pricing analytics

INFORMATION FOR MANAGEMENT DECISIONS

PRICING ANALYTICS

Pricing Decision Process

Problem

Question

Information Decision

From Information to Decision

Analytical Approach 3 Levels

Analytical Approaches

Level I Pricing Effects Level II Promotion Level III Predictive Analytics

Pricing Effects Introduction Level

Sales Trend vs. Competition Breakeven Analysis Price Elasticity Price-Mix-Analysis

Sales Trend vs. Competition Problem/Issue

Sales last month down 20% YOY while Price 15% higher than all competitors

Does higher price cause the sales decline?

Pricing Effects

Sales Trend vs. Competition Analysis

Our Price Comp A Comp B Sales YOY % Change

Sales Declining

Pricing Position Not Change

Pricing Effects

Sales Trend vs. Competition Lesson Learned

No correlation between sales decline and higher pricing position Always analyze sales and pricing trends. Sales or pricing snapshot often leads to a wrong conclusion.

Pricing Effects

Breakeven Analysis Problem/Issue

5% of price reduction increase sales by

30% Is it a price change successful?

Pricing Effects

Breakeven Analysis Analysis

Not necessary! Current GP = 10% Price Reduction = 5% GP Afterward = 5% Need to double the sales to breakeven!

Pricing Effects

Breakeven Analysis Lesson Learned

Breakeven analysis should base on margin not sales. Price Elasticity affects sales not margin

Pricing Effects

Price Elasticity Problem/Issue

How to determine incremental sales prior to price drop?

Price Elasticity or Pricing Sensitivity

Analysis

Pricing Effects

Price Elasticity Analysis

Price Quantity $ 5.00 881 $ 4.80 956 $ 5.00 881 $ 5.50 728 $ 5.00 881 $ 4.70 997 $ 5.40 755 $ 4.90 917 $ 5.30 784 $ 5.40 755 $ 5.30 784 $ 4.80 956 $ 4.70 997 $ 5.30 784 $ 4.70 997 $ 4.90 917 $ 5.20 815 $ 5.30 784 $ 5.30 784 $ 5.30 784 $ 5.00 881 $ 5.00 881 $ 5.10 847 $ 5.00 881 $ 4.70 997 $ 4.60 1041

Ln(P) Ln(Q) $ 1.61 6.78 $ 1.57 6.86 $ 1.61 6.78 $ 1.70 6.59 $ 1.61 6.78 $ 1.55 6.90 $ 1.69 6.63 $ 1.59 6.82 $ 1.67 6.66 $ 1.69 6.63 $ 1.67 6.66 $ 1.57 6.86 $ 1.55 6.90 $ 1.67 6.66 $ 1.55 6.90 $ 1.59 6.82 $ 1.65 6.70 $ 1.67 6.66 $ 1.67 6.66 $ 1.67 6.66 $ 1.61 6.78 $ 1.61 6.78 $ 1.63 6.74 $ 1.61 6.78 $ 1.55 6.90 $ 1.53 6.95

Coefficients Standard Error t Stat P-value Intercept 10.00 0.00127 7859.64492 0.00002

1.609437912 -2.00 0.00079 -2544.94319 0.00012

ln(Q) = 10 - 2 ln(P) Price Elasticity: -2

Pricing Effects

Price Elasticity Lesson Learned

Sell Price vs. Quantity Data

SAS, R or other tools for more products Log-Linear Demand /Price Model Make sure PE is statistically significant

Pricing Effects

Pricing Mix Problem/Issue

Average Selling Price dropped in the last three months!

But there is no pricing action What happened?

Pricing Effects

Pricing Mix Analysis

Aggregated level price changes can happen due to product mix or channel mix changes.

Sales Units Price 2014 2015 2014 2015 2014 2015 Product A $ 50,000 $ 40,000 500 400 $ 100 $ 100 Product B $ 20,000 $ 30,000 1000 1500 $ 20 $ 20 Total $ 70,000 $ 70,000 1500 1900 $ 47 $ 37

Pricing Effects

Pricing Mix Lesson Learned

Price change could result from the multiple mix changes such as product mix, channel mix and segment mix It is very common!

