- The Rise and Limitations of Rule-Based Pricing
- Rule-Based Pricing Powered by Pricing Consultancy
- Mastering Pricing Precision: Rule-Based Pricing Enhanced with Elasticity Insights
- Revolutionizing Strategies with ML-Based Pricing Solutions
- The Power of Deep Learning in Second-Generation AI Pricing Solutions
- Take your pricing to the next level with Competera AI / ML solution
The evolution of retail pricing solutions can be divided into distinct periods. Initially, the dominance of large consultancy agencies was challenged by the emergence of the first technology-driven solutions capable of analyzing historical data. However, these early solutions were costly and posed integration challenges.
The second wave of solutions brought advancements, allowing the creation of rudimentary predictive models. Nevertheless, they remained complex to onboard and operate effectively.
It wasn't until the mid-2010s that the retail industry witnessed the arrival of second-generation AI /ML solutions, which disrupted the monopoly held by industry giants and made optimal pricing accessible to all types of retailers.
To help you navigate the historical landmarks in the pricing tech history of retail and ensure your business is on the path to optimal pricing, we have compiled the most significant events. By understanding this journey, you can guide your team in the right direction. Let's begin!
At the beginning of the modern retail era, retail pricing primarily relied on manual rule-based approaches.
The concept of rule-based pricing software emerged in the early 2000s when retailers began exploring ways to streamline their pricing processes and gain a competitive edge.
Rule-based pricing was one of the initial approaches to automate and optimize pricing decisions. This software solution relies on predefined pricing rules set by the company. These rules could include factors such as cost-plus pricing, competitor-based pricing, or demand-based pricing.
Often, it wasn’t even software, but an Excel sheet with certain rules from retailers and formulas to generate pricing recommendations based on these established rules. For example, an electronics retailer may set a rule to always price their products 5% lower than their main competitors, just that simple.
While this approach brought efficiency to pricing processes, it could not often adapt to real-time market dynamics and individual customer preferences.
Recognizing the limitations of rule-based pricing, businesses started incorporating pricing consultancy services into their software solutions.
Firstly, pricing consultants collaborate with businesses to develop pricing rules tailored to their specific needs and monitor and evaluate the effectiveness of pricing rules in achieving desired outcomes.
Moreover, pricing advisors ensure that the rules are logical, consistent, and aligned with the company's objectives. They also provide strategic guidance by understanding the specific goals, challenges, and competitive landscape of a business.
As a retailer, you had more control over pricing decisions while benefiting from expert guidance. However, it has certain limitations. Take a lack of scalability with dependency on expert opinion, for one.
Plus, it was difficult to capture complex relationships between pricing variables and customer behavior manually. One pricing advisor just couldn’t handle adjusting flower prices for Mothers’ Day on Sunday, beer promotions for the Champions League final on Tuesday, as well as watching the tea and coffee stock, cause it’s pouring rain the whole week.
Retailers had to consider more advanced market approaches.
The concept of elasticity pricing dates back several decades, with economists recognizing the importance of understanding customer responsiveness to price changes.
The idea of price elasticity is essential for retailers to understand the impact of price adjustments on their sales and revenue. Few brief examples:
Elastic Demand: If a product has high price elasticity, a small price change will lead to a proportionally larger change in demand. For instance, If the price of a soft drink increases by 10%, and as a result, the quantity demanded decreases by 20%, the demand for the soft drink is considered elastic.
Customers are highly responsive to price changes and are likely to switch to alternative brands or reduce their consumption.
Inelastic Demand: On the other hand, products with low price elasticity have inelastic demand. If the price of a life-saving medication increases by 10%, the quantity demanded may only decrease by a minimal amount, as customers are often willing to pay a higher price due to the necessity and limited alternatives available.
Cross-Price Elasticity: Price elasticity can also apply to the relationship between two products. For instance, if the price of coffee increases, and as a result, the demand for tea increases significantly, it indicates that these two products have a positive cross-price elasticity.
As markets became more dynamic, businesses realized the need to consider price elasticity in their pricing strategies. Rule-based pricing software, integrated with elasticity pricing, takes into account the responsiveness of customers to price fluctuations.
By analyzing past sales data, customer behavior, and market conditions, the software can determine optimal pricing levels that maximize revenue and profit margins. This approach enables businesses to make pricing decisions that reflect the varying levels of customer sensitivity across different products and market segments.
