In today’s digital age, data is king. With the ability to collect, analyze, and interpret large amounts of data, businesses can gain valuable insights into customer behavior and preferences, and use this information to make strategic decisions.

Companies gather data on various customer attributes such as demographics, buying habits and preferences, which help them segment customers based on their likelihood of responding to certain prices.

This data is then used in combination with macroeconomic trends, such as inflation and currency exchange rates, to create an overall pricing strategy that takes into account current market conditions.

Businesses also use big data analytics to identify price sensitivities for different customer segments, allowing them to adjust prices accordingly, and monitor competitor pricing strategies.

By using predictive analytics algorithms such as machine learning and artificial intelligence, businesses can track competitors’ prices and see how their own pricing strategies compare.

This information can then be used to inform a company’s dynamic pricing decisions, helping them stay competitive while still maximizing profits, and this is where data analytics is becoming increasingly important.

By using big data analytics to inform their dynamic pricing strategies, businesses can also reduce their need for manual price adjustments and make sure their prices remain competitive in an ever-changing market.

In this blog post, we will discuss how businesses can leverage big data analytics to create smart pricing strategies that are tailored to their customer base and maximize profits.

Definition of dynamic pricing

Dynamic pricing is an increasingly popular business strategy that allows a company to adjust its prices in response to changes in demand.

This pricing structure ensures that companies are able to maximize their profits while also giving customers access to discounted rates that they may not have been able to get with fixed prices.

Furthermore, dynamic pricing helps businesses manage their inventory and revenue by allowing them to adjust the price accordingly based on whether product demand is high or low.

Overall, dynamic pricing is most useful for companies who want to make sure that they are getting the best value for their products, while also ensuring customer satisfaction.

What is Big Data?

Big data is a relatively new term that refers to the large volume of data that is made available to businesses from a variety of sources.

These could be business transactions, social media or even machine-to-machine data systems such as meters or sensors. However, the raw data by itself is largely useless.

A business must choose the best way to harness this data and analyze it at a granular level in order to better engage with their customers and put together data-driven pricing strategies.

By doing so, big data can be the key to the success and profitability of a company.

Big data is based on science. It uses software which can apply intelligent business decisions to each of the items in a company’s product range, regardless of whether there are ten or ten thousand of them.

Big data identifies factors such as the national or global economic situation, sales-rep negotiations and a customer’s product preferences.

By analyzing these factors, big data intelligence is able to find the optimal price that a customer is willing to pay and come up with an ideal pricing strategy based on this.

How Organizations are Using Big Data?

Companies operating in all industries  are benefiting from the information that big data can provide.

Data-driven analyses is assisting businesses in learning more about their customers’ spending habits and applying the correct pricing strategy to their products in order to return a better profit margin for their company.

There are already many industries that are using data-backed pricing strategies to great effect.

Rick Kostick, founder of cosmetics company 100% Pure, was attracted to using big data on their website due to the promise of:

“a machine learning algorithm that is able to predict which customers will leave your site without purchasing any of your products (with 99%-plus accuracy) and the capability to offer only this group a steeper discount than normal to entice them to purchase before leaving”.

By using this type of discount pricing strategy, 100% Pure saw its online sales increase by 13.52% in just three months.

In the world of car insurance, companies such as Progressive are providing more competitive quotes to entice potential customers.

This is largely possible through the use of data generated by telematics devices that are placed on the dashboard of a car.

These devices measure specific driving factors such as speed, cornering and how hard the driver is breaking.

Rather than an insurance quote being based on traditional averages such as age or gender, the telematics data is able to calculate a more precise level of risk which is directly associated with a customer’s style of driving.

Businesses using dynamic pricing can change prices multiple times a day, in real-time, to react to changes in demand.

This data-driven price optimization strategy is commonly used in industries with a lot of competition and where prices often fluctuate.

How does data influence the dynamic pricing algorithm?

Quantity of data

Not all data points are accessible or even applicable for every business. If a retailer is new to the market, for example, it may not have access to customer testimonials.

In addition, companies are rightly very cautious about using personal consumer data. However, since the machine learning model is always adapted to the individual case, it can be designed to deliver good results even without these data points.

For example, personal data is not even necessary for price optimizations at the product level.

As mentioned above however, machine learning approaches to dynamic pricing are particularly tailored to learning from large data sets.

The greater the quantity of data available, the better the machine learning model can be trained to make accurate forecasts.

A machine learning algorithm learns from past price changes and the effect the changes have on sales. Therefore it’s goods to have data from 2-3 prices changes per product with a significant amount of associated sales.

Quality of data

The quality of data collected will have a direct impact on the machine learning model built for dynamic pricing. The higher quality of data, the easier it is to utilize it and to build out features to guide the system. Good quality data is when:

• The data is complete.
• The data is clean.
• The data is consistent.

The following are some industries where dynamic pricing is commonly used:

• Airline industry: Airlines have used dynamic pricing for years to maximize revenue. Flight prices can fluctuate multiple times a day based on factors such as demand, time of day, and length of the flight.
• Hotel industry: Hotels also use dynamic pricing to fill rooms during slow periods and increase rates during busy times. Rates can fluctuate based on seasonality, demand, and location.
• Event ticketing industry: Event ticket prices can fluctuate based on demand, time of day, and seat location. Dynamic pricing is commonly used for tickets to sporting events, concerts, and theatre performances.
• Retail industry: Retailers increasingly use dynamic pricing to compete with E-Commerce businesses. Product prices can fluctuate based on demand, time of day, and location.

Examples of Companies Using Dynamic Pricing from Different Industries:

Dynamic pricing is becoming more and more accessible to all sorts of companies, B2B & B2C.

Given below are examples of companies that use dynamic pricing from different industries.

‍Amazon

How is Amazon using dynamic pricing as a winning pricing strategy?

