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Three reasons often initiate the introduction of professional pricing software in retail, wholesale and e-commerce:
In this situation, it makes sense to consider technical support that enables better pricing decisions with less manual effort.
Important: The individuals involved retain financial responsibility as well as the substantive understanding of the pricing. No one leaves the proverbial “driver’s seat.”
In retail and e-commerce, dynamic pricing is often equated with frequent price changes. In our view, this is not a mandatory characteristic. We define dynamic pricing as adaptive pricing. Over time, it is regularly (“dynamically”) checked whether the factors and market conditions that determine the pricing have changed compared to the last price setting. These factors include, for example:
If the factors have changed, a decision is made as to whether a price change is necessary. Theoretically, you can make price adjustments in real time (“real-time” or “near real-time”). However, if “price stability” is part of the price strategy you wish to maintain, you evaluate “dynamically” whether the benefit of a price change and update outweighs the disadvantage of sacrificing the desired price stability. You frequently check whether prices need to be changed and decide just as frequently whether you actually implement the price adjustments in accordance with market changes.
You can perform this regular review of how the market and other factors have changed manually. Likewise, you and your pricing managers can manually check whether you need to manually optimize the current price for each of your products.
The manual review and analysis of the factors that determine your pricing can be automatically taken over by a tool. This automation saves time and makes the work in price management and the adjustment of prices much more efficient.
Pricing managers spend less effort working “in pricing” and can devote more time to working “on pricing,” such as developing smarter pricing strategies.
Pricing software does not only help to automate price decisions—we refer to this as “price automation.”
Pricing software also provides support for price decisions as such. Setting the best price—in light of a defined price strategy—is referred to as price optimization.
Among the applications that support Price Automation and Price Optimization, there are some synonymously used terms: Pricing Software, Price Optimization Tools, software solutions for pricing, Dynamic Pricing Solutions and some other creative terms.
The selection of pricing software is at least a medium-term commitment and means a short-term investment in implementation. This decision should not be taken lightly.
Because more than half of all companies are dissatisfied with their purchasing decisions for software. A survey of 3,500 companies (350 of them in Germany) shows that 60% (63% in Germany) regret at least one technology purchase of the company in the last 18 months.
Of these, 59% describe the financial impact of the regretted software purchase as “monumental” and “significant.”
While incorrect decisions in software purchases are +68% more likely to be based on a ChatGPT search, successful software decisions are +56% more likely to start with the support of experts.
In short, professional support in the selection process of suitable pricing software is worthwhile from an entrepreneurial point of view.
Source: Capterra’s 2025 Tech Trends Survey, Sample: 3,500 Software Buyers
Important aspects have already been described above, such as how pricing software delivers entrepreneurial added value. We now want to supplement these different sources of added value.
Pricing software…
In retail and e-commerce, software support for pricing is divided into two levels.
The first stage involves decision rules applied in rule-based pricing. An internal pricing manager determines the strategic direction of the pricing and defines corresponding decision trees, which are processed at scale by a pricing system. These rules begin with a definition of the scope, e.g., assortment segments and item roles (e.g., all key value items/KVIs in category XYZ).
Subsequently, rules and rule priorities are established. For example, target prices and target margins are specified, relevant competitor prices are defined, maximum distances to competitor prices are determined, price gaps to related products are maintained, the manufacturer’s suggested retail price (MSRP) is set as a maximum price, and an absolute minimum margin is set as a price floor (“safety net”).
The result of this price calculation is transparent and verifiable at all times. If a price is classified as incorrect, the underlying rule can be systematically corrected. The configuration of price rules promotes thinking in terms of mechanisms and processes required for objective pricing. Pricing based on gut feeling or “expert assessment” is replaced by data-driven pricing.
This experience of being able to recalculate prices increases trust in an automated price calculation. This trust is important for the second level.
The second level builds on the user-defined decision rules and considers them as framework conditions. These framework conditions define a possible price range. Within this price range, an optimal price is set in accordance with the specified pricing strategy, which maximally achieves the target value of a so-called target function. This process and the underlying technology are understood as price optimization.
In this process of automated pricing, artificial intelligence or, more accurately, machine learning takes place. Algorithms predict the demand volume for different price points within the possible price range. Therefore, this more advanced approach is also referred to as demand-based pricing. For each of these price points, all financial metrics and KPIs can then be derived: revenue, contribution margin per unit, total contribution margin, product margin (“gross margin” in %), and units sold. Depending on the chosen objective of the pricing strategy, the optimal price that leads to a maximum result is automatically selected. This price optimization can also be achieved dynamically while adhering to constraints: “Set prices so that a minimum revenue growth of 2% is maintained and the contribution margin is maximized.”
Price optimization not only works for individual products in the range, but also for larger range sections. Technically, cross-price elasticities for related products (substitute and complementary products) are taken into account in pricing.
The advantage of demand-based price optimization is an improvement in decisions regarding price changes. Sales and profit as well as their ratio are maximized compared to purely rule-based pricing. The price optimization software sets its own prices in such a way that the best results for the company are achieved in view of the chosen pricing strategies. The demand forecast underlying price optimization simulates the effects of price updates before they are implemented. This forecast ultimately also provides the basis for making pricing decisions and thus for the release of price recommendations by internal pricing managers.
But even if the possible, user-defined price range is specified by an internal expert, the optimal price or the underlying demand forecast can no longer be recalculated. This circumstance can be perceived as a disadvantage in an early phase of the software introduction, which is resolved with increasing experience of the internal team and professional change management.
In the retail industry and in e-commerce, at least six data sources are connected to pricing software:
The evaluation of different providers of pricing software is ideally carried out (a) comprehensively, (b) objectively and (c) efficiently.
A comprehensive evaluation refers both to (a) a longlist of relevant providers that is as complete as possible and to (b) including all relevant evaluation dimensions and criteria as completely as possible. These evaluation criteria include
An objective evaluation means that the (a) method, (b) evaluation tools, and (c) evaluation consolidation into a recommendation are determined in advance (“ex-ante”) and applied equally to all software options—i.e., without bias.
An efficient evaluation ensures that internal resources are used as productively as possible when evaluating pricing tool options. Efficiency in the evaluation is ensured (a) by a step-by-step prioritization in the evaluation process and (b) by the introduction of the relevant project assets by an experienced expert.
In the course of a career in price management of a company, pricing managers introduce one or two pricing systems. Often it is the first selection ever or the second introduction in a long time. This experience gap can be effectively bridged by external support and the selection project can be accelerated in terms of content and time.
The Price Management Institute has developed a proven selection methodology for this purpose and implemented it in several practical projects: The PMI Pricing Software Roadmap® provides a systematic approach that implements pricing software projects with low risk and profitably. The proven methodology includes all the necessary deliverables, which include the experience and learnings from more than a dozen projects on software selection and implementation and can be used at short notice.
If you would like to get to know our approach, please arrange a meeting with us.
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