The document appears to show sales data over time for a marketing class (Mktg 559). It includes the following information: - Sales percentages over time for Mktg 559 that decrease from 45% to 10% - Rotten tomato critic ratings and box office revenues (in millions of dollars) for four movies labeled Lee's - The final box office revenues and production costs (in millions of dollars) for the four movies The data suggests that it is examining the diffusion of new movie releases over time and comparing box office revenues to production costs using percentages, critic ratings, revenues and costs.

2. Mktg 559

3. Mktg 559
45%

4. Mktg 559
45%
25%

5. Mktg 559
45%
25%
20%

6. Mktg 559
45%
25%
20%
10%

7. Mktg 559 Rotten
45% 86%
25% 75%
20% 18%
10% 42%

8. Mktg 559 Rotten Lee’s
45% 86% $25 m
25% 75% $12 m
20% 18% $10 m
10% 42% $17 m

9. Mktg 559 Rotten Lee’s BO
45% 86% $25 m $30 m
25% 75% $12 m $10 m
20% 18% $10 m $8 m
10% 42% $17 m $7 m

10. Mktg 559 Rotten Lee’s BO Cost
45% 86% $25 m $30 m $100 m
25% 75% $12 m $10 m $22 m
20% 18% $10 m $8 m $18 m
10% 42% $17 m $7 m $16 m

12. do you think
the Bass Diffusion Model applies to ....

13. Revolutionary products
or
Evolutionary products?

14. Usability features
or
Desirability features?

15. Forecasting
Time Series and Regression
for New Products

16. 1967 predictions
Artificial human organs
Credit cards would eliminate money
Lasers would be in common use

17. 1967 predictions
No cars in city centers
Hunger reduction
Cars will be driven by robots

18. Box Office Returns
Can one predict the successful diffusion of a
new MOVIE?

19. Box Office Returns
Can one predict the successful diffusion of a
new MOVIE?
Bass Diffusion Model

20. Box Office Returns
Urband Legend:
Total Sales = 2.5 * first week sales

21. Box Office Returns
Urband Legend:
Total Sales = 2.5 * first week sales
Time Series Analysis

22. Box Office Returns
“Seven” took 198 days to get to $100 mil
“Spiderman 3” took 2 days to get there

23. Box Office Return
- Regression models with critic ratings, star cast
etc
- Stock market games

24. Box Office Return
- Regression models with critic ratings, star cast
etc
- Stock market games
Regression Analysis

25. For existing companies the need is to determine how
much of the current product they are likely to sell..
Markets
Existing New
Time Series Analysis
Existing Regression Analysis
Products
New
Forecasting–

26. Time Series
Simplest Method is EXTRAPOLATION
Volume
of Sales
Time
Past Present Future
Forecasting–

27. Typical Time Series Data
Year Sales
1996 37
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

28. Typical Time Series Data
Year Sales
1996 37
Set of evenly spaced numerical data
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

29. Typical Time Series Data
Year Sales
1996 37
Set of evenly spaced numerical data
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

30. Typical Time Series Data
Year Sales
1996 37
Set of evenly spaced numerical data
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

31. Typical Time Series Data
Year Sales
1996 37
Set of evenly spaced numerical data
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

32. Typical Time Series Data
Year Sales
1996 37
Set of evenly spaced numerical data
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43 Forecast based only on past values
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

33. What would a plot of the data tell you?
Year Sales
1996 37
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

34. Plot data and connect the dots
Year Sales
1996 37
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

35. Connect the dots and add a trend line
Year Sales
1996 37
1997 40
1998 41
1999 37
2000 45
2001 50
2002 43
2003 47
2004 56
2005 52
2006 55
2007 54
2008
Forecasting–

36. Lets try moving averages, lag functions
Year Sales 3 year
1996 37
1997 40
1998 41 39.33
1999 37 39.33
2000 45 41.00
2001 50 44.00
2002 43 46.00
2003 47 46.67
2004 56 48.67
2005 52 51.67
2006 55 54.3
2007 54 53.7
2008
Forecasting–

37. Weighted Average
Moving Average weights equally
Weighted
Period Year Sales Avg'
1 1996 37 What would happen if you differentially
weighted the data?
2 1997 40
3 1998 41 39.9
4 1999 37 38.8 t-1 0.5
5 2000 45 41.8
t-2 0.3
6 2001 50 45.9
7 2002 43 45.5 t-3 0.2
8 2003 47 46.4
9 2004 56 50.7
10 2005 52 52.2
11 2006 55 54.3
12 2007 54 53.9
13 2008
Forecasting–

