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Simplified Forecasting masterclass CPA Australia Congress 2016 udpate

Simplified Forecasting masterclass CPA Australia Congress 2015, updated after Newcastle presentation March 2016

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Simplified Forecasting masterclass CPA Australia Congress 2016 udpate

  1. 1. Simplified Forecasting Masterclass 1
  2. 2. Warm up exercise 2
  3. 3. Instructions There are ten questions. You choose a range so that you are 90% confident you have the answer inside your range. Write down the question number, low and high. Some as tables, some as individuals. 3
  4. 4. Instructions (cont) For example, “Q1: How many plays did Shakespeare write?” Your answer could look like this: Q1: 10 to 20 4
  5. 5. 5 QUIZ!!! Hidden in separate slide
  6. 6. Now please hand your page to your neighbour Statistically, there is a 99.9% chance that you got at least 6 correct. 6
  7. 7. The answers 7
  8. 8. Conclusions Most people doing this don’t get as many right as expected. Humans tend to be overconfident. So now you’re all extra analytical … 8
  9. 9. Download Slides (SlideShare) http://goo.gl/tkJWwS PDF: https://goo.gl/IKNJ3n 9
  10. 10. Introduction Tim Richardson, CPA, B.A., B.Sc, M. Acct. 10
  11. 11. My Career Developer at small, innovative IT firm. Large clients. Large European firm acquires small Aus IT firm (to access technology and clients) Now even larger clients, in Asia. → Sydney → Jakarta. Works with Finance Director. Joins Philips, put into Finance talent pool → Singapore → Eindhoven (NL). Controller then Finance Director for retail then manufacturing and sourcing. Back to Australia. Equity raising, CFO of a fairly large international online retailer, then started GrowthPath. 11
  12. 12. Your Career Accounting has twice been found to be the most likely profession to be replaced by expert systems (modern learning AI) In one survey, retail checkout was the only job more likely to go. In another, it was call centre staff pushing accounting to #2. 12
  13. 13. The accountant of the future … 13
  14. 14. Table Introductions Please appoint a row leader Survey your table-mates for their forecast experience and how important it is to their role Time budget: 6 minutes 14
  15. 15. Glossary “linear”: an equation of a straight line. sales = 10000 + 56X is linear Sales = 10000 + 56^1.6 * sqrt(sales_last_year - fc)^2 is not linear. 15
  16. 16. Glossary ‘Regression’ is finding a formula which explains the shape of data. Called a trend line in spreadsheets. More advanced methods are not limited to straight lines; they can find non-linear lines, like exponential lines. “Correlation” scores how close the line is to the data. 16
  17. 17. Glossary ‘sand-bagging’: putting in artificially high costs or low sales, expected to be later ‘thrown overboard’ when needed, as in a hot air balloon. ‘gaming’: manipulating forecasts to reach objectives. ‘sensitivity analysis’: changing an important input (like selling price) to see how it affects the outcome of a forecast. ‘big data’: collections of massive amounts of business data which never makes it to accounting records and used to be invisible. 17
  18. 18. “A good player plays where the puck is. A great player plays where the puck is going to be” Wayne Gretzky, ice hockey great. 18
  19. 19. 19 STRATEGY Points of difference Competitive Advantage Barriers to entry Plan, Objectives Operations Business Control Are we on track? Corrective action. Are there surprises and opportunities? Traditional forecast Influential Forecasting
  20. 20. It’s not just about numbers. There are people too 20
  21. 21. True or False? Politics is a sign of a less effective forecast: True or False? 21
  22. 22. Forecasting and anthropology Forecasting is a numerical process But it also involves opinion, power, politics and the allocation of resources. Processes which do not affect resource allocation are not important. Processes which do affect resource allocation will be challenged. Changing how you do forecasting can therefore meet resistance. 22
  23. 23. Forecasting outcomes How important is this outcome of forecasting: to accurately predict the future. Rate your answer from 0 = Not important to 10 = the most important outcome 26
