In brief
- The Chinese online retailer Shein is a major player in the South African apparel market, but many listed retail executives prefer to downplay the threat to their businesses.
- For years, our core 22seven data has shown Shein’s rise. Acknowledging that this data might not be representative of the greater population, we analysed a brand-new dataset: de-identified transactions from 20,922 new customers.
- While there are some differences in the data, there is one striking similarity: Shein’s spending share is nearly identical. This proves that the retailer’s meteoric rise is no hoax.
- Due to the low prices of garments and a relatively high free-delivery threshold, a new type of Shein shopper has emerged who aggregates purchases on behalf of individuals. But the data shows that these ‘super users’ can’t explain all of Shein’s growth: the majority of spend comes from individuals.
Things have been happening at Shein. The Chinese retailer, which is already one of the most profitable fashion companies in the world, recently moved its headquarters to Singapore, doubled its global profits to more than US$2bn, and is awaiting a stock market listing in London or New York.
We have been tracking local spending at Shein for some time. In Rise and Shein, we showed that average spending is 2–4x higher than at competitor retailers, and Shein customers also tend to be younger than the average shopper, which bodes well for future growth. The Road to Shein tracked the spending journey of a new Shein customer and highlighted the dilemma facing traditional store-based retailers: if they offer their customers an online experience, they might inadvertently train them to shop elsewhere.
Yet despite exponential growth in this country, many market players continue to downplay the threat posed by Shein. They point to anecdotal evidence of small numbers of customers in very specific demographic segments skewing the numbers. The problem is that most investors can’t challenge this narrative because they don’t have the data to support their views.
This is where Reveal steps in. By equipping investors with a large sample of actual spending data from real consumers, we can interrogate underlying market trends that are likely to influence the future prospects of listed companies. Our goal is to challenge the selective disclosure provided by company management. Company reports usually offer rose-tinted explanations of how consumers engage with the company’s brands, and they shield outsiders – investors – from emerging threats to the business. We want to equip those investors with empirical information to allow them to make better-informed investment decisions.
Let’s get to it.
A brand-new dataset
In the past, most of our spending data has come from the budgeting app 22seven. The data provides a ‘live’ view of users’ debit and credit-card spending across all South African banks and financial service providers. A perception, however, has been that it may skew towards consumers who are more digital-savvy – that it’s not representative of the greater population, in other words.
To address this concern, we recently acquired anonymous and de-identified transaction data for 20,922 consumers. Most of these are Capitec bank customers. The median income across the sample is R16,369 pm, with an average age of 31. We showcased the value of this complementary alternative dataset in last month’s report about Pick n Pay’s unbundling of Boxer.
Now we’re turning our attention to Shein. By analysing the apparel market using the two datasets, we’ll highlight differences and consistencies to prove that Shein’s growth in this country should not be ignored.
To simplify things, we’re going to call the 22seven data the Core Data, and the Capitec data the New Data.
Our analysis will look at customer spending at all apparel retailers during the four months of overlap between the two datasets: October 2023 to January 2024.
Sample size by income and age
- Both data sets are large: Core Data tracks apparel spending by 53,379 users; New Data tracks apparel spending from 20,922 consumers. In total, there are more than 480,000 transactions at apparel retailers over 4 months ending January 2024.
- Sample sizes within income brackets are also large. Core Data is overweighted in high-income brackets, yet the number of consumers in each income bracket is significant in both datasets.
- The majority of consumers are young. Sample sizes for consumers younger than 55 are substantial in both datasets.
SAMPLE SIZE BY INCOME
SAMPLE SIZE BY AGE
Market share comparison
How does spending share differ across the datasets?
First, a disclaimer: no dataset is perfect. Interrogating the two sets is not meant to show that one is better or worse than the other. Both are large samples and have different strengths; knowing the differences will enrich the interpretation of the results.
For simplicity’s sake, we’re going to highlight two income groups for this comparison: R7.5–10k pm and R30–40k pm. The charts below show market share for the two groups, followed by the retailers that show the most variance in each group.
Observations:
- Spending share for listed retailers is much higher in New Data than in Core Data. In most cases, it is closer to industry estimates of market share.
- In Core Data, ‘Other merchants’ (a grouping of smaller outlets) accounts for a greater share of overall spending.
