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When the technology isn’t working right, the customer calls you. But what if you contacted the customer before they even became aware of the problem? Many Support Services teams are finding success by pivoting from reactive support models to predictive and proactive support. This topic was explored in depth during the “Enabling the Journey from Reactive to Predictive to Proactive Support” virtual summit.

To understand why businesses are gravitating toward predictive and proactive support, it’s important to understand the current business environment. There are two megatrends that are currently disrupting the business model of technology providers:

  1. Commoditization of feature functionality
  2. Acceleration of new consumption models

These trends are fueled by the way companies have historically developed their products. The tendency is for products to add new features and consequently grow more complex. As proof, 73% of TSIA members said it requires a high degree of technical and business acumen to support their products.

Unfortunately, the proliferation of features has far outpaced customers’ ability to consume. This divergence has created a consumption gap. Customers have been slowing down on decisions to refresh their technology. CapEx spending has declined and customers are relying on vendors to get the most out of their purchase.

The impact on the tech industry has been dramatic. While product revenue has declined, service revenue has accelerated. In the second quarter of 2020, service revenue has grown to 69% of total revenue. It should be noted that this service revenue is primarily recurring revenue that is dependent upon renewals.

Let’s be honest: tech companies like to make, sell, ship, and then forget about it. Tech companies have always offered support, of course, but it was typically reactive support. They would wait for the customer to call with issues.

That strategy doesn’t work anymore. Now customers are asking suppliers to take more responsibility for delivering business outcomes. You have to train, drive adoption, and ensure the customer gets the ROI they were promised. In order to do all that, you need to:

  • Know what’s happening at the customer location
  • Be connected
  • Analyze the data to predict what will happen next
  • Take proactive steps to achieve better outcomes

The most mature organizations are predictive and proactive. They anticipate needs and act to ensure business outcomes. To do this, you need predictive data analytics.

Of course, the COVID pandemic has changed everything. Employees cannot get on site, so we’re seeing extreme remote services. Customers are engaging remotely and are more willing to share data. In response, tech companies are investing in AI and data, and are finding new ways to deliver everything virtually.

AI Meets Big Data

One company that has made tremendous strides with AI and data analytics is Sysmex. When they switched from analog to digital data collection, Sysmex went from having too little information to a deluge of information.

To make sense of this data overload, Sysmex began creating evidence-based programming.

These efficiency tools helped to increase connectivity, driving IoT connections up over 90%. This connectivity has made it far easier for Sysmex service teams to support their products.

In addition, there was a shift in the demands placed upon Sysmex’s workforce. Employees who were once specialized workers within a specific niche needed to become generalists within a larger network. They needed to be able to support multiple products in multiple areas.

To support those products, employees need to expand their knowledge base. Sysmex began creating training materials and documentation pulled from past service experiences. They created a knowledge base that could be shared across their entire service organization spanning multiple continents.

Sysmex wanted to explore the capabilities of using AI in its support services. Although Sysmex usually prefers to build its own tools, they decided to pursue a different path to overcome some of the obstacles they encountered while trying to incorporate AI.

The main obstacle to AI integration was a lack of standardization in Sysmex’s service documentation. Service reports are written in free text in multiple languages. Also, there were many instances where customers referred to parts or products by slightly different names. All of these variations created data that was difficult to decipher. As a result, the “dirty” data often produced irrelevant results.

Enter Aquant. The AI platform from Aquant offered a solution to Sysmex’s issues with scrubbing out the dirty data from the company’s vast store of information. Aquant was able to sort through the data quickly, simplify data complexities, and deliver valuable insights on Sysmex’s products.

In addition, Sysmex liked the fact that they were able to leverage the collective AI implementation experience of Aquant’s other customers. This shared knowledge, combined with the product knowledge of Sysmex’s experts, positioned Aquant as a tangible tool that Sysmex’s service engineers are using to drive faster resolutions for customers.

One specific area where Aquant has helped Sysmex’s service professionals is prompting better questions. It is often difficult to get customers to fully explain the symptoms they are experiencing. The Aquant AI guides the service team to ask the right questions to pull all necessary information.

The combination of Sysmex’s data analytics and Aquant’s AI capabilities is allowing Sysmex to not only react to customers who are experiencing difficulties, but also be proactive and predictive in preventing problems.

The High Costs of Remaining Reactive

Elekta, a TSIA STAR Award winner for Best Practices in Field Services, is another company that has successfully moved from reactive support to more predictive and proactive service models. The company credits its processes, real-time data, and AI for its ability to make this transition.