Pricing Effects

Promotion Evaluation Medium Level

Potential Incremental Sales Baseline Forecast Margin Exposure Breakeven Analysis Test/Control Group Setup

Potential Incremental Sales Problem/Issue

Predict Sales Increase Eliminate Unreasonable Expectations

Promotion Evaluation

Potential Incremental Sales Analysis

Price Elasticity Competition Market Share Previous Promotion Wishful Thinking


Potential Incremental Sales Lesson Learned

Difficult but not impossible! Predict sales range and possibility Try to eliminate unreasonable thinking


Baseline Forecast Problem/Issue

Forecast without promotion Trends prior to promotion Seasonality Current market share


Baseline Forecast Analysis

Excel based forecast – analytic tools Time series forecasting – more than 10 statistical models Multivariable regression models


Baseline Forecast Lesson Learned

Process commonly ignored Critical step for promotion revaluation Make sure your promotion sponsor agrees Baseline Forecast prior to the promotion


Margin Exposure Problem/Issue

Price Drop = Profit Loss or Margin Leakage Margin Exposure accesses the potential risk


Margin Exposure Analysis

Identify the exposing areas Assume worst possible outcomes Calculation of margin loss needs to be done at sales order or invoice level not at aggregate level


Margin Exposure Lesson Learned

Always ask a following question: Can we afford to lose that much money if the promotion goes south? Average selling price should never be used in calculating margin exposure


Control Group Problem/Issue

Have the consensus on promotion evaluation and eliminate future dispute

Be prepared for unexpected changes of

market place


Control Group Analysis

Blinded Group: Similar set of promotion targets Preselected Group: The baseline group selected by promotion sponsor Randomized Group: Randomly divide the set of targets into test and control groups ANOVA Test: Statistical significant or not?


Control Group Lesson Learned

Less controversy and commonly adopted by promotion sponsor and pricing professional Best promotion evaluation approach!


Predictive Analytics Advanced Level

Outlier Identification Association - Shopping Basket Analysis Principal Component Analysis Margin Bridge Analysis Classification – Decision Tree

Outlier Identification Problem/Issue

Outliers often mislead analysis and promotion evaluation

Always try to eliminate them prior to any

analytical approach

Predictive Analytics

Outlier Identification Analysis


IQR Rule: Q3+1.5x(Q3-Q1) Regression Model (Robust, Cook-D etc.) SAS, SPSS, R and SQL Data Mining Tool

Outlier Identification Lesson Learned

Outliers are segment/cluster driven Single variable based outliers vs. multi-variable based outliers The outlier in one segment might not be the outlier in another segment


Association Problem/Issue

Opportunity for effective cross-sell Critical step to price product bundles Understand price elasticity between anchor

and attach Discount applied to targeting anchor not

attach


Association Analysis


Excel Data Mining Tool Add In SAS Enterprise Miner/R

Association Lesson Learned

Multiple levels of associations could occur Could well miss your shot i f anchor and attach are f l ipped Anchor is usually more price elastic than attach Never discount your price on attaches


Principle Component Analysis Problem/Issue

Your price can be affected by many factors PCA is a variables reduction process Always prioritize the most influenced

factors


Principle Component Analysis Analysis


Correlation matrix SAS Factor procedure Excel Data Mining Add In

Principle Component Analysis Lesson Learned

Principle component analysis is not factor/cluster analysis Must eliminate any possible outliers prior to run PCA Always better to run PCA before predicting pricing impact


Margin Bridge Analysis Problem/Issue

A way to analyze pricing/margin changes impacted by various mixes


$5,000

-$2,000

-$1,500

-$1,350 -$800

$1,500

$2,000

$3,000 $5,850

Year-2015 Pricing Costs Segment Mix Channel Mix Product Mix SalesEnhancement

New Product Year-2016

Margin Bridge Analysis Analysis


It is not Price-Volume-Profit Analysis in accounting and it can not be achieved by using simple algebra Multiple Linear Regression involved (GLM) and some assumptions are made

Margin Bridge Analysis Lesson Learned

Time consuming to transform the data and test the hypotheses More than likely the results are directional rather than solid science


Classification and Decision Tree Problem/Issue

Regression trees for prediction Quickly identify price sensitive segments Systematically search for low-hanging

fruits Evaluate existing pricing actions Classify potential targets using the data

from prior promotions


Classification and Decision Tree Analysis


Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines

Classification and Decision Tree Lesson Learned

Require a large amount of data Easily understandable and transparent Great visualization of representation However, trees must be pruned to avoid over-fitting of the training data


QUESTIONS?

PRICING ANALYTICS

pricing analytics

Economy & Finance