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What is a data analytics pricing model?

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What is a data analytics pricing model?

Every company must have a model to price its goods - it's a method to form the best price for a product or service. Your pricing model is a fundamental task shared between the marketing and accounting teams as pricing can increase sales, drop margins or send customers to your competitors. In a tough economic environment, sound pricing models are crucial. A data analytics pricing model provides a clear, consolidated view of your sales history, allowing you to make strategic pricing decisions.


Data analytics helps you to also include a variety of factors into your pricing model such as product life cycle, competition, and customer perceptions. It’s important to be able consider these when determining the best pricing model for your company. We've examined the three most common models: cost-plus, competitor-based, and value-based, and how data analytics can help manage each one across your customer base.

Cost plus pricing

When people think of the term ‘pricing model’, cost-plus pricing is what comes to mind. This is the simplest form of pricing as it is just a matter of pricing your products above cost. Simply total all of your costs and add the margin you want on top to determine the price. The benefit of this model is that there is no strategizing. There is very little data analysis or market research involved. Due to this, cost-plus pricing has been considered a good starting point for a new company with little overhead.

However, cost-plus pricing is harder to manage over time as you may not be able to predict all of your costs since costs can fluctuate. If, for example, your company calculates your costs and adds a 15% margin, this may work well for the first quarter. But if some unexpected cost comes up, such as a supplier raising their prices, your margin may be cut to 10%. A data analytics solution will help manage these unforeseen costs and you can set up alerts to advise when margins drop beyond a set threshold.

Competitor based pricing

Rather than using costs as a benchmark, this model is based on setting your prices according to your competitors’ pricing. This is common when companies are vying for the same contract with government in health or construction. When you are in a market with a product that is not unique or where prices are already established, it’s best to set your prices somewhere in the middle but data analytics can help you do modelling for tenders so you can put forward desired volumes to receive the preferred price.

On the other hand, if you are offering a better product with new features or more value, you should consider pricing your products higher than your competitors. And setting your prices below your competitors is similar to cost-plus pricing as this depends on your resources. Are you to be able to withstand unexpected costs? If not, you risk impacting your profit margins. In any case, your pricing should be close to those of your competitors if you’re in a highly competitive market.

The drawback to competitor-based pricing is that you don’t have a strategy that addresses the unique needs and concerns of your company. By developing your own pricing model, you can focus on adding value by offering better products at the right price. Data analytics will allow you to determine best selling products, in what markets and to what customers which will help drive a more efficient pricing policy.

Value based pricing

Value-based pricing is setting your prices based on what your customers believe your product is worth and what they are willing to pay. The more value your product offers your customers, the more money they will be willing to pay. Rather than looking at your costs or competitors, value-based pricing requires you to look to your customers. By getting to know the people who decide whether to purchase your product, you ensure that you understand what your customers truly want, and that you are offering the most value for the best price.

When determining the price point for a product, consider factors such as whether your product is different from your competitors. Will it help your customers to save time or money? Will it help your customers gain a competitive advantage? What features can you develop over time? Answers to these questions will help you determine your product’s value and whether your customers are willing to pay for it. Once you know your customers are willing to pay for your product, you can set a higher price point and then raise prices as you add more value. The downside to value-based pricing is that it takes time. You must be willing to invest the time to get to know your customers and understand their needs to set effective prices this way.

Data analytics allows you to compare and assess different pricing models

With data analytics, you can price according to your target market. Analytics enables companies to dramatically improve profitability by developing optimal pricing strategies to win more contracts and offer the most value to customers. Combining pricing with analytics allows you to leverage your data to understand both the internal and external factors affecting profitability at a granular level.

In spite of the wealth of data available, many companies are still in the dark when it comes to understanding their customers. Yet, facing growing complexity and a omni-channel business environment, companies must be able to answer fundamental questions such as “Who is my most profitable customer?” and “What is my most profitable product or region?” Answering these questions can help you understand your customers and their buying behaviors to create the most effective pricing models. In other cases, analytics can highlight your most unprofitable customers so you can realign their discounts or other incentives to increase profits. With analytics, you have a mechanism that functions as both a catalyst and a metrics engine for managing profitability.

Written by Phocas Software
Phocas Software

Empowering businesses with intuitive data analytics, driving informed decisions for growth and profitability. We make people feel good about data.

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