Understanding the DataBehind Pricing            Dan Barlow, epaCUBE
AgendaUnderstanding the Data Behind Pricing            Composition of a Price Matrix          The Science Behind Pricing  ...
Composition of a Price Matrix Begins With Segmentation           PRODUCT SEGMENTATION• Proper Grouping of Like Material• P...
Strategic Matrix Design Typical Nature Behind Most Matrix CreationsCustomer Segment    Product Segment      Strategic Outc...
The Price Matrix    A Relationship Of Pricing By Customer Groups to Product Groupso   Based Heavily on    Strategy, and Si...
The Science Behind Pricing    The Math That Backs Up The Business Strategyo   Statistical Evaluation Of Typical Sell Price...
Defining The Middle         13, 18, 13, 14, 13, 16, 14, 21, 13MEAN= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count  ...
Definitions         13, 18, 13, 14, 13, 16, 14, 21, 13MEAN      = 15   Represents the Average of the Data.MEDIAN   = 14   ...
Standard Deviation      13, 18, 13, 14, 13, 16, 14, 21, 13 A Numerical Representation of How Well The Mean Represents the ...
Going The DistanceMEAN      13, 18, 13, 14, 13, 16, 14, 21, 13= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count      ...
Going The DistanceMEAN      13, 18, 13, 14, 13, 16, 14, 21, 13= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count      ...
How Far Is Each From Mean?MEAN     13, 18, 13, 14, 13, 16, 14, 21, 13       -2.83 -2.83 -2.83 +2.83 +2.83 +2.83           ...
How It Lines UpMEAN     13, 18, 13, 14, 13, 16, 14, 21, 13  6.51     9.34   12.17          17.83   20.66 23.49       -2.83...
Empirical RuleMEAN   13, 18, 13, 14, 13, 16, 14, 21, 13                       68%                       95%               ...
Bad Segments Corrupt Good MathBad Data In…Well, You Know…    PRODUCT SEGMENTATION    • Dissimilar List Price Points • Diff...
Histograms    Present Numerical Data in a Way to Make a Pointo   Snapshot of Data in Defined Groupso   Just Enough Bars to...
Reading The Graph is Simple                      SYMMETRICAL                   NON-SYMMETRICAL POSITIVELY (RIGHT) SKEWED  ...
Which Was Our Sample Data?MEAN     13, 18, 13, 14, 13, 16, 14, 21, 13  6.51     9.34   12.17             17.83      20.66 ...
A Nice Thing About A Histograms!   BI-MODAL SPLIT                    MULTI-MODAL SPLIT
Segmentation Errors The Impact of Improperly Segmenting Customers or Productso Data Splits Create Averaging Errorso Leavin...
Dealing With Bad BehaviorWhen The Problem Is Not A Segment            EXCEPTIONAL ORDERS• Skews Data Positive or Negative ...
Exceptional OrdersGetting Your Arms Around What Is Normal              THE EASY STUFF              • Cost Overrides       ...
The Sales History FileUsed To Derive The Analysiso The Source File For Logical Analysiso The Evaluation Process       Typ...
Sales Outliers The Most Profitable And Accurate Matrix Allows Flexibilityo One Solution Is To Remove Them From The Calcula...
Sales Outliers The Most Profitable And Accurate Matrix Allows Flexibilityo One Solution Is To Remove Them From The Calcula...
Blending Art With ScienceI Know What Happened, But Here Is What I WANT To Happen Analytic Result                    Strate...
Blending Art With ScienceAnalytics = Where You Were; Strategy = Where You’re Headedo   Consider Customer And    Vendor Rel...
Timing Is EverythingVendor Price Increases Are The Perfect Storm
SUMMARYBuilding A Price Matrixo Limited Sales Transactionso Remove The Obviouso Research The Not-So-Obviouso Look For Patt...
Dan BarlowepaCUBEPhone 817-337-3158Email dbarlow@epacube.com
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Understanding the Data Behind Pricing - Dan Barlow, epaCUBE

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Distribution recognizes the need to develop talent for pricing within their organizations. Owning this core competency is now critical for survival. Many distributors have historically outsourced this capability, but are now ready to adapt this skill set internally and own the most important component to their customer relationship. In this session, we will discuss the transactional datasets used for pricing analysis and provide an understanding to the importance of blending knowledge with data to derive accurate pricing for customers. We will point out potential pitfalls in pricing that lead to commercial issues, and discuss the use of segmentation to derive contextual performance models.

