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In the the final article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning,  we see how applying the Machine Learning can lead to better service resolutions... 

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Congratulations, if you have been following this series of articles across the last few weeks you are now a cognitive technology guru! (If you haven't don't fret! we've written a whole white paper on the topic which is available to fieldservicenews.com subscribers.)

In the series we've tried to give you an understanding of how and why the field service industry is applying these new technologies, and you see the writing on the wall: positive results for your customers, your employees and your financial success.

Now it’s time for you to go through the machine learning process yourself in order to answer the most important question ever asked in a field service scenario: “What is the best resolution to a given problem?”

If we break this question down into smaller operational components, we first need to maintain a set of wide range problems represented in groups which share similar characteristics. We then label these groups. Let's say, in our case, how the problems were fixed or repaired. Then, we need to create a learning process that labels a new problem based on its characteristics.

 

The Learning Process

 

 

 

 

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The learning process requires “real world” raw data and actual business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive. The business experts are the people with extensive knowledge of hands-on technical support and service (e.g. field service managers with years of service and product experience.)

The next step is called data preparation. This step is required to screen the raw data, remove duplicate or irrelevant records, handle missing data attributes, and extract indicators and features that are needed in the learning process.

"Free text", such as the problem description of these examples, is then extracted into a bag-of-words model. This is a representation of the description text with meaningful information (disregarding semantic relationships in the sentences).

The next step is called the training process. This is where the actual learning happens. Different types of learning algorithms are applied to a data training set (a subset of the entire data to learn from).

The learning process requires “real world” raw data and business experts. In our example, this data will be historical incidents and service requests that could most likely be predictive.

The choice of those metrics influences how the performance of the machine learning algorithms is measured and compared. Accuracy is one of the metrics to evaluate and classify models. It calculates the number of correct predictions made by our training model over all kinds of predictions being made. Another metric, called the confusion or error matrix, is a two dimensional table that visualizes “actual” versus “predicted” results for different questions, called the “classes”. It shows if a model is confusing two classes (e.g. commonly mislabeling one as another).

Aptly named, the confusion matrix visualizes “actual” versus “predicted” results for different questions, called the "classes". It shows if a model is confusing two classes.

 

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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In the third article in our current series of articles from field service solution provider Astea focussing on Artificial Intelligence and Machine Learning we now take a look at some of the practical applications...

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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Thanks to early adopters and industry analysts, you know that augmenting your service business with intelligent systems can be useful in enhancing organisational performance. Now let’s review four examples of how field service companies can apply these technologies in order to gain insight and improve performance.

 

1. Dynamic Scheduling

Dynamic scheduling provides optimised schedule and planning of field service agents. It’s constantly updating in response to events in real-time.For example, when a new high priority service ticket is received, it needs to be scheduled for immediate execution and all other tasks must be postponed.

There’s an element of AI in this process because the scheduling system must perform its task of scheduling this service order intelligently. In other words, it must think and thus deduce that simply prioritising this service ticket is not enough. All other tickets must be postponed in order to ensure that the high priority one is resolved first.

For example, let’s say a call has arrived in the system and agents Steve and Karen have the same skills and are both available. The system decides to assign Steve as the agent for the call by considering the nearest agent rule. However, by looking back at history and identifying patterns based on call attributes and agent performance, we find that while Steve is located closer to the customer site and is more experienced, Karen is the right agent to dispatched. 

Why? The pattern shows that Karen has a higher probability to resolve this call on the first visit as compared to Steve. This prediction can be done only if the scheduling system reviews historical data and learns from it. We can even find that having more experience and arriving earlier to the site doesn’t necessarily deliver better results.

 

2. Customer Contact Center

Call center agents play a key role in customer satisfaction. They are usually the front line of the customer’s experience, so it’s crucial to invest in systems that improve the way call centers are evaluated.

Two of the important indicators to assess the efficiency of call centers are “first call resolution” and “average time in queue”. Calls are typically analyzed manually by the agent who is listening to the call and determining the context. Agents also have access to a knowledge base to help them.

Unfortunately, having a human evaluating each call to determine the course of action is time-consuming and does not guarantee the best solution.

 

3. Service Contract Renewal

Service contracts are one of the most important parts that a field service organisation can ensure ongoing revenue from customers. However, these services are provided for a limited timeframe

only, after which customers have to renew their contract in order to continue receiving the services. It is very critical for the businesses to understand the underlying reasons that impact contract renewal. Traditional ways to evaluate the chance of renewal are based mostly on client satisfaction survey results. However, a lot of important information is captured as free text from emails, notes, problem descriptions, etc. while call center and field service agents interact with customers in trying to resolve service requests.

