Unit 5. Excel Forecasting

Transcription

1 Introduction to Data Science and Analytics Stephan Sorger Unit 5. Excel Forecasting Disclaimer: All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only Some material adapted from: Sorger, Marketing Analytics: Strategic Models and Metrics Stephan Sorger 2016; Data Science: Excel Forecasting; 1

2 Outline/ Learning Objectives Stephan Sorger 2016; Data Science: Excel Regression; 2 Topic Description Applications Uses and stakeholders for forecasts Time Series Extrapolating existing data collected over time Causal Incorporating multiple variables for greater accuracy Smoothing Identifying trends in data

3 Forecasting: Applications Stephan Sorger 2016; Data Science: Excel Forecasting; 3 Product Promotion Quantity of product to manufacture Price Forecasting Selection of promotion vehicles Sales Calculate price for break-even point Place (Distribution) Estimate type and quantity of channels Track expected vs. actual sales Support Staff support centers

4 Forecasting: Methods Stephan Sorger 2016; Data Science: Excel Forecasting; 4 Method Time Series Description and Usage Leverage known sales history to extrapolate future sales Best for rapid predictions of short-term future sales Resources required: Low Accuracy: Low - Medium Causal Analysis Examines underlying causes to predict future conditions Best for in-depth analyses of sales Resources required: High Accuracy: Medium - High

5 Forecasting: Method Selection Stephan Sorger 2016; Data Science: Excel Forecasting; 5 Accuracy Life Cycle Stage Degree of accuracy required Time Series: Lower accuracy Causal: Higher accuracy Forecasting Method Selection Stage in product life cycle Time Series: Maturity stage Causal: Other stages OK Data Availability Availability of data Time Horizon Time Series: Less data required Causal: significant data required Span of time considered Time series: One quarter or so Causal: Potentially longer Resources Availability of time and money Time Series: Fast and cheap Causal: Slow and expensive

6 Forecasting: Time Series Stephan Sorger 2016; Data Science: Excel Forecasting; 6 Stock Price Technical stock analysts study stock trends over time to predict future direction Time

7 Forecasting: Time Series Stephan Sorger 2016; Data Science: Excel Forecasting; 7 Raw data Period Sales Period Period Period Period Period Period Period Period 8??? Sales $150 $140 $130 $120 $110 $ Time

8 Forecasting: Time Series: Regression Stephan Sorger 2016; Data Science: Excel Forecasting; 8 Output Description Value in Our Example R-Square Goodness of fit of line with data 0.75 Intercept Point where line crosses Y-axis Slope Coefficient for time variable 4.85 Sales = (Intercept) + (Slope) * (Time, in Periods) Sales = (103.1) + (4.85) * (8) = 142.0

9 Forecasting: Time Series Stephan Sorger 2016; Data Science: Excel Forecasting; 9 Sales = (Intercept) + (Slope) * (Time, in Periods) Sales = (103.1) + (4.85) * (8) = Trend Line + 8 Sales $150 $140 $130 $120 $110 $ Time

10 Forecasting: Causal Analysis; aka Multivariate Analysis Stephan Sorger 2016; Data Science: Excel Forecasting; 10 Value Investors: Seeks to find intrinsic characteristics of companies which can cause significant stock growth Causal Analysis examines root causes of marketing phenomena $400 Apple Stock Price $300 $200 $100 $0 iphone 1 iphone 3G iphone 3GS ipad 1 iphone

11 Forecasting: Causal Analysis; aka Multivariate Analysis Stephan Sorger 2016; Data Science: Excel Forecasting; 11 Market Conditions Sales decline in recessions Example: Consumer goods Distribution Competitive Environment Airline fare wars Example: United Airlines Product/ Service New products can drive sales Example: Apple Brand Strong brands can drive sales Example: Audi Factors Driving Sales Pricing Promotion Sales Experience Support

12 Forecasting: Causal Analysis; aka Multivariate Analysis Market Conditions Distribution New outlet store can drive sales Example: H&R Block expansion Competitive Environment Product/ Service Brand Factors Driving Sales Promotion Social media can drive sales Example: GEICO Sales Experience Skilled salespeople drive sales Example: Nordstrom Support Price drops can drive sales Example: Walmart Pricing Disgruntled customers hurt sales Example: Dell Computers Stephan Sorger 2016; Data Science: Excel Forecasting; 12

13 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 13 If you were the head of analytics at Acme Realty, how would you predict home prices?

14 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 14 Time Series Forecasting Home values in town of Hillsborough, CA Source: Zillow.com Raw Data

15 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 15 Time Series Forecasting Home values in town of Hillsborough, CA Home values over time Source: Zillow.com Scatter Plot

16 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 16 Time Series Forecasting Regression Analysis: Y: Dependent variable X: Independent variable Watch for: -Labels -Units

17 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 17 Time Series Forecasting R Square: OK Significance F: Good Coefficient: Intercept: Coefficient: Date: P Value: Good

18 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 18 Output Description Values in Our Sales Example R-Square Goodness of fit of model to data Intercept Point where line crosses Y axis Coefficient 1 Coefficient for Time Home Value = (Intercept) + (Coefficient 1) * (Time) = (216.07) + ( ) * (Time) Example: Estimate home value in 2011: Home Value (2011) = ( ) * (2011) = $2.31 M Watch for: -Units -Precision

19 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 19 Causal Forecasting Home values in town of Hillsborough, CA House size: K square ft -Lot size: K square ft -Bedroom quantity -Bathroom quantity Source: Zillow.com Raw Data

20 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 20 Causal Forecasting Regression Analysis: Y: Dependent variable X: Independent variables Multiple Independent Var.: -House size -Lot size Watch for: -Labels -Units