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Understanding the Data Behind Pricing - Dan Barlow, epaCUBE

  1. 1. Understanding the DataBehind Pricing Dan Barlow, epaCUBE
  2. 2. AgendaUnderstanding the Data Behind Pricing Composition of a Price Matrix The Science Behind Pricing Bad Segments Corrupt Good Math Dealing With Bad Behavior Blending Art With Science
  3. 3. Composition of a Price Matrix Begins With Segmentation PRODUCT SEGMENTATION• Proper Grouping of Like Material• Prevents Inconsistencies in Pricing• Typically Provided By The Vendor CUSTOMER SEGMENTATION • Proper Grouping of Like Customers • Drives Consistency in Strategy • Typically Provided By The Sales Team
  4. 4. Strategic Matrix Design Typical Nature Behind Most Matrix CreationsCustomer Segment Product Segment Strategic OutcomeA Sales Rep or Product Segments The Matrix Price IsManagement Are Usually Derived Off SomeDecides What Provided By The Sales History, butOther Customers Vendor Or More in Line WithThis Particular Grouped By A What Sales Feels IsCustomer Should Commodity ReasonableBe Priced Like Status
  5. 5. The Price Matrix A Relationship Of Pricing By Customer Groups to Product Groupso Based Heavily on Strategy, and Simple Matho Analysis Can Be Difficulto Customer Segments Usually Lack Structureo Limited Visibility To See Performance Concernso Do Not Know Who To Change and By How Mucho Lack Agility to Hide In Vendor Updateso They Simply Age With Limited Upkeep
  6. 6. The Science Behind Pricing The Math That Backs Up The Business Strategyo Statistical Evaluation Of Typical Sell Price Performanceo Used To Derive:  Typical Performance  Suggested Price Points  How Things Compare  What is Too High and What is Too Low
  7. 7. Defining The Middle 13, 18, 13, 14, 13, 16, 14, 21, 13MEAN= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count = (135) / 9MEDIAN = 15 13, 18, 13, 14, 13, 16, 14, 21, 13 13, 13, 13, 13, 14, 14, 16, 18, 21MODE = 14 13, 18, 13, 14, 13, 16, 14, 21, 13 (13, 13, 13, 13)(14, 14) 16, 18, 21 = 13
  8. 8. Definitions 13, 18, 13, 14, 13, 16, 14, 21, 13MEAN = 15 Represents the Average of the Data.MEDIAN = 14 Represents the Centermost point in the Data.MODE = 13 Represents the Most Repeated of the Data.
  9. 9. Standard Deviation 13, 18, 13, 14, 13, 16, 14, 21, 13 A Numerical Representation of How Well The Mean Represents the Data PointsMEAN= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count = (135) / 9 = 15 So…How Far Is Each Data Point In Comparison to The Mean?
  10. 10. Going The DistanceMEAN 13, 18, 13, 14, 13, 16, 14, 21, 13= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count = (135) / 9 13 - 15 = -2 = 15 18 - 15 = 3 13 - 15 = -2 If You Add These 14 - 15 = -1 13 - 15 = -2 Together, You Get 0, 16 - 15 = 1 So You Square The 14 - 15 = -1 21 - 15 = 6 Values! 13 - 15 = -2
  11. 11. Going The DistanceMEAN 13, 18, 13, 14, 13, 16, 14, 21, 13= (13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) / Count = (135) / 9 13 - 15 = -2 2 = 4 = 15 Total = 64, Then… 18 - 15 = 322 = 9 13 - 15 = -22 = 4 You Divide 64 / (Count – 1) 14 - 15 = -1 2 = 1 13 - 15 = -2 = 4 So, 64/9-1 or 64/8 16 - 15 = 122 = 1 =8 14 - 15 = -12 = 1 21 - 15 = 6 2 = 36 Lastly, We Take The Square 13 - 15 = -2 = 4 Root of 8 = 2.83
  12. 12. How Far Is Each From Mean?MEAN 13, 18, 13, 14, 13, 16, 14, 21, 13 -2.83 -2.83 -2.83 +2.83 +2.83 +2.83 15
  13. 13. How It Lines UpMEAN 13, 18, 13, 14, 13, 16, 14, 21, 13 6.51 9.34 12.17 17.83 20.66 23.49 -2.83 -2.83 -2.83 +2.83 +2.83 +2.83 15
  14. 14. Empirical RuleMEAN 13, 18, 13, 14, 13, 16, 14, 21, 13 68% 95% 99%
