There are two approaches to optimizing retail prices for foreground and background items.Approach 1 — Rule-based dynamic pricing.
If a retail / eCommerce 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 profit growth potential, relying on their experience and intuition. ●
They develop specialized dynamic pricing rules for these segments and input them into a dynamic pricing platform. ●
They monitor the results, searching for the optimal 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 maintains complete control over the pricing process and manages any changes. Disadvantage of the method
: With the rule-based approach, it becomes challenging to consider all the critical factors that influence pricing efficiency, particularly the relationship of SKUs within a category, such as product cannibalization, substitutes, complementary goods, traffic drivers, and 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 chosen criterion — gross profit, revenue, or sales volume in pieces. AI-driven algorithms identify the optimal set of prices under prevailing conditions. The resulting optimal price set enables retail or eCommerce companies to achieve maximum gross profit, revenue, or sales volume at the assortment level.
Machine learning algorithms assess cross-impacts among products and then segment items based on their roles in consumer baskets: basket drivers, traffic drivers, profit drivers, complementary goods, substitutes, and more. Each segment receives an optimal pricing strategy aligned with the retailer's business goals. Such solutions also enable retailers to optimize prices for specific product categories and swiftly determine optimal prices for new items. Advantage 1 of the method
: The approach helps avoid product cannibalization
within categories and consider the cross elasticity of items.Advantage 2 of the method
: AI-driven algorithms help identify situations where competitive pricing falls short. This occurs when all competitors err by setting prices too high from a consumer perspective, resulting in diminished willingness to pay and disappearing demand.
Algorithms assist retailers in finding a new optimal price and reigniting retail demand
. Advantage 3 of the method
: readily detect short-term demand surges due to temporary product shortages or unforeseen spikes in demand. In such cases, the pricing solution promptly raises the price of relevant SKUs and maintains it until demand normalizes.
This approach significantly enhances
retailers' gross profit
You can explore an example of such a success story here: CASE STUDY
Advantage 4 of the method:
Assortment-level automatic price optimization substantially boosts profits for both brick-and-mortar stores and eCommerce shops. Generally, eCommerce stores experience faster gross profit growth compared to offline stores, as they can adjust prices more easily and rapidly.
For more information on implementing AI-powered Demand-based Dynamic Pricing and how it works, visit: AI-DRIVEN PRICE OPTIMIZATION