Collecting the Right Data

Subcontractor Construction Bids: Best-Practices for Collecting the Right Data… Now!

Looking back at 2021, what have you learned about the projects your specialty trade business bid on that you can apply to the year ahead?  Can you say with confidence (and accuracy) why you lost a bid or if you made the profits you expected on the jobs you won?

Specifically, do you know who your most profitable GCs are and why? 

  • Where is the sweet spot for job size? What is the result of taking jobs that are too small or too large? 
  • Which projects delivered the best margin? 
  • Do you know the GC Supers who can make or break a project? 
  • Do your estimators have the insights necessary to bid on the right jobs to make you the most money while mitigating your risks? 
  • And finally, do you trust your existing data?

To answer all those questions with confidence, you would have had to consistently track the right data points for each and every project over time.  If you had this data in-hand now, I’d bet your approach to 2022 to bidding on projects in the future would significantly different. 

As I mentioned in a previous blog post, there are three categories of insights that good data can produce:

  • Project Execution Risk
  • Margin Certainty
  • Pricing Optimization

To reap the benefits of these insights, there are 4 things you should consider:

1. Start Collecting the Data Now

If you want to have a better predictor of the future, you need to start collecting the correct data. NOW!

Even starting with 3 months of the correct data can give you some great insights, but imagine the information you would have if you had been collecting a year’s worth of data. And while insights on 20 projects can give you a good glimpse, imagine the picture you and your team would have if you track every single project you bid on over the next year: the more projects, the better the insight. 

2. Start with the Right Types of Data

If you are thinking about cost estimating data, you are like everyone else. But successful contractors want better models to drive a greater chance of winning the most profitable jobs. The right data model allows you to make better decisions regarding which project you should bid on, why, and in some cases, why not.  

How do you get there? The more data you have, the more confident you will be in your resulting insights. To clarify, I’m not talking about more “types” of data; I’m talking about the largest data set that supports your desired insights. And even all with the multiple variables, a focus on more of the right data types will deliver a more precise set of trends and patterns that will significantly change how you go about bidding projects in the future.

Let me give you some examples of data I think can change your 2022 outcome.

GC Data – I’m sure there are GCs you bid a lot of work to, but still don’t win as much work as you want. Win/Loss is easy to track. Armed with this insight, you should either stop bidding with those GCs or show them how much time you spend bidding their jobs. This makes a better case that it is your turn for the next project. You should also be tracking which of your GCs are most profitable and why. Start tracking this consistently and the findings might surprise you.

GC Superintendent Data – We have been told that the #1 risk factor for project success is the GC Super. While we don’t always know who this is when the project is bid, if this is tracked over a large enough sample size and combined with Margin, you would have a better idea of how to price the next project.

Job Size – What happens when you take jobs too small or jobs too large? Can you consistently make money by taking on bigger projects OR does the larger risk of things going wrong have a pattern of turning a GREAT year into just a GOOD year? Again, collecting data by job size over a large sample should give you concrete insight on where your sweet spot is.  

Internal Project Team – Let’s work on the assumption that you are estimating consistently and there is money to be made when you win a project. We know different teams, with different skill sets and experience, can handle the unforeseen risk in a project’s delivery. They deliver at different production rates. There are lots of intangibles when it comes to this and you certainly have pretty good intuition, but collecting real data over a large sample size will give you a more definitive answer.  

The MOST IMPORTANT Data Point of All…Margin  

There are plenty of reasons to take a low margin or high margin on a job. But for today’s purpose, did you bring that job in where you thought you would?  

The most successful companies track margin on a job-by-job basis. The above data points we mentioned are things that affect margin one way or another. They are not independent from each other. A certain project team is great at certain types of work with certain GCs and of certain sizes. Again, you understand this from a qualitative point of view. BUT when you start tracking margin, you will also know it quantitatively. THIS is the difference, and THIS is the important distinction.

If you consistently collected the data types above over time, the analysis might surprise you: the GCs that give you the most work may not be the most profitable. Your job size sweet spot may be different than you think. And lastly, the difference between jobs you take vs. deliver is wider than you might expect.

Consider How Your Organization is Collecting Data

You have data in your company somewhere. It is most likely scattered across multiple systems and in various spreadsheets. There is a better way.

RhinoDox is a comprehensive bid management platform built to not only help estimators automated, manage and generate proposals, but also collect data from each project and turn it into aggregated reports that allow you and your team to identify and predict the right jobs you should spend more time trying to win.  

Why does it work? Because there isn’t any additional data entry. The process is consistent and therefore the data is comprehensive. 

If you are interested in understanding at a deeper level the insights gained from historical performance data then we should talk.

Justin Ullman