In a world where we’re bombarded daily — even hourly — by data points on everything from stock prices and market cap to retail sales and consumer spending to cryptocurrency’s ups and downs to the impact of innovation on the future of payments, commerce and financial services, we’re all just flying blind when it comes to the things that really matter to businesses and consumers.
We’re flying blind because we lack relevant data to build and then use the right frameworks to make confident, reasonable decisions that guide our businesses, and even our economy.
Despite being inundated with data, we’re amazingly lost.
We see evidence of this every day.
Put on a Retail Happy Face
Apocalypse, Schmapolcalypse, reports say, as a strong economy and historic levels of unemployment have driven people to open their wallets and buy more stuff than they have in a very long time.
Yup, physical retail is doing just fine — despite all the talk of doom and gloom.
Yet, according to Fung Global Retail & Technology, 6,985 stores closed in 2017, up 229 percent from 2016, and well above the number of stores which closed in the year that started physical retail’s death spiral: 2008. Retailer bankruptcies were up 30 percent in 2017, with a number of familiar names in that list of 662 firms: Payless ShoeSource, Toys R Us and The Limited.
At the same time, the market caps of the top 20 retailers have lost more than $230 billion over the last two years, and mall operators are sucking wind as overstocked anchor stores shutter underperforming locations, which delivers a death knell for the stores that depend on anchor store foot traffic to lure customers in. Some analysts project that one in every four of the 1,100 malls that operate today will close by 2022 — just four years from today.
Simultaneously, U.S. Census Bureau data suggests physical retail is just fine, thank you — accounting for more than 90 percent of all retail sales, with lots of analysts using this data point to push back on the notion that physical retail is really in trouble.
This whole eCommerce thing is kinda overblown they say, pointing to data that, on the surface, seems to suggest that all is well in the land of physical retail.
The slowing growth is off a big base — and the fast growth of online commerce is coming off a very small one, they say, so it will be a long time before in-store sales really tank.
But tell that to the nearly 7,000 stores that closed last year, and the nearly 700 that declared bankruptcy.
And the department stores that collectively closed 550 of their brick-and-mortar locations last year.
And the bookstores, music stores, office supply companies, sporting goods and apparel retailers who have seen or are seeing big chunks of their in-store business nosedive, as consumers shift their purchases online — and to one online player, in particular.
Part of the disconnect with the Census data is that it measures averages, but nothing is ever average. The average height of a female in the U.S. is 5’4” — even though few of the women reading this are exactly that height. The average temperature in Boston in February is 37 degrees Fahrenheit, but on Saturday it was 52 degrees. A week ago it was 28 degrees.
Census officials admitted to us two years ago (before they didn’t) that their recordkeeping systems weren’t set up to track transactions in a digital world — where the lines between on and offline are converging and digital channels turn manufacturers into brands from which consumers buy directly (think Apple and Dell) — so they didn’t count.
Compounding matters is the fact that many retailers who report data to the Census Bureau aren’t set up to report things such as buy online and pick up in-store accurately.
What we get from the Census, then, probably underreports online’s cut of retail sales — but no one really knows by how much.
So, we live, quarter after quarter, with these inconsistencies, putting a happy veneer on the state of traditional retail while it slowly sinks.
An interesting side note is that even using the Census Bureau data as it stands today, projecting the growth in online sales and the slowing average growth in physical retail sales, we see the 50/50 split of online to offline happening just about three decades from now.
In a blink, we’ll be there — and very likely before we get data from the Census Bureau telling us that.
Then there’s the years-long debate that’s raging among economists over whether GDP (Gross Domestic Product) is even the most accurate way to measure how well an economy is doing.
Much of GDP is based on consumer spending: price multiplied by how much people buy. It was devised when the world was largely a manufacturing economy and manufactured goods drove the output of goods and services.
And when there was always a price paid by the consumer to procure those goods and services.
But the internet, mobile apps, smartphones and advances in network capacity have changed the definition of output and the business models that drive the exchange of value between suppliers and consumers in some cases.
Today, more than two-thirds of the U.S. population owns a smartphone — double what it was just seven years earlier. By the end of 2018, eMarketer reports that a full third of the world’s population — some 2.5 billion people — will own one.
