Using predictive analytics to improve field service management

Key Takeaways:

Here’s a quick overview of how predictive analytics improves field service management:

• Predictive analytics leverages historical data, statistical algorithms, machine learning, and AI to anticipate future occurrences such as equipment failures, parts requirements, and technician performance.
• Predictive analytics provides answers to the question of “what will happen,” and prescriptive analytics takes it one step further to say “what we should do about it.
• Combining both enables field service companies to shift from reactive to proactive, then to prescriptive decision-making, improving efficiency and customer satisfaction.
• By consolidating data from IoT-enabled equipment, technician smartphones, and customer touchpoints, businesses can gain a holistic view of operations and performance.
• The key to success is gathering big data, measuring the right metrics, not the most metrics, and creating statistical models to predict future performance.

These days, we hear a lot about the importance of data, analytics software, big data, and other related topics. It’s not a fad. Computer science has reached new heights in terms of power and compute capabilities, opening the door to numerous new possibilities in every industry.

Google, Amazon, Facebook, and Apple — the famed GAFA — are at the heart of the digital revolution. But they’re certainly not the only ones involved. Today, every company is affected by the explosion of data and the potential it holds. And companies in the field services industry are definitely part of the trend.

Field service analytics has emerged as a crucial component of this digital transformation. It helps businesses optimize their operations and provides insights that drive better decision-making.

What is Predictive Analytics?

Predictive analytics is the branch of advanced analytics that focuses on forecasting future outcomes based on historical data. It uses statistical algorithms, machine learning, and AI predictive analytics tools to find patterns and trends in large datasets. Predictive analytics aims to give actionable insights to help organizations predict future events and improve their decision-making process.

Predictive analytics uses powerful computer predictive data analytics tools to analyze the massive volumes of data generated in today’s digital world and predict the future. The capability can be very useful for field service companies, especially when you understand the types of predictions that are possible.

What is the difference between predictive and prescriptive analytics?

Predictive analytics glances at what might happen in the future, while prescriptive analytics takes it one step further by suggesting concrete actions to attain the desired goal. In practical terms, predictive analytics seeks to answer the question of, ‘What will happen?’ while prescriptive analytics tries to address, ‘What should we do about it?’

For example, predictive data analytics may have determined that a piece of machinery will probably fail in the next month. Prescriptive analytics would provide actionable strategies for scheduling maintenance, ordering replacement parts, and reallocating resources to help prevent downtime.

Field service management requires both approaches. Predictive analytics assists in the early identification of potential trouble before it occurs, and prescriptive analytics ensures that the response will be optimal, thereby maximizing efficiency and effectiveness. By combining these analytics techniques, field service companies can transition from reactive to proactive to prescriptive decision-making, resulting in higher performance and customer satisfaction.

Predicting the future from the past

Anything that involves change can be predicted: spare parts inventories, maintenance tasks, field technicians’ schedules — even changes related to customer satisfaction levels. But to make these predictions, you need the relevant data and the appropriate analysis tools.

The integration of predictive data analytics tools allows field service companies to streamline their workflows. These tools are essential for processing vast amounts of information and delivering actionable insights. 

Statistical analysis of historical data

Predictive analytics technologies use sophisticated computer algorithms to analyze large volumes of statistics and reveal trends that are not obvious or even detectable by humans looking at the data.

In field service management analytics, data from previous service calls is analyzed to create statistical models that help forecast future trends. For example, analytics algorithms can help to determine the probability of customer behaviors, equipment failures, field technician performance, stock levels for particular parts, and many other items.

Prescriptive analytics vs Predictive

Traditionally, field service management analytics relied on descriptive and diagnostic analytics. These approaches looked back at historical data to learn about past performance or explain why a particular outcome occurred. They were great for analytics and reporting on field service KPIs but couldn’t forecast future trends or suggest proactive actions.

However, predictive analytics takes it a step further. It employs advanced algorithms, predictive analytics techniques and AI predictive analytics tools to analyze historical data and make accurate predictions. For example, traditional methods can explain why a piece of equipment failed, but predictive field service management analytics can predict possible failures and suggest preventive measures.

In addition, predictive analytics tools can process large datasets from multiple sources, which traditional methods find difficult to process efficiently. This capability enables field service companies to make data-driven decisions in real-time, improving operational efficiency and customer satisfaction. The key advantage in today’s competitive field service landscape is that businesses can shift from reactive to proactive management by embracing predictive analytics.

