There are two approaches to optimizing prices of foreground and background items.Approach 1 — Rule-based dynamic pricing.
If a company optimizes its pricing from a low base — for example, price calculations are based on a system of fixed markups and scheduled promotions — then, with an agile rule-based dynamic pricing system, pricing experiments can provide impressive growth.
How it works: ●
A pricing specialist identifies segments and groups of items with the profit growth potential, based on his experience and intuition. ●
He creates special dynamic pricing rules for those segments and puts the rules into a dynamic pricing platform. ●
He monitors the results, seeking for the optimum price.
Note that high agility of the rules settings and powerful analytics tools are crucial for pricing efficiency in this case.Advantage of the method
: the pricing specialist keeps the pricing process entirely under control and manages any changes. Disadvantage of the method
: with the rule-based approach, it is difficult to consider all the important factors that affect pricing efficiency, in particular, the relationship of SKUs within a category — product cannibalization, substitutes, complementary goods, traffic drivers, profit drivers. Approach 2 — Machine learning algorithms for fully automated assortment-level dynamic pricing. Price optimization, based on demand sensing.
Assortment-level automatic price optimization maximizes a selected criterion — gross profit, revenue, or sales volume in pieces.
AI-driven algorithms find the optimal set of prices under current conditions. "Optimal" means, implementing that price configuration, the retail company achieves the maximum gross profit margin, revenue, or sales volume at the assortment level.
Machine learning algorithms evaluate goods' cross-impact and segment items by their roles in the consumers' basket: basket drivers, traffic drivers, profit drivers, complementary goods, substitutes, and others. Then each segment gets its optimal pricing strategy according to retailer's business goals.
Such solutions also allow retailers to optimize the prices of a specific category of goods and rapidly find optimal prices for new items. Advantage 1 of the method
: The approach helps to avoid product cannibalization
within categories and to consider the cross elasticity of items.Advantage 2 of the method
: AI-driven algorithms help identify situations in which dynamic competitive pricing does not work. It's when all competitors make a mistake and charge prices which are too high from the point of view of consumers, so both the willingness to pay and demand disappear.
Algorithms help retailers find the new optimal price and reignite retail demand
. Advantage 3 of the method
: Advanced pricing tools easily recognize short-term raises in demand caused by short-time shortages of goods or unforeseen rush demand. In such a case, the pricing solution quickly raises the price of the relevant SKUs, and keeps it before the moment when the demand normalise again.
This approach empowers retailers to significantly accelerate
their gross profit
You can study an example of such a success story here: CASE STUDY
Advantage 4 of the method: Assortment-level automatic price optimization increases profits significantly both in brick-and-mortar stores and in eCommerce shops.
Generally, eCommerce stores gain speedier growth in gross margins compared to offline stores, since an eCommerce company can change prices easier and faster than an offline store.
You can find more information about how to launch AI-powered Demand-based Dynamic Pricing and how it works here: AI-DRIVEN PRICE OPTIMIZATION