21 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 21 Causal Forecasting R Square: Good Significance F: Good Coefficient: Intercept: Coefficient: House size: Coefficient: Lot size: P Value, House: Good P Value, Lot: Poor

22 Forecasting Example: Acme Real Estate Stephan Sorger 2016; Data Science: Excel Regression; 22 Output Description Values in Our Sales Example R-Square Goodness of fit of model to data Intercept Point where line crosses Y axis Coefficient 1 Coefficient for House Size Coefficient 2 Coefficient for Lot Size Home Value = (Intercept) + (Coefficient 1) * (House Size) + (Coefficient 2) * (Lot Size) = ( ) + (0.6468) * (House Size) + ( ) * (Lot Size) Example: Estimate home value for house size = 4,000 square feet and lot size = 22,000 sq. ft Home Value = ( ) + (0.6468) * (4) + ( ) * (22) = $2.64 M Watch for: -Units -Precision

23 Smoothing: Original Data Set Stephan Sorger 2016; Data Science: Excel Forecasting; 23 Raw data Period Sales Period Period Period Period Period Period Period Period 8??? Sales $150 $140 $130 $120 $110 $ Time

24 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 24 Calculations Period Sales 3PMA* ** *3 Period Moving Ave **( ) / 3 = 105 Sales $150 $140 $130 $120 $110 $100 Chart after 3PMA Smoothing Smoothed; 3PMA Time Exponential Smoothing: Similar to 3PMA, but emphasizes recent values

25 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 25 Trendline: Linear (right-click on data series to pull up dialog box)

26 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 26 Trendline: 3PMA

27 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 27 Data Analysis Moving Average

28 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 28 Enter: Input Range Labels in First Row Interval: 3 Output Range

29 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 29 Results: Same as manual calculations Can offset inherent lag by shifting entire output up one cell

30 Smoothing: 3 Period Moving Average Stephan Sorger 2016; Data Science: Excel Forecasting; 30 Alternative: If you do not have access to Analysis ToolPak Enter formula =AVERAGE(B6:B8) Copy formula down column

31 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 31 Alternative: Exponential Smoothing Similar to Moving Averages but gives higher weight to recent data

32 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 32 Alternative: Exponential Smoothing Similar to Moving Averages but gives higher weight to recent data Analysis ToolPak Exponential Smoothing

33 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 33 Alternative: Exponential Smoothing Enter values: Input Range (as usual) Output Range (as usual) Damping Factor (DF) Damping Factor = (1 a) a = smoothing constant a = 0.1 DF = 0.9 high damping, peaks smoothed a = 0.9 DF = 0.1 low damping, little smoothing a = 0.5 DF = 0.5 compromise

34 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 34 Alternative: Exponential Smoothing Enter values: Input Range (as usual) Output Range (as usual) Damping Factor (DF) Damping Factor = (1 a) a = smoothing constant a = 0.1 DF = 0.9 high damping, peaks smoothed a = 0.9 DF = 0.1 low damping, little smoothing a = 0.5 DF = 0.5 compromise DF = 0.5

35 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 35 Alternative: Exponential Smoothing Enter values: Input Range (as usual) Output Range (as usual) Damping Factor (DF) Damping Factor = (1 a) a = smoothing constant a = 0.1 DF = 0.9 high damping, peaks smoothed a = 0.9 DF = 0.1 low damping, little smoothing a = 0.5 DF = 0.5 compromise DF = 0.1

36 Smoothing: Exponential Smoothing Stephan Sorger 2016; Data Science: Excel Forecasting; 36 Alternative: Exponential Smoothing Enter values: Input Range (as usual) Output Range (as usual) Damping Factor (DF) Damping Factor = (1 a) a = smoothing constant a = 0.1 DF = 0.9 high damping, peaks smoothed a = 0.9 DF = 0.1 low damping, little smoothing a = 0.5 DF = 0.5 compromise DF = 0.9

37 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 37 Removing seasonal elements: -Toys -Snowblowers -Bikinis Example: San Francisco Airport (SFO) Average Temperatures

38 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 38 Removing seasonal elements: -Toys -Snowblowers -Bikinis Example: San Francisco Airport (SFO) Average Temperatures: Line chart shows distinct seasonality

39 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 39 3 Step Process: 1. Compute Annual Average (average of all monthly temperatures) 2. Compute adjustment value Temperature Index 3. Compute deseasonalized value: (Temperature) / (Index)

40 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 40 3 Step Process: 1. Compute Annual Average 2. Compute adjustment value Temperature Index 2A. temperature divided by annual average) 3. Compute deseasonalized value: (Temperature) / (Index)

41 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 41 3 Step Process: 1. Compute Annual Average 2. Compute adjustment value Temperature Index 2B. Find average for all months All January, All February, All March, 3. Compute deseasonalized value: (Temperature) / (Index)

42 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 42 3 Step Process: 1. Compute Annual Average 2. Compute adjustment value Temperature Index 3. Compute deseasonalized value: (Temperature) / (Index) (divide Temp, orig. by Temp. Index )

43 Smoothing: Deseasonalization Stephan Sorger 2016; Data Science: Excel Forecasting; 43 Deseasonalized Temperature -Smaller range: (50 to 70) (54 to 65) -Less periodic tendency (annual cycles) -Upward trend more noticeable

44 Outline/ Learning Objectives Stephan Sorger 2016; Data Science: Excel Regression; 44 Topic Description Applications Uses and stakeholders for forecasts Time Series Extrapolating existing data collected over time Causal Incorporating multiple variables for greater accuracy Smoothing Identifying trends in data