MKTG353 CSUF EXAM 1

Write the correct sales planning process (hint: five steps).

1. Analyze

2. Set Goals

3. Develop Plan

4. Execute

5. Evaluate

Briefly interpret the simple regression equation. (Y=B0+B1X1)

Y = Dependent Variable (Sales/Revenue/Profit)

X1 = Independent Variable (What you can change; Plan; ex: Advertising Amount $)

ß1 = Period 1 (End product; One Year later)

ß0 = Period 0 (In the beginning)

If ß1 = Positive, then the regression is positive; If B1= Negative, then the regression is negative.

Briefly interpret the regression output tables. (Adjusted R, Observations, Coefficients, & P-Value)

Adjusted R - Better Fit Model (High the value = the Better the Fit is)

Observation - Data Point within a Data Set

Coefficients - Sales Unit (Regression Equation)

P-Value - Test Null Hypothesis (Relationship with Predator & Response; if P = 0 means unlikely; if P < 0.5, Statistically Significant

What is e in a Regression Equation?

e = the residual

Forecasting

Predicting or estimating a future event or trend

Forecast using Regression:

Historical Data

Observable Behavior

Known Data

Consumer's Past Behavior

Forecast to Predict:

Future Data

Unobservable Behavior

Unknown Data

Consumer's Future Behavior

Forecasting Steps:

1. Understand the relationship between X & Y: Run regression to estimate the coefficient and the intercept using data

2. Forecast the future Y: Comput the estimated Y (y^ = ß0 +ß1x1)

Absolute Percentage Error (APE)

Helps us determine how much we can rely on the forecasting method (how accurate is it?)

EX: For every 1 million cases, there is an error of 22.96% (i.e., forecast will be off by 22.96%; either positively or negatively)

APE Equation

(Actual Sales - Predicted Sales) / Actual Sales

Time Series Regression

Many Time Periods and One Y

Cross-Sectional Regression

One Time Period but Many Ys

Buying Power Index (BPI)

Is a multiple-factor index; expressed as a percentage of the total market; Indicates a geographic market's ability to buy

Custom BPI Estimation

When using secondary data, the data available to you may not be in the format (measurement, unit) you need to create new variables by modifying the existing variables.

Custom BPI Estimation Steps (4 Steps).

1. Select Existing Variables

2. Create New Variables Using the Selected Variables

3. Standard Variables

4. Distribute Importance Weight

How does regression determine coefficients for variables?

Beta coefficients indicate direction and magnitude of prediction line; When the beta coefficients of Xs are known, you can estimate y using Xs.

Standardizing Variables

Converting to percentage standardizes variables.

Moving Average (MA)

Conceptually, n period moving average is the average of the sales in the past n periods

Moving Average (MA) Equation

Average Sales / N (Periods)

Mean Absolute Deviation (MAD)

Standard to judge if the sales forecasting methods can predict sales better

Mean Absolute Deviation (MAD) Equation

Predicted Sales - Actual Sales / N (Periods)

Mean Absolute Percentage Error (MAPE)

the average of the absolute values of the percentage forecast errors; a metric used in marketing to measure how accurate predictions are.

How to Find Mean Absolute Percentage Error (MAPE)

1. Calculate the absolute value of all the errors

2. Divide the absolute value of each error by the original data point

3. Multiply by 100 to get the percentage error

MAPE Interpretation

A lower MAPE value indicates a more accurate prediction; A higher MAPE value indicates a less accurate prediction

Understand Exponential Smoothing (ES)

Using Historical Data and a Piece of Your Insight

Is a type of moving average. However, exponential smoothing weights the most recent observations heaviest for good reason

Understand Exponential Smoothing (ES) Equation

= p1Sales(t) + (1-p1)Predicted Sales

EX: If p1 = 0.3

0.3Sales(t) + (1-0.3)Predicted Sales

Two-Sided Market

Buyer benefit from more sellers; Sellers benefit from more buyers.

Two-Sided Market Examples

Groupon, Airbnb, Uber, Amazon, Newspaper, TV, Google, Yelp, Etc.

How to turn Raw Data into Marketing Insights?

1. Cleaning up and making sense of data

2. Finding Patterns in Data

3. Preparing for strategic data analysis