Who hasn’t heard of Amazon? The former bookstore is now one of the biggest (if not) the greatest online retail store in the world. It sells everything.

Books, pipes, electronics, wine and more. One of the reasons they have grown into one of the biggest companies in the world is their use of dynamic pricing.

Amazon changes prices on its products every 10 minutes!!!

According to research, Amazon changes their product prices on average every 10 minutes. Which means that on average, product prices change 144 times a day, 1008 times a week and 52.560 times a year.

This amount of price changes is insane. But it also leads to Amazon being able to offer the right price at any given moment.

All the data Amazon gathers from customers’ buying behaviour, competitors, profit margins and inventory is used to increase profitability.

And with success!

• ‍Uber:

Uber uses dynamic pricing for two different reasons – for boosting profits but also for making sure that taxis are covering all demand.

When demand for taxis in a certain region is high, Uber automatically increases prices for customers to make these rides more attractive for Uber chauffeurs.

These prices make sure that people that do not care about the price, pay significantly more than normal, but it also results in more Uber drivers coming to the area in which demand is high.

When the demand is met, the prices go back to normal. People also call this surge pricing.

• ‍AirBnb:

Although AirBnB acts as a mediator – it is a rental platform which connects properties with renters – it also has dynamic pricing function built, in its platform.

AirBnB calls this function “Smart Pricing”, and it automatically changes the prices of your property if certain factors are changed, such as season, demand, property features or location.

Next to this, people are also able to add their own constraints, such as a minimum price per night.

AirBnB states that people who use the “Smart Pricing” function are 4x more likely to receive a booking than people that do not use this function.

They state that this function alone increases revenue for people with 12%. And Amazon profits from this too, by receiving more commissions.

• Airlines:

The airline industry has long been a pioneer in the use of dynamic pricing. Airfares are continually adjusted based on factors such as seat availability, time of booking, and competitor pricing.

By analyzing large volumes of historical and real-time data, airlines can determine the optimal price

for each seat, maximizing revenue and load factors.

‍The importance of data in dynamic pricing

With the ongoing boom in online shopping, implementing dynamic pricing has never been more important than it is right now.

Prices need to make sense within an increasingly competitive landscape, and your business’ pricing model needs to be ready to adapt to fluctuations in customer demand and purchasing behaviors.

The ability to take quick, informed action around pricing has a massive impact on overall profit margins.

Traditionally, pricing in retail was set based on static price rules that utilized a limited amount of data inputs (e.g. cost base, conversion rates, etc.)

With this approach, massive amounts of important data – both transaction data and non-purchase data – went under utilized. Data which could inform smarter, more agile pricing decisions!

In today’s hyper fast, highly competitive retail landscape, data-based dynamic pricing strategies are harnessing the power of this consumer data and using it to drive pricing decisions.

The explosive growth of big data and the potential it contains for developing AI and machine learning approaches to pricing strategies has unlocked new opportunities for intelligent pricing solutions.

Machine learning technology takes dynamic pricing to the next level, as it can process much larger data sets and can consider various influencing factors to predict the effect of price changes.

For companies retailing online, consumer behavior and the data generated by it should be a major focus.

By considering consumer behavior when approaching pricing, companies continue to price at the value a customer ascribes to a given product, they also work to manipulate that perception of value, measure, and increase it.

Inputs to this new approach to pricing come both from the data generated by the buyers’ behavior, as well as from the larger competitive landscape.

Today, thanks to artificial intelligence and machine learning, retailers can more readily get a robust view of what both competitors and customers are doing at any given moment, as well as a better sense of the influences and reasons behind their buying behavior.

The wealth and sheer quantity of data online consumers generate is enabling new, better-informed strategies to drive customer happiness and company profitability.

Benefits of using big data in dynamic pricing

Big data is revolutionizing dynamic pricing by providing improved insights and more powerful predictive models.

• By gathering and analyzing a variety of real-time data points, businesses can adjust prices to match customer demand and maximize their profits while still offering competitive products or services.
• With the right data, companies can tailor their campaigns to individual customers with pinpoint accuracy, measuring customer preferences in areas such as product selection and price sensitivity.
• Furthermore, big data provides even small enterprises with the ability to gain valuable insights about customers—data that would have been too expensive to collect on their own.
• Dynamic pricing is a software-driven approach to streamlining sales strategies and improving customer satisfaction.

Here are some ways that businesses can use data analytics to set dynamic prices:

• Monitor competitor pricing: By analyzing competitor pricing data, businesses can gain insights into market trends and adjust their own pricing accordingly. For example, if a competitor lowers their prices, a business might respond by lowering their own prices to remain competitive.
• Analyze customer behavior: By tracking customer behavior data, such as purchase history and browsing behavior, businesses can gain insights into customer preferences and adjust pricing to meet their needs. For example, if customers tend to purchase more products at a certain price point, a business might adjust pricing to maximize revenue.
• Use predictive analytics: Predictive analytics involves using data to forecast future trends and identify potential opportunities or challenges. By using predictive analytics, businesses can anticipate changes in demand and adjust pricing accordingly.
• Monitor supply and demand: By tracking supply and demand data in real-time, businesses can adjust pricing to meet changing market conditions. For example, if demand for a product suddenly spikes, a business might increase pricing to maximize revenue.

Conclusion

The use of big data analytics in dynamic pricing is quickly becoming a necessity for businesses looking to maximize profits. It has revolutionized the way businesses approach pricing.

By leveraging the power of big data analytics to inform their pricing strategies, companies can better align prices with customer demand and remain competitive in today’s ever-changing market.

The use of big data, advanced analytics, and machine learning in dynamic pricing strategies can provide businesses with a significant competitive advantage, enabling them to adapt to market conditions and customer demand in real time.