Why Knowledge Transfer is Failing in Field Service
In our previous article in this series of excerpts from a recent white paper published by Aquant we explored the significant challenges field service companies are facing as they try to navigate the huge demographic changes that are underway at the moment as the ageing ‘boomer workforce is replaced by their millennial cohorts. In today’s article we explore why the current knowledge transfer tools are letting field service companies down.
The labour shortage forces organisations to choose between immediate needs, such as having their most senior staff in the field addressing urgent customer issues, and long-term goals of documenting their knowledge and training new employees.
Knowledge Transfer in FIeld Service Requires Tools Fit-For-Purpose:
As most managers know, when resources are tight, the biggest fires get put out first and smouldering issues continue to be put off until they can’t be ignored.
It’s not that companies haven’t tried to harness insider info and scale training. There are mobile apps and field service tools designed to capture notes from the field.
However, change management can be a bigger barrier than the C-suite anticipates, leaving managers tasked with motivating the workforce to use the technology that some in the field consider clunky or time-consuming. And even when these tools are successfully put into use, it’s difficult to make these notes and comments that are captured on customer tickets actionable.
They’re often riddled with typos and contain information about multiple tasks in one long, free text form. How can any organisation parse that information and use it effectively?
This challenge is at the root of why so many internal knowledge bases are missing the deep insights of employees in the field. Plus, most of these solutions are static databases, as opposed to connected learning tools that know what information is necessary, can prompt employees to ask the right questions, and then figure out logical solutions based on partial inputs.
Digitally savvy employees are used to using tools like Siri that understand their location and habits and can offer intelligent solutions without the user having to do all the legwork.
Uncover Existing Data to Fill in the Gaps in Your Field Service Knowledge Base
People make the best mentors and trainers, and those with deep on the-job knowledge often excel at diagnosing obscure problems that newer employees may have never experienced, but these deeply knowledgeable employees only have so much time and ability to impart their wisdom.
As organisations seek to meet high customer service expectations, human knowledge must be combined with an artificial intelligence discipline called machine learning in order to democratize that knowledge.
Use Machine Learning Technology to Distribute Existing Knowledge
Customer-facing organisations have far more information and institutional knowledge squirrelled away than most managers and executives realise.
There are free text notes, product images that sit within and outside of CRM, ERP, WFM, and other databases. Technology partners that leverage Machine Learning (the process of computers improving responses with experience) can capture this unstructured information and add it to the knowledge base, alongside real-time data, producing a rich and interactive pool of information that all employees can draw from.
The right application can make actionable recommendations and predictions based on this data, helping teams solve customer and service challenges efficiently.
Apply Natural Language Processing on Top of the Information Mountain
With the amount of data in play, it’s not enough to simply convert current and historical information into structured data which can easily be indexed and searched.
The problem is that different customers or regions might have different terms for the same issue. Plus, case notes about this issue might contain typos and misspellings, making it difficult to manually identify and categorise records. A solution that offers Natural Language Processing, in combination with Machine Learning, digs deep into the historical information and acts as a translator.
It will understand the root issue regardless of how it’s described by analysing the past examples, whether it’s faulty equipment or new installs. It will map these different ways of describing issues back to the same solution—even if the descriptions contain mistakes. In addition to helping call centre agents and techs in the field, it’s an essential learning tool to help employees level-up by quickly accessing critical data to get the job done.
In the final feature within this series of excerpts we will look at two more ways field service companies can utilsie the data within their existing records to help solve the field service skills gap as well as an industry case study from a leading high tech organisation in the 3D printing space.
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