Profit growth with dynamic pricing:
Benchmarks by retail industries. Factors causing slowing down and accelerating growth

Profit and sales growth varies depending on the specialization of the retail chain — such as food retail, pharmacy / drugstore, DIY, and so on.
Below are the growth ranges for some segments after implementing dynamic pricing:

Each marketplace has its own specifics: rules, technical features, and a composition of sellers (retailers who sell the product).

As a result, the profit growth with dynamic pricing for the same retailer varies depending on the specific marketplace.

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.

Reason 2 - Lack of price change history.

➜ The process of changing prices online is simpler than in brick-and-mortar stores. That's why online stores often accumulate significant sales history at different price points. This data allows reducing the number of pricing experiments, therefore algorithms optimize prices faster.

➜ Offline retailers often lack price change history for non-food items, so training algorithms requires more experiments, and price optimization takes longer.

Reason 3 - Offline stores' channels for notifying customers of prices are less efficient.

Offline stores have fewer communication channels with customers. Customers' reactions to prices take more time (they need to get to the store), and there are geographical limitations. In grocery retail, many customers only learn about a new price during their visit to the store.

Online stores have very high price transparency. Customers can open five websites in a couple of minutes and compare prices. An attractive offer generates traffic and sales much faster than in offline stores.

Reason 4 - Material barriers in the brick-and-mortar store.

At any price, a store can't sell a product if:
✘ It was in the store's warehouse, but hasn't hit the sales floor.
✘ It's out of stock in the store's warehouse.
✘ It doesn't have a price tag.
✘ It's not visible on the shelf.
✘ And so on.

➜ Eliminating such "material barriers" increases store sales and profits. This is a very common indirect effect of implementing dynamic pricing.

➜ The direct effect of implementing dynamic pricing in brick-and-mortar stores is seen later, after the "barriers" problems have already been resolved.

➜ There are far fewer such delays due to "interferences" in online stores. Errors do occur, but they are much easier to identify and eliminate.

➜ Outcome:
In retail chains, the main increase in profits often occurs after the pilot project has ended.
An example of a delayed effect in food retail:

In some retail companies, internal price-setting processes are working incorrectly due to ERP and related systems settings and suboptimal logic embedded in them.

Before launching dynamic pricing, retailers often do not aware about the weaknesses of their IT system related to price setting.
Dynamic pricing requires monitoring of sales results and prices. Dedicated analyst sees odd sales fluctuations, makes questions to the store, and detects a lot of internal errors. For example, the process of taking SKUs out of promotion is not formalized, there are accounting issues, the repricing logic generates losses in some cases, etcetera.

If a company has IT errors related to price data exchange, profit growth will slow down; it will require to first eliminate the discovered "bugs" in the IT system and processes. The optimal price will not be efficient if customers do not see it.

The more manual actions were in the retailer's pricing process before the implementation of dynamic pricing, the higher profit the retailer will gain after the implementation of dynamic pricing. In such cases, the "low base effect" works.

Not all retailers aim to increase profit with pricing automation.

For some retail companies, the main goal of automation is to relieve talented employees.
Such retailers want their commercial department to work more efficiently with product matrix, assortments and suppliers, to search for new demanded positions, strengthen supply chains and stop spending 30-50% of working time on routine price calculations in Excel.

In projects with such goals, profit growth is usually lower than in other retailers.
The reason: responsible employees focus on other aspects and do not seek additional growth opportunities. It is enough for them that the platform generates correct prices, and profit growth covers the cost of implementation.

If the company's work culture is focused on constant analysis, searching for growth points, and testing hypotheses, the result of dynamic pricing implementation is higher. Dynamic pricing software makes conducting price experiments and introducing new pricing strategies and approaches dramatically easier and faster.

The size and speed of the increase in gross profit within a pilot test depend on several parameters of that pilot.

One of these parameters is which product segments and categories are involved in the pilot:

➜ If only categories with small revenue and profit and with rare sales are introduced in the pilot, the increase in money terms will also be small.

➜ If a wide range of products are included in the pilot, including popular items with high sales, the increase in money will be significant and will occur faster.