What are the benefits of predictive analytics in field service?

As in all industries, the shift to a digital world where people have easy access to information on the internet and instant responses on smartphones has changed the balance of power between field service providers and their customers. Now, more than ever, the customer is king.
 
Your customers know all about you and your competition. This knowledge creates increasingly high expectations for responsiveness, mobility, visibility, quality of service and other performance factors. Field service providers have no choice but to evolve their operations to meet these demanding performance standards.
 

An important tool to increase customer satisfaction

But, let’s not beat around the bush. This evolution is not necessarily easy for field service companies. While things have changed in recent years, field service companies have not traditionally been among the first to adopt modern operations.

American field service providers have led the way, showing the benefits that can be realized by moving to digital operations. In Europe, companies such as Praxedo have been advocating for paperless processes in the field service management industry for more than a decade. So, things are moving in the right direction.

The analytics and reporting on field service KPIs plays a pivotal role in ensuring that businesses can measure and enhance their performance standards effectively. These metrics help companies understand operational gaps and implement strategies to improve service delivery.

The predictive analytics tools available today allow field service management companies to offer efficient and innovative service management capabilities that exceed customer expectations. And it’s all thanks to smarter use of the massive amounts of data about service activities and customers that field service companies can now collect.

How do you apply predictive analytics to field service management?

To successfully integrate predictive analytics logic into your field service management process, start with the following steps.
 

Convert to big data

In today’s digital world, you can collect data almost everywhere. For example, you can use technicians’ smartphones to collect and transmit data about their daily service activities. Equipment can also communicate data about its operating state and send alerts in case of failure. These connected objects are all part of the Internet of Things, and all customer contact points — call centers, web portals, emails — can receive the data that’s collected.
 
The key for field service companies is to think in terms of big data and adopt the tools needed to collect centralize all of this data in their information system. With centralized data, you have a detailed, global view of your customer activities and performance levels.
 

Track relevant metrics for your business

The challenge with big data is that it’s really big. And you can quickly find yourself overwhelmed, drowning in an ocean of information. It’s very important to start by defining a clear and easily understandable set of benchmarks and metrics that will help you better understand your company’s operations and evaluate performance. Identifying more metrics is not better. You must identify the right metrics.
 

Anticipate future performance

With predictive analytics techniques, such as artificial intelligence and machine learning, you can define statistical models based on historical data for any type of installation or maintenance work completed by field technicians.

These statistical models result in analyses that predict future trends and allow you to anticipate, for example, how spare parts stocks will need to be managed or impending failures on a specific type of equipment. With preconfigured machine learning models, you can extract useful information from the analyzed data even faster.

Together, all of these factors point to the undeniable fact that field service companies must take a keen interest in new predictive analytics technologies. Whether it’s AI predictive analytics or understanding the differences between prescriptive analytics vs predictive, these tools will soon be essential to keep up with the frantic race to deliver ever-higher performance levels.

Conclusion

Predictive analytics is no longer optional for field service companies aiming to stay competitive. Historical data can be used to generate precise predictions, and when combined with prescriptive actions, businesses can proactively address potential failures, optimize inventory, and exceed customer expectations. The journey begins with data centralization, the right metrics, and models that predict change. Businesses that adopt these tools now will be the ones providing the responsiveness and service quality that today’s customers are looking for. To see how Praxedo can help, request a demo and start turning your field service data into a competitive advantage.

FAQs:

1. What is predictive analytics in field service management?

It’s the application of historical data, statistical algorithms, and AI to predict future events such as equipment failures, parts requirements, and technician performance.

2. What is the difference between predictive and prescriptive analytics?

Predictive analytics is used to predict what will happen, and prescriptive analytics is used to suggest what to do to get the best result.

3. How does predictive analytics differ from traditional analytics?

Traditional descriptive and diagnostic analytics provide insights into past performance, while predictive analytics can predict future trends and recommend proactive actions.

4. What data is needed for predictive analytics in field service?

Data from technician smartphones, IoT-connected equipment, call centers, web portals, and emails, all centralized into one system for analysis.

5. How does predictive analytics improve customer satisfaction?

It enables businesses to predict issues and act quickly, addressing the increased customer demand for responsiveness, visibility, and service quality.

6. How do you start using predictive analytics in field service?

Start by consolidating your data, establishing a set of relevant metrics, and creating statistical models to predict future performance.