“Powered by AI” – with this label, software manufacturers promise retailers unprecedented possibilities in price optimization. The problem is that the applications often fail to deliver what they claim. The AI algorithms are poor or there is no real AI built in at all. Therefore, retailers should take a close look when choosing an AI pricing provider. We show you how to find the strong, genuine AI providers among the many black sheep.
How artificial intelligence works in pricing solutions
First, developers usually speak of machine learning instead of AI, because machine learning is the subset of methods used in AI-based price optimization software.
Complex machine learning algorithms in price optimization are able to analyze huge amounts of data from many different sources, such as inventory, price, competitor prices, season, and product category. By recognizing specific patterns in the data, the algorithms can indicate how price changes would affect demand and profit, allowing retailers to implement data-driven pricing strategies that more effectively align with their business goals, such as maximizing sales or clearing stock. However, it’s not always easy for users to judge how well the algorithms perform and how accurate their forecasts are. At least, not without talking to developers and assessing their AI approach.
Key indicators of genuine AI usage
The potential of powerful AI algorithms is massive, but how can you check the quality of an AI pricing tool before making a purchase decision? Take time to get your questions answered in a personal conversation. If the AI pricing provider is not able to give the needed information or doesn’t want to disclose documents, this is a huge red flag.
Here are some important aspects that you should ask about:
1: How does the AI learn?
Does the data model learn through supervised or unsupervised learning? Many unsupervised data models require a lot of time before they can provide reliable, high-quality recommendations.
2: What algorithms are used?
BERT, XG, Q-Learning – each new algorithm has its own strengths and weaknesses. Choosing the right algorithm for the specific use case is crucial for the quality of the price recommendations.
3: How is the effectiveness of the data model measured?
R-squared score, F1 score or accuracy percentage – there are many ways to measure the accuracy and reliability of an AI data model.
4: How often does the AI pricing provider retrain the AI models?
As with any software, a data model should be updated regularly, for example to eliminate bias, improve security and accuracy, map new functions and work more resource-efficiently.
Assessing algorithmic transparency
Many people are too enthusiastic about the performance of AI pricing software to question how the algorithms actually arrive at their results. However, business is not a place for blind trust. Transparency of AI should be a key criterion in the selection of any AI pricing provider.
Retailers must know the most important facts about the algorithm in use: What data is processed and how? How accurate and reliable are the outputs? This is the only way retailers ensure that the AI pricing tool works in line with their business objectives and corporate values.
If you want to read more about the challenges of trust in AI, we recommend reading: AI’s trust problem by Harvard Business Review.
Analyzing data integration and analysis capabilities
Even the best algorithm is of little use if it only works on a small database and data integration is prone to errors. Therefore, we encourage retailers to go deep on asking about how their future AI pricing provider processes data.
Data sources
Ask about which data sources will be worked with. Are all relevant sources included? AI pricing software should work with a comprehensive dataset, including historical sales data, inventory levels, weather data and competitor prices to enable robust and accurate pricing strategies. Some solutions like 7Learnings even include marketing data to allow for cross-optimization of sales and performance marketing campaigns.
Data quality management
What are the provider’s practices for ensuring data accuracy, completeness, and consistency? Professional data quality management includes regular cleaning, validation, and updating of data to reach the highest possible accuracy of AI predictions.
Integration capabilities
How will the solution integrate with your existing systems? Make sure data can be accessed easily and data will flow in real-time to allow for immediate price adjustments.
Security & privacy
Ask how your future AI pricing provider is handling data security and is aligned with data privacy protection regulation? When data leaves your systems, you don’t want to risk your compliance and protect your company data as best as possible.
Scalability
When you are on a growth journey, make sure your AI pricing provider can handle even massive increases of data from your side. You want to choose a scalable solution that can process high and complex volumes of data without compromising performance.
Requesting proof of AI performance
Real-life results speak for themselves: reputable AI pricing providers not only integrate a few short customer quotes on their website. They provide customer success stories from various industries in which companies are named, and results are listed. And they are willing to talk about how they measured the performance to allow retailers a realistic impression of how results should be interpreted.
With 7Learnings, for example, companies regularly achieve uplifts in profits in the double-digit percentage range – after just a few weeks. INTERSPORT Krumholz saw an uplift of +118% in profits after introducing predictive pricing, while shoe manufacturer Tamaris halved its operational time and reduced discounts by 5%.
Want more examples of what’s possible with 7Learnings? Read more about our customer successes.
The importance of choosing the right AI pricing software
AI pricing software is one of the best application scenarios for artificial intelligence in companies. This is because companies regularly achieve double-digit growth in sales within a short period of time, as confirmed by BCG. While the use of AI in pricing was still rare two or three years ago, a rush for the technology has now begun. Those who do not keep up are in danger of being left behind.
However, AI is not just AI: companies need to gain at least a basic understanding of how AI pricing tools work in order to evaluate how good an AI pricing provider really is. After all, there is a gold-rush atmosphere in the software world and many providers sell inferior quality under the label of AI. However, with the right AI pricing provider at their side, retailers gain various benefits: They strengthen their market position, increase their profits and improve their future competitiveness in every aspect.
