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3 Ways In Which Predictive Analytics is Optimizing Data Leveraging for Service Companies

In this special guest feature, Shahar Chen, CEO & Co-founder of Aquant, discusses how predictive analytics will reshape service companies in every industry. There are several technical aspects involved in predictive analytics, including data mining, modeling, artificial intelligence and machine learning. For service companies, it could mean huge savings and a reshaping of the industry. Shahar is an entrepreneur with an innovative spirit and a passion for business and sales. He brings more than 15 years of business and technical expertise in enterprise software, consulting for Fortune 500 companies on field service management, improving business processes, and implementing best practices. As an enterprise sales executive, Shahar has a proven track record of driving multi-million-dollar yearly sales in the field service and workforce management industries.

Predictive analytics will reshape service companies in every industry. For those unaware, predictive analytics is a branch of analytics that uses data to make predictions about future events. There are several technical aspects involved in predictive analytics, including data mining, modeling, artificial intelligence and machine learning. For service companies, it could mean huge savings and a reshaping of the industry.

In fact, we are already seeing industries rapidly transition to new models, based on the insights of artificial intelligence. There is no end to the ways in which predictive analytics can help service companies optimize their data leveraging practices when imbued with a creative edge. Doing so allows them to get ahead of the curve.

In the years ahead, service companies will rapidly adopt predictive analytics as standard practice. The companies that embrace this new technology sooner rather than later will have a solid foundation already in place, giving them a leg up on the competition.

Adopting A.I. analytics allows companies to not only improve their data analysis efforts but also reduce costs in that department. With Machine learning analysts can focus on the big-picture implementation of data strategies, allowing service companies to reduce analysts while improving efficiency.

One of the areas in which predictive analytics really shines is in risk analysis and reduction. In fact, the technology can quickly provide some quick wins in this area. Using machine learning can help companies leverage historical data to analyze risk and provide strategies that will deliver the maximum return. For service companies that deal with machinery and continued maintenance, it’s easy to see how this would be hugely beneficial.

Predict Downtime

Downtime is more than an inconvenience for large, complex companies. The cost of the interruption compounds the longer that downtime lasts, with employees prevented from completing tasks and transactions not being completed. A recent study by Gartner suggests that large companies may be losing as much as $540,000 for every hour of downtime due to technical failure. Globally, downtime costs companies $647 billion per year. It’s frustrating that this downtime is largely preventable.

One of the most measurable ways in which machine learning can help service companies is through the prediction of downtime. Service companies that maintain systems stand to gain a lot from reducing unplanned maintenance in their customer’s systems. Being able to assess equipment and then schedule necessary maintenance well in advance of equipment failure is incredibly beneficial.

Improve Efficiency of Technicians

Artificial intelligence provides a clearer picture of significant issues, ensuring that service companies send repair techs with the right expertise and tools to get the job done, and shorten planned downtime as well. Without predictive analytics, companies often base their repair schedules on intuition and experience. While valuable, this strategy is often inaccurate and leaves companies out of step with reality. They may show up to a job site to find that an unexpected part has broken and they lack the necessary tools to fix it. Even the most outstanding service technicians aren’t able to predict the future.

Predictive analytics can improve efficiency in these areas. The AI systems are able to analyze historical data at scale, beyond what is possible for human data analysts, to determine not only when a machine is likely to require maintenance, but also which parts are most likely to break. Machine learning allows technicians to ensure that they arrive at each job site with an understanding of what is required and the correct tools for the job. Equipment sensors may be able to give an exact diagnosis for each individual problem or provide an accurate guess when data is not available. Additionally, predictive analytics can reduce repeat service visits by optimizing parts management.

Optimization of Demand Analysis and Pricing

A failure to properly optimize prices for services can be a huge drain on small and large companies. Continually optimizing your pricing ensures that you can deliver maximum return. It allows companies to adequately test different pricing levels, measure elasticity and determine the best prices for their products and services.

Predictive analytics can help service companies identify new opportunities, both in market-wide demand and with individual customers. A.I. can provide insights into which customers are most likely to be interested in upselling opportunities, and help companies position themselves to capitalize on industry trends.

Conclusion

Downtime and poor business decisions cost companies billions per year around the world. Service companies who stake their future on being able to provide a higher value service than that of their competitors would be foolish to avoid embracing predictive analytics. It enables them to reduce downtime, predict upcoming technical issues and ultimately provide a more efficient service in all aspects of their business.

 

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