There is a perception in ecommerce that a focus on promoting best selling products is a sound strategy. That is perfectly logical, reasonable, and wrong.
In truth, it’s a strategy borne out of a fundamental misunderstanding of how the ecommerce challenge differs from in-store. Online, promoting only best selling products is a waste of time, money and effort.
Product promotion via recommendation and search builds on one of retail’s oldest techniques – the best seller list, which first appeared in ‘The Bookman’ in London in 1891.
This best seller list is successful offline because limited space for stock and the need to keep production costs down means the imperative is to streamline demand.
Bestseller lists do this in three ways:
- People buy what is popular
- Showing people the best sellers makes them buy more
- Best seller lists encourage purchases from the top of the list, not the long tail.
Ecommerce, however, presents very different challenges, and demands a different view of promotion.
Online, space is practically unlimited so the imperative is simpler – maximise sales. As a result, promotion is not about encouraging a focus on the best sellers only; it is about maximising product exposure in an intelligent way. In this context, intelligent means relevant – that is, exposing more of the right products to the right people, more of the time.
In terms of the top seller list, those online priorities change the rules. Yes, the aim is still to encourage people to buy more, but we can now expose more products, not fewer. In simple terms, that's means adding a crucial new ingredient to the top seller list - volatility.
That volatility is crucial. Getting it right optimises product exposure and maximises sales by ensuring top seller lists reflect both long term best sellers and short term sales trends.
But ‘getting it right’ is no simple task. It demands a new approach; one that is more grounded in science than it is in art and intuition, and with good reason.
To find out more, read our whitepaper "Online Merchandising and the Bandit Problem"