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.

1. 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

39. Trend
Cyclicality
Seasonality

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!