1. Troy Nunez cell:925.381.9704
A Brief Summary of a More Comprehensive Analysis
I completed an evaluation on the returns of our marketing spend by channel and by product. Advertising
channels: TV, Radio, Print, and Digital. In order to calculate returns, I created a multi-variat regression model
using STATA. Using regressions allows for isolating and quantifying each variable. In other words, it provides
each marketing channel’s ROI independent of one another. I believe this analysis highlights many skills that
are both applicable and rare in the marketplace. It also highlights the robust approach I take in delivering an
analysis.
Key deliverables:
Return on marketing investment1
Lagged effects of marketing spend2
Price Elasticities3
Identify Competitive Products
Analyzed Promotional Lift
How each marketing channel effects each product
Optimum marketing spend
Peak demand
Identified Cost savings for times of unresponsive marketing
Included in the model:
Marketing spend
Seasonality
Holidays/closures
Exhibit Changes
Income
Price
1.
Return on marketing investment: All returns were significant at the 95% confidence level. We can see here
what returns in dollars we would get (y-axis) by each additional dollar spent (x-axis).
*
Note that in this case it is not concerning that our sales are not greater than our investment, as this is a
granular look how total marketing effects a subset of the overall business.

2. 2.
Lagged effects of marketing spend: Significant at the 95% confidence level
In an effort to determine the best strategy for marketing spend; we needed to determine how long we could
wait in-between campaigns, before returns would erode. Based on the persistence of our advertising, we
could determine how far apart we could spread our advertising dollars to be most efficient. I was able to
determine that 50% of the effects of advertising persist until just under the 2-week mark (first red line) and
10% of the effects remain through 6 weeks (second red line).
3.
Price Elacticities: Here we see the original price of each product where each line converges (x=0,y=0). We can
see the percentage change in sales (y-axis), based on the percentage change in price (x-axis). In the curve
emphasized below, we would interpret this, as an increase of just over 50% in price will result in a decrease of
just over 60% in sales for that unique renewal line through the Membership channel.
*
Each curve is significant at the 95% confidence level. Product labels withheld. Each line represents a unique
product. We are able to accurately estimate the effects of price changes, allowing us to find the optimal
price.
0
.2
.4
.6
LaggedCoefficients
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Weeks
Lag Advertising Effects

3. Results: The model fits the data well, accounting for 68% of the daily variation in Individual sales. The red
vertical line indicates the date of the price increase, which my model accurately forecasted. My model
improved the existing model by more than 25%, and had a variance of less than 5%.
0
10
20
30
40
50
Sales
01jul2014 01sep2014 01nov2014 01jan2015
Date
Individual Individual Forecast
Forecasted Sales vs Actual Sales
R-squared .68
Individual