Pricing analytics helps brands track a wide range of pricing metrics with
front-line analytical tools and gives insights to pass the competition. This
analysis utilizes historical data to know how earlier promotion and
pricing activities affect sales, brand, and customer pricing perception. It
involves recognizing weaknesses and opportunities in the competitors'
price strategies and using them for better sales & revenue.
Pricing analytics assist brands in understanding how product prices and
promotions influence profitability and the steps they take to optimize
their price structures. Brands could leverage their prices and consumer
data to design suitable pricing models to achieve their sales objectives.
Let's overview historical pricing analytics, its advantages, and how you
can improve sales using historical pricing data analytics.
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.
Need assistance in tracking competitor prices? Or need historic price
insights for your brand? Or follow the efficiency of online promotions?
Contact Actowiz Solutions now!
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