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Pricing Analytics
  • Data Visualization
  • Retail
  • Data Analytics

Pricing Analytics in Action: Retaining Customers and Amplifying Profits

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Last Updated : 06-February-2024

Editor’s Note: In a business landscape characterized by constant change and fierce competition, our blog takes you on a journey through the intricate realm of pricing data analytics solutions. We delve deep into the pricing strategies, models, and real-world applications that empower businesses to maximize profitability while retaining customer loyalty. Each price point is a story waiting to be uncovered, and through the insights shared here, we aim to equip you with the knowledge and pricing analytics tools to craft your narrative of success in today's data-driven marketplace.

💡 Do you know the key factor for purchase in this omnichannel business era is the price tag?

Platforms like Google Shopping, Trivago, Bankbazaar.com, Pronto, etc. allow customers to easily compare prices of the same product across providers. In many cases better the offer – the higher the sale.

Hence, the pricing and analytics within an industry can have a crucial role in enhancing revenue and retaining customers in the long run.

Pricing data analytics models utilizes predictive analytics to enable informed decisions on pricing strategies, ultimately leading to increased profitability.

In “Getting Pricing Right by Larry Montan, Terry Kuester, and Julie Meehan”- research shows that price management initiatives can increase a company’s margins by 2 to 7% in 12 months, yielding an ROI between 200 and 350 percent, clearly indicating that pricing metrics are highly important.

Ways to increase value with pricing analytics

1. Know your Customer Base -

To make effective use of pricing analytics platform, it's essential to gain a deep understanding of your customer base and their purchasing behavior. This involves collecting information about your existing customers, such as their buying patterns and preferences.

Knowing Price Elasticity helps understand your customers, as it helps determine how responsive they are to changes in product prices.

Price elasticity measures how a change in consumption of a commodity relates to a change in price.

Formulaically, it's expressed as:

Price Elasticity of Demand = % Change in Quantity Demanded / % Change in Price

Elastic – Demand decreases when the price fluctuates.

Inelastic – Demand remains the same when price changes.

price elastic demand

Transaction data of customers can be used to help leaders understand price elasticities, and cross-elasticities, and ultimately optimize pricing strategies.

  • Own Price Elasticity

Depicting the reciprocal connection between shifts in your pricing and variations in customer purchase & sales volumes, this analyses how sensitive customer demand is to changes in product prices.

  • Cross-price Elasticities

Assessing the influence of alterations in the price of one product can influence the demand for other products, revealing the interplay between different items in the market for customers.

Advanced Pricing Analytics solutions can evaluate the following customer metrics: -

pricing analytics solutions
Embrace the age of hyper-personalization by discovering how consumer markets can effectively leverage the trend.

Dive into the world of Dynamic Pricing and its significance in catering to the hyper-personalized consumer market.

2. Testing diverse Price Ranges

Experimenting with various price levels is another effective method for profit optimization using advanced pricing analytics solutions. Consider offering products or services at different price tiers, like premium versus basic options, and assess which one generates higher revenue over time.

This approach proves particularly valuable when introducing a new product or service. By testing different price points before finalizing one, you can ensure that the selected price maximizes company profits.

Companies can employ diverse Pricing Data Analytics models, such as the Van Westendorp approach, Choice-based Conjoint modelling (CBC), Gabor-Granger, and Econometric Demand Modelling to determine the optimal price point.

  • Van Westendorp Approach: This model assesses customer price sensitivity by asking survey respondents about acceptable and expensive price ranges, helping determine an optimal price point that balances profitability and customer acceptance.
  • Choice-Based Conjoint Modeling (CBC): CBC analyzes customer preferences by presenting them with different product attribute combinations and prices, enabling businesses to identify the most desirable product features and pricing strategies.
  • Gabor-Granger: Gabor-Granger involves experimenting with price changes and measuring the resulting demand shifts, providing insights into price elasticity and the impact of price adjustments on sales.
  • Econometric Demand Modeling: This model employs statistical techniques to estimate how various factors, including price, affect product demand, allowing businesses to make data-driven pricing decisions based on historical sales data and market dynamics.

3. Develop Segmented Pricing Strategies

A powerful application of pricing analytics models involves segmenting customers into distinct price tiers, which are determined by their specific purchasing behaviours and preferences. For instance, if one group of customers tends to make frequent purchases, it can be beneficial to offer them more substantial discounts or reward points to foster loyalty. Conversely, for customer segments that make infrequent purchases, lowering prices can serve as an incentive to encourage them to buy more.

