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Optimising prices with AI: transparency as the key to success

Since the explosion of digital technology in our societies, artificial intelligence (AI) has played an increasingly crucial role in the pricing of products and services. It enables companies to optimise their prices according to various factors such as demand, competition, market trends, etc. 

However, with this increase in the use of AI, the transparency of models is becoming essential, particularly in pricing. Indeed, pricing is an extremely strategic discipline for companies, who therefore have a major need to understand how AI-powered tools use their data to create optimised prices.


First of all, a quick definition:

Transparency of AI models means making the processes and decisions of algorithms understandable and accessible to end users. 


In the case of AI for pricing, transparency is crucial because it strengthens the trust between the SaaS pricing solution and its customers. This trust can grow in stages: 

Initially, users tend to test solutions, sometimes on a limited basis. They want to be sure that the tool is working properly.

Once this test phase is over, global deployment and the first successful price optimisations reinforce confidence.

The third confidence phase leads to an increase in the rate of adoption of price recommendations.

Once all these stages have been passed, it is possible for users of pricing tools to automate more and more tasks and free up their time to concentrate on high added-value missions.

At PricingHUB we value transparency and want to share with you (as simply as possible) how our pricing recommendations are developed.

Optimised prices thanks to our rules engine

Our rules engine allows you to adjust prices using a vast set of rules based on multiple attributes such as:

  • Custom rounding 
  • Min and max 
  • Price index
  • Margin constraints
  • Price consistency, etc. 

Our module offers the ability to create multiple pricing scenarios, providing the flexibility to estimate the impact of each scenario on the key KPIs identified, with or without taking into account elasticity assumptions. 

We enable our customers to compare the different scenarios to ensure informed, data-driven decision-making.

Our rule engine aims to ensure a strategic and transparent approach to price construction, in order to optimise not only profitability but also user productivity.

Optimised prices thanks to our module based on consumer price sensitivity

Our AI-based module creates sustainable value through a two-phase approach:

  1. The learning phase

This first phase lasts from 2 to 8 weeks and is designed to explore the price elasticity of your products. To do this, we carry out a large number of price iterations, while respecting a global constraint in order to evaluate your global and granular elasticity curves.

    2. The optimisation phase

Once the learning phase is complete, we can launch the price optimisation phase. During this phase, the aim is to propose relevant, granular and real-time recommendations in line with your objectives and constraints, to ensure mass adoption.

To do this, our module offers the option of selecting several parameters to frame the pricing iterations: 

  • 1 quantitative objective based on the main KPIs: profit, turnover and sales volume
  • 1 quantitative constraint that can be based on the main KPIs but also on: optimised stock management, unit margin, etc.

Thanks to a methodology based on reinforcement learning, our module dynamically measures and adjusts price iterations in order to propose granular price recommendations on a daily basis for vast product catalogues.

Displaying recommendations in the PricingHUB application

Whichever optimisation module you choose to use on the application, we allow you to access information related to recommendation breakdown directly in the application.

The transparency of our tool means that you can:

  • Understand in detail how the price is constructed, for example: 
    • Respect for intervals
    • Personalised rounding
    • Reference price, etc.
  • Identify its position in the market: we offer our customers the opportunity to understand the context in which the price recommendation was issued and its impact on key performance indicators.

By promoting transparency, we enable our customers to navigate a complex pricing environment with confidence, maximising profitability and optimising productivity. Transparency is at the heart of our approach, as we believe that a clear understanding of the algorithmic processes is essential for widespread adoption and successful use of our solutions.

Thus, by adopting a transparent, AI-based pricing strategy, businesses can not only improve their financial performance but also benefit from a relationship of trust between customers and partners. At PricingHUB, we’re proud to help you do just that. To find out more, come and talk to one of our experts!


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