And manufacturing accounts for about 11.7 percent of our output, down from 25.4 percent in 1947.
The intersection of apps and mobile phones and technologies like GPS have inspired entrepreneurs of all stripes to create new and/or to enhance existing digital intermediaries that bring two or more stakeholder groups together. In some cases, these virtual platform businesses may be new, but the business models that underpin them have stood the test of time over more than 3,000 years.
Igniting platform businesses — virtual or physical — often means having one side of the platform subsidize the other who pays (gladly) for the ability to efficiently access it.
The emergence of these models reveals that the standard measure of GDP totally ignores the economic value consumers get from all the free stuff.
How much does it count in GDP? Well, that would be nothing — even though consumers are getting a ton of value.
Take Venmo (and P2P more broadly) — a service that’s free to both the sender and the receiver. Because of that perk, the economic value of making it easier for Venmo and P2P networks to move money between parties isn’t captured anywhere: It’s a big zero as far as most economists are concerned. Worse yet, banks and PayPal are dinged when they can’t point to a specific plan to monetize that service as a standalone, even though there are enormous efficiencies and benefits to all sides when the friction is removed from letting people pay this way.
This deficiency becomes a much larger issue when applied to ad-supported digital content platforms — from Facebook to Google to television networks — that exist today.
In a recent whitepaper published by Economist and Global Economics Group Chairman David Evans, he writes that in 2016, American adults spent 437 billion hours consuming content on ad-supported media across all channels. Those consumers thought that time was worth something; otherwise, why would they divert their attention? And if they valued it just at even an after-tax minimum wage rate, Evans says consumers must have believed they got at least $2.8 trillion in value from those services, since they continued to visit them day after day, hour after hour.
At the same time, the lack of a consistent way to interpret the data we do have can create market imbalances with far-reaching impacts.
Two weeks ago, eBay’s decision to intermediate payments wiped about $8 billion in market cap from PayPal in the space of 48 hours. A close examination of the facts suggests PayPal isn’t staring down the barrel of a massive eBay risk either short- or long-term, given the complexities associated with building, igniting and maintaining a two-sided platform.
We see it regularly whenever Amazon announces it’s expanding its presence beyond its retail/eCommerce playing field, even though the U.S. Census Bureau tells us eCommerce sales are diddly squat — and we know from other sources that Amazon has half of the diddly.
Amazon announced Shipping With Amazon last Friday, and UPS and FedEx, together, lost $25 billion in market cap ($18 billion for UPS and $7 billion for FedEx). That came a few days after UPS took it on the chin for getting squeezed over the costs of supporting eCommerce deliveries.
Months earlier, when it was revealed that Amazon had applied for a wholesale pharmacy license in 12 states, signaling its move into the $560 billion-a-year pharmacy market, pharmacy benefit managers Express Scripts, Caremark and Optimum saw their stocks plummet too — 4 percent across the board — even though stepping into the regulated world of prescriptions is a very new business for Amazon, and we really have no idea how large the online pharmaceutical market is.
And, when Amazon bought Whole Foods in August of 2017, Kroger saw its market cap slashed from $30 billion — already down from $34.2 billion at the start of the year — to $18 billion in September 2017. It’s recovered since to $24 billion — $10 billion off where it was this time last year, even though grocery is an extremely fragmented business in the U.S., with Amazon and Whole Foods’ share combined representing less than 3 percent, using 2016 data.
But then again, who really knows, since measuring online sales is a real crapshoot. What’s driving these market movements is observing the devastating impact Amazon has made on the retail sectors it has entered, regardless of U.S. Census Bureau data reports that suggest nearly all retail sales happen in a physical store.
We spend a lot of time in our world talking about the importance of Big Data and the impact of artificial intelligence (AI) and machine learning on crunching the massive amounts of data that washes over us each day — well, that maybe drowns us each day.
It’s been reported that we generate 2.5 quintillion bytes of data daily. A quintillion is 1 million trillions. In other words, it’s a lot.
But instead of leaving it all up to the machines to crunch endless amounts of data, maybe what we need is to spend more time creating the thoughtful frameworks that could lend clarity to some of innovation’s most basic questions and then go out and get the right data to answer them.
Data, in the absence of those frameworks, becomes just a bunch of numbers on a page.