  24. 24. Forecasting outcomes How important is this outcome of forecasting … To choose the best future from a forecasted selection of possible futures 27
  25. 25. Forecasting outcomes How important is this outcome of forecasting … To understand how to influence the future by understanding the link between decisions made today and future outcomes 28
  26. 26. Now ... Now that you’ve seen all three questions, do you change your first two scores? Are there other questions to add? 29
  27. 27. Our objective today Something simple: why? Something relevant to management Something useful for decisions: why? 30
  28. 28. Why is simplicity so valuable? Easier to understand. When someone else can understand your forecast and own it to convince others of the right course of action, you have communicated effectively. Faster to prepare More time for other things 31
  29. 29. When is a number useful to management? 32
  30. 30. Forecasting through the ages …. We need a definition of forecasting that excludes things like: ● tea leaves ● tarot cards ● chicken entrails ● signs from god Why do we reject these methods today? Why did people do them anyway? 33
  31. 31. The use of prediction Centuries of evidence that humans find obviously inaccurate forecasts useful anyway. It’s very interesting to think about why this is the case. 34
  32. 32. Problem with stone-age forecasts lack cause and effect. They make predictions, but don’t show how we change affect the future. There is no diagnostic feedback. If the forecast was wrong, why? 35
  33. 33. A good forecast method Write three to five characteristics of a good forecast process Try to include organisational aspects about the process (who, how long, how is it discussed) not just technical aspects. What trade-offs must you make when choosing a forecast process? A perfect forecast would take one second and be 100% accurate. But in reality, what compromises do you make? 36
  34. 34. And for bonus points…. Sensitivity analysis / multiple scenarios based on cashflows with a clear separation of relevant and sunk costs 37
  35. 35. DECISION FOCUS: Helps focus on what decisions, impacts and opportunity cost (tradeoffs) CAUSE AND EFFECT: Shows how decisions will affect the future SIMPLE: People get it, own it, use it. FEEDBACK: if the future surprises us, our forecast should be diagnostic: what happened, in a way we can action. CASHFLOW: Very useful to forecast on the cash effects of decisions. Possible answers: A good forecast … 38
  36. 36. Predicting the future Apart from tea leaves, how can we make predictions about the future? What is the traditional method of forecasting? 39
  37. 37. Traditional forecasting Traditional forecasting believes the secret to the future is based in the past. Often there is a lot of data. The temptation is to believe that the answer is there, if only we apply more and more ‘power’. Statistical forecasting (linear regression, non-linear,... neural networks) is based on discovering trends and patterns in history to tell us what will happen. This is a very common myth (e.g. Asimov’s Foundation books). These methods can become so complex and powerful they approach a priesthood. Most managers won’t understand them. 40
  38. 38. Advanced tea leaves Traditional forecasting can be “enhanced” with advanced methods to mine historical data for patterns which will repeat in the future. But this is, in my opinion, largely a waste of time. This is advanced tea leaves. 41
  39. 39. Random quiz If my sales are normally distributed, how much of the time will my sales be below two standard deviations from the mean? Write down the answer. You have 10 seconds. 42
  40. 40. Simplification though focus and aggregation 43
