In the movie Twins, a mother gives birth to… you guessed it… twins, played by Danny DiVito and Arnold Schwarzenegger. But instead of evenly distributing the good and bad qualities of the parents, all of the good qualities - kindness, intelligence, athleticism - concentrate in one and the… not so good qualities concentrate in the other.
No spoilers, I’ll let you figure out which character is which.
The average of these children resemble the parents. The actual twins… do not.
A good operator can tell anyone who asks who their “average” client is.
And depending on the founder it looks one of two ways.
First, we have our numbers lady. She knows her KPIs backwards and forwards. She rattles off the technical average to a decimal points - $709.11 per transaction and engages for two projects a year. Their average is a statistical calculation across the whole.
The second is the gut feel guy. He sits down with ever customers and shakes their hand before every deal. He knows they usually have families, like bowling, are worried about retirement, and didn’t vote because ‘they didn’t like either of ‘em’. Their average is a profile they see most often day in day out.
Both think of these as their economic engine – the segment that keeps the bills paid. They build products around them, design sales funnels for them, and model their brand with them in mind.
However, both of these operators might be missing a big part of the picture.
What if the “average” isn’t representative, “typical” doesn’t exist, or a minority ACTUALLY drives the economics? You could be making decisions based on the wrong idea of who you serve.
Just your not-so-average Josefina
I was recently working with a client where we analyzed their client data and came to some interesting insights (and this is altered to make sure it’s anonymous):
They told us the average customer spent a few hundred dollars and comes back a couple of times. This was based on what the saw most often.
The numbers showed average customer spend was $2,000 per year over 6 purchases. This was based off averages across all their transactions. A technical answer.
This was the assumption their whole business model was based on.
However, as we stratified the customers another picture emerged. Both of these were misleading.
The top 25 percentile of customers represented 70%+ of their revenue. The top 10% was close to 50%.
Most of their business was being driven by a customer profile they didn’t really consider.
In fact, there were really three tiers of customers:
The Regulars - $6k over about 20 transactions
The Occasionals - $1500 over 3 transactions
The One-timers - $200 in their one and only transaction
There were a few big ‘ah ha’ implications that jumped out immediately to the founder.
A lot of the marketing was designed around the One-timers because they closed with the most frequency. So when looking at sales data and who converted the best - it was the low price, high volume clients. But these weren’t their best customers.
A lot of the product was built for the regulars since they did the most work and provided the most feedback. But these weren’t who was shopping.
90% of the headaches were from One-timers who required a lot of time to set up, asked a lot of questions, complained about pricing and value, and ultimately didn’t come back.
The Regulars were the real economic engine but that didn’t mean we should dismiss the rest of the customers. Truth was, the Regulars were actually Occasionals who then grew into a new level of needs that had them buying a lot more.
The (over) simplified solution that came out of this was that the marketing and sales funnels needed to target Occasionals and do more to filter OUT One-times.
The product then needed to be a ladder that focused on escalating Occasionals to Regulars.
Better alignment of the marketing and product around the ACTUAL customers who drove value dramatically improved the business.
Capacity improved with less time getting burned on high-maintenance, low price customers.
Revenue grew as the same amount of resources got deployed to a better customer through more efficient channels.
Both profits and satisfaction - both of the team and the clients - jumped.
No extra work, no extra dollars thrown at ads, just recalibrating based on the numbers.
Share this with your most “above-average” friend
Do the math
Here’s a simple way to figure this out.
Download your customer data from whatever POS, CRM, etc. program you use. This is probably the trickiest step depending on your business and how good your system is at exporting data.
Total revenue by customer or client.
Sort the list by customer revenue highest to lowest and total all client revenue.
Add a field for percent of total by taking each customer’s total and dividing by the total for all clients. This gives you what percent of your total revenue this customer represents and, if a few clients represent a high portion of revenue for example, informs you where you are vulnerable.
Now add another field after this to determine the cumulative percent of revenue. Next to each client’s percent of total, add their percent and the percent of all the clients above them.
Look over your total list and see where natural breaks occur. See how many customers make 50% of your revenue. 80%? 90%?
Lastly, take these blocks you’ve created and investigate the common patterns, behaviors, or characteristics of these clients/customers. Do they make larger purchases on average? Return more frequently? Are they typically clients in a specific industry?
Identify who they are, what they do, where they find you (or you find them), and most importantly, what matters to them.
You can make big revenue gains without adding new customers or growing traffic.
Improve your customer mix by recalibrate your sales funnel to the most profitable groups or the best entry points
Focus sales not only on new customers but on lower tier existing customers
A lot of times it is easier to grow the customers or clients you have than find new ones.
OK Good Operator - let’s see what you got
“The Numbers” are more than financials
Operators not steering with any numbers is a well documented problem and most who do it, know they need to improve.
But equally as common is the data-focused operator who leans heavily on the numbers but misinterprets them.
Make sure you’re taking the right lessons from the right numbers.
Best,
Chase “yakitiyak yakitiyak don’t talk back” Spenst
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