- Unpacking the variance: Core Data reflects lower spending at major listed retailers and a greater share at mall-based retailers (e.g. Cotton-On, H&M) and online retailers (e.g. Superbalist). Controlling for income, we can infer that Core Data represents a more urban consumer: closer to malls and able to accept delivery.
- Although we only have four months of New Data, it’s clear that combining the two datasets provides a far richer view of spending and an opportunity for more detailed analysis of spending and growth. This is an exciting prospect for Reveal and its clients.
MARKET SHARE BY RETAILER
DATASET VARIANCE BY RETAILER
Market Share: New Data less Core Data
So, where’s Shein at?
For all the subtle differences in the data, the most striking observation is the similarity in one key area: Shein’s spending share in both samples is nearly identical. Both datasets still rank spending at Shein in the top five among apparel retailers.
How can that be? Customers in New Data spend much more at Ackermans, Pep and Mr Price, yet a significant portion of their spending still goes to Shein. The prevalence of online spending in both samples is probably more similar than one might expect.
This insight alone adds significant credibility to our observation that Shein is changing the landscape in the apparel market. The impact has been noted in Core Data for years and is now validated by our New Data. It would be foolish to think otherwise.
And in case you need a reminder, Shein’s enormous spending share has grown from zero in only four years.
(Side note: Superbalist is the largest online retailer in Core Data, but it features less prominently in New Data, which reflects a market share structure that is more aligned to existing industry norms.)
The rise of the ‘runner’
For such a serious market competitor, shopping at Shein is not as simple as one might expect. Since all items are manufactured in China and shipped from there, delivery times are slow and customs fees come into play. Similarly, returning items is a headache.
There’s also the issue of the free delivery threshold. For a company shipping from China, the R600 threshold is actually quite low, but some of the garments are so cheap that it’s difficult to reach that threshold. (Deliver costs R150 for orders less than R600. By comparison, Superbalist delivery is free for orders over R500 and R60 for orders under the threshold.)
Shein’s R600 minimum order has given rise to a new kind of online shopper – a reseller or ‘runner’ who leverages social networks, WhatsApp and increasingly affordable local courier services to order from Shein on behalf of others for a small surcharge. Here’s an example on Facebook Marketplace and Instagram.
Are these runners, with their increased shopping frequency and higher basket spend, contributing meaningfully to Shein’s market share?
Again, we looked at the numbers.
Our evidence shows that 6% of Shein customers purchased more than five times from the retailer in the four-month period under analysis. Yet despite their relatively small numbers, these outliers account for 26% of spending at Shein in the Core Data, and 16% in the New Data.
That sounds like a lot, but it only helps if we compare the figures to a competitor retailer. All retailers have ‘super users’, the question is whether Shein’s are markedly different.
Let’s look at Mr Price and Ackermans, both of which also had shoppers who purchased more than five times during the period. At Mr Price, those regular shoppers accounted for 21% of spending – a share almost equal to Shein’s regular shoppers. However, as the purchase frequency increases, the contribution of these super users decreases at Mr Price and Ackermans compared to Shein. At 10 purchases per month, Shein’s super users spend 3x more than Mr Price’s and 9x more than Ackermans’.
But these super users alone aren’t enough to explain Shein’s tremendous success. If we assume that Shein’s super users account for 9% of spending value, then Shein’s adjusted market share is ~6.3% instead of ~7%, which is still a major chunk. Therefore, even though there is evidence of so-called ‘runners’ boosting Shein’s spending share, it’s safe to say that they vast majority of spending still comes from individual shoppers.
PROPORTION OF TOTAL SPEND FROM CUSTOMERS WITH MORE THAN 5,10 OR 20 TRANSACTIONS DURING THE PERIOD
What have we learnt?
- Despite a complicated delivery and returns process, consumers in South Africa, including those in lower-income bands, have not been deterred from shopping online at Shein. This mirrors a trend that has been observed worldwide.
- 'Runners’ or aggregators are more prevalent among Shein shoppers, but these high-spend users are not solely responsible for Shein’s outsized success over the past four years.
- Shein’s low-price fashion offering speaks to a gap in the market. South Africa is not protected by lower average incomes or logistical complexity.
- Given that the market shift has happened so rapidly, and data in the public domain is relatively opaque, it is understandable that apparel executives may downplay the threat. But the threat is valid, as we have now demonstrated across two large samples of real transactions from more than 70k actual consumers.
