So many businesses track a list of KPIs, but have never reflected on what they're telling them.
Today we break down a system for creating the optimal KPI stack in your business.
But first, our sponsor.
A MESSAGE FROM SMB Financial Fundamentals Cohort
MASTER YOUR NUMBERS, LIVE!
Last week I announced I was launching my cohort waitlist and I opened enrollment on Monday. So far we have 10 people enrolled and I expect more on the way!
Over four weeks we’ll help you:
- Learn how to decode your financial statements
- Identify key results drivers for your situation
- Select, track, and act on your key metrics
- Use your numbers to make better decisions and tell your story
We won’t just stick to theory… we’re going to dig into YOUR numbers.
We're combining both pre-recorded sessions and live sessions to deliver the most value possible in the least amount of time.
After this cohort, you’ll walk away with a complete system on how to analyze, digest, and use your numbers to improve your business outcomes.
In version 3 of Financial Statements Decoded, I charged $1,500.
But since this now “version 1” again, I’ve reduced the price as you go through this curriculum for the first time with me. Next round the price will be increased, so this is the lowest you’ll ever see it ($795).
We start classes on March 11th, so only a few weeks left to enroll.
I hope you'll join us!
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HOW TO BECOME A KPI MASTER
Too many businesses are tracking both the wrong KPIs and the wrong number of KPIs. Let me explain.
When you start a business, things are simple. Let’s use the example of a door-to-door salesman. This salesman knows he has to make $100 a day to buy his food and pay for rent. He knows he makes $5 per sell, so he needs 20 sales over the course of the day. While he can work 24/7, common courteous says you can only visit homes between 9am and 7pm, so he’s limited to 10 working hours.
That means he needs 2 sales an hour. So, what is this salesman going to track? He’s likely going to notice that on average he needs to visit 15 homes to get a sale, so he needs to visit 30 homes per hour. He’s start out counting both homes visited and sales (or dollars) per day.
As he got to the $100, he’d be relieved and either continue to work or stop, knowing he’d met his personal “quota.”
But there are a million more things he could track: time per house, visits and sales sales per home value range, visits and sales per neighborhood, visits and sales per customer gender, etc.
His boss would tell him “you can only spend 2 minutes per home” because otherwise you can’t get to enough homes. But he only needs 2 sales, so couldn’t he spend 30 minutes per home if they both resulted in sales? At some point you’d go down the rabbit hole of calculation correlation between length of visit and sale or not. When is not enough time or when is it too much time?
Surely there would be some data you could extract…
Now pause. Hopefully you saw where we went wrong. We went from two metrics (houses visited and total sales) to three or four layers deep in a snap of your fingers.
The problem becomes that sure you might be able to extract some useful information as you go deeper, but how does that get applied out in the field?
At some point the level of complication makes the job the salesman is out there to do harder… actually sell.
New businesses are really good at focusing on only the essential things:
- Sales calls/client touches
- Bank balance
- Baseline expenses
But as the business gets more complicated, so do the metrics they want to track.
Over time, one metric after another gets added, where eventually they’ve created a whole “dashboard” of 10 or 20 different metrics that they “track” on a regular basis.
But just as 2-3 swing thoughts get in the way of my golf game, too 10 to 20 metrics get in the way of getting any real value.
So, how do we pick the right metrics and know how many to pick?
I’ve talked about the process of establishing goals and picking the right metrics in the past, so we’re going to focus on creating the right metric profile for your business.
Connecting KPIs & Reporting Levels
Leading metrics are forward looking indicators of a future outcome or event. Lagging metrics look back at whether the intended result was achieved.
Leading are typical inputs, meaning you can control the number and timing and change them on the fly.
Lagging metrics are outputs, meaning you’re looking back at a past result and seeing how you did.
Leading metrics are predictive of the outcomes of lagging metrics.
Alone they’re helpful, but together they’re like magic.
A few examples of leading versus lagging:
Leading >> Lagging
Sales calls >> New Customers
Customer Contracts >> Revenue
Employee training hours >> Productivity improvement
Interviews conducted >> New hires
Advertising spend >> More sales call bookings
You’ll notice in the few examples I provided there are multiple levels in the funnel. Sales calls can be booked based on outreach, advertising, or brand awareness. Tracking the channels can be helpful, but too much of this can easily obscure the actual impact.
There are two keys:
- Connecting the leading and lagging measures, so you can make sure the data “makes sense.”
- Finding the right level of reporting for the right level of the organization.
Executives may want pipeline numbers, sales department leaders total sales calls, and sales managers want information by channel.
Matching the KPI with the appropriate level is key to having the most useful data.
An exercise I like to do is KPI mapping, where we start with the outcome goal we want and map backwards to the “input” sources for that lagging measure. You then determine who sees what numbers and on what schedule. The steps are as follows:
- Determine a goal you’re trying to reach
- Determine the ultimate output and map each input to that output
- Once you’ve reached the “beginning,” highlight the most important numbers
- Assign owners to the numbers
- Determine a schedule on which they’re reviewed and who’s involved
Reporting Cadence Design
Once you’ve established the key levels of data ownership, you need to start thinking about the appropriate cadence for reviewing the data. I like to think about this in MVD (minimum viable dose) terms, as that is the only way to make sure you don’t get metric overwhelm on any different cadence.
Reporting cadence seems simple, but it can easily become complicated.
At the highest level, you’re determining what metrics to look at on what reporting schedule.
But as you get into the weeds, this is where the “too many metrics” problem is really highlighted.
It’s easy for leadership to create a dashboard that mixes all different types of metrics and end up with a lot of disconnected data.
The key with reporting cadence is that the data served up at each cadence makes sense. I think of this at two levels:
- Summary data
- Drill-down data
Summary data is high level data that provides an overall picture of the business. The drill-down data goes multiple levels deep within a piece of the summary data.
So, we want to establish a cadence:
- Weekly: a few key leading measures
- Monthly: summary data and drill-down data, on separate reporting. Includes both leading and lagging measures.
- Quarterly: focus on summary data with selected drill-down data. Includes leading measures predicting yearly results and lagging measures based on monthly and quarterly results.
- Yearly: forecasting future results and drill-down data for whole business. Includes leading in forecasting, but all others are lagging.
Weekly tracking should be tied and validated to what you’re viewing on a monthly basis, just as monthly leading measures should be tied to quarterly or annual lagging measures.
Bringing it together
From an executive level, you still end up looking at a lot of metrics. You still have the responsibility to look at drill-down data for different departments. To maintain this, you want to establish reporting schedules that allow you to review the data in sub-sets.
For example, instead of looking at gross margins by product every month, look at it on a quarterly or annual basis.
This is where it turns from science to art.
An executive schedule could look like this:
- Weekly: pipeline changes, AR/AP data, bank balances
- Monthly: Financials & drill down pipeline data
- Quarterly: Financials & gross margin review
- Yearly: Financials, forecast, & cost analysis
What’s most important is custom to your business or industry, so it’s important everyone goes through this thought exercise for themselves.
I’ll be talking about this more in the future, mapping the whole process, so stay tuned for that.
Next week I’m going to talk about telling a story with your numbers.
