The nearly universal experience of a technician needing multiple visits to fix a given asset remains a frustration for both brand promise and a servicing organization’s bottom line. At the enterprise level, these visits can account for millions in lost revenue. First Call Completion is thus a make-or-break metric for servicing organizations, especially so for those maintaining assets in remote locations. Every trip costs the enterprise, whether the goal is completed or not.
The Predii Smart ServicingTM platform ingests Hobart Service historical data, extracting symptom insights from the notes left by dispatchers in their CRM. These insights are connected to the parts used by Hobart Service technicians to resolve the customer's issues.
When a customer calls with an issue, the dispatchers are able to enter their symptom and are immediately provided with a list of which parts are the resolutions to that symptom – and a ranked list of who currently has those parts stocked on their van. They are able to immediately select which technician is most prepared to resolve the issue without having to return a second time with the correct part.
Predii TechSelectTM automatically suggests which technician to dispatch based upon how many of the suggested parts they are currently carrying.
Various roles have their own expertise within the enterprise. For example, a dispatcher might be a trained expert in customer relations, but not an expert in technical details for each of your product lines. They leave the failure-to-resolution knowledge to the technicians, who understand diagnostic procedures, parts requirements and labor operations. But if it were possible for the dispatchers to have deep technical knowledge – or be provided a tool that did that thinking for them – they could resolve many service delivery issues before they occur.
Extracting expertise from one functional role and empowering the others in the service delivery chain is the key to digitally transforming brand promise.
Every customer interaction can be a source of expertise – whether it is a phone call, an email, or an in-person visit. The customer describes the problem, an expert from your organization diagnoses the underlying failure, and a technician performs the fix. These complaint, cause, and correction data points are saved in your databases as unstructured textual data.
This data is gold, but difficult for service providers to leverage at an enterprise level.
When fifty customers describe the same issue fifty different ways, AI is the perfect solution for connecting the true meanings together. However, while AI is getting better and better at understanding general human language, these generalist AI platforms don’t translate well to the unique format of service orders and enterprise servicing data.
These service orders do not always follow the grammatical conventions that generalist natural language processing systems are built to understand, and, on top of that, every enterprise has product terminology and processes that are specific to their organization. The insights that generic AI and text analytics solutions are able to extract are noisy, too generic, or simply incorrect – and the amount of tuning required to get to accuracy standards is a huge data science investment.
Extracting reliable symptom-to-resolution guidance from historical servicing data requires domain-adaptable tools.
The next generation of text analytics adapts to the specialized nature of enterprise data. Read our walkthrough of the difference between standard text analytics and augmented analytics platforms that automate data preparation – bringing AI transformation far closer to turnkey status.
Predii's ability to extract the relationships from Hobart Service's textual data without requiring hundreds of hours of manual data tagging efforts enabled efficient AI transformation.
“Clearly I would recommend Predii to anyone who has a service organization with historical service call data. At Hobart Service, our mission is to deliver premium service, nationwide, from each of our 100 locations and 1,500 factory trained technicians. With Predii we are able to put new smart technology into the hands of our teams. Our dispatchers are empowered to make the data-based decisions, in real-time, that make an impact on first call completion, increasing our customer’s equipment uptime. Compared to the competition, Predii is the AI company taking the time to truly understand us as a customer and our industry."
Predicting parts requirements prevents repeat visits. By connecting the real-time complaint of the customer against historical customer complaints and the parts used to resolve them, Predii TechSelectTM empowers dispatchers to make instant expert-level decisions.
What if you woke up on the right side of the bed every morning?
What if your technicians started every morning with an easy, slam-dunk repair?
When we showed Hobart Service dispatchers our Predii TechSelectTM dashboard, we expected them to love the cost savings and reduced frustrations the tool empowers by knowing which parts were required before dispatching a technician.
Instead, they took one look at it and said: "I'm going to use this to make sure my guys have an easy repair to start their day, every day, so they can start on a roll. Wouldn't that be great?"