For many brands, pricing strategy boils down to guesswork — shooting in the dark and hoping consumers are willing and happy to pay. However, the ‘throw it at the wall, and see what sticks’ pricing strategy leads to big pricing mistakes. Pinning down an optimal price for products requires a clear picture of ideal customers, understanding each customer segment’s behavior, a solid grasp of your product’s value, and an analysis of competitors. Pricing analytics can help brands track a wide range of pricing metrics with cutting-edge analytical tools and use insights to get ahead of their competition. This analysis uses historical data to understand how previous pricing and promotion activities affect brand, sales, and customer price perception. It often involves identifying opportunities and weaknesses in competitors’ pricing strategies and exploiting them to improve sales and revenue.
Pricing analytics helps brands understand how product pricing and promotions affect profitability and the steps they can take to optimize their pricing structures. Brands can leverage their pricing and consumer data to design appropriate pricing models for achieving their sales goals.
Pricing analytics uses historical pricing and demand data to understand how pricing activities have affected profitability and overall brand. It also helps to optimize a brands’ pricing strategy for maximum revenue. Manual tracking of pricing for brands with numerous product lines, multiple selling points, different customer tiers, and complex product bundles is a huge challenge. Brands from every sector and industry vertical, manufacturing and distribution to retail and eCommerce, can benefit from pricing analytics.
There are three types of pricing analysis:
Descriptive pricing analytics involves analyzing historical data to evaluate how customers have perceived and reacted to pricing fluctuations in the past. It analyzes metrics such as month-on-month sales growth, average revenue per customer, year-on-year pricing changes, or changes to the number of registrations to a particular service over a specific period.
Although brands can’t accurately predict how pricing changes will reflect sales, they can use predictive pricing analytics to get insights into the best possible chance of doing so. Predictive pricing analyzes historical data with statistical algorithms and machine learning to predict the price and trends of products in the future. It also helps brands to optimize their prices with future goals.
Prescriptive pricing analytics is the opposite of descriptive analytics. Unlike descriptive analytics that helps brands explore their historical data to understand customer response after an event, prescriptive analytics help brands design better and more informed strategies. With prescriptive analytics, brands can shape their growth strategies to achieve more sustainable results over the long term.
While analyzing the metrics to understand pricing optimization, brands can also gather valuable insights into their customer’s price perception. Pricing analytics helps brands understand which customer segments are the most (and least) profitable and how each segment responds to specific pricing strategies. With historical pricing data analytics, brands can also intelligently link pricing and promotions by first determining customer price sensitivity then gauging the effectiveness of promotions.
Historical pricing analytics means eliminating guesswork from deciding the optimal pricing for a given product. By analyzing historical pricing data, brands can discover how their past pricing and promotional decisions impact profitability. Based on this historical data, they can also test various pricing strategies like value-based and dynamic pricing. It also allows brands to learn which customer segments are most likely to respond positively to price change. These insights from pricing analytics will drive more effective (and profitable) pricing decisions.
Many brands have gaps in their pricing strategy — underpriced or overpriced tiers, pricing leaks, markup errors, or neglected upsell opportunities. Tiered pricing models are prevalent in subscription-based brands where brands offer tiers to meet the needs of diverse customer segments. With historical pricing analytics, brands can improve their pricing tiers and get insight into the right number of tiers and optimal prices for each. Pricing analytics will comb a brand’s historical data to find tier pricing mistakes to improve sales and revenue.
Promotional pricing decisions are critical for any brand, as pricing perception is directly linked to consumer demand and profits. Brands have to carefully plan promotions that include variables such as list prices, special offers, advertisements, and discounts while ensuring profit margins. With predictive analytics, brands can determine optimal discount levels, keep a close eye on the competition, and announce promotional offers when customers are likely to purchase. Historical pricing analysis also helps predict revenue and determine optimal locations and platforms for promotional ads.
Not all sales channels bring equal revenue to your brand. Historical pricing analysis can help you determine the most effective quality, volume, and revenue channels. Brands must understand which marketing and sales channels bring quality leads that convert to paying customers. It also helps to determine which eCommerce channels are most profitable so you can optimize your budget and identify channels you should be investing in as a part of future customer acquisition strategies.
Here are a few pricing analytics metrics that can help brands to understand customer behavior towards pricing:
WTP, also known as price sensitivity, is the maximum price your potential customers are willing to pay for your service or product. It is an essential part of pricing strategy since you have no other way of understanding whether your product can yield an augmented product value. Numerous factors are responsible for a customer’s willingness to pay, and it’s not static. Brands must track willingness to pay for all customer segments to ensure that the product is priced competitively and drives maximum profit while staying in line with current market conditions.
Feature value analysis, also known as relative reference analysis, measures the most important features to customers in relation to other features of a product or service. Analyzing critical features to customer segments will help brands price products based on basic or premium components. It can also help to better bundle your services or products so you can drive the most revenue.
The average revenue per user is the revenue generated from the sum of active users divided by the total number of users in a monthly time frame. Delving deeper into ARPU can help brands compare numbers with rivals and check how all products or customer segments perform.
Lifetime Value offers a complete picture of a user’s journey and the average revenue that the user will generate throughout their engagement as a customer with your brand. It helps brands determine various economic decisions such as marketing budgets, profitability, forecasting, and resource allocation.
A successful and profitable brand needs to balance its customer acquisition cost or CAC. It is about spending the right amount of resources and time to drive new customers without jeopardizing their lifetime value and revenue. Correct calculation of CAC helps brands to quantify their sales funnel and determine the efficiency and profitability of their strategies.
Historical pricing analytics is a powerful tool, and it can make a huge difference to a brand’s potential by increasing sales and unlocking incredible profitability in a relatively short time. Historical analysis of pricing and promotions data will help brands get better marketing returns than relying on traditional pricing approaches.
Leveraging pricing analytics will prevent brands from blindly reacting to competitor price changes and support solutions for scaling up price transformation efforts. By using historical pricing data, brands can more effectively segment their customers for marketing and promotion strategies. Properly utilizing predictive analytics and past sales data can help cut costs and keep profit margins high by adjusting production and prices according to market trends.
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