How Businesses Cater to the Long Tail
https://miloszkrasinski.com/the-long-tail-effect-theory-in-practise-explained/
This article talks about the long tail effect and how businesses can economically benefit from catering to the long tail. Chris Anderson, who popularized a theory about the long tail effect, theorized that consumers in the modern economy are increasingly shifting away from a smaller number of popular “hit” products (the head) and towards more individualized niche products (the tail). The rise of the internet has allowed businesses to stock a wide range of items, since they do not have to constrain themselves to the limits of shelf space, creating more choice for the consumer. As stated by the article, “modern customers are becoming more interested in products and services that seem unique and fit their individual needs and tastes, creating more demand for a greater number of more unique items”. Some specific examples of businesses that have found great success in catering to the long tail are Amazon, Netflix, and iTunes. These online companies generate a lot of revenue from selling small numbers of many different, less popular items that appeal to a wide range of customer preferences. The “unlimited shelf space” of an online market allows for these companies, creating a longer tail and therefore attracting a larger/more diverse set of customers. Then the question arises, how do these companies connect buyers with such obscure, niche items? According to the article, companies like Amazon have utilised long-tail keywords in their product listings and descriptions to expose users to niche markets. In fact, 57% of Amazon’s sales come from long-tail keywords. Finally, the article also talks about how such companies have implemented complex recommendation systems to match users with long-tail items and thus generate more revenue from them.
In class, we discussed the long tail of a power law distribution in the context of popularity. We also learned that power law distribution has been found in many areas such as book sales, word frequency in papers, calls to a phone number, and citations to an academic paper. As discussed in the article, this distribution also applies to items on Amazon, movies/documentaries on Netflix, and music on iTunes. All of these companies have features that provide the users with the most popular items offered by them, so users tend to click on these items and thus a rich-get-richer effect results. These businesses proliferate this rich-get-richer effect by displaying the most popular items when a user puts in a broad keyword (e.g. “clothes” for Amazon). These popular items are subsequently purchased more and more, explaining why the popularity of items on Amazon closely follow a power law distribution. The article expands on the idea of the long tail: the latter part of a power law distribution; in other words, the many items holding very little popularity. Because the internet has allowed these companies to keep an essentially unlimited inventory of items, they can appeal to a lot of niche markets, and this is how much of their revenue is generated. Users looking for very specific niche items can find them instantaneously due to the companies’ utilization of long-tail keyword. These companies also share another similarity in that they all have systems for recommending these niche items to users that they believe these items will appeal to. By having this massive selection of products and a way of connecting users to the many obscure items, these companies make a lot of money by catering to the (very) long tail.