Benchmarking is the process of comparing one’s business processes and performance metrics to industry best practices from other similar companies. The metrics that are typically analyzed are those that focus on efficiency, reducing costs, and improving performance. Successful benchmarking then allows companies to understand their competitive position with others in their space and make actionable business decisions based on that information.

This all sounds pretty straight forward, but it is an imperfect science and requires hard work and commitment to the process itself in order to be successful. The following are some of the common challenges that you may encounter along the way:

1. Avoid a Complicated Model

Start simple. Pick one or two high value, yet simple metrics and focus on getting them really dialed in. You can always add complexity later as you start to better understand the variables.

2. Accessibility of Data

Once you’ve selected your metrics, you may learn that you are not currently collecting the requisite data to analyze your performance.  You may need to introduce process changes or technology solutions to help you capture the information you need.

3. Compare Yourself Against “Like” Competitors

Once you have the data you need it will be important to compare yourself against people in your space who closely resemble your business. Even small subtleties may create gaps between two businesses and those gaps can render data sets to be very different.

4. Staff Resistance & Resource Constraints

Implementing new process and technology may be viewed by staff as “big brother.” It is important to engage with your staff and help them to understand why you are collecting this information and why it is valuable.

So how do you get started? One of the most important things to recognize is that perfection is the enemy of the good. You won’t be perfect on your first try so you have to be committed to continual reevaluation of the model. Make sure that you plan accordingly and are ready to revisit and adjust the model frequently. A simple set of steps to get going are:

• Collect Data
• Compare the Data to Your Peers
• Analyze the Data
• Take Actions to Improve your Performance
• Repeat

As far as the data you are collecting is concerned, here are some examples of data you might want to consider for your model: