Download presentation

Presentation is loading. Please wait.

Published byLilliana Tufts Modified over 4 years ago

1
Chapter 4 Revenue Producing Machine Ted Mitchell

2
A Marketing Machine Producing Revenues From the 4Ps The Marketing Machine Inputs to The Marketing Machine Price Tags Product Quality Promotion Place Revenue Output from The Marketing Machine Revenues Revenue

3
It is usually move convenient Managing marketing machines when their output is measured as dollars of sales revenue rather than as units of quantity sold. Output = (Conversion rate, r) x Input Quantity Sold, Q = (conversion rate, r=Q/π) x π Revenue, R = (conversion rate, r=R/π) x π

4
Typology of Demand Producing, Quantity Sold, Q, Marketing Machines Two-Factor Model Calibrated from a Single Observation Two-Factor Meta-Model Calibrated from a minimum of two observations Input from Positive Elements of Marketing Mix, π Promotion, Place, Product Type #1 Quantity Sold, Q = r x π Conversion rate, r = Q/π Quantity Sold, Q = (Q/π) x π Type #3 Quantity Sold, Q = m x π Conversion rate, m = Q/π Slope-Intercept version Q = a + b(π) Input from Negative elements of the Marketing mix Price Tag, P Type #2 Quantity Sold, Q = r x P Conversion rate, r = Q/P Quantity Sold, Q = (Q/P) x P Type #4 Quantity Sold, Q = m x P Conversion rate, m = Q/P Slope-Intercept version Q = a - b(P) See Chapter 3 for details

5
Chapter 4 Goal: build a Typology of Basic Revenue Machines Two-Factor Machine Single Point of Observation Two-Factor Meta-Model Two or More Points of Observation Positive Input From Marketing Mix, π Promotion, Place, Product Quality TYPE 1 Revenue, R = r x π TYPE 3 R = m x π R = a + m(π) Negative Input From Marketing Mix, Price Tag, P TYPE 2 Revenue, R = r x P TYPE 4 R = m x P R = a – m(P)

6
Two-Basic Types of Revenue Machines 1) The Simple Two-Factor Model using a single point of observation for its calibration of the conversion rate, r 2) The Meta-Model using a minimum of two observations for its calibration of the meta- conversion rate, m

7
Two Basic Types of Input 1) Positive Elements of the marketing mix, π, that increase value to customer: Promotion, Place, Product quality 2) Revenue generating element of the price tag, P, to the customer, that reduces value to the customer

8
Typology of Basic Revenue Machines Two-Factor Machine Single Point of Calibration Two-Factor Meta-Model Two or More Points of Calibration Positive Input From Marketing Mix, π Promotion, Place, Product Quality Type #1 Revenue, R = (R/π) x π Type #3a: demand extension Revenue, R = P x Q Revenue, R = P(a + b( π )) Revenue, R = aP + bP( π ) Type #3b: direct observation Revenue, R = a + m π Negative Input From Marketing Mix, Price Tag, P Type #2 Revenue, R = (R/P) x P Revenue, R = Q x P Type #4a: demand extension Revenue, R = P x Q Revenue, R = P (a-b P ) Revenue, R = a P – b P 2 Type #4b: direct observation Revenue, R = a + m P

9
Basic Type #1 Revenue Machine A single point observation of Revenue, R, and a Positive Input, π Two-Factor machine is R = r x π Calibrate the conversion rate, r = R/π Poor forecasting tool and the conversion rate should not be used as a standalone performance metric

10
Basic Type #2 Revenue Machine A single point observation of Revenue, R, and a Negative Input, Price Tag, P Two-Factor machine is R = r x P Calibrate the conversion rate, r = R/P Classic Definition of Revenue Revenue, R = (Quantity, Q) x (Price, P) Poor Predicting Power, Useful Diagnostic

11
Typology of Basic Revenue Machines Two-Factor Machine Single Point of Calibration Two-Factor Meta-Model Two or More Points of Calibration Positive Input From Marketing Mix, π Promotion, Place, Product Quality Type #1 Revenue, R = (R/π) x π Type #3a Revenue, R = P x Q Revenue, R = P(a + b( π )) Revenue, R = aP + bP( π ) Type #3b Revenue, R = a + m π Negative Input From Marketing Mix, Price Tag, P Type #2 Revenue, R = (R/P) x P Revenue, R = Q x P Type #4a Revenue, R = P x Q Revenue, R = P (a-b P ) Revenue, R = a P – b P 2 Type #4b Revenue, R = a + m P

12
Basic Type #3a Revenue Machine A two point observation of Quantity, Q, and a Positive Input, π, and a price tag, P Extension of the Demand Machine Have the Demand machine, Q = a + bπ Multiply the Demand by the Price tag (P x Q) = P x (a + bπ) Forecasted Revenue, R = aP + bP(proposed, π)

