Financial Forecasting For WordPress Businesses
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A financial forecast estimates a company's financial performance over a period of time. It is created using historical internal data, external indicators, and qualitative and quantitative methods. Qualitative methods include customer research while quantitative methods include time series analysis, regression analysis, and decomposition. The forecasting process involves identifying the problem, relevant variables, data collection approach, assumptions, model selection, forecasting, and verification. Examples of financial forecasts for WordPress businesses include estimating increased support needs from sales growth and creating a 3-year forecast for theme revenue, costs, and profits.
Financial Forecasting For WordPress Businesses
- 2. I am Christie Chirinos Co-owner, Business Development Lead: Caldera Labs (makers of Caldera Forms ). You can find me at @cicichirinos Hello!
- 3. I am Christie Chirinos Slides, links, resources, etc.: calderaforms.com/WCUS2017 Hello!
- 4. 1. Start With What Theoretical understanding leads to more informed thought processes.
- 5. “ A financial forecast is an economist's best guess of what will happen to a company in financial terms over a given time period.
- 6. How much money do we think we’re going to bring in?
- 7. Why should I care?
- 8. Why should I care? “Those who fail to plan, plan to fail.”
- 9. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Make better decisions ◉ Prioritize tasks ◉ Assess performance ◉ Valuate businesses
- 10. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Make better decisions ◉ Prioritize tasks ◉ Assess performance ◉ Valuate businesses
- 11. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Hire
- 12. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Hire & ◉ Fundraise
- 13. “ Financial forecasts have diminishing marginal returns.
- 14. “ The time value of money (TVM) assumes that a dollar today is worth more than a dollar tomorrow.
- 15. 2. Factors What matters when creating a financial forecast?
- 16. “ A financial forecast is an economist's best guess of what will happen to a company in financial terms over a given time period.
- 17. “ Using historical internal accounting and sales data, in addition to external market and economic indicators, a financial forecast is an economist's best guess of what will happen to a company in financial terms over a given time period.
- 18. “ Using historical internal accounting and sales data, in addition to external market and economic indicators, a financial forecast is an economist's best guess of what will happen to a company in financial terms over a given time period.
- 19. “ Using historical internal accounting and sales data, in addition to external market and economic indicators, a financial forecast is an economist's best guess of what will happen to a company in financial terms over a given time period.
- 21. 3. Methods How do we create financial forecasts?
- 22. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 23. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative
- 24. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 25. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 26. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 27. ⅛ xt−2 +¼ xt−1 +¼ xt +¼xt+1 +⅛xt+2 Simple moving average: quarterly data
- 28. Moving average: 3 month data August September October November December Revenue 800 1000 1000 ? ? 3 mo SMA - - - ? ? 800 + 1000 + 1000 3 = 933.33
- 29. Moving average: 3 month data August September October November December Revenue 800 1000 1000 ? ? 3 mo SMA - - - 933.33 ?
- 30. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 ?
- 31. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 ?
- 32. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 1016.66
- 33. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 34. x̂ t+1 =αxt +(1−α)x̂ t (1) Exponential moving average
- 35. Exponential smoothing August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 1016.66
- 36. Exponential smoothing August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 1016.66 3 mo EMA 800 880 928 976.8 1006.08
- 37. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 38. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 40. Trend Growing or not growing Cyclicality WordPress updates Seasonality Black Friday
- 41. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
- 42. Y = a + bX + u Linear regression Dependent variable Intercept Coefficient Independent variable Residual
- 43. Y = a + bX + u Linear regression Thing you want to know Where we’re starting How much X matters Thing you think will influence Y How good we feel about this relationship
- 44. Y = a + bX + u Linear regression Return on WordCamp attendance?
- 45. Y = a + bX + u Linear regression Return on WordCamp attendance? Based on # of people you networked with?