It is important to note that Elekta produces radiotherapy solutions for cancer patients around the world. Needless to say, it is urgent that Elekta’s equipment functions properly to provide vulnerable patients with potentially life-saving therapy.

Given the high stakes involved, Elekta was eager to move beyond a reactive approach to fixing broken equipment. When a machine breaks down, it is out of commission for approximately 66 hours while a field service engineer is dispatched to repair it. That means patients cannot receive treatment for almost three days.

In contrast, Elekta found that a machine is only out of commission for 13 hours when the repair is proactive and predictive. Having a field service engineer replace a part before it fails reduces machine downtime by 80%. That’s a big deal when you’re talking about treatment for cancer patients.

So how was Elekta able to shift toward proactive and predictive support? The answer is digitalization. The company now boasts more than 18,000 connected devices, representing about 80% of its total portfolio.

The connected devices are constantly delivering a wealth of data to Elekta. To sort through that data, Elekta employs an AI algorithm that is able to identify problems. On average, Elekta is now able to predict when to replace a part three weeks before part failure.

To make this all work, Elekta stressed the importance of setting up the proper infrastructure to support proactive and predictive services. Information technology, parts logistics, customer support channels, data science and all parts of the ecosystem must be connected to succeed. Of course, the technology must also be engineered appropriately to provide the real-time data that is the backbone of the predictive and proactive model.

Although Elekta had been working on the transition away from reactive services well before 2020, the weaknesses of the reactive model became even more apparent during the COVID pandemic. It became increasingly difficult for service engineers to travel and for replacement parts to be delivered. As such, machine downtimes would likely have expanded if Elekta had not already embraced more proactive and predictive methods.

The Power of the Cohort

Qlik is a software company that provides an end-to-end platform that includes data integration, user-driven business intelligence, and conversational analytics. It has found an innovative way to use that data and analytics proactively.

Like many data-driven companies, Qlik receives a great deal of information. It then becomes a challenge to sort that data effectively. This was especially true in the area of problem management. The company had plenty of records of incidents and customer questions, but what should it do with all of that information? Solving that puzzle was the key that changed Qlik from a reactive company to a proactive company.

The first step in Qlik’s transformation was shifting the mindset of the service team. Instead of waiting for problems to arrive – a very reactive mindset – engineers needed to start thinking about how they could prevent problems.

Next, Qlik needed to develop a fuller understanding of the customer lifecycle. To do this, Qlik began to split customers into different cohorts based on how long they had been using Qlik’s products. Thus, customers new to the platform would be grouped together and tracked as they moved through the customer journey from onboarding to renewal.

The value in the cohort idea is that it allows Qlik to compare apples to apples. They began to see clear quantitative and qualitative patterns that indicated when a customer was falling below benchmarks for adoption, training, satisfaction, and engagement as examples. Not only could Qlik identify when the support team needed to reach out to the customer, they could also get fast feedback on improvements made to their onboarding process. In other words, the cohort model allowed Qlik to be predictive and then proactive.

This predictive, proactive approach is especially important for subscription businesses. If your services are purely reactive, the customer may not tell you about problems until it is time to renew the subscription. By then, it’s too late. Customers are more likely to cancel a subscription if they are currently having trouble with the product. It is far better to intervene throughout the lifecycle to prevent problems.

But Reactive Support Isn’t Going Away

To be clear, there will always be a reactive component to Support Services. Things break and they need to be fixed. Nevertheless, there is real value in trying to become predictive and proactive.

A predictive, proactive approach allows you to fix the problems before they actually become problems. This will eliminate many of those reactive situations. Equipment won’t break down unexpectedly, downtimes will shrink, and the customer does not have to approach you for help. It all adds up to a better experience for customers.

To learn more about how you can move your business toward predictive and proactive support, watch the full “Enabling the Journey from Reactive to Predictive to Proactive Support” virtual summit.

 
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Vele Galovski

About Author Vele Galovski

Vele Galovski is vice president of support and field services research for TSIA. Using his nearly 30 years of industry experience, he has consistently helped companies both large and small drive double-digit top-line growth with a proven retain, gain, and grow strategy. Vele has also written a book, The Perpetual Innovation Machine, which describes a holistic approach to management based on ambitious goal setting, data driven analysis, skillful prioritization, inspiring leadership, and the lost art of employee engagement.

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