Chapter 4 Revenue Producing Machine Ted Mitchell.

Chapter 4Revenue Producing Machine

Ted Mitchell

A Marketing Machine Producing Revenues From the 4P’s

The Marketing Machine

Inputs to The Marketing Machine

Price TagsProduct Quality

Promotion

Place

Revenue Output from The Marketing Machine

RevenuesRevenue

RevenueRevenue

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

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, πPromotion, Place, Product

Type #1Quantity Sold, Q = r x πConversion rate, r = Q/πQuantity Sold, Q = (Q/π) x π

Type #3∆Quantity Sold, ∆Q = m x ∆πConversion rate, m = ∆Q/∆πSlope-Intercept versionQ = a + b(π)

Input from Negative elements of the Marketing mixPrice Tag, P

Type #2 Quantity Sold, Q = r x PConversion rate, r = Q/PQuantity Sold, Q = (Q/P) x P

Type #4∆Quantity Sold, ∆Q = m x ∆PConversion rate, m = ∆Q/∆PSlope-Intercept versionQ = a - b(P)

See Chapter 3 for details

Chapter 4 Goal: build a Typology of Basic Revenue Machines

Two-Factor MachineSingle Point of Observation

Two-Factor Meta-ModelTwo or More Points of Observation

Positive Input From Marketing Mix, πPromotion, Place, Product Quality

TYPE 1Revenue, R = r x π

TYPE 3∆R = m x ∆πR = a + m(π)

Negative Input From Marketing Mix, Price Tag, P

TYPE 2Revenue, R = r x P

TYPE 4∆R = m x ∆PR = a – m(P)

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

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

Typology of Basic Revenue MachinesTwo-Factor MachineSingle Point of Calibration

Two-Factor Meta-ModelTwo or More Points of Calibration

Positive Input From Marketing Mix, πPromotion, Place, Product Quality

Type #1Revenue, R = (R/π) x π

Type #3a: demand extension Revenue, R = P x QRevenue, R = P(a + b(π))Revenue, R = aP + bP(π)Type #3b: direct observation Revenue, R = a + mπ

Negative Input From Marketing Mix, Price Tag, P

Type #2Revenue, R = (R/P) x PRevenue, R = Q x P

Type #4a: demand extensionRevenue, R = P x QRevenue, R = P(a-bP)Revenue, R = aP – bP2

Type #4b: direct observationRevenue, R = a + mP

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

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

Typology of Basic Revenue MachinesTwo-Factor MachineSingle Point of Calibration

Two-Factor Meta-ModelTwo or More Points of Calibration

Positive Input From Marketing Mix, πPromotion, Place, Product Quality

Type #1Revenue, R = (R/π) x π

Type #3aRevenue, R = P x QRevenue, R = P(a + b(π))Revenue, R = aP + bP(π)Type #3bRevenue, R = a + mπ

Negative Input From Marketing Mix, Price Tag, P

Type #2Revenue, R = (R/P) x PRevenue, R = Q x P

Type #4aRevenue, R = P x QRevenue, R = P(a-bP)Revenue, R = aP – bP2

Type #4bRevenue, R = a + mP

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)

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)

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 xx x

x x x

x

x

x x

x

aP = intercept

R = aP + bPπ

Revenue = kπa

π = Advertising Budget

R = Revenue

x x x

x x x

x x

x xx x

x x x

x

x

x x

xLinear Revenue Meta-Machine is a secant that approximates the Revenue function

R = aP + bπP

R = kPπa

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 – bP2

Lower Price Sells More Units

Price per Cup$3.90

2,200

$4.00

Quantity

Sold

2,000

Demand Equation

Q = a - bP

Revenue = 2,000 x $4.00Revenue = $8,000

TJM

Revenue Machine converting Price Tag looks like

R = P(a-bP)R = aP - bP2Revenue

Price per cup0

TJM

Implies that there is an optimal price, P* for maximizing revenue

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

Revenue

Price per cup0

TJM

R= P(a-bP)R = aP - bP2

R= a-mP

Typology of Basic Revenue MachinesTwo-Factor MachineSingle Point of Calibration

Two-Factor Meta-ModelTwo or More Points of Calibration

Positive Input From Marketing Mix, πPromotion, Place, Product Quality

Type #1Revenue, R = (R/π) x π

Type #3aRevenue, R = P x QRevenue, R = P(a + b(π))Revenue, R = aP + bP(π)Type #3bRevenue, R = a + mπ