- 46. Y = 50 + 25X + u Linear regression Return on WordCamp attendance? Based on # of people you networked with? Where we’re starting How much X matters
- 47. Y = 50 + 25X + 25 Linear regression Return on WordCamp attendance? Based on # of people you networked with? Where we’re starting How much X matters How good we feel about this relationship
- 48. Y = 50 + 25(2) + 25 Linear regression
- 49. Y = 50 + 25(2) + 25 Y = 50 + 50 + 25 Y = 100 + 25 Y = 125 Linear regression
- 50. Y = a + b1 X1 + b2 X2 + b3 X3 + ... + bt Xt + u Multiple regression
- 51. 4. Process How do we answer a question?
- 52. Process 1. Identify the problem 2. Identify relevant variables 3. Decide how to collect data 4. Make assumptions 5. Choose a model that fits 6. Forecast 7. Verify
- 53. 5. Examples Some WordPress-specific applications
- 54. How much more support for an increase in sales?
- 55. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales
- 56. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets
- 57. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket
- 58. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket 3. Decide how to collect data: HelpScout
- 59. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket 3. Decide how to collect data: HelpScout 4. Make assumptions
- 60. How much more support for an increase in sales? FACT: Last week, we sold 20 licenses. We also had 10 new support tickets. The week before we sold 20, and had 12 new support tickets. ASSUMPTION #1: We have 11 support tickets per every 20 new sales.
- 61. How much more support for an increase in sales? FACT: Data collection in HelpScout says we spent an average of 15 minutes on each ticket. ASSUMPTION #2: Each ticket is 20 minutes of support tech time.
- 62. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket 3. Decide how to collect data: HelpScout 4. Make assumptions 5. Choose a model that fits
- 63. How much more support for an increase in sales? August September October November # of new sales 60 70 80 ? # of addt’l tickets 33 39 44 ? Addt’l minutes of tech time 495 585 660 ?
- 64. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket 3. Decide how to collect data: HelpScout 4. Make assumptions 5. Choose a model that fits 6. Forecast 7. Verify
- 65. How much more support for an increase in sales? August September October November # of new sales 60 70 80 90 # of addt’l tickets 33 39 44 50 Addt’l minutes of tech time 495 585 660 750
- 66. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets, minutes to resolve each ticket 3. Decide how to collect data: HelpScout 4. Make assumptions 5. Choose a model that fits 6. Forecast 7. Verify
- 67. How much more support for an increase in sales? August September October November # of new sales 60 70 80 100 # of addt’l tickets 33 39 44 46 Addt’l minutes of tech time 495 585 660 690
- 68. Help me build a 3 year forecast for my themes
- 69. Y1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 0 0 0 0 0 0 500 600 1200 1300 1500 1500 6600 Costs 0 0 0 0 0 400 100 100 200 200 200 200 1700 Profit 0 0 0 0 0 (400) 400 500 1000 1100 1300 1300 4900
- 70. Y1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 0 0 0 0 0 0 500 600 1200 1300 1500 1500 6600 Costs 0 0 0 0 0 400 100 100 200 200 200 200 1700 Profit 0 0 0 0 0 (400) 400 500 1000 1100 1300 1300 4900
- 71. Y2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 1600 1700 1800 1900 2000 1500 1500 1400 2400 2500 2600 2700 6600 Costs Profit
- 72. Y1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 0 0 0 0 0 0 500 600 1200 1300 1500 1500 6600 Costs 0 0 0 0 0 400 100 100 200 200 200 200 1700 Profit 0 0 0 0 0 (400) 400 500 1000 1100 1300 1300 4900
- 73. Y2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 1600 1700 1800 1900 2000 1500 1500 1400 2400 2500 2600 2700 23,600 Costs 200 200 200 200 200 200 200 200 400 400 400 400 3,200 Profit
- 74. Y2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL Revenue 1600 1700 1800 1900 2000 1500 1500 1400 2400 2500 2600 2700 23,600 Costs 200 200 200 200 200 200 200 200 400 400 400 400 3,200 Profit 20,400
- 75. Y1 Y2 Y3 Y4 Revenue 6600 23,600 15,100 19,350 Costs 1700 3,200 2,450 2,825 Profit 4900 20,400 12,650 16,525
- 76. Any questions ? You can find me at ◉ twitter.com/cicichirinos ◉ calderaforms.com ◉ christiechirinos.com Thanks!