Pricing analytics describes the range of metrics and software used to understand and explain how pricing affects a business. This analytical software is often used to help companies gain a more thorough understanding of how profitability differs for a range of different pricing options and how far pricing optimization could improve the potential profitability of products and services.
Pricing analytics is fast becoming essential to a company’s overall growth strategy. By helping businesses understand how their prices affect profitability and what they can do to optimize their pricing structures, pricing analytics offers enormous potential to help companies grow.
Using a wide range of different metrics, along with cutting-edge analytical software, companies can now use pricing analytics to get ahead of their competitors. Here’s a brief overview we hope helps the reader learn more about the nature of pricing analytics and how to put its tools to work.
Types of Pricing Analytics
No business can predict the future with perfect accuracy, but predictive pricing analytics gives companies the best possible chance of doing so. This form of analytics involves the use of historical data, which is analyzed and then used to make informed predictions on what might happen in the future.
Predictive pricing analytics incorporates statistical algorithms and machine learning to ensure the best possible results for a company. Once these metrics have been gathered and analyzed, businesses can begin to optimize their prices with future goals in mind.
Descriptive pricing analytics involves the use of historical data, which can be analyzed to evaluate how changes have been perceived in the past, and how customers have reacted to pricing fluctuations before.
Typically, descriptive pricing analytics involves the analysis of metrics such as month-on-month sales growth, year-on-year pricing changes, average revenue per customer, or changes to the number of sign-ups to a particular service over a particular period of time. All of these metrics can be used to give a business a complete picture.
Prescriptive pricing analytics can be seen as the opposite of descriptive analytics. While descriptive analytics enables companies to explore their data to understand why customers have reacted the way they have after an event, prescriptive analytics is used to help companies create better, more informed strategies before they do so.
Decide the pricing strategy to be used
Without research data, pricing strategies could be highly skewed and biased basis a few people defining the pricing model. Using data, you can determine the pricing strategy that works for a business to create the most sticky customers and increase profitability. Some of the most commonly used pricing models are:
- Cream pricing: First movers in the market use this pricing model to drive profitability with premium pricing.
- Penetration pricing: Pricing into an existing market to break a monopoly or duopoly by sacrificing profitability and pricing low.
- Low pricing: Everyday low pricing operates on scale and volume rather than per-unit pricing. This helps with everything from sourcing at scale to undercutting the competition, even on non-event-based pricing days.
- Cost-plus pricing: This pricing model adds a percentage profit as a line item on cost. This model causes prices to fluctuate basis input costs.
- Demand-based pricing: Lastly, this pricing analytics model looks at market sentiments and other external demand-based factors to determine the price.
Average revenue per user (ARPU)
The average revenue per user (ARPU) is the net value of the revenue for a pre-defined period divided by the number of users. This metric is essential to understand if there is an uptick in each user’s revenue. ARPU allows organizations to calculate if their pricing model suits their market, competitive benchmarking, value analysis, and more.
An example of this pricing model is a phone manufacturer that launches multiple phone variants a year across different features and price points. Higher the ARPU, the higher the profitability of the brand.
Customer lifetime value (CLV) and customer acquisition cost (CAC)
Customer lifetime value (CLV) and customer acquisition cost (CAC) are important metrics in pricing analytics research. When CLV outweighs CAC, that is a win for your brand. You want to spend the least amount of money to bring in a customer and then what’s required to keep them as customers for your brand for long, so they continue to spend with your brand.
Tools to conduct pricing analytics
We have looked at the benefits of pricing analytics and the different types with examples and their nuances. Let’s now dive deeper into how to conduct pricing analytics. While data is at the heart of any pricing strategy, how you collect and analyze that data is imperative.
Research data can be in the form of qualitative research or quantitative research. Using the right mix of both offers the maximum value for pricing analytics.
Van Westendorp pricing sensitivity
The Van Westendorp pricing sensitivity allows researchers to understand the value of fundamental psychological price points and how much consumers are willing to pay or spend on a product or service. This method will enable researchers to look at a range of prices that users are willing to pay and the dropoff in consumers based on perceived value or lack of it.
The choice-based modeling technique calculates willingness to pay for specific products or services, tradeoffs on features, and more using the conjoint analysis and maxdiff analysis modeling techniques.
These quantitative research methods are the most widely used methods by researchers to understand customers’ choices and the price that customers are willing to pay.
Focus groups are a qualitative research method to gather in-depth information about a product or service. Using this model for pricing analytics allows brands to leverage the continuous discovery model and keep evolving or tweaking prices based on various macro and micro factors in the market and demographic segmentation.
Gabor-Granger price modeling
Gabor-Granger is a pricing research technique for determining a revenue and demand curve for a specific product or service. This survey research model helps determine a product’s or service’s price elasticity.