Ecommerce, COVID, and How Americans Spend

Everybody is at home: no more parties, no more business drinks, no more date nights on the town, or concerts, or even hikes, it seems (I live in LA and they closed the parks and trails in the area). Folks staying at home means a massive surge in screen usage, and a corresponding surge in social media usage — anything from Reddit to Facebook. What does this mean for ecommerce?

That depends on your target market, and the relevant distinction is the nature of their work — not their age or average income.

Based on first order impact on discretionary spending alone, we should expect a more than 1/3 increase in online spending from the average american consumer under 40 who is stuck at home and didn’t have any cut to income (specifically a ~38% increase). However, for anyone who has suffered a reduction in their income, we can expect that reduction in income to result in a much larger reduction in discretionary spending (specifically a 40% reduction would cause an ~87% reduction using the example below).

While a glut in screen usage means overall cheaper impressions for e-commerce sites, that will only translate to profitability depending on conversions. Whether you get those conversions will depend on to whom you are advertising, and the meaningful distinction here is the nature of their work, not their age, income bracket, etc. Folks in non-quarantine-sensitive industries, whose income stays the same, will have more budget than before to spend on online purchases. Folks who take a pay cut reduce discretionary purchases disproportionately to their income reduction.

Eyeballs Are On Sale, and Ecommerce Brands Are Feeling The Difference

Paid social media advertising, the bread and butter (or heroine) of direct to consumer ecommerce is the closest thing we have ever had in history to a liquid central exchange between human attention and USD. This gives us reasonably efficient (if not always transparent) pricing of how much it costs to put your pixels in front of another person’s eyeballs.

Rather than going too Econ 101 and putting a supply vs demand curve here, let’s just acknowledge that this is a huge influx of supply for eyeballs-on-screens, which means it’s cheaper to buy space in front of eyeballs (cheaper CPMs: cost per impression). And, at least some buyers of ads have stopped paying for paid social (e.g. someone promoting any kind of in-person event).

E-commerce brands are feeling the difference, and naturally have to taken to twitter to make sure everyone knows about it.

This makes sense at the top level: if a consumer is buying anything, they’re buying it online. And if they’re looking at anything besides their wall, or cat, or roommate, it’s probably a screen.

But, depending on the cohort to which you advertise, you could be seeing falling conversions, or, at the very least, failing to optimize your ad spend.

Obviously, folks who haven’t gotten pay cuts (or lost their jobs) are more likely to make a purchase, but going through the actual cases shows that the difference may be even more dramatic than you might originally think. This is relevant because it shows that it’s worth doing the work to figure out and optimize to which cohort your ads are served, whether you’re doing so to improve already good conversion rates or to fix bad conversion rates.

Meet Hannah and Rachel (The Average American Consumer Under 40)

Meet Hannah and Rachel. Hannah and Rachel both match the exact average income and spending patterns of American adult consumers born 1981 or later (basically thirty nine or below — see analytics note [1]). The difference between them is that Hannah works in an industry largely unaffected by having to do remote work: let’s say she works at Tech Startup X as a customer experience specialist (or whatever that startup calls the human you finally fight your way to after rejecting multiple customer service answers from robots), and Rachel has a job that is indirectly affected by the quarantine (but not completely shut down) — perhaps at a post production company that edits ads for agencies: she’ll keep her job, but get a pay cut.

Pre-quarantine both of them earn $58,628 (post-tax) per year. About 90% of this is spent and accounted for, meaning $52,874 of annual expenditures. A little over half that annual spending is “fixed,” and let’s call the rest “discretionary” (see analytics note [2]).

(all spending data from Bureau of Labor Statistics, Consumer Expenditure Survey 2018)

Which leaves about $24,290 of discretionary spending per year after fixed costs are covered. Let’s see what happens to that discretionary spending (which is what ecommerce businesses are vying for) during a quarantine.