However, rule-based pricing models with elasticity typically treat elasticity as a static factor. This fails to account for the dynamic nature of customer preferences, changing market conditions, and evolving competition.
Moreover, other important pricing factors such as product features, promotions, or customer segmentation, weren’t adequately considered, leading to suboptimal pricing decisions.
As we see, the journey of retail pricing towards AI/ML pricing has been a result of advancements in technology, the availability of vast amounts of data, and the need for more sophisticated pricing strategies.
So, first-generation AI / ML pricing tools incorporating machine learning were developed during the early 2010s. Let’s see what this brought to the table.
These tools marked a significant advancement in pricing solutions, moving away from manual modeling and simplistic pricing rules that were previously utilized.
These initial ML-based solutions provided retailers with the capability to dynamically calculate elasticity during each repricing cycle, employing basic linear models. The ML breakthrough eliminated the need for extensive consultation and manual recalculation of elasticity, which was traditionally dependent on expert opinions.
In this approach, the impact of price changes on profits and sales is predicted for each product, forming the basis for optimization. The pricing process is revolutionized as ML-based pricing takes a targeted approach, reversing the traditional workflow.
Rather than defining rules and conducting analyses to achieve KPIs, the pricing manager sets the objectives, and the algorithm generates the optimal prices. By doing so, the system ensures that the desired targets are met even before the prices are implemented, providing a more efficient and confident pricing strategy.
However, a drawback of this approach was its heavy reliance on the availability of accurate data for each product to accurately calculate elasticity. Furthermore, elasticity was still regarded as a static factor within pricing rules, failing to fully capture the dynamic nature of customer demand.
It is worth noting that the effectiveness of these ML-based pricing tools heavily relied on having a sufficient amount of historical price changes.
Consequently, certain products within a retailer's assortment might not be covered by the new price suggestions. Additionally, retailers with a large number of low-selling products might find this pricing method less applicable and beneficial in their context.
Finally, we get to the point of pricing evolution where the software is currently at.
The landscape of AI pricing solutions underwent a significant transformation with the emergence of second-generation solutions in the late 2010s. These advanced solutions harnessed the power of neural networks trained on diverse and extensive datasets.
Using deep learning techniques revolutionized the analysis of correlations between various factors and sales. Rather than solely focusing on the relationship between price changes and sales, these solutions delved deeper, considering the interplay of multiple contextual factors and learning from their impact. This shift provided a deeper understanding of customer behavior and generated valuable insights.
Unlike their predecessors, second-generation AI/ML pricing solutions required less guidance to generate meaningful recommendations and were less susceptible to sparse data due to their consideration of multiple factors.
Deep learning-based second-generation AI/ML pricing solutions excel in optimizing prices based on evolving demand trends. They analyze past business policies, leveraging a wide array of pricing and non-pricing factors and information.
They will analyze consumer behavior on a sunny day, plus your competitor’s prices, plus data on sales on all sunny Wednesdays, plus holidays, and tons of other factors.
In addition to historical transactional data, these solutions take into account trading constraints, pricing architecture, inventory availability, competitors' pricing, cannibalization effects, and other crucial information.
Furthermore, the second-generation solutions exhibit flexibility in adapting to market changes, such as the entry of new competitors or business restructuring. These solutions can adapt and generate insights without the need for extensive guidance or expensive expert support.
In summary, the second-generation AI pricing solutions powered by deep learning techniques have brought about a paradigm shift in pricing strategies.
Today, these solutions enable retailers to optimize prices based on complex market dynamics, generate valuable insights, and adapt to changing business environments.
Competera is a cutting-edge second-generation AI/ML pricing solution that bestows advanced pricing capabilities upon retailers.
By using AI/ML algorithms, Competera diligently examines extensive data sets, encompassing historical sales, market trends, competitor pricing, and customer behavior, delivering retailers an exclusive blend of pricing technology.
Complemented by a group of skilled pricing professionals who collaborate closely with retailers, Competera not only offers ongoing support but also imparts expert guidance, ensuring pricing success.
If you want to discover how Competera can contribute to the flourishing of your business, we encourage you to contact us today and elevate your pricing endeavors to a new echelon.