Dynamic pricing technology as an effective way of optimizing your business

pricing-tools

Price is one of the most important decisions which company makes about their product. The key challenge for any business is managing it in a constantly changing circumstances and environment. Today customers have access to more information than ever before, so online marketplaces forces businesses to change their strategies on the go to be able to stay competitive within the sector they operate in.

One of the effective strategies for solving these issues is to use dynamic pricing methodologies and tools.

What is dynamic pricing?

The dynamic pricing is a strategy that involves setting flexible prices for goods or services based on real-time demand.

Companies use dynamic pricing in two formats:

1.Dynamic pricing based on product groups

In this scenario, companies use machine learning/artificial intelligence algorithms or just statistical grouping to offer different prices to different groups. It can be realized through some analytical forecast that sets a greater tendency to pay for a product depending on the type of device and geographical and demographic reasons, which as the result showing a certain price to a certain group of buyers. 

For example, German-based startup 5Analytics provides a software to analyze each customer’s historical purchase data by applying machine learning to provide personalized prices.

2.Dynamic pricing linked to the time of purchase

This type of pricing means that the price rises or falls depending on the time of purchase. One of the simplest examples is the price reduction at the end of the month, when sellers need to fulfill a sales plan. In a more complex version, companies use a tool that allows you to quickly change the price, increasing the profit from the sales and depending on the level of demand. 

The most common example is Uber, which dynamic pricing algorithm adjusts the ride fares based on time and distance of your route, traffic and peak hours, current rider-to-driver demand.

Which industries usually use dynamic pricing?

1. Airlines

A ticket for the exact same flight with the same destination and at the same date can have a number of different prices for different customers. Because airline sales moved online earlier than other categories and because airlines are expected to charge different prices for the same ticket bought on different days, it was easy and acceptable for airlines to move to dynamic pricing.

2. e-Commerce

Retailers, especially e-commerce companies like Amazon and eBay use dynamic pricing for personalized pricing. If you consistently buy from Amazon or another e-commerce website, prices will be higher. Algorithms calculate the loyalty level of each customer and set the price lower if a person is a newcomer.

3. Hospitality

For hotel management and tour companies, seasonality is an important factor. Using time-based pricing that means increasing prices during peak season and lowering when the season ends increases profitability. During the peak season, hotels’ supply needs also increases that’s why charging guests higher is not an immoral idea but the goal of the management should be finding the highest price that consumers are willing to pay.

4. Car Rental

Prices of rental cars fluctuate depending on season and day-of-the-week effect. According to a study made by Thinknum in 2018, weekend prices are more expensive than weekdays and summer prices are higher than winter prices. 

5. Retail

Physical retail is one of the first industries to use dynamic pricing. Primarily relying on seasonality, stock levels and Key Value Item pricing, retailers managed prices in physical stores to optimize their financial outcomes.

How Dynamic Pricing can be helpful to your business?

1. With the use of dynamic pricing, you get to increase prices on the products whose demands have risen – netting you more profits and sales.

2. Dynamic pricing can help you beat the competition quite easily. You can take the shopping preferences of your customers to provide a better experience but at a cheaper price compared to your competitors pricing all thanks due to dynamic pricing.

3. By implementing dynamic pricing, your business stands to benefit by remaining profitable. This provides flexibility and freedom to focus on other aspects of the business.

4. Dynamic pricing helps provide indirect control over the inventory – allows you to provide discounts for overstocked products to reduce their numbers or have a higher price on higher demand items to maintain the supply chain while earning more revenue.

How can AI/ML improve dynamic pricing?         

Software powered by machine learning gains knowledge from data to find the approaches to solving a problem itself, without direct programming. The more data is being fed to a machine learning system, the more it learns from it and improves its performance. In other words, such software doesn’t need detailed instructions on decision-making in a given situation.

The best use-cases of AI and ML-based dynamic pricing solutions typically involve large amounts of daily transactions where demand fluctuates and consumers are willing to pay a dynamic price.

AI and ML allows more extensive data analysis, which results in richer solution functionality. Pricing software with built-in machine learning pricing models has the following features and capabilities:

1. Customer segmentation.This solution can find and detect hidden relationships among data representing customer characteristics, including behavior patterns, search or/and booking history, demographic features, income, device, willingness to pay, and determine customer groups with high accuracy.

2. Analysis of the current market and information about competitors. Competitor pricing is one of the most influencing factors that must be assessed for a price recommendation along with some other external factors: the tool helps to identify industry trends, seasonality, weather, location, production costs.

3. KPI-driven pricing. Businesses can set up a product to align pricing recommendations with performance metrics, such as margin, turnover or profit maximization, etc.

4. Real-time market data analysis. It’s possible to automatically optimize prices to changing demand and market conditions in real-time without specifying complex pricing rules.

5. Price elasticity calculation. These solutions give users the capability to define price elasticity to predict whether customers will accept a new price before taking a pricing decision.

price-optimization

Additionally, according to AIMultiple research in 2022, there are a few main points and constraints to be taken into account while creating the dynamic pricing tools:

  • To get the best results, software should be able to integrate with existing company systems like CRM or ERP to get the most accurate data.
  • Some dynamic pricing tools have “freemium” options where you have access to a few features and have to pay for complete service. If you are looking for a long-term solution, a subscription method would fit best.
  • Software-as-a-Service vs in-house systems: Cloud-based solutions may reduce the risk of data loss but increase the cost. 

Conclusion

Although there may be a several concerns around dynamic pricing, the reality is that it can maximize profits and track expenses. Using algorithms and machine learning, you’ll employ price optimization, ensuring you set your goods and services at the best price.
If supply and demand impact your product significantly, dynamic pricing may be the best strategy to ensure a steady flow of sales. With insightful strategy and attention to detail, you’ll find that a dynamic pricing model can help take your business to the next level.

Altabel Group has experience in implementing such solutions using Artificial Intelligence and Machine Learning algorithms, so please feel free to reach us out to know more about it, we would be glad to turn your business ideas into reality!