Dynamic Pricing Algorithm: How it works?

Pricing Management

If you are grappling with the idea of pricing techniques in the new year, you are not alone. 2024 has arrived. Consumers are in the festive mode impacting the demand-supply curve across the retail industry. In the fluctuating market, you want to maximize returns and receive an optimal amount for your product/service, and you don’t want to price yourself out of the market. Indeed, pricing is a tricky Job. Adopting an AI/ML-powered dynamic pricing algorithm enables you to adapt prices according to the market trends, personalizing your customers’ requirements, driving revenue goals, and gaining competitive advantages, to name a few of the merits of the algorithm concerned.

In this article, you will learn all about the dynamics of the ‘Dynamic Pricing Algorithm’ in detail. Everything related to the concept including its practical implementation, benefits, criteria to choose models, traditional vs. dynamic pricing, challenges, if any, and to address those challenges and many more. Let’s explore!

What is Algorithmic Pricing?

What according to you happens to be the main driver of the competition today in the retail industry, particularly, e-commerce, online retail, and brand? Wouldn’t you say it is the ‘Price’? Yes, you’d! When you offer attractive prices to your customers, that gives you an edge over your competitors. Offering attractive prices has now become a much more striking value proposition for companies, big or small.

Algorithm Pricing refers to the process of real-time price adjustments aligning with company goals by leveraging AI (Artificial Intelligence) and ML(Machine Learning) capabilities. It is considered a powerful pricing strategy tool that retailers adopt to gain a competitive advantage in the market. The price adjustment is based on competitors’ activities, supply and demand, delivery timing/schedule, etc. Algorithm Pricing helps companies to maximize profit, and boost ROI (Return on Investment) while attaining business goals.

Yes, when you say the ‘Best Price’, it does not mean it is the ‘Lowest Price’. This is the underlying essence of Algorithm Pricing. It helps you gain near-real-time market visibility, your competitors’ price movements, and automatic comparison of prices in the market leading you to gain a competitive edge, and you get this opportunity to read your customers’ minds, how they’d respond to varied product pricing levels, what pricing you settle for at that moment to motivate them to buy the very product! The entire process leads you to boost your sales, maximizing your profitability.

Why Algorithm Pricing?

Depending upon your unique business requirements, the algorithm helps you to gain complete control over the pace of selling, i.e. how fast or slow you sell your product/service.

Thus, Algorithm Pricing helps in:

• Fetching optimum profits as it finds the exact price and no guesswork involved.
• Time-saving as it allows you to take care of vital activities rather than setting up your prices manually. If you do it manually, it can take up hours of your day. Instead, why not focus on other important activities like replenishment, scouting, and planning your next move? Think over!
• Helps you to have substantial control over your inventory as well as the market.

Algorithm Pricing Features

As discussed above, Algorithm Pricing is a price management process that leverages the massive power of AI/ML technology, and data analytics, to help pricing managers, pricing heads, and other primary stakeholders in making valuable pricing decisions aligning with company goals. External factors such as competitor pricing, consumer demand, and market/economic trends influence the process. Price elasticity of demand, a significant pricing concept, also acts as an important parameter that helps in determining how a certain change in price impacts or affects consumer demand. I will cover this topic specifically in later blogs, stay tuned!

Meanwhile, the following features and capabilities help in diving deep into the topic:

• Dynamic Pricing: Dynamic pricing algorithm, also known as demand pricing, surge pricing, or time-based pricing is a flexible, partially or fully automated algorithm pricing strategy where prices change/ fluctuate based on market demand. Here, they don’t personalize customers’ unique requirements but adjust as per competition price, demand, supply, inventory status, subsidiary product prices, clearance goals, the spike in demand, and then, set an optimal price.
• Personalized Pricing: Here, the algorithm pricing focuses on the personalization aspect of customers and potential buyers. It personalizes customers’ unique requirements. It takes into account price sensitivity, differences in consumer tastes(as all are unique tastes), and varied statistical information about potential customers/buyers. Customers’ demographics give valuable insights into their unique requirements.
• Optimized Pricing: AI/ML-powered data-driven methods help in optimizing pricing strategies for varied categories and products in the market. CVP(Consumer Value Perception) is a major element under this feature/capability of Algorithm Pricing.
• Optimal Pricing: For instance, an AI/ML-enabled Algorithm Pricing Management System helps in suggesting new promotional offers to improve the performance of the baseline promotion calendar. Yes, the algorithm can determine near-optimal pricing parameters, discover missed opportunities, and offer suggestions to improve the misses!
• NLP Capabilities: The algorithm Pricing feature boasts of NLP capabilities that help in leveraging textual and visual data, like those of product descriptions and images.
• Demand Modelling for Merchandizing/Inventory Management: Algorithm Pricing sees stock level, and selling velocity data while going for price optimization. As a result, to avoid circumstances like stockouts and overstocks, pricing can be adjusted (increase or decrease). Moreover, demand modeling can help in the inventory optimization process.