Using this unstructured textual information, with the aid of machine learning algorithms, we can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to classify the root cause of problems that lead to a customer not renewing a contract.

Machine learning algorithms can perform a sentiment analysis to detect a customer’s emotions about the service or specific product. This allows companies to learn why a customer does not renew a contract.

 

EXAMPLE: “Your customer support is great, however battery only lasts for 30 minutes.”

When analyzing the sentiments in this sentence, we can classify that the cause that could potentially lead to contract non-renewal is not related to the service, but rather is based on the product itself. As a corrective action, we can propose an upgrade to a newer model in which the battery life has been significantly improved.

With sentiment analysis, companies can respond quicker to signals, track changes in customer sentiment over time, determine if particular customers feel more strongly about their services, and predict the likelihood of contract renewal.

4. Inventory Management

Efficient inventory management revolves around knowing your demands. On one hand, companies must ensure that they have enough stock in their warehouses to meet the ongoing demands. On the other hand, they must preserve a small quantity of slow-moving items. Traditional solutions rely on formulas to reach static sets between pessimistic and optimistic values, and because of that, the chosen buffer level does not guarantee that the stock level fully satisfies the demand.

The way in which data can be incorporated from different external sources, including non-business data, helps machine learning models to find a correlation between the different variable factors and to optimize stock levels dynamically.

For example, feeding weather conditions and inventory data into a machine learning model can identify that specific parts tend to fail when the temperature outside is above 90 degrees Fahrenheit. In case the weather forecast shows that a heat wave is approaching, several proactive actions can be initiated, like ordering a sufficient quantity of items that are likely to break down so that those items can be used for predictive maintenance visits. Essentially, the machine learning model predicts the necessary stock level and determines how much inventory to order by using the past records of weather and failures.

The machine learning model can predict the necessary stock level and determine how much inventory to order by using the past records of weather and failures.

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

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Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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In the second article in our current series of articles from field service solution provider Astea focussing Artificial Intelligence and Machine Learning we explore how and why field service companies are adopting these emerging tools... 

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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There is a lot of buzz around machine learning and AI in the field service industry, but are service organizations actually adopting it? How are they applying the concepts to their business operations? And lastly, what results have they experienced thus far? We turn to some of the predominant service management industry analysts to help answer these questions.

Let’s tackle adoption trends first. Gartner predicts that by 2020, 10% of emergency field service work will be both triaged and scheduled by artificial intelligence, up from less than 1% in 2017. (1)

And by 2022, Gartner predicts that one- third of complex field service organizations will utilize machine learning to predict work duration and/or parts requirements, rising from less than 2% today. (2)

In addition to analyst predictions, another way to look at adoption trends is to ask how many companies plan to deploy machine learning in the near future. In a recent Gartner survey of 50 leading field service organizations, over 25% indicated they had artificial intelligence or machine learning projects planned for the next 12 to 18 months. (2)

Aberdeen Group’s research is in alignment with Gartner’s. According to a recent survey of customer experience management leaders by Aberdeen, only 14% of their organizations were currently using machine learning and only 9% were using AI. Yet 40% of the surveyed companies are planning to deploy machine learning and 34% are planning to deploy AI.

 

Adoption Results:

How have service companies applied intelligent systems to their operations? What have their results been thus far? 

Again we turn to the analysts at Gartner who state that field service companies will use AI and machine learning in their customer service channels to gain better insights, increase self-service and improve productivity.

Specific AI applications in customer service include:

• Intelligent case management routing and workflow;
• Robotic process automation (RPA) tooling;
• Knowledge management;
• Chatbots, virtual personal assistants (VPAs) and natural language processing (NLP);
• and finally, sentiment analysis and emotional detection in social media.(2)

Customer service appears to be the most popular application thus far for field service, but what other business functions could AI improve? According to Aberdeen, another application is managing the customer experience. Retaining existing clientele is top-of-mind for almost all companies. In fact, companies using cognitive technologies achieve 6.5 times greater year-over-year increase in customer retention rates, compared to all others.(3)

We’ve seen positive results from AI and machine learning when applied to customer service and the customer experience. But can these technologies improve life for your employees, too? 