13
Basic Type #3b Revenue Machine A two point observation of Revenue, R, and a Positive Input, π Two-Factor Meta-machine is R = m x π Calibrate the conversion rate, m = R/π Create a slope-intercept equation of Forecasted Revenue, R = a + m(proposed, π)

14
Type 3: Revenue, R = P x (a + bπ) R = aP + bπP π = Advertising Budget R = Revenue x x x x x x x x x x x x aP = intercept R = aP + bPπ

15
Revenue = kπ a π = Advertising Budget R = Revenue x x x x x x x x x x x x Linear Revenue Meta-Machine is a secant that approximates the Revenue function R = aP + bπP R = kPπ a

16
Basic Type #4a Revenue Machine A two point observation of Quantity, Q, and a Negative Input, Price Tag, P Extension of the Demand Machine The slope-intercept equation of the meta- demand machine Q = a – bP Multiply by The observed Price tag. P (P x Q) = P x (a-bP) Revenue, R = aP – bP 2

17
Lower Price Sells More Units Price per Cup $3.9 0 2,200 $4.00 Quantity Sold 2,000 Demand Equation Q = a - bP Revenue = 2,000 x $4.00 Revenue = $8,000 TJM

18
Revenue Machine converting Price Tag looks like R = P(a-bP) R = aP - bP 2 Revenue Price per cup 0 TJM Implies that there is an optimal price, P* for maximizing revenue

19
Basic Type #4b Revenue Machine A two point observation of Revenue, R, and a Negative Input, Price Tag, P Two-Factor meta-machine is R = m x P Calibrate the conversion rate, m = R/P Create a slope-intercept model of Forecasted Revenue, R = a +m(proposed, P) Linear estimate of the quadratic

20
Revenue Price per cup 0 TJM R= P(a-bP) R = aP - bP 2 R= a-mP

21
Typology of Basic Revenue Machines Two-Factor Machine Single Point of Calibration Two-Factor Meta-Model Two or More Points of Calibration Positive Input From Marketing Mix, π Promotion, Place, Product Quality Type #1 Revenue, R = (R/π) x π Type #3a Revenue, R = P x Q Revenue, R = P(a + b( π )) Revenue, R = aP + bP( π ) Type #3b Revenue, R = a + m π Negative Input From Marketing Mix, Price Tag, P Type #2 Revenue, R = (R/P) x P Revenue, R = Q x P Type #4a Revenue, R = P x Q Revenue, R = P (a-b P ) Revenue, R = a P – b P 2 Type #4b Revenue, R = a + m P

22
Two Basic Definitions of Price Create lots of confusion! 1) The Marketing Definition 2) The Accounting Definition Marketing Managers Use Both

23
Two Basic Definitions of Price 1) The Marketing Definition is that selling price is a price tag that signals the customer as to the amount that must be given up to acquire the product 2) The Accounting Definition is that selling price is the average revenue generated per unit sold.

24
Two Basic Definitions of Price 1) The Marketing Machine that produces Revenue uses the Price Tag as an Input Revenue, R = (Quantity, Q) x (Price Tag, P) R = Q x P 2) The Accounting Machine that produces Revenue uses the Quantity sold as the input and the Price is the Conversion rate, P = R/Q Revenue, R = (Conversion rate, P = R/Q) x (Quantity Sold, Q) R = (R/Q) x Q R = P x Q

25
Confusion due to Different Definitions of Price in the Revenue Machine Avoid the Mistake

26
The Simple Two-Factor Revenue Machines Are most useful for diagnostic purposes when comparing two performances between two machines 1) Revenue, R = (R/π) x π 2) Revenue, R = Q x P Not very useful for forecasting or optimization purposes

27
For Diagnostic Purposes You want to explore the differences between two performances Revenue due to P and Q 1) a machine and an benchmark performance 2) a machine and a standard performance 3) a machine and an average performance 4) a machine and its previous performance

28
Two-Factor Revenue Performance Café #1 Quantity of Cups Sold, QQ 1 = 2,000 Selling price per Cup, PP 1 = $4.00 Sales Revenue, R =P x Q$8,000 Do NOT Forget: If you know 2 of the 3 elements, you can calculate the third element of the Two- Factor Machine

29
Compare the Revenue performance to another typical machine Café #1Café #2Difference #2-#1 Quantity of Cups Sold, QQ 1 = 2,000Q 2 = 2,200 Q = 200 cups Selling price per Cup, PP 1 = $4.00P 2 = $3.90 P = -$0.10 Sales Revenue, R =P x Q$8,000$8,580 R = $580 Identify the impact the P and the impact Q had on the R R = Impact of Q + Impact of P You can see the differences in the two performances

