Why you need to think about Optimization – not just Automation – when it comes to retail pricing

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The retail market changed dramatically in 2021 and 2022 due to the effects of the pandemic, the war, and supply issues. Following the right pricing strategy is even more vital than before in this environment. Pricing is one of the most critical levers to increase profitability in retail. In fact, on average, a 1% improvement in pricing drives 6% profits. Finding the right pricing strategy isn’t just an option for you as a retailer. It’s a necessity. “How can you ensure that your pricing strategy is powerful enough to weather the economic swings?” or “Is there a golden standard for pricing in retail?” are the typical puzzles that a pricing manager often encounters. To answer these questions, we will dissect the two pillars of pricing: automation and optimization.

Now more than ever, adaptability is of the utmost importance in the retail industry. Hence, your pricing strategy should become dynamic. ‘Dynamic pricing‘ is a pricing strategy where prices are adapted frequently according to changes in the market environment. With dynamic pricing, the need for automation increased dramatically. Saving time while getting better results sounds like the ideal situation. But it is not the whole story. Automation without optimization in dynamic pricing leaves significant profit potential untouched.

Is automation a silver bullet for Retail Pricing?

The straightforward answer is “no” if you decouple automation from optimization. To explain,  automation itself does not mean that decisions are optimized. Often, automation technology does not translate to optimization. Many retailers make the common mistake to automate pricing with a single or a set of inefficient and umoptimal rule-based approaches. 

Popular rule-based pricing strategies

Cost-based pricing                        

  • Cost-based pricing uses production or material costs as the basis for the pricing strategy.
  • A certain profit level or minimum margin is necessary to set the correct prices.
  • A price floor and a price ceiling are set based on the costs.
Pros Cons
  • Simple calculation

  • Security in the contribution margin via fixed margins

  • The basis of the pricing strategy is exclusively based on costs

  • Takes neither customer behavior nor competitor prices and offerings into account

  • Neglects customers' willingness to pay

  • No maximization of total profits possible

Competition-based pricing        

  • E-commerce pricing for a product or service considers the prices of the competition.
  • Typically, businesses use this method where competitors sell similar or the same products.
  • Prices are based on pricing rules that establish a fixed margin (e.g., always 5% cheaper) or a specific ranking (e.g., among the top 3 cheapest) relative to the competition.
  • This pricing strategy requires a deep understanding of the relevance of different competitors and their price changes on their sales.    
Pros Cons
  • Aggressive growth strategy with a focus on revenue Easy to implement when competitor prices are available
  • Potentially higher profits are possible
  • Neglects the customer's willingness to pay                                                        
  • Neglects production costs and stock constraints
  • Relies on competition to act intelligently
  • Can lead to a downward price spiral in the marketplace
  • Does not work for private label products

Rule-based pricing only considers selected pricing factors without applying a systematic measurement of willingness to pay. The more products in the assortment, the more complex the rule system becomes and the higher the effort to maintain it. For instance, these rule sets get unmanageable with increased complexity (e.g., more SKUs, stock constraints, seasonality). 

These traditional pricing methods have been further affected by the e-commerce explosion and the overall digitization of the market in recent years. Hence, this has led to a massive increase in consumer and sales-related data retailers must contend with. The sheer volume of available data makes it increasingly challenging for retailers to evaluate it correctly and continuously.

How to do automated pricing right?

First, it is essential to realize that automation does not mean optimization. With a rule-based pricing approach, retailers automate the pricing process but do not necessarily reach optimal results. Just a reminder: With just a 1% improvement in pricing, your business can drive up 8% profits. To ensure that the price rules are up-to-date with current market conditions, retailers must regularly test their performance (e.g., with AB testing). This process costs companies a lot of time and effort to manage effectively. Even worse, many companies do not have a good monitoring process. These companies opt for the second-best solution and leave significant margin potential behind. Instead of searching for the correct pricing automation, you should look for an optimal pricing strategy that is automatable.

Automated dynamic pricing does wonder when adapting prices continuously to the volatile market. When it comes to dynamic pricing, the key is to comprehensively analyze and understand the wealth of data at a company’s disposal. Good quality data, but particularly good analytics, can help companies identify factors often overlooked – such as the broader macro economic situation, consumer purchasing behaviors, and external influences – and reveal what drives optimal prices for each customer segment and product. New advances in price optimization technology allow retailers to harness the full potential of their data while effectively setting prices that maximize their KPIs. Hence the pricing process is not only automated but also optimized.

How predictive pricing combines automation and optimization

Our predictive pricing software processes information regarding seasonality, inflation, inventory, competition, demand, and more to forecast a price that would bring in the highest returns for a retailer. And, unlike other pricing strategies, the benefit of predictive pricing is that prices are optimized in advance, with retailers knowing their impact before they go live. That is why we at 7Learnings call the approach predictive pricing.

The technology regularly runs different scenarios and test approaches to determine the optimal prices before going live with any adjustments, ensuring any change is positive. Scenario testing is beneficial for conventional e-commerce retailers, as they often deal with frequent changes like seasonal or rotating product assortments that undergo several changes throughout the retail cycle.

With predictive pricing, online retailers benefit from all the advantages of machine learning,accurate forecasts and powerful optimization algorithms. The applications evaluate current data and anticipate its development so that price adjustments are made earlier than with other available methods. Whereas such future scenarios and their effects previously had to be stimulated manually, the management effort of doing so is now reduced while the quality of the price optimization increases.

Automatic optimization to business goals

The ML-based pricing application observes the market and continuously optimizes different prices without requiring manual intervention from pricing managers. With huge assortments and all the associated data points that go with it, it is too expensive and time-consuming for companies to analyze thousands of products manually. Predictive Pricing can identify narrow segments, determine what drives value for each one, and match that with historical transactional data. This allows companies to set optimal prices based on data for clusters of products and segments. Automation also makes it much easier to replicate and tweak analyses, so it’s not necessary to start from scratch every time, as the pricing algorithm learns and adapts with data over time.

If your business aims for a certain margin or inventory level, the pricing application automatically adjusts individual product prices to achieve these goals. The ML-based pricing application observes the market and continuously optimizes different prices without requiring manual intervention from pricing managers. Machine learning algorithms in predictive pricing evaluate databases to recognize patterns, derive conclusions, and ultimately find solutions to previously defined problems. They do not need explicit instructions to solve the problem. Users do not have to rely blindly on the algorithm’s price recommendations, as they always retain control. Managers can vary their stored corporate targets and simulate how price adjustments will affect all relevant KPIs. Completely manual price optimizations also remain possible. The results of predictive pricing speak for themselves.               

Predictive pricing – the gold standard for successful business

Machine learning-powered predictive pricing has proven that automation and optimization can go hand in hand. With its help, online retailers can significantly reduce application configuration and monitoring efforts, incorporate more factors into calculating optimal prices, and determine valid forecasts for various events. Companies that do not use this technology in some industries are already at a competitive disadvantage. This trend will spread to other sectors.