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Automated Troubleshooting: How Knowledge Management Aids Troubleshooting Through Self-service Channels

6 mins read

Troubleshooting, by dictionary meaning, stands for the process of analyzing and solving complex problems for organizations. Troubleshooting support helps solve complex customer issues using advanced technical knowledge. It involves multiple devices thus in-depth knowledge of features and functions of entire process. Experts and field agents require seamless integration with knowledge base to help with that.

  •         Consistent performance
  •         Reducing agent workload
  •         Customized solutions
  •         Complete backend support

Steps to troubleshooting

Identify the problem

Troubleshoot support implies the provision of expert services to a consumer. It is usually the L3 level of support. For this, it is essential for an agent to accurately access the issue being encountered and degree of its complexity. Agent must then try to explain to customer about the issue and convince them to not self-serve unless an expert arrives in a professional capacity.

Pinpoint the issue

Once grounds for problem are set, it is essential to also continue narrowing down the issue without interfering in a manner that complicates it further. The requirement of troubleshooting can be quite immediate as in cases pertaining to network troubleshooting or can be casual and subject to study as in cases of device or product troubleshooting.

Reverse create the solution

Failure of contact center staff over calls to remediate an issue quite clearly establishes the levels of difficulty. A module like decision tree can be used to reverse program problem tracing it down to its origin and rooting the solution. Germane problems can be solved using this method of study. While an entire product division is educated on practical fronts, the time duplicated to first study product, find probable cause, and devise solution is altogether removed and encapsulated into one-time experiment. This can thus be documented as firm knowledge.

Mend the leaking hose

When you find your garden’s hosepipe leaking, you don’t cover it in mud expecting the leakage to stop. You dig cause for leakage and replace the pipe altogether while taking actions to prevent recurrence. Similarly, when you find an issue with your product while studying it, you don’t just cover up original problem and wait till other possible issues arise. Product team must immediately sit down to devise solutions in case any such problems arise alongside making stout alterations to the product for better usage stability.

While troubleshooting support for each device may be different, basic functionality of all devices of a group is the same. All android mobiles will have similar solutions to issues regarding device configuration or SIM set up. All Wi-Fi routers will be similar buttons and ports binding to same output, just in a slightly different manner. This brings us to step up troubleshooting as an amalgamation of human brain and technical support.

All similar solutions can be grouped and passed on as general training material while any specific difference can be advocated during process training. Also, every such solution ever created, predicted, or disseminated can be saved and uploaded to organization’s knowledge base. A solution once uploaded, post-verification, is ready to be disseminated even by a bot or through various other modules.

Automated troubleshoot support

While integration of an enterprise’s knowledge base as a plug-in over almost all channels helps the users to independently type in problem, find relevant solutions, and execute them; complex issues can lead to graver problems if evaded from under a supervisory expert. However, putting every step of automated support under experts’ purview defines the basic meaning of automated support itself. Technologies like augmented reality, virtual reality, and mixed reality for recent developments, eliminate the above stated problem to a great extent.

Use of mixed reality helps in bringing solution to customer’s doorsteps while bringing the complaint to expert’s doorsteps. Delivery of solutions on a real time basis helps satisfy customers and close tickets faster. Swift is the word for millennials and using mixed reality fits just in. Automated support in troubleshooting for enterprises is a shortcut preventing them from losing customers or facing employee attrition.

Knowledge management in troubleshooting support

While troubleshooting refers to quickest replies in minimum possible time frame, a major ingredient for the same is presence of a spotless and concrete knowledge base. Knowledge management system assures the use of correct software and keeps their integrations updated. All knowledge published follows a chain of command. It is created by subject matter experts and is run through admins. Admins then process this under the assessment of super admins who then verify, make final edits, and publish that content with proper tagging.

Using a knowledge base to create and store intuitive knowledge further simplifies the process of knowledge dissemination. Decision trees help to create a schematic board that starts with problem statement at the top and tapers down to an exact solution. Between the opening statement and closing remark, branching out are various possibilities of other situations that could lead be the cause of problem. Narrowing down to the exact cause helps with identifying best fit solution. Agents can then simply conduct a question and answer type of call, explain root cause of the issue, gain customer’s trust, walk them through solution, and close the ticket.

Visual how-to guide is yet another interactive way to create a solution guide. Having a DIY approach, agents themselves can identify the solution and create a visual guide alongside product research teams. Simply drag and drop the solution set of images, attach needed description, and upload. Guide is ready to be accessed by both agents and self service users. It simplifies troubleshooting to a great extent by lending engaging support.

AI chatbots use the programmed intelligence of ML and NLP to create and disseminate solutions via a conversational approach. They are connected with the backend that keeps them updated with recent knowledge in their feed. A supervisor often takes random samples of conversations being conducted by the bot. With intent identification, the bots can now automatically read keywords from tickets and generate a list of all solutions in each possible scenario. Petty queries with repeated issues are deviated towards chatbots. They thus provide solutions while still keeping the visitor engaged. It improves credibility of the solution, organization, and process as a whole. Best fit of AI chatbots in troubleshoot support is due to their customizable yet omnichannel enabled integration.

Magic of technological advancements has now ceased to put end-users at a disadvantage due to lack of technological knowledge. Network troubleshoot support can zero down the exact device and pinpoint time to seconds. It can trace existence of the issue and resolve it from a distance in an automated manner. This development of technology has now trumped up speed, clarity, and quality of the solutions delivered.

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