When implementing dynamic pricing, sales and profits grow faster for online stores
than for offline retail. This is the case in most instances; the difference between online and offline metrics can be studied HERE
Why sales and profits grow slower in offline retail: Reason 1 - Manual price tag changing. ➜
The assortment of a hypermarket consists of around 20,000 SKUs;
a supermarket has around 5,000 SKUs.
The resources of stores allow for changing 200-500 price tags per day
, which is 1-10% of the assortment. Note that first they always change the price tags of the products whose cost price has changed and the price tags of KVIs. ➜
A significant share of
the store's profits
potential is "hidden" in
products from the Foreground
and Long Tail
segments. AI algorithms calculate the optimal set of prices for these goods:--
If the price of a product is lowered, the algorithms increase it to the optimal value; sales remain at the same level, while gross profit grows.--
If the price of a product is overestimated, the algorithms reduce it to the optimal value, and gross profit increases due to the growth of sales.
The optimal price set ensures the growth of sales and profits, recovering the reduction in margin on KVIs. To calculate the optimal price set, the AI algorithms require sales data at different price levels
At the same time, the "Exploration or Exploitation" dilemma is solved: the task is to spend as little time as possible searching for the best price and to exploit such a price for as long as possible.
On average, the algorithms need to test 3-4 prices
at the start of optimization. In offline retail, for relatively rarely purchased Foreground and Long Tail products, each price should be unchanged for 2-3 weeks to measure the demand at that price