Proactive and Predictive Analysis: The Future of IT Support

Digital transformation in industries means that tech support needs to be more proactive and easily available to customers and employees. Downtime and gaps in productivity due to unforeseen events or technical difficulties are no longer considered a valid reason for delays in deliverables or loss in productivity. Most companies have efficient user support processes that are reactive, meaning, they help solve a problem after it has occurred. Customers reach out to the support team every time they have a problem through multiple channels; the support team then troubleshoots the issue and offers a viable solution. However, employees and customers want more than just a solution to their problems- they require IT support that is quick, easily accessible, reliable, and hassle-free.

Imagine being able to solve a problem before the employee calls or to be able to predict and resolve an issue before the employee is even aware of it. Predictive customer support is when a customer receives the required assistance not just before they call, but even before they realize they have a problem. Think of it like an experienced customer relation executive who, based on the customer’s history with the company, can predict the type of assistance that the customer needs, creates a solution strategy, and applies the strategy all before the client even encounters the issue.

If you have any concerns about whether you want to make a switch from the reactive to the proactive and predictive model- think about whether you’d rather solve a problem that has occurred, or if you’d prefer to prevent the problem from occurring in the first place.

Predictive Approach vs. Reactive Approach

In today’s workplaces, the use of various elements such as mobility, outsourcing opportunities, offering software as a service, and cloud-delivered services means that there is a greater need for technology and a higher chance of technology breaking down and halting productivity, which in turn surmounts the pressure on companies to find more efficient and automated ways to deliver support services. Most employees find that accessing support services takes up too much of their time and end up choosing not to flag the issues and instead suffer in silence. While we can’t quantify the cost of this hesitation, we can understand that it causes a significant decrease in productivity and employee morale and confidence. Even the best standard user-support processes are largely reactive in their approach- providing solutions only after a problem has occurred.

Businesses can no longer afford to operate support services only in a reactive mode, the tried and tested processes such as ITIL, service desk incident, and root cause analysis is all reactive processes. In order to be a step ahead of their competitors, companies need to be able to identify and diagnose technical issues before customers report them.

Advancements in predictive technology have revolutionized the operation of businesses- automation facilitates efficient, accurate, and prompt service, and artificial intelligence use company data to make a predictive analysis which can improve business operations. The result of this Proactive-Predictive approach is a fast, reliable, and effective support system.

Reactive approach	Proactive Approach	Predictive Approach
This model is used in most businesses. A client experiences an issue or problem.	Client devices, network and services are monitored 24×7 through automation technology and IT analytics.	Predictive analytics and technology allow for synchronous real-time intervention.
The client then contacts the user-support department to report the problem.	Automatic alerts are programmed to provide data about possible issues.	Predictive analytics identifies issues and attempts to prevent them from happening.
Escalation of more than one issue may be required for onsite dispatch.	Issues may go unnoticed as it causes very little disruption to productivity.	With this approach, dashboards and diagnostic tools are utilised to facilitate quick action.
Low employee productivity is usually observed with reactive SLAs (as identifying and reporting issues as well as strategizing and providing solutions to issues may delay resolution).	Proactive support providers will reach out to the clients with warnings, resolution strategies and solutions- even before the problem has occurred. Thus not impeding productivity.	IT performance and employee productivity levels are high. This is because there is less burden on the IT department thanks to predictive diagnostics and automated technology, and employees don’t need to wait for resolutions.
Even when the SLAs are met, client satisfaction is not ensured (as there is a loss in productivity, delays, expenses etc.)	Employees have a better experience with user-support and are more pleased not to have the hassle of reporting and waiting for resolutions	Employees enjoy an enhanced user experience as early warning tools prevent any unnecessary downtime.

After all, the most productive employees are the ones with access to uninterrupted, responsive and reliable information and tools that they need to do their job.

Why Is Proactive Support Better?

Companies have more access to data about their customers and products than ever before. This data, through predictive analytics and AI, can be used to provide personalised customer experiences and predict incidences in the future with respect to a customer’s needs. For example, Volvo uses this sort of analytic technology to predict when certain cars or parts need to be serviced through their ‘Early Warning System’. The company then recommends services and maintenance plans to their customers before a problem has even occurred.

