The 80/20 rule and the retail assortment:
Chris Anderson published a highly influential book “The Long Tail,” about the skewed demand curve (80/20 rule, 80% of sales come from 20% of products) and the potential for niche retailers. However, I believe that for the sake of elegance in theory, he over simplifies the argument for a new business model. In particular, he ignores three important aspects of retail: Assortment, Stock ratios, and the Buying cycle
Anderson’s basic theory is that as tastes have broadened and more consumers move away from “mainstream hits”, the demand curve at the tail end becomes fatter. This trend, coupled with on-line retailing with its reduced distribution and inventory costs, makes catering to those fringe customers more lucrative.
From personal experience, I agree with his initial statements: the internet has made it both easier to find, and shop for more exotic products. However, I don’t believe the business model of retail has fundamentally changed.
For one, Anderson describes one problem as carrying a broad array of products that don’t generate a more uniform proportion of sales. Retailers are aware of this distribution curve; however, they refer to it as an “assortment.” There will always be underperforming products, but having a consistent, broad, and coherent assortment defines the brand or store, and creates a desirable shopping destination I believe Anderson misses this point when he writes:
On a store shelf or in any other limited means of distribution, the ratio of good to bad matters because it’s a zero sum game. Space for one eliminates space for the other. Prominence for one obscures the other. If there are ten crappy toys for each good one in the aisle, you’ll think poorly of the toy store and be discouraged from browsing. Likewise it’s no fun to flip through bin after bin of CDs if you haven’t heard of any of them.
A hypothetical store that only carries these top 20 percentile products would be a very odd store – a hodgepodge of mostly the “hits” from different categories or genres, with a few one-hit wonders. It would be as if a clothing store had 70% blue jeans, a few t- shirts, a women’s shoe or two, and sometimes a handbag or watch. On-line or brick and mortar, I’m not sure if this is a desirable shopping destination. Lastly, a store would never carry a truly “crappy toy.” A product may not sell well, but it always has a purpose. Maybe it was stocked as a show or display piece to make other products look more enticing or to support a marketing campaign.
Secondly, his theory almost completely ignores the second half of retailing: stock. Andersen makes a passing reference to inventory management when he writes:
Traditional retail economics dictate that stores only stock the likely hits, because shelf space is expensive. But online retailers (from Amazon to iTunes) can stock virtually everything, and the number of available niche products outnumber the hits by several orders of magnitude. Those millions of niches are the Long Tail, which had been largely neglected until recently in favor of the Short Head of hits.
Having a balanced sales to stock ratio, is the one of the central concepts of retailing. But he ignores the fact that to cater a wide range of tastes, still means more inventory risk and higher capital costs. Assume that due to “infinite offering” the 80/20 ratio flattens a bit to 70/30. If the entire market is $100 million across 100 SKUs, a “traditional” retailer that focuses on the hits only has to carry 30 SKUs to generate $70 million of sales, while a niche retailer must carry 70 SKUs to make $30 million. The problem is exacerbated in certain categories, such as apparel, where each SKU requires multiple sizes to be salable. If you were to look at turn (a metric showing the amount of inventory needed to support sales), a niche retailer will almost certainly have a lower turn and greater cash needs than a mainstream one.
The graph below shows that even at the tail of the curve, a minimum amount of inventory still must be purchased.
Finally, recognizing the ratio of sales to products is one thing, identifying them in advance is essentially the main goal. Accurately predicting which products will be the hits is not as easy as it sounds. Depending of the retailer, buyers have thousands of SKUs to choose from and must predict the demand of each, often 6 months in advance.
The two graphs show a “medium” tail, but still skewed sales distribution: about 50% of the products contribute to 80% of the sales. But those products were only 70% of the stock. Did the buyer not recognize the demand curve? No, the buying strategy was consistent with the demand curve. The second graph shows the same products sorted by investment. Here, you can see that the approach was basically correct: about 80% of the investment went into 50% of the products. He or she simply did not buy 100% correctly.
Also, we haven’t even talked about margin…