Field service organizations increasingly feel the pressure to maximize the productivity and efficiency of their workforce and get every job right on the first try. While schedule optimization gets the right technician to the right place at the right time faster than ever, 15-30% of jobs still require a repeat visit, and many jobs are not completed within the promised service window. There is plenty of opportunity to improve customer satisfaction while increasing schedule accuracy and first time resolution. In order to satisfy the rising expectations of the modern customer, field service management needs to get smarter and more predictive.
An increasing number of industries and businesses are exploring artificial intelligence, machine learning, and predictive analytics. Most organizations are not taking advantage of these technology enablers primarily because they feel they lack the necessary skills in house to manage the solutions. But if AI is delivered in a way that is pre-packaged and easy to deploy, requiring minimal resources, then field service organizations stand to gain significant benefits.
Boost efficiency, productivity and customer lifetime value
ClickSoftware’s Predictive Field Service is the culmination of over 20 years of establishing field service best practices and enabling technology to empower service organizations to achieve desired outcomes. Predictive field service (also known as predictive workforce intelligence) anticipates service fluctuations and automatically adjusts business processes accordingly. The solution is powered by our Machine Learning Cloud, which applies artificial intelligence across the service chain to find hidden patterns in data and make predictions that matter to service businesses. predictive workforce intelligence combines historical data with external sources to provide increasingly accurate predictions of important metrics (such as the duration of tasks or the likelihood of appointment cancellations) that have a material impact on key service business decisions. By seamlessly incorporating artificial intelligence into field service processes, organizations can benefit from more accurate resource plans, schedules, and improve customer satisfaction.
Machine Learning Cloud is seamlessly integrated with Click Field Service Edge, so minimal configuration is required to start taking advantage of service predictions. With field service best practices baked into the solution by our field service data scientists, there is no need for customers to modify the system. Key insights and performance improvements are communicated to stakeholders to ensure alignment with goals and enrich learning. Reports can also be generated in advance of implementing Machine Learning Cloud’s predictions to fully qualify the impact.
Smarter Service Predictions with Every Interaction
ClickSoftware’s Machine Learning Cloud empowers service organizations to provide better service to end-customers with predictive insights derived by field service specific AI and data science. Machine Learning Cloud makes data-driven decisions. It identifies the patterns in historical data, finds relevant parameters that might affect job duration, and constantly tunes itself to reduce the gap between predicted and actual times. With Machine Learning Cloud, service organizations can:
Make More Accurate Predictions About Job Completion
Predictive Job Duration estimates the most precise duration for each job, based on all relevant task and engineer properties. ClickSoftware’s Machine Learning Cloud uses historical assignment data to build prediction models for a specific assignment’s duration. If historical duration data is not available, data is augmented using location-based algorithms. Predictive job duration is used both when a new task is created and once a task is assigned to an engineer. Up to now, service companies have tried to predict the duration of tasks based on estimates by task type; however, duration is impacted by many parameters which are not considered with other methods, like required skills, technician efficiency, customer type, installed products, etc. In the past, these estimates have typically remained static, while the parameters involved are actually dynamic. For example, as technicians become more proficient with fixing a certain issue, the job duration will get shorter. Our solution will learn this and optimize accordingly.
Predictive Job Duration automates key decisions for schedulers and dispatchers that directly impact the customer experience and the bottom line such as:
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