Put yourself in the customer’s shoes, imagine their happiness and satisfaction on receiving an alert for potential trouble, and giving them instructions on how to best manoeuver around it. This can drastically improve the support team’s performance and reduce negative user experiences.

Automated tech in the form of diagnostic tools and dashboards also favour home users- whether small business owners or employees forced to work from home during the pandemic. Instead of waiting for the problem to occur and not being able to work until it is fixed, employees will instead receive an alert for a potential issue which can be solved long before it affects productivity.

Predictive analytics help organisations develop system improvements to enhance performance, avoid common disruptions and improve employee productivity. It looks at common occurrences, patterns, probabilities and trends, it then measures variables to predict future events.  Advanced analytics capabilities include ad hoc statistical analysis, predictive modelling, data mining, text analytics, optimization, real-time scoring and machine learning. Using these, companies can analyse problems that have occurred in the past, make a root cause analysis and proactively create strategies and solutions in order to prevent any future repetitions and disruptions.

Automation and AI facilitate machine learning which allows companies to accurately and efficiently identify any underperforming or faulty technological assets which could result in downtime. This is done by determining “normal” behaviour and performance levels, and comparing it to past disruptions and breakdowns. Predictive analytics is then used in identifying patterns or abnormalities which can help in predicting the occurrence of any technical issue. The more data there is to run analytics on, the more accurate the predictions are.

Steps to Predictive Analytics

There are four major steps to make predictive analytics work:

1. Find the right data: Understand the organisation’s goals and identify the most relevant data with respect to these goals.
2. Use this data to determine a predictive model: Once you have relevant data from multiple sources, consolidate this data and select the best possible algorithm that aligns with the company’s goals. The algorithms are first tested on smaller subsets of data until the most accurate required result is obtained.
3. Deploy the best model: Use all the data collected on the best algorithm obtained to create the best suited predictive model.
4. Continuously monitor the model to ensure efficacy as the data can degrade over time. The model needs to be constantly updated and adjusted by collecting new data or even by adjusting the algorithms.

Benefits of a Proactive and Predictive Approach

Here are a few ways in which a proactive and predictive support approach is better than a traditional reactive approach:

1. Better for making decisions: Proactive support helps companies plan better and make better decisions. It aids companies in the analysis of data to help them better predict future outcomes and therefore take the necessary steps in order to deal with them better.
2. Effective Budget Planning: When you’re able to predict future outcomes, you’ll also be able to better plan your budget for those scenarios. There will be a decrease in the number of ad hoc expenses.
3. Cost Saving: Preventive measures taken towards predictable IT issues helps cut down on risks and expenses that might otherwise surmount when the problem occurs. This preventive method is not only cost-effective in the traditional sense, but also saves the company a lot of money as it keeps employees happier and more productive with there being fewer unforeseen IT issues that are interrupting their workflow. A report from OpenSpan showed that companies operating with a predictive approach to customer service can lower their call centre operating costs by 25%.
4. Increased Uptime: With proactive monitoring and predictive strategies in place, you can easily identify areas of vulnerabilities or critical issues and fix them before they cause any sort of serious disruption in the workflow.
5. Improved turnaround time: With proactive support, issues are identified before the user reports it, this gives the help desk time to take action much faster. All issues will receive priority and the major issues can be identified easily because of automated and effective resolution tools.
6. Can improve Customer Loyalty: According to a study by Forbes, proactive customer service can boost customer retention and loyalty from 3 to 5%. When the support team proactively reaches out to customers, it reduces escalations and problems which in turn results in higher customer satisfaction.

In conclusion

Predictive analytics are used to forecast disruptions and outages allowing companies to proactively plan for operations to run more smoothly and efficiently. Downtime in businesses can cost millions and even result in the loss of customers, and when that happens, resources are diverted from the business’ core operations to IT and support teams. Taking a proactive and predictive approach to user-support helps companies plan better, make smarter decisions, run smoother, improves productivity and results in happier employees and customers. At least with user-support, the future certainly is predictable.