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Financial Forecasting For WordPress Businesses

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Christie Chirinos

calderaforms.com/wcus2017

Published in: Internet
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Financial Forecasting For WordPress Businesses

  1. 1. Financial Forecasting For WordPress Businesses
  2. 2. I am Christie Chirinos Co-owner, Business Development Lead: Caldera Labs (makers of Caldera Forms ). You can find me at @cicichirinos Hello!
  3. 3. I am Christie Chirinos Slides, links, resources, etc.: calderaforms.com/WCUS2017 Hello!
  4. 4. 1. Start With What Theoretical understanding leads to more informed thought processes.
  5. 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. 6. How much money do we think we’re going to bring in?
  7. 7. Why should I care?
  8. 8. Why should I care? “Those who fail to plan, plan to fail.”
  9. 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. 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. 11. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Hire
  12. 12. Why should I care? “Those who fail to plan, plan to fail.” Financial forecasting helps ◉ Hire & ◉ Fundraise
  13. 13. “ Financial forecasts have diminishing marginal returns.
  14. 14. “ The time value of money (TVM) assumes that a dollar today is worth more than a dollar tomorrow.
  15. 15. 2. Factors What matters when creating a financial forecast?
  16. 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. 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. 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. 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.
  20. 20. 3. Methods How do we create financial forecasts?
  21. 21. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  22. 22. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative
  23. 23. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  24. 24. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  25. 25. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  26. 26. ⅛ xt−2 +¼ xt−1 +¼ xt +¼xt+1 +⅛xt+2 Simple moving average: quarterly data
  27. 27. Moving average: 3 month data August September October November December Revenue 800 1000 1000 ? ? 3 mo SMA - - - ? ? 800 + 1000 + 1000 3 = 933.33
  28. 28. Moving average: 3 month data August September October November December Revenue 800 1000 1000 ? ? 3 mo SMA - - - 933.33 ?
  29. 29. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 ?
  30. 30. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 ?
  31. 31. Moving average: 3 month data August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 1016.66
  32. 32. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  33. 33. x̂ t+1 =αxt +(1−α)x̂ t (1) Exponential moving average
  34. 34. Exponential smoothing August September October November December Revenue 800 1000 1000 1050 ? 3 mo SMA - - - 933.33 1016.66
  35. 35. 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
  36. 36. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  37. 37. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  38. 38. Trend Cyclicality Seasonality
  39. 39. Trend Growing or not growing Cyclicality WordPress updates Seasonality Black Friday
  40. 40. Qualitative ◉ Customer research ◉ Market research ◉ Delphi method Methods Quantitative ◉ Time series ○ Rule of thumb ○ Smoothing ○ Decomposition ◉ Causal ○ Regression analysis
  41. 41. Y = a + bX + u Linear regression Dependent variable Intercept Coefficient Independent variable Residual
  42. 42. 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
  43. 43. Y = a + bX + u Linear regression Return on WordCamp attendance?
  44. 44. Y = a + bX + u Linear regression Return on WordCamp attendance? Based on # of people you networked with?
  45. 45. 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
  46. 46. 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
  47. 47. Y = 50 + 25(2) + 25 Linear regression
  48. 48. Y = 50 + 25(2) + 25 Y = 50 + 50 + 25 Y = 100 + 25 Y = 125 Linear regression
  49. 49. Y = a + b1 X1 + b2 X2 + b3 X3 + ... + bt Xt + u Multiple regression
  50. 50. 4. Process How do we answer a question?
  51. 51. 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
  52. 52. 5. Examples Some WordPress-specific applications
  53. 53. How much more support for an increase in sales?
  54. 54. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales
  55. 55. How much more support for an increase in sales? 1. Identify the problem 2. Identify relevant variables: # of sales, # of tickets
  56. 56. 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
  57. 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 3. Decide how to collect data: HelpScout
  58. 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 4. Make assumptions
  59. 59. 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.
  60. 60. 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.
  61. 61. 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
  62. 62. 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 ?
  63. 63. 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
  64. 64. 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
  65. 65. 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
  66. 66. 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
  67. 67. Help me build a 3 year forecast for my themes
  68. 68. 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
  69. 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. 70. 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
  71. 71. 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
  72. 72. 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
  73. 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 20,400
  74. 74. 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
  75. 75. Any questions ? You can find me at ◉ twitter.com/cicichirinos ◉ calderaforms.com ◉ christiechirinos.com Thanks!

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