All Else Equal, Hannah is Probably Spending ~38% More on Online Shopping

Although Hannah’s income has remained the same, the breakdown of her discretionary spending has changed. She is no longer using discretionary spending for restaurants, bars, concerts, gas, etc., which frees up a lot of cash. Using consumer spending data, we can estimate that she just freed up about 28% of her discretionary budget by being stuck at home (see analytics note [3]).

(all spending data from Bureau of Labor Statistics, Consumer Expenditure Survey 2018)

Unless Hannah starts saving more money, this would mean that she has $6,691 of free cash in her annual discretionary budget (about an extra $558/month) for “spending time” at home (see analytics note [4]). Allocating that extra budget to every category of at home expenditures (like, say, online shopping) proportionally would imply a 38% increase in each category (see analytics note [5])

This is the impact from first order forced budget changes alone: there is, of course, an additional effect of buying the same things but online instead of in person, which would make the increase even greater in the online shopping category. There are also additional factors like the increase in screen usage being more focused on computers than phones (which usually means better conversions), which would provide an additional bump. We could debate whether the reallocation of that capital is proportional across all at home spending categories, but the higher level point is clear (anecdotally, based on instagram, I don’t have any trouble believing many folks have increased their spending on alcoholic beverages, for example, by almost 40%) .

This would imply that if you are advertising to Hannah, or can make sure you start advertising to Hannah, you should not only expect lower CPMs, but way better conversions for the duration of quarantine.

All Else Equal, a ~40% Pay Cut for Rachel is an ~87% Decrease in Discretionary Spending

What about Rachel the film editor? No gathering of people means no new filming means no new ads for her post production house to edit: half of her coworkers got fired as the company tried to reduce costs and the rest (Rachel included) received a forty percent pay cut — but she’s one of the lucky ones for still having a job at all (see analytics note [6]).

The frustrating thing about fixed expenses is that they don’t go away when your income falls, which means that the proportional impact on discretionary spending has a multiplier because that’s where the entire income hit goes. In this case that is going to be ~2x the reduction, with the numbers we’re using as an example specifically creating an 87% reduction in discretionary spending as a result of a 40% pay-cut. This is because your fixed costs now account for 90% of your income.

Remember, the numbers above are annual: Rachel has $261.70 a month to spend now.

And that’s not accounting for things like now having to buy more groceries, actually, as you are never eating out and no longer having lunch provided by work. If Rachel has a bill coming through for an unusual expense that she was expecting this month’s salary to cover, it isn’t getting paid (you could slightly shrink this impact by ascribing all of the unaccounted for income to discretionary spending, but I’m not sure it makes sense to do so: see analytics note [4], again)

This roughly squares with the experience of a friend of mine who is in nearly exactly this boat, working in editing, 40% pay cut, etc. After cancelling all their subscriptions they actually ended up with only $200/month after fixed costs not including groceries.

Suffice to say, no matter how low CPMs are, you are not going to convert Rachel to a purchaser right now. But, also, why are we even talking about ecommerce at this point?

Rachel Is Not OK, and America Needs Rachel

Given a recent FT poll indicating that 73% of Americans have had their family’s income reduced by COVID, with 24% saying it had been hit “very significantly” Rachel’s situation is more than an abstract subset of American consumers having a rough time: it’s a portrait that hopefully captures and brings to life the havoc this pandemic has wreaked on the financial stability of American households.

And, for many of those households, it isn’t a 40% pay cut — it’s 100%. It’s immediate insolvency, and bills they cannot pay this month. And, if the situation persists, the number of those financially shipwrecked by COVID will grow, as missed bills mean missed income for someone else, and falling income affects the ability to pay debts, which in turn affects asset prices etc. The amount of government stimulus, both directed at businesses and at individuals, required to prevent a downward economic spiral in this environment is going to be mind-boggling — and it’s not a forgone conclusion that we will be able to pull it off.

