Without question the North Star for every merchant is sales. Sales is the lifeblood of companies, and unless you’re a large cap company paying out dividends, everyone pays attention to sales.
But we’d argue too much attention.
Look, we don’t blame people: sales data is relatively easy to get. As software has propagated across the market, merchants everywhere have point of sale or accounting software to give them a sense for their current period sales, and how those values compare to figures in the past.
Yet what if there were better data that more objectively told you when you would miss your sales goals? No brainer, right?
There are “four corners” of marketing:
The customer “corner” is related to how many customers you’re seeing. How many are new? How many are repeat? How is this metric trending? Is your customer count increasing while that of your competitors are decreasing, or vice versa?
Then next relevant metrics are derivatives of sales per customer: how much your customers spend, and how is that changing. In an ideal world this would be 1-to-1, meaning that you’d be able to clearly identify every guest that visits your business. Of course this is impossible, though with credit card data you can track those who spend with card on a semi-anonymous basis.
In other words, you’ll not know that it’s John Doe who lives on 123 Main Street (though we do), but you’ll know that some unique card number comes in, how often they come in, and if you’ve got a good POS and CRM, what that guest is ordering when they do visit.
The third corner is transactions per customer: how many transactions over some period of time your customer makes. Another way to think about this is customer frequency and recency, which combine into customer lifetime value. You should once again think about how this can be compared to your competitors; since there is not an infinite supply of customers businesses are often trading market share. Understanding how you’re stacking up is critical.
Lastly, the final corner is sales per transaction, or average check. Once again it would be nice to tie this to each individual customer so you could know who your high spenders are and who are the proverbial dogs. And it’s useful to compare this against the market: if your competition has a higher check average, maybe you should increase your prices.
All of these metrics have been hidden from brands for a long time. The largest Tier 1 retailers are fortunately rich enough to engage the card networks (Visa, Mastercard, et al.) and other service providers to compile their own analysis on these metrics.
But it ain’t cheap, and depending on the data source you need to bring an army of engineers to wrangle it into something actionable.
However, with democratization of the same data (and the heavy application of data science), these metrics are no longer hidden and can even be made actionable.
For example, we’ve not only been pulling out these insights, but wrapping them in prescriptions to tell you when you have a problem, what it’s worth, and even activating marketing to relevant audiences – based upon their actual spend behavior – to drive 10x+ higher marketing ROI (thank you PhDs whom payments bros will never hire).
What was once reserved for Walmart and Amazon is becoming available for everyone else willing to do a little self-education.
Access to this data should instigate an entire change in how a business thinks about marketing, operations, development, hiring, and other capital allocations.
Obviously the should implies that the human capital behind a business is logical and rational.
Here’s what we mean.
Today if you’re a retailer of any size you stack rank your stores, usually on YoY comp metrics or something, then determine which stores need help. You assume the ones at the top are the gold standard and the ones on the bottom merit assistance. Maybe if you’re lucky you can push some of the bottom performing stores to the middle and some of the middle performers to the top.
So what happens next? The business takes its marketing axe and goes to work on those underperforming stores.
Annnnnd you’d be wasting your limited resources.
Just because your stores are down doesn’t mean it’s a store-level issue. Similarly just because a store is up doesn’t mean it’s run by some savant.
Macro market trends have material impact on performance and while they’re out of your control retailers should be intelligently leveraging that insight to steer efforts into more productive endeavors.
Welcome to the concept of marginal utility.
Marginal utility is an economist’s fancy term for, “Don’t spend effort on shit that doesn’t move the needle.” A simpler way to think about this is understanding (and quantifying) potential market opportunity and exploiting it where possible.
A simple example can be demonstrated in this graphic:
The problem is without sufficient density “local market” benchmarking tools are literally entire states. It’s even worse when they’re relegated to enterprise operators, because consumers don’t think like that. Jim Bob doesn’t wake up and decide to only spend his money at McDonald’s or Burger King.
Surprise: Jim Bob will spend his money at non-chain restaurants.
But when you have virtually all cardholder spend it’s a different game.
We get surgical in a local market, looking at spend down to the competitive outlet based upon where your own customers shop when they’re not buying from you.
Once a retailer can agree that marginal utility matters, and deploying resources against stores with limited excess demand is a waste, then you can get on with activating the right customers, measuring performance of those campaigns both against your own stores and against your competition, seeing if you’re actually taking share of wallet and, quoting our friendly economist, “moving the needle”.
We used the sales metric for our example, because that’s what’s most familiar to retailers, but a more leading metric would be something like unique customer visits or customer frequency and recency.
In other words, sales is a lagging indicator, a second order metric. First order metrics (frequency, recency, and lifetime value) are also made transparent with immense scale of cardholder and POS data, which we’ve made available.
For even better news, all this data allows us to predict marketing opportunities (looking at changes in discretionary spend and consumer-level behavior), execute marketing around unique customer touch points, and influence these first order metrics with full transparency into ROI against competitor dynamics so there’s no confusion about what’s really driving returns.
Aben’s co-founders discuss precisely how restaurants can step up their game on the Restaurant Technology Guys podcast (we encourage all of those that serve the restaurant industry to earmark and listen to this podcast – it does a fantastic job covering tech trends in the industry.)
Retail is an industry that’s low on margin, and low on time. Knowing that you can now leverage better data to literally 10x marketing returns it should be criminal to operate without it. But hey: Amazon wouldn’t be Amazon if their competitors operated with data as rationally as Amazon did. Will their competitors finally step up?
Add comment