Make decisions for your business based on data, not instinct

Are you making business decisions based off actual data or on gut instinct? Oftentimes, it’s both. If you lean more towards making “guesstimates,” or just plain old instinct, then you may be making the wrong decisions and putting your company at risk.

Top-performing IT solution providers (TSPs) rely on data insights to make important business decisions instead of just relying on instinct. They make decisions based off of real data, which gives them the insights and knowledge they need to not only improve their financial and operational performance, but see where there is room for improvement.

To make the right decisions, you need to have accurate data, which means looking at the right metrics. Metrics are the quantitative measurement of data in relation to what you are actually measuring. This means most of the activities and interactions that are going on day to day in one’s business creates a great deal of records and data. For instance, understanding that each day a TSP is logging ticket after ticket creates a wealth of knowledge and insight – if one can take a step back to harness this data, which can help identify certain trends in running a business.

This means you need to implement systems to track operational metrics with data. The metrics that you collect and analyze may differ from business to business. You might find value in measuring different types of operational data depending on the ease of reporting and the complexity of data systems.

Measure service levels and tech performance

A good place to start would be tracking some key metrics to measure service levels and tech performance. This would include: 

• Number of managed services (MS) contract endpoints
• Number of end users supported by managed services contracts
• Number of MS contract tickets generated
• Average hours per ticket
• Average hours per endpoint
• Number of MS contract endpoints managed per MS engineer
• Number of MS contract tickets per MS engineer

Note that this data needs to be studied regularly to make data-driven decisions. This way, you can evaluate if these are the right metrics and see where you need to make performance improvements.

When evaluating the metrics, look for the possibility that change in the company environment has occurred. For example, you might notice the total amount of tickets generated has significantly increased month over month. At first glance, it might seem that the provider is experiencing a noisy environment from clients. However, a closer look could reveal that the provider had many new accounts each month. That could cause the ticket increase, as opposed to a noisy or inefficient environment.

What to look for within your data

Looking for changes and discrepancies in data are most valuable to you. Building on the ticket increase example above, let’s say you notice an uptick in tickets from 10,000 one month to 20,000 the following month with no major increase in clients.

This would be a red flag and you need to take a closer look at what is causing this issue. Was there an increase of unactionable automated alerts skewing the data? Has there been a change in the configuration of the tickets? Since you want to steer clear of guesses as to the ticket increase, you’ll need to further analyze the unique situation before making conclusions.

You may want to examine whether the average hour per ticket is rising, which means your business is moving in the wrong direction. This will have a correlation on the company’s service gross margin, since more engineering resources will be needed to complete more tickets. 

This is the type of metric you want to examine before it’s too late to do anything about the problem. Any lost revenue from typically profitable clients will continue to damage the company’s gross margin. By watching operational metrics in real-time, you can take action before you see the profit losses in the month-end financials.

In addition, longer average hours per ticket eventually lead to lower customer satisfaction and a higher risk of losing clients, which will figure into that loss of profit. 

How the right data helps you plan for the future

Having accurate data is crucial to growing your business, which includes looking at the right metrics to help you plan for the future and for staffing purposes.

A good example of this is understanding the number of Managed Services (MS) contract endpoints managed per MS engineer. Let’s say that number is currently 400. If your company is bringing on a new client every other month that needs to support 200 endpoints, that means that by year-end, you should have six more clients, or 1,200 endpoints. If there is no loss of clients overall, the company should reasonably expect to add three more employees to meet the additional needs of the endpoints. This data offers a more realistic method for gauging the number of new staff, compared with a “feeling” that more employees are needed.

Another way to look at data for future staffing purposes is if you can measure the number of MS contract tickets per MS engineer that they can close. If that number is, for example, 240 tickets closed in a quarter, and you currently have 10 engineers, that means you can reasonably expect to close 2,400 tickets a quarter. If you can effectively reduce volume of tickets by 10% or 240, it means you have that much availability in your staff for future clients’ requests. If, on average, a new client creates 120 tickets a quarter, you would be able to handle onboarding two new clients before future hiring is necessary.

Help employees understand the data

The more that employees understand the day-to-day results from the metrics within their department, the more buy-in you’ll get in terms of meeting goals. This helps them understand how their performance relates to the company’s goals and what they are expected to achieve weekly, monthly, and quarterly.

In addition, readily available data shows employees that goals are set based on actual data, not on guesses. 

In the same way, managers can use metrics to become better informed about their direct reports. For example, managers can create standardized forms of evaluation, such as examining the service metrics for the department and for each individual team member. This allows both the manager and their direct report to agree on expectations on how success is defined and what they should evaluate.