## Sunday, June 7, 2015

### Video CPM as a Function of Demand - Complete

# Cost Based CPM from fit to elasticity curve evaluated at actualDailyCost

## Wednesday, June 3, 2015

### ClickMePushYou

## The Puzzle

*If you click on *me...

*I* will disappear.

**Try clicking me away!**

### No, dooon't!

## How did you do?

Your score = 6 - (number of clicks + page_refreshes)

## Tuesday, June 2, 2015

### Player size restricted Video CPM as a function of Demand

# Experiment description and Analysis

# Biggest client tactics representing about 9% of total video revenue were duplicated with all prior restrictions (geo, site, device etc.), additionally restricted to Player size > 400 X 300 and budget-density tested at 1X, 2X, 6X and 12X daily budget on 25, 12, 4 and 2 buckets respectively. For each of these budget points we have the spend, cost and impressions in the player size inventory.

# We are clearly having trouble delivering at scale for the higher budget density - we are delivering only about 35%. For example, to hit the actual budget density points, every tactic in a line item (targeting different numbers of user buckets) should have had the same cost, yet on 5/19/15 the cost in "2 buckets" is only 129 instead of 379. Hence, while the goal was to explore a range 12X the current total video revenue, in practice we've only been able to get to about 4X.

# From querying our database

# we know that about 35% of all our impressions and 35% of video cost are for player size > 400X300, so for all the video tactics which weren't restricted by player size as part of this test, we uniformly apportioned 35% of spend and cost (assuming margin was same) and 35% impressions in the tested user buckets to player size restricted inventory.

# The above spend cost and impressions were added to those for the tested tactics,

# and CPM vs daily cost was plotted and fitted to a power law.

# Why a power law? Because cost (CPM) elasticity of demand (total cost) is the ratio of the logarithmic derivatives, which is just the exponent in a power law.

# The Calculator

# dailyCost = (1-margin)*dailySpend

# actualDailyCost = (spendFraction + (1-spendFraction)*costFraction) * dailyCost

#
Cost Based CPM from fit to elasticity curve evaluated at actualDailyCost

Revenue CPM = CostCPM/(1-margin)

## Monday, April 27, 2015

### Annual Video spend to Cost CPM calculator

# Experiment description and Analysis

# 13 biggest client tactics representing about 9% of total video revenue were duplicated with all prior restrictions (geo, site, device etc.), additionally restricted to Tier I-III inventory and budget-density tested at 1X, 2X, 6X and 12X daily budget on 25, 12, 4 and 2 buckets respectively. For each of these budget points we have the spend, cost and impressions in the TierI-III inventory.

# From other sources (Atul's presentation) we know that about 15% of all our impressions (50% of all avails) are in Tier I-III, so for all the video tactics which weren't restricted to TierI-III as part of this test, we uniformly apportioned 20% of spend and cost (assuming margin was same) and 15% impressions in the tested user buckets to TierI-III inventory.

# The above spend cost and impressions were added to those for the tested tactics,

# and CPM vs daily cost was plotted and fitted to a power law.

# Why a power law? Because cost (CPM) elasticity of demand (total cost) is the ratio of the logarithmic derivatives, which is just the exponent in a power law.

# How do I put in formulas? dailySpend = AnnualSpend/365

# dailyCost = (1-margin)*dailySpend

# actualDailyCost = (spendFraction + (1-spendFraction)*costFraction) * dailyCost

#
Cost Based CPM from fit to elasticity curve evaluated at actualDailyCost

Revenue CPM = CostCPM/(1-margin)

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