38. Exponential Smoothing
Exp Sophisticated weighted average
Year Sales Smooth
1996 37 37
1997 40 37.0
1998 41 39.7 This Forecast =
1999 37 40.9
2000 45 37.4
2001 50 44.2
last forecast
2002 43 49.4 +
2003 47 43.6 alpha * (last actual - last forecast)
2004 56 46.7
2005 52 55.1
2006 55 52.3
2007 54 54.7
2008 54.1
Forecasting–

39. Exponential Smoothing Tool
Single-parameter exponential smoothing is easy with Excel’s ToolPak. Click
on Tools on the menu bar, select the Data Analysis option, and then in the Data
Analysis dialog box, click on Exponential Smoothing.
22
Forecasting–

40. Single-Parameter
Exponential Smoothing (Figure 7-4 )
1. Enter the
smoothing 2. Enter problem
constant in D2. information in
range. Notice D26
does not have a
value because it
is to be forecast.
3. Click on Tool,
Data Analysis, and
the Exponential
Smoothing to get
the Exponential
Smoothing dialog
box shown next.
23
Forecasting–

41. Exponential Smoothing Dialog Box
4. Click the OK button to get the results
shown previously in Figure 7-4.
1. In the Input
Range line enter the
range of the data.
The result shown is
$D$6:$D$25
2. Enter the
Damping factor. It
is 1 - α.
3. In the Output
Range enter the
location of the
results.
24
Forecasting–

42. Forecast using
Regression Models
Forecasting–

43. Linear Regression
 Identify dependent (y) and
independent (x) variables
 Develop your equation for the
trend line
Y = a + bX
Forecasting–

44. Interpretation of Coefficients
Y = a + bX
27
Forecasting–

45. Interpretation of Coefficients
Y = a + bX
Slope (b)
27
Forecasting–

46. Interpretation of Coefficients
Y = a + bX
Slope (b)
Y changes by b for each 1 unit increase in X
27
Forecasting–

47. Interpretation of Coefficients
Y = a + bX
Slope (b)
Y changes by b for each 1 unit increase in X
27
Forecasting–

48. Interpretation of Coefficients
Y = a + bX
Slope (b)
Y changes by b for each 1 unit increase in X
Y-intercept (a)
27
Forecasting–

49. Interpretation of Coefficients
Y = a + bX
Slope (b)
Y changes by b for each 1 unit increase in X
Y-intercept (a)
Average value of Y when X = 0
27
Forecasting–

50. Regression is to understand
relationships
E(Y) = a + bX i
Y b>0 Y b< 0
X X
Forecasting–

51. A maker of golf shirts has been tracking sales and advertising
dollars.
Predict sales for $53,000 advertising
Sales $ (Y) Adv.$ (X)
1 130 32 Y = 92.9 + 1.15X
2 151 52
3 150 50
Y5 = 92.9 + 1.15(53) = 153.85
4 158 55
€
5 ? 53
€
Forecasting–

52. Excel Regression Tool
Tools --> Data Analysis -->Regression
30
Forecasting–

53. 1. In the Input Y Range 3. Click on the OK
line enter the range of button to get the
the Y data. The result Regression
shown here is $C$7:$C
$16
Regression Dialog Box Summary Output
shown next.
2. In the Input X
Range line enter
the range of the X
data. The result
here shown is $B
$7:$B$16
31
Forecasting–

54. Excel’s Regression Tool
The slope and intercept are read from E15:E16 and yield the regression
equation below. The multiple R, R squared, adjusted R, standard error, and F
and t statistics are shown also.
32
Forecasting–

55. What if you had data like this?
Forecasting–

56. Second-Order Model
2
E(Y) = a + bX1i + cX
1i
Linear Curvilinear
effect effect
Forecasting–

57. Second-Order Model Worksheet
Create X12 column.
Run regression with Y, X1, X12.
Forecasting–

58. Non Linear Regression
5
€
36
Forecasting–

59. Eg. Toy Manufacturer
 How is weekly toy sales affected by
 changes in levels of advertising,
 the use of sales reps vs. agents for calling on retailers, and
 local school enrollments?
Toy Sales = Advertising(X1)+ sales rep/agent(X2)+ school enrollment(X3) + e
To do this, we need to dummy code: sales rep = 1 or agent = 0.
Y = 102.18 + 3.87X1 + 115.2X2 + 6.73X3
Forecasting–

60. Multiple Regression
Excel’s regression
tool can be used
to do multiple
regression. Just
list ALL the X
variables when
designating the
Input X Range;
C7:D16 in this
example.
38
Forecasting–

61. Model-based forecasting methods
Regression with other factors
 Sales = a intercept + b (advertising) + c (price)
 Develop model on half of past data
 Test model on other half of data
Forecasting–

62. PROS AND CONS?
Markets
Existing New
New
Products
Time Series Analysis
Existing Regression Analysis
Forecasting–