Pricing Analytics
Pricing analytics involves the use of historical data to determine the best prices to set for future sales.
Combining pricing with analytics can create a mechanism that functions as both a catalyst and a metrics engine for managing profitability.
Data driven, statistically analyzed analytics will ensure pricing strategies align with business and brand strategies and avoid the external pressures that trigger pricing failures.
Few decisions have as large an impact on the success of your business as optimizing prices.

The goal of pricing analytics is to provide the right price for every product, for every customer segment, through every channel.
Our pricing analytics approach helps businesses get actionable insight to formulate best pricing decisions:

1) Sales Trend vs. Competition
2) Breakeven Analysis
3) Price Elasticity
4) Price-Mix-Analysis

5) Potential Incremental Sales
6) Baseline Forecast
7) Margin Exposure
8) Price sensitivity analysis
9) Customer Segmentation intelligence
10) Simulations
Helps businesses in:
Our Pricing Analytics Techniques

a. Outlier Identification
b. Shopping Basket Analysis
c. Principal Component Analysis
d. Margin Bridge Analysis
e. Neural Networks
f. Statistical segmentation methods
g. Correlation & price regression analysis
h. Predefined Optimization Algorithms
i. Decision Tree / Classification and Regression Trees (CART)

j. Decision Forests
k. Purchase probability model
Better planning of pricing changes
Better planning of pricing  promotions
Optimizing pricing
Identifying pricing opportunities
Better understanding about customers
Our pricing analytics solutions provides answers to critical pricing and promotions questions:

1) What are the key measures and metrics you want to track?
2) What are the key areas of margin leakage you want to monitor?
3) Where should we be allocating marketing spend to maximise ROI?
4) Do we have the right portfolio of products?
5) How do we set pricing and promotional offers to maximise overall margin?

Our predictive models deliver value is through:

1) Cost Savings
2) Growing Revenue
3) Increasing Return on Investment (ROI)
4) Managing Risk

5) Optimizing profit
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Decision Science Services
AI > Decision Science