And to those who really just came here to increase ROAS; I do hope this was useful to you. But I also hope you’re a little mindful about how and where you celebrate your success — it may be in poor taste to be flaunting your stats on social media while millions of individuals and families are suffering through the loss of some (or all) of their income, with no clear end in sight.

— — — — — — — — — — — — — — — — — —

If you’re interested in this stuff, come join the discussion at r/MeritStore

— — — — — — — — — — — — — — — — — —

Analytical Notes:

[1] The reason I’m choosing this age-based cohort is that the same dynamic here will exist across multiple categorizations — age group, income level, etc. The meaningful difference here is in type of work (specifically whether it is affected by quarantine) and there isn’t a clean way to cut that in consumer report data. They have breakdowns by industry but those don’t map cleanly on to “can or can’t do during pandemic”. Using this cohort I get a broad swath of the population, and a small enough income and savings rate that it highlights the impact (both the positive impact on Hannah and the negative impact on Rachel). The same impact exists at higher income levels, it’s just eased by more spare cash at the end of the day etc. In the end, this decision is in service of the point of the analysis in this case — to be illustrative, not comprehensive. I’m writing a medium article trying to explain a dynamic, so this suffices as a clear demonstration of that dynamic. If I were still working at a fancy hedgefund and were trying to trace the economic impact of this to make specific estimates that would result in conclusions re: trades, I would go through the work of trying to parse which industries’ incomes were affected and by how much, what exactly those specific consumers spent money on, etc. Spending the time doing that wouldn’t really add to my point here, but if you really, really want to see how this looks from another cut ask me and maybe I’ll just pull it up — or go do it yourself, the data is public.

[2] Our “fixed” is just grouping together housing and utilities, fixed vehicle costs (e.g. leases, maintenance, insurance), healthcare, other insurance (like life insurance), education, and groceries (food purchased for inside the home). This isn’t precise — there are likely fixed payments that are missing, e.g. phone plans, etc. Likely a larger portion than what we ascribe to “fixed” expenditures are fixes. Since that would only increase both of the effects we’re highlighting here (the multiplier of pay reduction to reduced spending and the significance of out of house spending vs total discretionary spending), I’m not too fussed about it.

[3] This is probably not all of the out of house expenditures, but it’s the easiest ones to identify (vacations, for example, are completely included) — I shortened the label names, so the included categories are: entertainment fees and admissions, public and other transportation, gasoline, fuels, and motor oil, and fuels away from home. The motor oil seemed odd because I would have thought that it could be included in aforementioned vehicle maintenance category, but they’re marked separately in the Consumer Expenditure Report.

[4] When I say “all else equal” this includes the spending 90% of income part. I could do the analysis where I reallocate this “saved” amount as spending to see the difference — it would slightly mitigate the impact but obviously not change the direction or higher level point. Part of the reason I didn’t do this is that I don’t really know if all of that is “saved,” it’s just not accounted for as spending in the consumer spending report. There are a few possible wiggles in this whole picture (for example, when you stop eating out your “fixed” grocery expenditure actually has to go up, or maybe you need to upgrade the quality of your internet if your working from home, etc.), which I would try to account for if I were making a comprehensive analysis of consumer spending in order to trace how much and where changes in spending were likely to affect which sectors, in order to figure out impacts on equity prices or corporate debt, but I’m just making a point here so I’m trying to keep the analysis high level and simple. If you’re that curious it could let me know and I can tell you how big of an impact it would be.

[5] It pains me to think this might be necessary to spell out, but the reason that a 28% reduction, when distributed proportionally back to the parts, creates a 38% increase is that you’re using a smaller base. I take 28 from 100, it’s a 28% (28 / 100) reduction. I then add 28 to 72, which is a 38% (28 / 72) increase. You are adding back onto a smaller base, so the % increase in that smaller base (and, if proportionally added, to each part of that smaller base) is larger than the original % removed from the larger base.

[6] No, I didn’t bother accounting for the change in tax rate. Like I said, it’s an illustrative analysis.