the past two and a half decades. Ideally, your customers can find the information they need to address their issue through self-service. However, companies still fall short when it comes to implementing technology that will help customers find useful information online.
So what happened over two decades ago that caused a shift in how companies publish information about their products and services? The answer is simple, technology and the internet.
Electronic Knowledge Management – History
Think of the evolution of electronic knowledge management since 1990.
In the beginning . . .
- Starting with the dawn of the internet, companies were able to publish online owner manuals and FAQs. This information helped customers find answers to their questions about a companies products and services.
- Soon after that, companies realized that customers needed a more efficient way to find this content. Thus, knowledge base technology emerged. Search engines helped to connect customers with content. However, the customer service industry was missing knowledge management procedures.
- As a result, organizations started to discover innovation in knowledge management process like Knowledge Centered Support (now called Knowledge Centered Service – KCS¹).
- The customer service industry then started a frenzy of creating content and improving the content creation process. Not all companies were successful.
- Online communities and social media started to take over where companies failed. Out of frustration, individuals not representing the company started writing and developing support content on the internet. Some of this was good and helpful knowledge, but some of it was wrong or misleading content.
- Somewhere along the way, technology helped to validate knowledge content with options for content users to vote, “like” (thumb up/down), or comment on knowledge content. This validation process helped but has not solved all issues that occur based on unvalidated knowledge.
Electronic Knowledge Management – Now
And this is where we are today. Companies still struggle to produce good and useful knowledge content while a flood of information about their products is continually published online by credible but also by unreliable third party sources.
Electronic Knowledge Management – Future
The future of knowledge management has already being laid out with emerging technologies that will explode making virtual agents, BOTs, augmented reality and artificial intelligence a reality.
But this technology may not be effective unless we improve the process for feeding the technology with quality knowledge content. In other words, your company will still need to validate content to ensure that the new wave of self-help technology has relevant information needed to be successful. The same struggle that companies face today to determine the most impactful and useful knowledge content is not going to change with advances in self-service technology.
Enabling the Future of Electronic Knowledge Management and Self-Service
Regardless if you are looking for content for your current knowledge management solutions or looking to build future self-help for your customers with BOTs and virtual assistance, one of the best sources to get content is the customer request or case management systems. Your company is probably receiving several customer requests every day. These requests may be from customer support service inquiries, questions submitted to your helpdesk and queries submitted on your website and knowledgebase.
While companies capture these requests electronically, many organizations are not analyzing this data to find out how to better serve their customers. With unstructured data analytics, you can cluster and categorize your most common customer information requests so that you can prioritize content for knowledge creation. As you improve your ability to create knowledge content based on demand, you will improve customer satisfaction. You will also create efficiency that will grow your capacity to better meet your client’s needs.
As stated, one of the best sources of information is the customer request or case management system. This is the system that you use to log your customer questions and information requests. The most valuable field to enable search and knowledge retrieval is the description of the request. This field is sometimes referred to as the “brief description.” To leverage unstructured data analysis you should ensure that the content in this field is in a format so the unstructured data analysis can cluster common requests.
Guidelines to Facilitate the Clustering of Common Customer Requests
Data in the customer description field should be as clean as possible so that unstructured data analytics can find similar customer requests. Getting accurate data in this field will require some discipline and employee training. Guidelines can also help to make sure that the content being added to your customer case management system is valuable and easy to find. To help ensure your customer description field has good data to analyze, you should consider implementing the following suggestions:
- Your customer description should be long enough to be specific and descriptive but not too long. Long descriptions ar more difficult for data analytic engines to analyze.
- The description should be one cohesive thought, not several unconnected run-on sentences.
- When entering the customer request, your team should use plane terms. In other words, they should use terms a customer would use to find that same request.
- You should use consistent templates and industry best practice for logging the description of each customer request.
CSLD Solutions Can Help!
Once these guidelines are in place, you can use unstructured data analytics to cluster customer request topics. With this information, you can better enable your current electronic knowledge management solution and begin to prepare for the future of BOTs and virtual assistants. CSLD helps companies develop, train and put these guidelines in place. Once in place, the CSLD Data Analytics for Knowledge Creation will cluster and categorize your most frequent customer information requests. Then, you will be able to prioritize content for knowledge creation.
Contact CLSD to learn more about our Data Analytic Engine for Knowledge Content Creation. We can show you how to improve your ability to create knowledge content based on demand. Ultimately, this will increase your ability to satisfy your customers.
¹KCS is a business methodology that was developed by the Consortium for Service Innovation™ (CSI, www.serviceinnovation.org)