How Can Historical Price Data Analysis Be Helpful to Your Business?

What is Historical Pricing Data analytics?

Pricing analytics utilizes historical price and demand data to understand how to price activities have exaggerated profitability and the exclusive brand. Brands from all sectors and industry verticals, from manufacturing & distribution to eCommerce and retail, can benefit from pricing data analytics.

There are three kinds of pricing analysis:

Predictive Pricing Analysis

Though brands can't predict how price changes will affect sales, they can utilize predictive pricing analytics to get insights about the best chances of doing that. Predictive pricing studies historical data using Machine Learning and Statistical Algorithms to predict prices and product trends in the future. This also assists brands in optimizing the costs with upcoming goals.

Descriptive Pricing Analysis

Descriptive pricing analytics studies historical data to assess how customers have supposed and reacted to price fluctuations. It studies metrics like average revenue per customer, month-on-month sales growth, year-on-year price changes, or total registrations to any particular services over a specific time.

Prescriptive Pricing Analysis

Prescriptive pricing analytics is contradictory to descriptive analytics. Dissimilar to descriptive analytics, which helps brands search historical data to know customer responses after the events, prescribed analytics assists brands in designing better and more well-informed strategies. Using prescriptive analytics, different brands could shape growth strategies to accomplish more workable results in the long run.

Advantages of Historical Pricing Data Analytics

Get insights about customer pricing perception

Pricing analytics assists brands in understanding that customer segments are maximum on minimum profitable and how every segment reacts to particular pricing strategies. Using historical price data analytics, brands can link promotions and pricing by defining customer pricing sensitivity and then evaluating the efficiency of advertisements.

Well-Optimized Pricing

Historical pricing analytics eliminates the guesswork in determining the optimum pricing for products. Through analyzing historical price data, brands can determine how past prices and promotional decisions affect profitability. Depending on the historical data, they could test different price strategies, including dynamic and value-based pricing.

Identify pricing tiers that work the best

Tiered pricing models are predominant in subscription-based brands, whereas brands provide tiers for meeting the requirements of different customer segments. Having historical pricing analytics, brands could improve their price tiers and find insights into tier and optimum prices for all. Pricing analytics would search a brand's historical data to find tier pricing errors to improve revenue and sales.

Plan Promotions and Pricing Strategies

Promotional pricing decisions are essential for any brand, as pricing perception is directly associated with consumer demands and profits. Brands need to carefully make promotions that include variables like list prices, advertisements, special offers, and discounts while ensuring profit margins. Using predictive analytics, brands could determine optimum discount levels, observe the competition, and declare promotional offers while customers are expected to purchase.

Find profitable channels

Historical pricing analysis could assist you in finding the most applicable quality, revenue channels, and volume. It also helps in determining the eCommerce channels which are most lucrative so you can optimize the budget and recognize channels you need to invest in as a part of future client acquisition tactics.

Metrics Tracking

Let's go through some pricing analytics metrics which can assist brands in understanding customers' behavior toward prices:

Price Sensitivity

Price sensitivity is the maximum pricing your prospective customers are ready to pay against your product or service. It is an essential part of price strategy as you have no other options for understanding if your products can yield an increased product value. Many factors are accountable for a customer's readiness to pay, which is not static.

Relative Reference Analysis

Relative reference analysis or feature value analysis evaluates the most vital features to customers concerning other characteristics of a service or product. Analyzing critical structures to customer segments would help brands price products depending on elementary or premium components. This can also assist in packaging your products or services better so that you can get more revenue.

Average Revenue Per User (ARPU)

The ARPU or Average Revenue Per User is revenue produced from total active users separated by users in the monthly time. Digging deeper into ARPU can assist brands in comparing numbers with opponents and observing how all customer segments or products perform.

Life Time Value (LTV)

Life Time Value, or LTV, provides a whole picture of the user's journey and an average revenue that a user will produce through their involvement as a client with your brand. This helps brands control economic decisions like profitability, marketing budgets, resource allocation, and forecasting.

Customer Acquisition Cost (CAC)

Any profitable brand requires to balance its CAC or Customer Acquisition Cost. It's about occupying the right time and resources to get new customers without risking their revenue and lifetime value. Correct counting of CAC assists brands in quantifying the sales funnel and regulating their tactics' profitability and efficiency.

Conclusion

Using pricing analytics can stops brands from thoughtlessly reacting to competitors' pricing changes and help solutions to scale up pricing transformation efforts. Using historical price data, brands can treat their customers more efficiently for promotion and marketing strategies. Properly using predictive analytics with previous sales data can assist in cost-cutting and keep higher profit margins by adjusting prices and production per the market trends.

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