Striking a balance between positive results for your customers and your employees is a best practice you’ll hear often when reading about cognitive technologies. The early adopters have proven it’s possible. Aberdeen’s research shows that companies using cognitive technologies enjoy 81% greater year-over-year increase in employee engagement. This is a bit of a paradox since in the short-term, some employees will be replaced by robots. Yet, when looking at the big picture it’s clear that the large scale impact of AI and machine learning will be improving employee productivity.(3)

 

Can this lead to financial success as well?

Thus far we’ve discussed positive results for your customers and your employees, but can cognitive technologies help drive financial success also? Absolutely!

Aberdeen’s data shows that companies using cognitive technologies enjoy 36% greater year-over-year increase in annual company revenue, compared to All Others. They also attain 63% greater annual improvement in customer lifetime value, compared to others.6

Companies using cognitive technologies enjoy 36% greater YoY increase in annual company revenue. They also attain 63% greater annual improvement in customer lifetime value.

 

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References::

1. Robinson, Jim et a “Critical Capabilities for Field Service Management.” Gartner, 27 March 2018.
2. Huang, Olive et a “Predicts 2019: CRM Customer Service and Support.” Gartner, 13 Dec 2018.
3. Minkara, Omer. “Cognitive Customer Experience: The Future is Here.” Aberdeen Group, April 2017.

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

---

Sponsored by: 

 

 

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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In a new series of articles from field service solution provider Astea, we tackle one of the burning questions being discussed amongst field service companies across the globe - what is Machine Learning and more importantly how can it be leveraged to improve service delivery? 

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Is Machine Learning a key topic for your organisation?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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There's probably no singular definition that would be accepted universally, but there are certainly some basic concepts. To understand those concepts, think about what machine learning actually does. The machine finds patterns in the data and uses these patterns to predict the future.

For instance, suppose that we have a data-set of one million emails that were tagged as spam. We could find patterns in that data-set that characterize spam, for example emails with all caps or with exclamation points, especially when they’re in the subject line.

Then, we can use those patterns to predict whether or not an email is spam.

 

Is Machine Learning the Same as Artificial Intelligence (AI)?

These two terms are often used interchangeably, but technically they do not mean the same thing, nor are they used for the same purposes.

We will start by defining artificial intelligence (AI) since machine learning has developed as a result of breakthroughs in the AI field. AI means that machines can perform tasks in “intelligent” ways instead of just being programmed to do a single task over and over. Performing a task intelligently implies that machines can adapt to different situations. Machine learning is considered to be a branch of AI, and it means that machines can be built to learn on their own and automatically improve their decision making through experience, all without human supervision.(1)

If you’re still a little fuzzy on the differences, we will define both concepts more simply:

• Machine learning consists of technology applications that learn by analyzing a pattern of historical and recent data.
• Artificial intelligence consists of technology applications that provide automated reasoning and decision-making capabilities. (2)

 

Origins of Machine Learning and AI

If you are wondering when these concepts were created, you can look all the way back to the original logical machines--computers. The computer’s end goal was that it could eventually function like a human brain. As we learn more about how the human brain works, we build that knowledge into artificial intelligence. And when you couple our deeper understanding of how the brain works with the massive amounts of data that the internet provides, you understand why AI and machine learning have grown so much in the last few years. 

According to AI expert Terrence Mills, “These two breakthroughs made it clear that instead of teaching machines to do things, a better goal was to design them to "think" for themselves and then allow them access to the mass of data available online so they could learn.” (1)

 

Why is Machine Learning so Popular These Days?

It’s all about data. Today, data is all around us. We are living in a data-driven world that’s only going to produce more and more data as time goes on. Humans and machines have become “data generators” that produce a tremendous amount of data every second. The amount of data created in the past few years is more than ever in the history of mankind and it is growing at a rapid pace.

The digital universe doubles in size every 2 years. By 2020, it will contain nearly as many digital bits as there are stars in the universe. 

In fact, the IDC estimates that by the year 2020, the accumulated volume of data will increase to roughly 44 trillion GB.(3)

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References::

1. Robinson, Jim et a “Critical Capabilities for Field Service Management.” Gartner, 27 March 2018.
2. Minkara, Omer. “Cognitive Customer Experience: The Future is Here.” Aberdeen Group, April 2017
3. Turner, Vernon. “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.” EMC Digital Universe with Research & Analysis by IDC, April 2014.

 

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Do you want to know more?!

