Pricing for a new era of retail

In our latest Viewpoints blog, Yossi Cohen, co-founder and chief technology officer of Quicklizard, discusses different pricing methods that retailers can use while evaluating their approach to private label.

Pricing affects not only the revenues and margins of a business but also drives market share, brand perception and customer loyalty. Despite this, it is still common for business owners and business leaders, including those in private label, to underestimate the impact of pricing. They often look at the cost of their products, consider their competitor’s rates, and make changes to selling price by a few dollars. While costs and competitors are important, they shouldn’t be at the heart of a pricing strategy.

The agile pricing approach is a transformative journey towards pricing that best reflects the retailer’s business goals. The need for an agile approach to pricing has grown due to continuous industry changes and disruptions. The ‘old’ method does not offer a continuous and iterative process that honors the changes both in a business or in wider market conditions

The speed and efficiency of the transition, as well as the agility to move between different strategies, will depend on the resources and infrastructure available. Whether that be from human teams to a tech stack that allows the retailer to implement this progressive methodology.

Automatic pricing is used by retailers to address two main pain points: sub-optimal pricing strategy vs. the excessive cost of pricing. By automatically pricing the items we are not changing the pricing strategy itself, but we are changing the pricing process making it cheaper and faster.

Using dynamic pricing, retailers can set the prices of their products to automatically move in relation to a competitor's price. But pricing optimization goes above dynamic pricing. 

Price optimization focuses on finding the price that maximizes a defined metric, such as company revenue, by considering many dynamic factors. Competitor pricing is just one of these factors. 

In the contextual bandit problem, a learner repeatedly observes a context, chooses an action, and observes the result for the chosen action only. Contextual bandit algorithms use additional side information (or context) to aid real-world decision-making. 

AI algorithms are based on historical data. Items that have multiple historic data points including price changes, transactional data can have models applied based on historical data. This accumulated data can be used to create a forecast or a pricing prediction.

When it comes to new products, historical data will not be sufficient to create a prediction model. In these cases, the second technique can be applied and reinforcement learning or machine learning comes into play. 

Examples for utilization of machine learning algorithms 
Product clustering algorithms group similar products together to create price segments. These groups can then be used to predict prices and demand for new products that have no sales data. More generally, machine learning can be a tremendous tool for insights:

Halo Effect - Complementary products are products that are closely related to the leading product and very often can’t be consumed alone. Mobile phones are an example of a leading product with many compliments, such as mobile covers, screen protectors, warranties, etc. The demand for the leading product generates the demand in its complement. The halo effect involves setting the price of the main product at the optimum level so that the demand for the complementary product increases, thereby maximizing the profits from both products together. 

Inter-Product Relationship - The demand for a product sometimes depends on complement and substitute products and their prices. For example,  private brands often have similar items in their portfolio. These items are substitutes. The demand for the private brand would depend not only on its pricing but also on the price movements of the generic brand. Brands like Coke and Pepsi are examples of substitutes. 

Customer Lifetime Value (LTV) - LTV is an estimate of the average revenue that a customer generates throughout their lifespan as a customer. This customer worth is used to support many economic decisions including marketing budget and resource allocation, profitability, and forecasting. Measuring the contribution of each of these product purchases on long-term profitability and user retention can be leveraged to make price changes that maximize the long-term value of your business.

In conclusion, agile pricing, dynamic pricing and the use of AI and machine learning are all tools in the private label retailers’ tool kit. When used correctly and in a methodological manner, each of these steps can help create more efficient and resilient organizations and minimize the effect of external changes. A robust pricing platform is an important step in helping companies on their journey to pricing excellence. 


Yossi Cohen headshot

As the co-founder and chief technology officer of Quicklizard, Cohen is responsible for the strategy and execution of our product and technology roadmaps. He lead the company's teams of developers, product managers, and data scientists in creating the best and most technologically advanced AI-pricing optimization platform. Prior to co-founding Quicklizard, he co-founded 3Base (acquired in 2012).

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