30
R = P x Q Price Factor Quantity Factor 0, 0 $3.90 per cup 2,200 cups Observed point ($4.00, 2,000) Observed Output = $3.90 x 2,200 = $8,580 revenue Observed Output = $3.90 x 2,200 = $8,580 revenue $4.00 per cup 2,000 cups Observed Output = $4.00 x 2,000 = $8,000 Revenue Observed Output = $4.00 x 2,000 = $8,000 Revenue Observed point ($3.90, 2,200) Q P

31
R = IP + IQ = $780 -$200 = $580 Price Factor Quantity Factor 0, 0 $3.90 per cup 2,200 cups Observed point ($4.00, 2,000) Impact of Q= $3.90 x 200 = $780 revenue Impact of Q= $3.90 x 200 = $780 revenue $4.00 per cup 2,000 cups Impact of P = -$0.10 x 2,000 = -$200 Revenue Impact of P = -$0.10 x 2,000 = -$200 Revenue Observed point ($3.90, 2,200) Q P

32
The Simple Two-Factor for Diagnostics Positive impact due to increase in quantity, ImpactQ=$780 Negative Impact due to decrease in price ImpactP = -$200 Net Impact = R = $780 + (-$200) = $500 The impact due to change in quantity more than off-sets the net impact of the price reduction

33
Revenue due to P and Q Café #1Café #2Impact of Changes Quantity of Cups Sold, QQ 1 = 2,000Q 2 = 2,200 Q = 200 cups Impact = $780 in revenue Selling price per Cup, PP 1 = $4.00P 2 = $3.90 P = -$0.10 Impact = - $200 in revenue Sales Revenue, R =P x Q$8,000$8,580 R = $580 Also use this for calculating Price Elasticity Elasticity of Price =( Q/Q 1 ) / (P/P 1 ) Elasticity of Price = (200/2,000) /(-$0.10)/$4) = 0.1/-0.025 = -4

34
Decomposition for More Diagnostic Detail The problem with using high levels of aggregation such as Total Promotion Budget rather than radio budget and print budget Total Revenue rather than revenue from pastry, large cups, small cups, etc. is you lose too much information

35
Example You find your total budget too aggregated for you analysis Revenue, R = (conversion rate, R/π) x (total promotion, π) Decompose the Two Factors into Three Factors Revenue, R = (Revenue returned by cost of radio spots, R/S) x (Ratio of Radio to Total Promotion, S/π) x Total promotion, π) R = (R/S) x (S/π) x π

36
Decompose The Aggregated Input Into A Multi-factor machine You need to have recorded total promotion, π, total revenue, R, and total radio spot expense, S

37
Decomposing the Revenue in the conversion rate Total revenue has been aggregated into revenue from pastry sales and from coffee sales You find that total revenue is too aggregated for your analysis

38
Transform from a Two-Factor to a Three-Factor Machine Revenue, R =(conversion r, R/Q) x Cups sold, Q You need to know the number of pastries sold, T, to expand the analysis Decompose to Three Factors Revenue, R = (Sales Revenue per pastry, R/T) x (Pastry per cup sold, T/Q) x Number of cups sold, Q R = (R/T) x (T/Q) x Q

39
Decomposing the Conversion Factor into a Multi-Factor Model You need to know that 600 pastries were sold in café #1 and 900 pastries were sold in café #2

40
Any Questions?

41
You may have to Calibrate the Revenue Producing Meta-π Machine Using the basic 7 steps for calibrating the Slope-Intercept Equation of the Meta-Marketing Machine Output = a – b(Input) Where a = calibrated value of the y-intercept b = calibrated value of the slope. ø/ I

42
Review the 7 Calibration Steps 1) Observe two inputs to the machine, π = π2-π1. 2) Observe two outputs of the machine, ø = ø2-ø2. 3) Establish the Meta-Machine, ø = m x π 4) Determine the meta-conversion rate, m = b = ø/π. 5) Set Slope-Proposed Point Equation, (ø – ø 2 )/(π-π 2 ) = m where the input is set at π=0 and the output is the y-intercept, ø=a. 6) Use observed values of ø 2 =y, π 2 =x, and the calculated value of conversion rate, m = b, to calculate the value of the y- intercept, ø=a, (a-y)/(0-x) = b a = b(-x) + y 7) Establish the Slope-Intercept equation of the meta- marketing model as Output = a + b(Input)

Similar presentations

Presentation is loading. Please wait....

OK

Chapter 17 – Additional Topics in Variance Analysis

Chapter 17 – Additional Topics in Variance Analysis

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google