Having made it to this blog, you likely know that learning from data is non-negotiable for growing your business. Here, “data” refers to information about your operations, finances, customers, and employees. But unless you’re in the “business of data,” creating an environment, process, and strategy for using that information usually doesn’t become a priority until you realize you have a problem that data can solve.
This data-as-an-afterthought approach tends to yield disparate data sources, repositories, and interpretations because there was no intentional stewardship of the data from the outset. It often also causes duplicated efforts which means wasted time and frustrating results. When you ask a single question of two different people in your company, you get two different answers because those two people aren’t aligned on where to find the relevant data, what the data technically represents, and how to apply it within the context of the question.
Suppose you present this question to two people at your company:
"Has technician retention improved over the past six months?"
Person 1 says, “Yes, retention is up 22%.”
Person 2 says, “Yes, retention is up 4%”
This is a common scenario, especially in home services where data and analytics become a priority well after daily operations are established.
Even though the answer is ‘yes’ in both cases, the implications are very different. The improvement of 4% may not even be significant, meaning there’s been no change at all.
Both numbers mean something, but which is relevant?
Which one answers the question you’re truly asking?
Which is effective for measuring progress?
Which, if improved, will fuel sustained growth?
The discrepancies aren’t inherently bad. They show you where you need better documentation and processes.
Analytics helps with this.
Analytics is the bridge between reporting and growth planning, and the technical options available for analyzing data have exploded over the past several years so it’s a hot topic for growing companies. It also happens to be my primary area of expertise. But it’s seldom where I first bring value to my clients because…ANALYTICS is not the starting point.
DATA is the starting point.
Consider the progression illustrated by Figure 1:
We must start with the information. Know what data you have. Know how it’s being collected, where to find it, and what it means. Data changes constantly along with your business, so this is never “done.”
Analytics rely on data inputs and will provide answers (correct or not). If the data is inaccurate, the answers will be useless at best (misleading at worst).
At first, there's value in analyzing areas of your business separately (marketing, sales, ops, team retention). As you learn and grow, you’ll want a more complete view.
Analysis includes a variety of techniques that explain or predict. While it’s shown here in the middle of the progression, it actually permeates throughout. For example:
Descriptive and diagnostic analytics provide clarity and performance snapshots, so you can iteratively improve Data, Accuracy, and Integration.
Predictive analytics reveal how you arrived at your current state, how best to improve performance (Process/Automation), and the impact that improvement will have on your business (Measured Progress).
When you’ve learned from the analytics results, you can improve your processes. This includes the front end of the progression (better data collection and documentation, greater accuracy, cleaner integration) in addition to the better procedures and automation created based on the findings.
With accurate information showing you where you stand and improved processes for continuously collecting and learning more, you can accurately track your growth and continue to adjust along the way.
Where to begin?
None of these steps in the progression has to be perfect to move on to the next. The key is understanding which pieces aren’t ideal, how those flaws affect your business outcomes, and how you will continue improving. And despite what you’re seeing about AI everywhere, it doesn’t have to be super advanced or cutting-edge.
1️⃣ List out the pain your data is causing in your company.
2️⃣ Ask your teams how their impact is restricted by the data available.
3️⃣ Calculate a ballpark impact that data issues have on your business (there IS a cost).
When you have an idea of what your roadblocks are costing you, you can make an informed decision about how to invest in your data and analytics strategy. Find someone you trust (within or outside of your business) to take it on.