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Pricing activities have a significant impact on business performance. To ensure that pricing decisions are optimized for maximum profitability, finding a way to quantify them is vital to success. Using pricing analytics to understand pricing patterns and trends provides companies with a data-driven approach to understanding their market.
What is Pricing Analytics?
Pricing analytics involves the collection, aggregation, and analysis of pricing data from various sources.
It allows businesses to identify opportunities for revenue optimization, understand the demand for their products and services, and visualize how customers respond to different pricing strategies, and anticipate competitors’ moves.
By analyzing historical data, companies can also identify which products are more profitable over time and adjust their prices accordingly.
Depending on the industry, businesses use pricing analytics in different ways.
For retail businesses, on the other hand, pricing analytics can be used to identify seasonality trends in sales and make dynamic pricing adjustments accordingly.
- Price Analysis: The process of examining and researching the cost of goods/services to determine the most suitable pricing structure for a given product or service.
- Price Optimization: A strategy used to determine the best prices for goods and services in order to maximize revenue from sales.
- Price Intelligence: The ability to collect, analyze, and interpret market information in order to make better pricing decisions.
The Importance of Pricing Analytics
Companies in just about every vertical can benefit from pricing analytics. Particularly large companies—which tend to have complex pricing structures and large product catalogs—benefit from the ability to analyze pricing data to identify opportunities for increased revenue.
Benefits of pricing analytics include:
Identify Pricing Opportunities
When companies have access to pricing analytics, they can identify opportunities that could lead to an increase in revenue.
This is especially true for businesses that want to switch from individual transactions to a recurring revenue model. By understanding customer demand for different products and services, the company can create bundles with multiple price points to maximize revenue per sale.
Pricing analytics can also be used to identify discount opportunities without the risk of margin leakage. Companies are able to analyze the data to determine which discounts will generate the most sales while still maintaining a healthy margin.
Optimize Pricing Strategies
Price optimization is an ongoing process, and companies often miss out on revenue opportunities due to complexities.
Complexity can occur in business in numerous ways:
- Large customer base with varying average deal sizes (e.g., enterprise, mid-market, and SMB buyers)
- Multiple product lines with varying pricing structures (e.g., one-time services, subscription models, tiered/package offerings)
- Fluctuating market conditions
- High transaction volume
- Multiple channels and go-to-market strategies
- Quote-based pricing
The odds that a business has found the perfect pricing structure for its products and services are low.
By analyzing historical data, businesses can identify gaps in their pricing strategy and fill them with more accurate, informed pricing decisions.
At its core, pricing analytics is mainly used to set optimal prices—at which point, the ideal amount of customers will buy from the company at a rate that isn’t too low to sustain a healthy margin.
Pricing analytics improve overall business profitability in numerous ways:
- Higher average revenue per user. When businesses can closely match their pricing model to customer demand and accurately forecast trends, they can increase the revenue generated from each customer.
- Better margins. Companies can analyze pricing data to identify opportunities for discounts, creating potential new revenue streams by incentivizing customers to purchase more.
- Reduced churn. When organizations understand where their pricing falls short, they can take steps to fix it and improve customer retention.
Pricing analytics give companies the customer data they need to increase and decrease prices based on demand.
By looking at behavior, demographics, seasonality, and other patterns, companies can use this data to better understand their customer base and provide a better product to them through their pricing options.
They can also use pricing analytics to tailor offers to specific buyer segments, ensuring they get the best value for their money.
With the right segmentation and targeting, sales and marketing teams can identify and resonate with their ideal customer profile (ICP) more effectively, lowering the total customer acquisition cost (CAC).
Focus on Profitable Channels
Beyond customer insights, one of the most valuable aspects of pricing analytics is the ability to identify profitable channels.
More than 40% of salespeople say prospecting is the most challenging part of sales. Data used for pricing analytics shows organizations exactly which accounts are generating the most revenue for the business, which they can use to find similarities.
By using data to determine where a majority of their high-ticket sales are coming from, businesses can direct more resources and budget towards those key areas and away from unprofitable channels.
Improve Operational Efficiency
By saving sales teams time during the prospecting stage and saving marketing teams time when targeting campaigns, pricing analytics help businesses save money and increase efficiency.
Companies can also use their data to automate certain parts of their business, such as creating product bundles for customers or offering discounts and promotions at the right times.
Most of these activities are time-consuming and error-prone with lots of guesswork involved. Pricing analytics minimize human error by providing reliable data that businesses can trust and use to quickly make decisions.
Pricing Analytics Metrics
To understand customer behavior and determine the right pricing plans companies need to follow the right data.
Here are the key business metrics for pricing analytics:
- Price Elasticity of Demand: The degree to which changes in price affect demand for products or services, measured by the percentage change in demand relative to a given percentage change in price. High elasticity indicates that alterations to prices can dramatically affect demand.
- Price Sensitivity: The degree to which customers are affected by changes in prices, measured by the amount of variation in sales or purchases that occur after a price adjustment. High sensitivity demonstrates that customers are either looking for better deals or actively avoiding certain prices.
