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How to find the right price with Big Data Analytics?

In the age of Big Data, Artificial Intelligence (AI), Data Analytics and Machine Learning, it is now possible to determine hidden market trends and customer preferences, among other things, in order to develop an efficient pricing strategy. With Big Data, new concepts such as predictive analysis and dynamic pricing have emerged. These are all tools that Business Intelligence (BI) is now using to help companies make faster and better tactical and strategic decisions and gain a competitive advantage.

How do you price the products or services you offer to your customers? How can you use Big Data and Data Analytics to optimize your sales prices? What are Business Intelligence and Dynamic Pricing strategies, and how can they help you optimize your prices?

The art of pricing: how to set the right sales prices?

Setting the selling price of a new product or service should not be done lightly. It is indeed important to find the right price both in the eyes of the buyers and for the company. But a question arises: what exactly is a fair price?

Let's see what the definition of a fair price is and what are the parameters to take into account when setting the selling price of your product or service.

What is a "fair" price?

A price is considered "fair" if it reflects the quality/price ratio expected by the buyer and if it complies with established standards. For the same product, the fair price perceived by the buyer may differ from one country to another, or even from one establishment to another.

For example, for the same product, such as beer, the cost will generally be higher if it comes from a Michelin-starred restaurant than from a local grocery store. In other words, the price that the customer is willing to pay for a given product or service depends mainly on the value that the customer places on the product or service.

Beyond the actual cost of the product, the right price must balance supply and demand. It must also respect the freely expressed interests of the seller and the buyers.

The main strategies for setting the sales price

There are several strategies to choose from when developing your pricing policy:

  • skimming strategy - this involves setting a high initial price to "skim" the market by targeting only a wealthy clientele, and then gradually lowering the cost as the product spreads, in order to adapt to the market and the competition;
  • market penetration strategy - this pricing policy sets a relatively low initial price in order to quickly conquer large market shares. The realization of large sales volumes allows the company to make a profit despite lower costs;
  • price alignment strategy - this strategy consists of aligning its prices with the competitors' and therefore closely following the evolution of market prices. This strategy is aimed at avoiding a price war and allows the company to avoid losing market share;
  • differentiated pricing strategy - this involves varying prices according to certain parameters such as the customer segment (professionals, individuals, students, retirees, etc.), the purchase period, the place of purchase, the distribution channel (in stores or on the Internet), or the economic context (crisis or recovery).

And finally, we cannot avoid mentioning the dangers of price wars. Price wars are an aggressive policy that consists in charging abnormally low prices in order to eliminate competitors. Not supported by a real competitive advantage, this strategy leads to the ruin of the company, both in terms of image and financially. The company's margins and profits are likely to shrink to nothing.

Setting the sales price: the parameters to consider

Pricing a product or service is a highly strategic operation that, if not done properly, can quickly ruin a company's commercial strategy. On the other hand, a good price optimization can contribute to increase sales volume and profits at the same time.

In order to determine the right marketing strategy to adopt and to develop an efficient pricing policy, many parameters must be considered by the company, including:

  • market segment(s) to be targeted;
  • the strategic positioning (or marketing image) of the company in relation to its competitors in the targeted markets;
  • the understanding of the market and the preferences/expectations of the potential customers;
  • the company's break-even point;
  • the psychological price, or average price acceptable to customers;
  • the price-product policy of the competition;
  • supplier prices;
  • the different prices charged by sales channel (stores, distributors, internet, etc.);
  • the sales context that can impact supply and demand, which takes into account, among other things, the economic and social context, the weather, the COVID-19 health crisis, and the evolution of the market price.

As a reminder, the objective of a good pricing policy is to ensure the profitability and sustainability of the company.

The importance of finding the right price

Finding the right price in the eyes of the consumer is therefore of the utmost importance. Indeed, the value of your product or service in the mind of the consumer, also called "perceived value", is not directly related to the cost of the product or service. This perceived value can be strongly influenced by advertising. If you align your pricing with this price point, and if it is well above cost, you can increase your margins and sales volume. On the other hand, if your price is much lower than the perceived value, the consumer may doubt the quality of your product or service.

So how to know the perceived value of a product or service, and therefore the price the customer is willing to pay? How can you optimize your price to maximize the attractiveness of your offer in order to boost your turnover?

We will see how Big data, data analysis, artificial intelligence and machine learning can help you to develop an efficient pricing strategy and optimize your prices.

Big data and data analysis: how to optimize the price of your products?

90% of companies still use Microsoft Excel software to create their financial plans or "forecasts", manage their pricing strategy and analyze data (also called Data Analytics).

Excel spreadsheets are among the most widely used Business Intelligence (BI) tools in France today. But in the era of Big Data, Excel is no longer sufficient to process and analyze the impressive amount of data collected by companies today. To exploit Big Data, more adapted BI software, such as Microsoft Power BI, IBM Cognos Analytics, Google Data Studio or Oracle BI, are used. Let's see how to take advantage of Business Intelligence to optimize your prices.

What is Business Intelligence?

Business Intelligence refers to a set of tools and processes that enable:

  • the collection and analysis of data from internal and external sources, including Big Data;
  • the enhancement of collected data, in order to extract marketing insights, in other words, knowledge that allows us to penetrate the minds of consumers and understand what drives them to act;
  • Identifying market trends and issues that need to be addressed;
  • creating synthetic dashboards, reports and data visualizations to make the results of the analysis usable for decision making.

The main objective of Business Intelligence is to accelerate and improve decision making through advanced data analysis in order to gain a competitive advantage.

Optimize your pricing strategy with Business Intelligence

Business Intelligence is a great ally in defining THE right pricing strategy. Especially if the company adopts BI tools integrating artificial intelligence and machine learning, two technologies that allow easier processing of megadata or massive data from Big Data.

More and more companies are now using Big Data, data analysis and machine learning to optimize their prices, and more specifically to find the right price for their products and services. Note that when Business Intelligence exploits Big Data, predictive data analysis and machine learning technologies, it is called Business Analytics.

A detailed analysis of customer data from Big Data allows us to understand their needs and expectations, preferences, behaviors and consumption habits. This facilitates the development of a pricing policy perfectly adapted to the targeted customer segment(s).

Dynamic Pricing, a data-driven pricing strategy

When it comes to pricing strategy, the use of machine learning gives companies the opportunity to adopt a pricing strategy that is on the rise now: dynamic pricing.

Also known as pricing intelligence, dynamic pricing is a pricing strategy that automatically adjusts the selling price of a product or service to market demands in real time. Such real-time price changes are made possible by the use of artificial intelligence and machine learning technologies.

This practice of dynamic pricing is currently growing in importance thanks to the development of e-commerce, and also thanks to solutions that allow automatic and remote updating of prices in stores. Among these types of solutions, we can mention the electronic shelf label with digital price display.

Good to know
: because it adjusts several million prices per day, the company that best embodies the practice of dynamic pricing is none other than Amazon.

In conclusion

Based on many factors (supply and demand evolution, competitors' prices, etc.), dynamic pricing is today an essential strategy for price optimization.

Finally, it is good to know that machine learning algorithms allow you to optimize your sales prices compared to the competition, to identify the right price the first time for all your products, to have a scalable pricing strategy, to determine the best pricing strategy, to maximize your sales while obtaining the highest possible margin.

Looking to optimize your pricing?

Let's discuss your objectives to find the right solution for your business.