Beyond this, conducting thorough customer research plays a pivotal role in understanding the underlying motivations that drive customers to choose your brand over competitors. This research can reveal which product features or services hold the greatest appeal to your customer base, allowing you to fine-tune your pricing strategies accordingly.

To gain these valuable insights, companies can employ various research methods such as focus groups, surveys, and interviews. By delving into the needs and motivations of different customer segments, businesses can tailor their offerings and adjust pricing to better align with customer expectations.

4. Analysing Competitors

To stay competitive, it's crucial to keep an eye on your competitors' pricing strategies, monitoring their models, discounts, and promotions. Employ automated competitive intelligence tools to track competitor prices over time, maintaining market competitiveness without compromising profit margins and embrace pricing analytics benefits.

Automated tools extract data for a real-time dashboard that serves as an early alert system for price and promotion changes. Armed with this knowledge, brands can anticipate competitors' moves, identifying opportunities for attracting new customers and retaining existing ones through discounts and special offers, ultimately boosting overall profits.

5. Optimizing Pricing Strategy

Price optimization is vital for businesses aiming to maximize profits and maintain competitiveness. Through simulations involving product-level elasticity, cross-elasticity, and the market basket effect, companies can pinpoint the ideal prices across their product range.

  • Product-level elasticity measures how demand or revenue changes when a single price is adjusted.
  • Cross-elasticity evaluates the impact of different prices on overall revenue.
  • The market basket effect delves into pricing scenarios with bundled products.

By considering these factors together, businesses can simulate various price points to determine the most lucrative ones.

However, achieving optimal pricing isn't a one-time task; companies must regularly adjust prices to educate customers about pricing dynamics. This practice prevents customers from growing accustomed to static discounts and promotes long-term profitability. By employing simulated models and adjusting prices systematically, businesses can increase revenue and margins, meet customer expectations, and enhance their position in the market, ultimately leading to stronger financial performance.

Here are the steps for price optimization: -


Pricing analytics use case: cracking the code to optimal pricing

Consider an electronics company, "TechPro," that specializes in manufacturing smartphones. Using pricing analytics solutions, TechPro aims to optimize the pricing of their latest smartphone model, "SmartX," to achieve maximum profitability while appealing to their target customer base.

Here's a hypothetical example of how pricing analytics case study example of how it can be applied to SmartX

Data Collection: Historical data on past smartphone models, including pricing, sales volume, customer demographics, and competitor pricing.

Data Mining: With data analysation, TechPro discovers that certain features like camera quality and battery life strongly influence customer purchasing decisions.

Model Design: TechPro develops a pricing model integrating production costs, desired profits, feature significance, and price elasticity to gauge customer responsiveness to price fluctuations

Coefficients Calculation: Using statistical techniques, TechPro estimates coefficients for different attributes like camera quality, battery life, and price.

Model Back-End: This allows TechPro to predict how changes in pricing and features will affect the sales volume of SmartX.

Model Front-End: TechPro creates a user-friendly interface where decision-makers can input different pricing scenarios and see the predicted outcomes in terms of sales volume, revenue, and profit.

Optimization & Simulation: TechPro simulates price changes, like raising SmartX's price by $50 for better camera features, predicting resulting sales and revenue shifts.

Based on the simulation results, TechPro decides on an optimal pricing strategy for SmartX.

To thoroughly master the pricing analytics phenomenon, delve into the rich and detailed content available in this ebook.

Ending words

In the dynamic world of pricing analytics, businesses wield the power to shape their destiny. As they fine-tune pricing strategies, segment customers, and competitors, the quest for optimal profitability unfolds. But the journey doesn't end here; it raises intriguing questions.

How will artificial intelligence further transform pricing analytics? What role will customer data play in the next pricing revolution? The answers await those who dare to innovate and explore. So, as we embrace the future of pricing analytics services, let's remember that in this data-driven age, the price tag isn't just a number; it's a story waiting to be uncovered, a strategy yet to be perfected, and a customer experience yet to be optimized.

We at Polestar Solutions are providing valuable support to businesses looking to optimizing pricing strategies for retention through pricing analytics services. By leveraging our expertise as an AI and Data Analytics powerhouse, we help unlock the full potential of their organisation data.

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