- Revenue per Customer: Measures how much each customer contributes to overall revenue for the company. Revenue per customer is relative for companies and business models, so measuring against industry standards is a useful way to identify the success of a pricing strategy.
- Quote-to-Cash Conversion Rate: The ratio of quotes that are accepted and converted into sales. A high conversion rate indicates the effectiveness of a pricing model.
- Average Order Value (AOV): The average amount a customer spends per order. AOV is relative—companies with higher-priced products may have a high AOV, but could still have a lower revenue per customer or fewer customers, meaning a less successful pricing strategy.
- Customer Lifetime Value (CLV): The total amount of money a customer is expected to spend with the company over their lifetime (closely related to customer loyalty). If customers are turning over before CLV has time to compound, a business should look into its pricing and how it compares to customer expectations.
- Gross Margin: How profitable a business is on a per-transaction basis, taking into account all costs associated with generating revenue from sales. Low margins can be offset by high sales volumes and vice versa.
- Profitability by Product or Customer Segment: The amount of income a business brings in from a specific product, service, or customer segment compared to the amount of resources used to generate that income.
Types of Pricing Analytics
There are three main types of pricing analytics: descriptive, predictive, and prescriptive.
Descriptive pricing analytics provide an overview of historical data, such as past sales and customer behavior.
- Average order value
- Revenue per customer
- Quote-to-cash conversion rate
Businesses use descriptive analytics for identifying trends, understanding buyer behavior, and performing feature value analysis, which they can then use to inform their pricing, product, and marketing strategies.
Predictive pricing analytics use data mining and machine learning to identify patterns in the data and make predictions about future outcomes.
- Price elasticity of demand
- Price sensitivity
- Customer lifetime value
Predictive analytics can help businesses anticipate buyer behavior and make decisions about the best pricing strategies for their products and services.
Since they are future projections rather than historical insights, they work best for companies with recurring revenue, which is predictable and allows for more accurate predictions.
Prescriptive pricing analytics involve advanced algorithms that identify optimal pricing plans and provide instant feedback on how changes to price would affect future performance.
It is similar to predictive analytics, but provides actionable recommendations rather than leaving that up to the business.
This kind of analytics is produced by software with price optimization built into it. Answering the question of “what should we do?” helps businesses adjust their prices in real-time to maximize profits and while maintaining competitive pricing.
Prescriptive pricing analytics can also help with customer segmentation and finding profiles with the highest potential lifetime value.
Pricing Analytics Software Features
The right pricing analytics software will have features that enable businesses to set the right retail prices or include the best software subscription options without sacrificing their bottom line.
Some features include:
Monitoring customer responses, competitor prices, and market conditions in real time allows businesses to quickly adjust their pricing strategies.
A pricing analytics tool can do this by tracking customer buying habits and pricing changes from competitors.
Alerts notify businesses when customer behavior changes or competitor prices drop. They can also indicate updates to customer and market data, such as when a customer’s buying behavior shifts or seasonality affects the market.
Competitive intelligence from automatic alerts help companies stay on top of their list prices, promotions, and discounts to avoid missed opportunities.
Some software has price optimization and data analytics built-in. If it doesn’t, it should integrate with other elements of the company tech stack, including:
ERP software is used to keep track of customer purchases, pricing history, and other transactional data.
Integrating with a CRM gives businesses access to customer information such as buying habits, preferences, and contact details. This helps them personalize offers for their customers and target the right audience with the right message.
To avoid data silos, analytics tools should integrate with business intelligence (BI) software. That way, businesses can use the same source of data for pricing, product, and marketing decisions.
Configure-price-quote (CPQ) software helps companies quickly generate accurate quotes and configure complex products. This integration helps businesses accurately reflect pricing changes in their CPQ system, ensuring customers are always offered the right price for the product or service they want to buy.
The billing process is a significant part of a business’s pricing strategy. Automated billing should feed into the pricing analytics tool so businesses can accurately track their revenue and profits for each customer.
People Also Ask
Why is pricing analytics important for SaaS companies?
SaaS companies often have large customer bases, multiple products and microservices, and numerous customer segments. Especially in the case of early-stage startups, finding the ideal customer requires experimentation, from pricing to positioning.
Pricing analytics helps SaaS companies identify their best customer segments and optimize their pricing structure based on that information.
How does pricing analytics improve profitability?
Pricing analytics uses data to help businesses find the most profitable pricing strategies. Analyzing customer buying habits, competitor prices, and market conditions in real-time helps businesses identify ideal opportunities for revenue and profit growth.
The right pricing analytics software can also provide recommendations on discounts and promotions that could bring in more revenue while minimally compromising potential profits.
How is analytics used for dynamic pricing?
Dynamic pricing requires businesses to frequently adjust their prices in response to changes in customer demand and market conditions. Analytics helps them identify the right pricing points, based on real-time data, that will maximize profits.