Briefly speaking, you can consider Algorithm Pricing as varied tool-kits customized to your specific requirements, and not just as pricing strategies. After all, it is not a one-size-fits-all toolkit. You choose what suits you the most. It is better to consult an expert in the Algorithm Pricing vertical in the industry.

Traditional Pricing is Dead. Dynamic Pricing is the Future.

Traditional retailing resorts to traditional pricing or fixed pricing only! Modern retailers who believe in the power of digital technologies like AI/ML, data analytics, pricing intelligence, and retail intelligence, resort to dynamic pricing algorithms for their pricing strategies. Now, what do you understand by Fixed Pricing? It means, there is no flexibility in pricing for retailers to attract more customers, and to build brand loyalty.

Let’s quote FORBES on Dynamic Pricing-

“In the not-so-distant future, virtually nothing will have a fixed price. Say goodbye to the familiar $1.99 and hello to a price between, say, $1.39 and $2.17, based on changing supply and demand. This is the phenomenon of dynamic pricing and it is already rapidly changing the way we buy goods and services and dramatically reshaping our economy.”

Traditional Pricing Origin

Dynamic Pricing has evolved as the Traditional Pricing approach started giving retailers challenging times to sustain in the market. The origin of traditional pricing dates back to the 1980s when retailers used to hire consultancies/agencies in their pricing decision-making process, to come out with a solid pricing policy for their business. The crux is, that they resorted to manual calculations of prices, and these calculations were purely based on human-centric analysis of the market, viz. cost, supply, demand, competitors, and so forth. Consequently, two types of pricing approaches were formed:

1. Premium Pricing: Retailers who wanted to compete in the market sought ways to differ from competitors. They wanted to sell unique products, and for that, they collected competitors’ prices manually and put them as records for future reference. They would decide whether to increase the price at some point in time in the future. This method was labeled as Premium Pricing.
2. Penetration Pricing: Adopting this strategy, retailers used to lower product prices than those of other players in the market. This pricing approach did help retailers to gain customer loyalty. however, the flip side was, that it failed to sustain profit margin and boost revenue for their business.

Today, these traditional pricing approaches still exist in the retail market. Sometimes they work favorably, but they lack long-term value quotient and fade soon. They lack sustainable growth prospects and lack efficiency in the implementation process. A manual process is prone to error and can damage reputation in the market. After all, it is hazardous to rely completely on your competitors to set prices correctly. If they make any mistake, you know the repercussions. You will lose credibility in the market.

Premium & Penetration Pricing: Disadvantages

Premium Pricing Disadvantages

• Difficult to maintain a high-profit margin
• The potential for growth is very low
• Vulnerability
• Managers need to focus on SKUs separately, not as a whole portfolio

Penetration Pricing Disadvantages

• Low Customer loyalty
• Very little scope for getting a high price/decent margin (expect a low price only)
• Threat to brand image with a bad reputation in the market
• Managers need to focus on SKUs separately, not as a whole portfolio

The Evolution of Dynamic Pricing Algorithm

“Necessity is the mother of invention!”

Heard it before? Well, it’s a common adage!

This adage perfectly suits the invention of Dynamic Pricing in the market. When the traditional pricing approaches started faltering, digital technologies like Artificial Intelligence, Machine Learning, Big Data Analytics, etc. were already in the midst of their evolutionary journey. As a result, the Dynamic Pricing Algorithm was developed.

During the 1990s, the prototypes of Dynamic Pricing Solutions were launched in the market. However, they were very expensive, and not an integration-friendly solution. Thereafter, with the beginning of the 21st century, newer versions of digitally-capable Dynamic Pricing Solutions emerged that were easy-to-integrate, and had high accuracy levels. The advantages were massive as those Dynamic Pricing

Engines were capable of considering myriad functions, viz., cross-elasticity, and demand fluctuation, while processing billions of data points in a go!

How Does Dynamic Pricing Algorithm Work?

Dynamic Pricing algorithm strategy is a pricing strategy where retailers adjust their product/service price offerings to ally with the changing demand in the market, aligning with respective business goals. It ensures scalability and enables pricing managers, and retailers to relocate from SKU-centric pricing to a portfolio-based pricing system for enhanced outcomes. Dynamic pricing algorithms help you set prices for varied target groups of shoppers, and consumers, bringing maximum flexibility in the ecosystem. The algorithm crafts an optimal value offering that is based on parameters like market trends, customer behavior, fluctuations, purchasing power of customers, and so forth.