Negative Input From Marketing Mix, Price Tag, P

Type #2Revenue, R = (R/P) x PRevenue, R = Q x P

Type #4aRevenue, R = P x QRevenue, R = P(a-bP)Revenue, R = aP – bP2

Type #4bRevenue, R = a + mP

Two Basic Definitions of Price

• Create lots of confusion!• 1) The Marketing Definition • 2) The Accounting Definition• Marketing Managers Use Both

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.

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

Confusion due to Different Definitions of Price in the Revenue Machine

Avoid the Mistake

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

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

Two-Factor Revenue PerformanceCafé #1

Quantity of Cups Sold, Q Q1 = 2,000

Selling price per Cup, P P1 = $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

Compare the Revenue performance to another typical machine

Café #1 Café #2 Difference #2-#1

Quantity of Cups Sold, Q Q1 = 2,000 Q2 = 2,200 ∆Q = 200 cupsSelling price per Cup, P P1 = $4.00 P2 = $3.90 ∆P = -$0.10Sales 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

R = P x Q

Price Factor

Quantity Factor

0, 0 $3.90 per cup

2,200 cupsObserved point ($4.00, 2,000)

Observed Output = $3.90 x 2,200 =$8,580 revenue

$4.00 per cup

2,000 cupsObserved Output =

$4.00 x 2,000 =$8,000 Revenue

Observed point ($3.90, 2,200)

∆Q

∆P

∆R = I∆P + I∆Q = $780 -$200 = $580

Price Factor

Quantity Factor

0, 0 $3.90 per cup

2,200 cupsObserved point ($4.00, 2,000)

Impact of ∆Q= $3.90 x 200 =$780 revenue

$4.00 per cup

2,000 cupsImpact of ∆P =

-$0.10 x 2,000 =-$200 Revenue

Observed point ($3.90, 2,200)

∆Q

∆P

The Simple Two-Factor for Diagnostics

• Positive impact due to increase in quantity,Impact∆Q=$780

• Negative Impact due to decrease in price• Impact∆P = -$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

∆Revenue due to ∆P and ∆QCafé #1 Café #2 Impact of Changes

Quantity of Cups Sold, Q Q1 = 2,000 Q2 = 2,200 ∆Q = 200 cupsImpact = $780 in revenue

Selling price per Cup, P P1 = $4.00 P2 = $3.90 ∆P = -$0.10Impact = -$200 in revenue

Sales Revenue, R =P x Q $8,000 $8,580 ∆R = $580

Also use this for calculating Price ElasticityElasticity of Price =( ∆Q/Q1) / (∆P/P1) Elasticity of Price = (200/2,000) /(-$0.10)/$4) = 0.1/-0.025 = -4

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

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

Decompose The Aggregated InputInto A Multi-factor machine

You need to have recorded total promotion, π, total revenue, R, and total radio spot expense, S

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

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

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

• Any Questions?

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-interceptb = calibrated value of the slope. ∆ø/∆I

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 – ø2)/(π-π2) = m

where the input is set at π=0 and the output is the y-intercept, ø=a.

• 6) Use observed values of ø2 =y, π2=x, and the calculated value of conversion rate, m = b, to calculate the value of the y-intercept, ø=a, (a-y)/(0-x) = ba = b(-x) + y

• 7) Establish the Slope-Intercept equation of the meta-marketing model asOutput = a + b(Input)