Customer Service, Industry Today

April 3, 2019

By Paul Whitelam, SVP, Global Marketing, ClickSoftware

Due to the ubiquity of personalized, on-demand services like Amazon Prime, Uber, and Airbnb, customer expectations have risen in recent years across industries, and manufacturing is no exception. This consumer technology has set a precedent for transparency into the service process, and today’s customers expect to be in more control than ever before. To meet customers’ needs, manufacturers must innovate, and AI-driven applications today offer the ability to maximize efficiency and effectiveness for both customers and service providers.

To ensure excellent customer service, companies should plan in stages: before the day of service, on the day of service and after the day of service.

Planning for success

In order for field service providers to deliver exceptional service, there are a series of decisions, such as training, that need to be made weeks, months, or even years in advance. Thoughtful demand forecasting and resource capacity planning give organizations the confidence to prepare appropriately for the day of service. Knowing the types of service calls well in advance, and in which regions, helps organizations to plan dependencies such as parts availability, shift patterns and even SLAs. In addition, ensuring in advance that field service resources are in the right locations with the right skills on the day of service allows field operations to minimize travel time, increase resource utilization, and respond more promptly to customers.

For manufacturers, data about asset performance is a key ingredient in this process, giving planners an understanding of patterns of particular machine failures. This helps align resources with demand, and enables organizations to accurately predict service and maintenance timeframes, and ensures successful execution of these services to meet both business and customer needs.

Executing with the right technology

Though careful, advanced planning is key for successful customer service, there are always unexpected factors like traffic, extreme weather, and customer cancellations that disrupt even the best laid plans. While these disruptions aren’t always predictable, factoring in real-time data can address what’s actually happening on the day of service.

Leveraging technology that automatically adjusts technicians’ schedules during the execution phase in response to real-time conditions has proven highly valuable. Combining sophisticated algorithms with actual intelligence and practical input from dispatchers, managers and field personnel, increases operational visibility and ensures a tightly run schedule. For instance, artificial intelligence (AI) can predict that torrential rain will result in heavy traffic, and proactively re-route a technician for an optimal journey. In another example, Internet-of-Things (IoT) sensors on industrial parts and systems, or environmental monitoring equipment, give service teams visibility into real-time asset performance. If a sensor initiates a service request, then real-time information on technician location means the most appropriate technician can be directed to the asset as soon as possible.

Analysis for future service excellence

Many businesses gain tremendous value from the data they collect — as reviewing historical patterns allows them to make the right adjustments to move ever closer to flawless service delivery.

Before companies can use historic information to improve their future service delivery, it’s important to ensure they’re collecting the right data. For manufacturers with a goal of increasing service efficiency and effectiveness, there are four types of data that are highly useful, including: historical job data, technician data, customer data, and environmental data.

Historical job data includes any information that was relevant to a prior service visit. This includes the task type and duration, success rate, and parts and tools used. Machine learning techniques can look at the correlations between inputs and successful outcomes and reveal what worked well in past visits, and what caused problems. In this way, historical data can uncover what’s needed to ensure the most efficient service day.

No two technicians are alike – each have their own skills, specialties, and experience that influence how they perform during a particular task, or even with a particular customer. It’s important to consider these specifics when assigning jobs, to ensure that the designated technician is well-suited – and is likely to be able to fix the problem quickly on the first visit, ensuring a positive customer experience.

Similarly, customers are diverse, and each must be treated as an individual when scheduling a service call. It’s useful to track individual customer data, including service history and previously stated preferences. Customer data can be especially useful in predicting the likelihood that a customer will cancel or fail to show when a tech arrives. Machine learning automates data analysis, helping organizations to make the right assumptions based on prior evidence, and making it possible to avoid the added costs and productivity loss that comes with cancellations and no shows.

There are also several external factors that can have a significant impact on service. Insight into environmental data, like historical traffic and weather patterns, can help develop more precise schedules in advance, in turn leading to reduced fuel costs, increased productivity, and reducing the risk of missing an important SLA.

Today’s wealth of data and machine learning technology is already delivering these benefits for industry innovators. With advanced planning, the business agility to respond to real-time conditions on the day of service, and thorough analysis, manufacturers can use the power of predictive field service to deliver increasingly efficient service coupled with exceptional customer experience.

Whitely Paul ClickSoftware, Industry TodayPaul Whitelam, senior vice president of global marketing at ClickSoftware, has more than twenty years’ experience leading multi-national marketing and product teams. Paul has worked on both the technical and business aspects of many areas that are fundamental to field service. This includes senior-level positions at Nokia (mobility and sensor technology), HERE (mapping and GIS), and Endeca (data management and analytics). Prior to his current role, he served as Group VP of Product Marketing, working with field service management leaders across a variety of industries.

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