There is a detailed white paper on this topic authored by Emily Hackman and Liron Marcus which is available to fieldservicenews.com subscribers within our premium content library... 

---

Sponsored by: 

 

 

---

Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content who may contact you for legitimate business reasons to discuss the content of this content.   

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Emily Hackman, Global Director of Marketing, Astea, looks at how the modern phenomenon of the connected customer is driving heightened service expectations that field service companies must meet...

In the last few years we have gone through a true revolution when it comes to digital connectivity.

The widespread adoption of tools that offer ever-greater connectivity amongst the general populace is increasing at ever-faster speeds. The end result of this increase in connectivity for businesses is a rising need for meeting rapidly heightening customer expectations when it comes to service quality.

Indeed, this is truly the era of the connected customer—consumers have become accustomed to having an unprecedented access to the knowledge and insight offered by the Internet right at their fingertips. And in a world were smart phones are virtually omnipresent, that access is 24/7/365, no matter where they happen to be.

 

Today’s consumers can instantly interact with friends and associates via text or social media, they can quickly summon a ride, make restaurant reservations, or order a gift with just a few clicks and swipes on their phone. Looking back even just a decade we would never have imagined the sheer pervasiveness of connectivity that we enjoy today and the huge impact it would have on our lives.

Yet for field service business, such increased levels of connectivity can be a double-edged sword offering both challenges and opportunities in equal measure.

Rising Expectations of the Connected Customer

Thanks in no small way to the companies like Uber and Amazon - who have embraced technology to not only disrupt the markets they exist within but also in many respects establish entirely new markets, the Connected Customer is intimately aware of the capabilities mobile computing bring to service operations.

Thanks to advances in mobility, their local florist or Pizza Delivery company can provide them with updates on their orders in real time. So why shouldn’t they expect field service technicians to be able to access those same or even more advanced mobile capabilities?

When it comes to service, connected customers now expect as standard:

• Real-time alerts when technicians are on their way to the job site/residence
• Technicians that will arrive armed with their individual customer histories and preferences
• A service organisation that can respond quickly to emergency calls
• The ability to receive real-time updates on the status of their service, both online and via their mobile phones
• Technicians that have full access to the repair information and parts that they need to complete the job

In fact, whilst just a few years ago mobile technology in and of itself offered a competitive advantage, mobility is now basic table stakes when it comes to field service.

And today, by harnessing the technology, service organisations are able to satisfy the needs of their customers. This can hugely effect how they refine and improve the customer experience, enhance their reputation, and reduce both employee and customer churn

Leveraging Customer Connectivity

In the world of enterprise, companies are rapidly embracing mobility.

According to data from Frost & Sullivan, 47% of North American businesses have at least 11 different mobile worker apps deployed, and 88% plan on introducing at least one new employee-facing app within the year.

According to the same data, companies have found that key mobility benefits include:

 

• More efficient business processes (49% of respondents),
• More productive employees (46%),
• Improved collaboration (46%),
• Cost savings (45%)
• More satisfied employees (44%),
• Enhanced customer engagement (43%),
• Competitive advantage (42%)

Of course, having a mobile solution in place does not automatically enable a service organisation to effectively serve the connected customer.

Focusing on reducing costs, whilst simultaneously improving productivity and efficiency is no longer the end game when it comes to mobility.

This is largely because whilst reducing costs and completing more jobs per day is good for the bottom line, they don’t necessarily assist in improving customer satisfaction and are not always in line with value creation in the long-term  and how quickly you can get them back up and running.

 

The brutally honest fact is that your customers don’t particularly care when you save money on fuel or can bill more jobs per month, they solely care about whether you’ve met your SLAUltimately, service customers simply want reliability and visibility. Did the service organisation get their technician to the job site quickly, armed with the right parts and repair knowledge? Were they able to complete the repair in one visit?

Every decision the service organisation makes should be weighed against a backdrop of the overall impact to the customer. The fact that customers are now highly connected makes it easier for service organisations to meet their needs, provided that they have their own robust mobility solution in place. But that is just the tip of the iceberg.

By leveraging analytics, the input your connected customers provide can help you understand consumption patterns and deliver a personalized solution—and potentially do so at a premium, creating new pricing models and differentiated service models, and establishing new revenue streams in the process.

 

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Click here to apply for a complimentary industry subscription to Field Service News and get the white paper "Preparing for the Connected Customer" sent directly to your inbox now

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