  15. 15. Bad Segments Corrupt Good MathBad Data In…Well, You Know… PRODUCT SEGMENTATION • Dissimilar List Price Points • Different Value Propositions • Wide Margin Spreads CUSTOMER SEGMENTATION • Some Customers Behave Differently • Diversified Expertise • Perceive Your Values Differently
  16. 16. Histograms Present Numerical Data in a Way to Make a Pointo Snapshot of Data in Defined Groupso Just Enough Bars to Represent a Pattern  Identify the Distribution of Data by Shape  Clarity of Variability  Centermost Point  Visualize Data Trends
  17. 17. Reading The Graph is Simple SYMMETRICAL NON-SYMMETRICAL POSITIVELY (RIGHT) SKEWED NEGATIVELY (LEFT) SKEWED
  18. 18. Which Was Our Sample Data?MEAN 13, 18, 13, 14, 13, 16, 14, 21, 13 6.51 9.34 12.17 17.83 20.66 23.49 -2.83 -2.83 -2.83 +2.83 +2.83 +2.83 POSITIVELY (RIGHT) SKEWED 15
  19. 19. A Nice Thing About A Histograms! BI-MODAL SPLIT MULTI-MODAL SPLIT
  20. 20. Segmentation Errors The Impact of Improperly Segmenting Customers or Productso Data Splits Create Averaging Errorso Leaving These Things Together:  Standard Deviation is Overstated  Mean Value is Wrong  Highs Are Too High  Lows Are Too Low  Drives Pricing Beyond Value Proposition  Overrides Will Reoccur Or Never Leave
  21. 21. Dealing With Bad BehaviorWhen The Problem Is Not A Segment EXCEPTIONAL ORDERS• Skews Data Positive or Negative • Is Not A Typical Purchase • Should Be Excluded SALES OUTLIERS • When The Segment Is Right • Performance Is Very Different • Best Profit Play Is Reclassification
  22. 22. Exceptional OrdersGetting Your Arms Around What Is Normal THE EASY STUFF • Cost Overrides EASY • Sell Contracts • Large Orders or Jobs HARD NOT SO INTUITIVE • Sales Overrides • Missing Contract Items • Special Pricing Agreements
  23. 23. The Sales History FileUsed To Derive The Analysiso The Source File For Logical Analysiso The Evaluation Process  Typical System Sales  Exclude Obvious Deviations  Investigate Items Out Of Statistical Norms  Include and Exclude Data And View Changes  Prioritize Effort to Data With Greater Deviations and Dollars
  24. 24. Sales Outliers The Most Profitable And Accurate Matrix Allows Flexibilityo One Solution Is To Remove Them From The Calculationso But Consider Movement Of Pricing To Align With Customer  More Like Market-Segment Editing  More Flexible Pricing Solution
  25. 25. Sales Outliers The Most Profitable And Accurate Matrix Allows Flexibilityo One Solution Is To Remove Them From The Calculationso But Consider Movement Of Pricing To Align With Customer  More Like Market-Segment Editing  More Flexible Pricing Solution  Handles Exceptional Behavior  Maximize Profit, Minimize Risk  Prevents Averaging of Data
  26. 26. Blending Art With ScienceI Know What Happened, But Here Is What I WANT To Happen Analytic Result Strategic ObjectiveWhat The Limited The Financial Or Sales Data Strategic Objective Represents As Desired Historically Accurate
  27. 27. Blending Art With ScienceAnalytics = Where You Were; Strategy = Where You’re Headedo Consider Customer And Vendor Relationshipso Limit Sales History To Reflect The Desired Commitment  New Vendor  Strategic Partner  New Products  Desired Customer  Unacceptable Performance
  28. 28. Timing Is EverythingVendor Price Increases Are The Perfect Storm
  29. 29. SUMMARYBuilding A Price Matrixo Limited Sales Transactionso Remove The Obviouso Research The Not-So-Obviouso Look For Patternso Quantify Overrides To Standard Deviationo Eliminate Segment Errors!o Execute Edits With Vendor Price Changes
  30. 30. Dan BarlowepaCUBEPhone 817-337-3158Email dbarlow@epacube.com
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