Under dynamic pricing algorithms, the core estimation emerges from the fine dependency between price and demand. While it is viable to depend on data relevancy with this estimation, you can calculate the revenue-optimal price for your company. Following is the formula for calculation -

Source- Linkedin

In the formula, the price (p) and the demand function (d(p)), act as the foundation, with dynamic pricing software engines expanding it to consider factors related to pricing as well as non-pricing fields. These factors entail procurement expenses, inventory costs, demand cannibalization, competitor pricing, promotional activities, etc. The algorithm’s accuracy level improves as it processes relevant data points only.

Demand Function Workflow: 4 Stages

In demand pricing algorithms, historical data forms the core for demand estimation.

Dynamic pricing algorithm leverages historical data to know about:

• Market trends
• Product prices
• Customers’ purchase behavior
• Production costs
• Real-time data about competitors’ prices, stocks

A typical workflow pattern shows four main stages :

• Dynamic pricing algorithm starts processing historical data on price points as well as the demand.
• Based on identified dependencies, the demand function is constructed.
• Advanced mathematical processes generate optimal prices based on pricing and non-pricing factors.
• Recommended prices are set to apply. After the application, the algorithm iterates, considering the latest repricing results.

The given pattern is a fundamental workflow pattern. Dynamic pricing algorithm unique models offer customized solutions to each retailer with unique requirements, taking account of their objectives as well as limitations.

Dynamic Pricing has advantages but has certain limitations, too. How?

Advantages

• You can sell even in downtimes. You lower your product price as demand is low. This way, dynamic pricing can act as a salvaging engine for your business.
• You can offer discounts, and sales at a time when people don’t want to pay the whole price. On top of this, the same customers can become your loyal customers once the demand comes back to normal stage. Thanks to your dynamic pricing algorithm that gave you the suggestion of lowering price, so that you could get minimum outcome even in downtimes.

Disadvantages

• Dynamic pricing algorithm has limitations, too. Inconsistent pricing leads your customers to lose trust in your brand and won’t come back to you again. They feel cheated.
• Moreover, overdoing dynamic pricing might lead you to lose business.

Exploration-vs-Exploitation Algorithms: Enabling Enhanced Outcomes

Exploration-vs-exploitation algorithms act as a significant enabler from the point when basic dynamic pricing models fail to deliver the desired results. It enables retailers to make informed, accurate pricing decisions fetching maximum returns. These capabilities enable retailers to have a competitive edge in the dynamic market.

Key benefits are:

• Highly Adaptable to Changes
• Optimized Decision-Making Enabler
• Enhanced Competitiveness
• Near Accurate Data-Driven Insights
• Improved Customer Experience (CX)

How to Choose the Dynamic Pricing Model?

While choosing a dynamic pricing model that matches your business requirements, consider parameters like your maturity level, your specific business goals, and your overall market strategy. Thus, to choose the best dynamic pricing algorithm, you take into account that the very model is providing prices that:

1. Helps in maximizing revenue, and profit: Dynamic pricing algorithms are designed in a manner that supports greater flexibility in price adjustments, in real-time or in dynamic market situations.
2. Helps in reducing customer churn: Your chosen dynamic pricing model should help in providing personalized prices to customers to connect. The algorithm should be capable of analyzing customers’ behavior, for personalization that helps in minimizing customer churn.
3. Helps to attract customers with competitive prices: The dynamic pricing algorithm model should be capable of anticipating competitors’ prices, and accordingly make your price adjustments. This’d help you stay competitive, and attract customers.
4. Helps to create improved CX and maintain loyalty: Does your dynamic pricing model provide personalized offers, discounts, and prices to your customers? Does it ensure to provide an enhanced shopping experience to your customers? Consider this aspect, too, as it helps build repeat business.
5. Helps to align with your business objectives: Ensure that your chosen dynamic pricing algorithm model makes price adjustments that align well with your business goals/objectives.
6. Helps to reduce cannibalization effect between specific products: Your chosen dynamic pricing model should be capable of generating prices for products without any scope of risks for KVI’s sales.

Dynamic Pricing Algorithm: Enhancing Your Business Goals

Dynamic pricing algorithm is not a choice anymore, but a necessity to stay competitive in the retail sector. If you want to maximize margins and expand your footholds in the market, then adopting an AI/ML-enabled pricing tool is a perfect step. Why not? After all, a dynamic pricing tool comes with a highly customizable rule engine that takes inputs in terms of targets and guardrails to tailor the best pricing strategy for your business. This makes managing multiple categories and hundreds of thousands of SKUs easy, helping you to maximize margins and expand your market.