  41. 41. Historical Data Jan 2010 Feb 2010 Mar 2010 Apr 2010 May 2010 Jun 2010 Jul 2010 Aug 2010 Sep 2010 Oct 2010 Nov 2010 Dec 2010 1 2 3 4 5 6 7 8 9 10 11 12 Sales $127,843 $188,151 $244,650 $233,176 $281,558 $321,796 $185,085 $257,522 $317,497 $356,247 $345,265 $213,637 Cont Margin $51,419 $75,317 $97,433 $92,728 $113,265 $130,243 $73,400 $102,840 $127,327 $142,239 $139,073 $85,928 CM% 40.2% 40.0% 39.8% 39.8% 40.2% 40.5% 39.7% 39.9% 40.1% 39.9% 40.3% 40.2% Overheads Wagebill $22,573 $22,711 $23,198 $22,915 $23,692 $23,830 $23,371 $25,012 $22,320 $22,408 $24,792 $22,597 Marketing $10,674 $10,008 $9,905 $10,070 $10,766 $10,493 $10,135 $10,536 $10,450 $10,094 $10,839 $10,226 Occupancy $20,065 $19,280 $19,739 $20,784 $20,670 $20,480 $20,926 $20,790 $20,406 $21,495 $20,785 $20,580 Insurance $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 Interest $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 Depreciation $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 Overheads $70,958 $69,644 $70,486 $71,414 $72,773 $72,448 $72,076 $73,983 $70,820 $71,642 $74,060 $71,048 Profit before Tax -$19,539 $5,672 $26,946 $21,314 $40,492 $57,796 $1,324 $28,857 $56,507 $70,597 $65,014 $14,880 -15.28% 3.01% 11.01% 9.14% 14.38% 17.96% 0.72% 11.21% 17.80% 19.82% 18.83% 6.97% 44
  42. 42. Seasonality. Linear trend shown How good is the correlation? At what time scale does seasonality become more important than trend? The correlation is poor, but it the trend still valuable? 45
  43. 43. Statistics (sales) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Seasonality 2010 4.4% 5.8% 8.4% 8.7% 9.4% 10.2% 5.5% 8.3% 10.5% 11.5% 10.6% 6.9% Seasonality 2011 4.4% 5.9% 8.7% 8.4% 9.2% 9.7% 6.0% 8.7% 10.4% 11.5% 10.1% 7.2% Seasonality 2012 4.4% 6.1% 8.7% 8.6% 9.2% 10.0% 5.8% 8.6% 10.6% 11.0% 10.7% 6.3% Avg 4.4% 5.9% 8.6% 8.5% 9.3% 10.0% 5.8% 8.5% 10.5% 11.3% 10.5% 6.8% Year on Year Sales Growth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL 2011/2010 2.7% 3.4% 5.6% -1.1% 0.4% -2.4% 11.4% 6.8% 0.8% 3.0% -2.7% 6.4% 2.3% 2012/2011 2.6% 8.65% 4.65% 6.79% 4.41% 7.56% 0.32% 2.89% 6.98% -0.76% 11.20% -8.34% 2.2% 46
  44. 44. Statistics: Profit Year on Year Profit Growth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL 2011/2010 4.9% 16.4% 10.6% -10.5% -5.4% -14.0% -138.4% 19.0% 1.5% 2.3% -12.4% 36.8% 0.5% 2012/2011 -2.5% 215.7% 8.3% 18.5% 7.7% 19.3% -130.4% 0.7% 13.2% -4.5% 24.6% -50.8% 8.2% Profit as % sales Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL 2010 -12.2% 0.9% 12.7% 13.1% 14.8% 16.6% -2.7% 10.8% 17.8% 19.6% 17.2% 6.4% 11.9% 2011 -12.48% 0.99% 13.26% 11.82% 13.90% 14.64% 0.92% 12.06% 17.88% 19.50% 15.45% 8.20% 11.7% 2012 -11.86% 2.88% 13.72% 13.11% 14.34% 16.23% -0.28% 11.80% 18.91% 18.77% 17.31% 4.40% 12.1% CM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL 2010 40.0% 39.9% 40.0% 39.8% 39.9% 39.8% 39.8% 39.8% 39.7% 39.9% 39.9% 39.9% 39.9% 2011 39.9% 40.0% 40.1% 39.7% 39.9% 39.5% 39.9% 39.8% 39.8% 39.8% 39.8% 39.9% 39.8% 2012 40.3% 39.6% 40.0% 39.8% 39.8% 39.9% 39.5% 39.4% 39.9% 39.7% 39.7% 39.8% 39.8% 47
  45. 45. Which is easier to forecast accurately Profit in a specific month Profit in a specific quarter 48
  46. 46. What we can we improve so far? Variations month to month are hard to forecast from historical data but the variations smooth out when you aggregate. We call this “swings and roundabouts” Mathematically, it’s related to ‘regression to the mean’ 49
  47. 47. First steps: A simplified forecast We first just take the existing historical P&L data and see where common sense leads us 50
  48. 48. 51
  49. 49. 52
  50. 50. 53
  51. 51. Based on the data we have so far, What drives the result? Use a simple annual growth for sale trend. Use last year averages for overhead costs Use avg for GM 54
  52. 52. Jan Feb Mar Q2 Q3 Q4 TOTAL Actual 2013 trend 2.20% Sales $147,178 $205,889 $293,025 $938,101 $841,460 $960,699 $3,386,352 $3,410,359 avg 39.80% GM% 39.8% 39.8% 39.8% 39.8% 39.8% 39.8% Margin $58,577 $81,944 $116,624 $373,364 $334,901 $382,358 $1,347,768 $1,359,649 Note avg Overheads Fixed $24,771 Wagebill $24,771 $24,771 $24,771 $74,313 $74,313 $74,313 $297,253 $306,171 Fixed $11,312 Marketing $11,312 $11,312 $11,312 $33,937 $33,937 $33,937 $135,747 $139,820 Fixed $22,153 Occupancy $22,153 $22,153 $22,153 $66,458 $66,458 $66,458 $265,831 $273,806 Fixed $2,087 Insurance $2,087 $2,087 $2,087 $6,261 $6,261 $6,261 $25,046 $25,046 Fixed $2,609 Interest $2,609 $2,609 $2,609 $7,827 $7,827 $7,827 $31,307 $31,307 Fixed $13,045 Depreciation $13,045 $13,045 $13,045 $39,134 $39,134 $39,134 $156,536 $156,536 Profit -$17,400 $5,967 $40,647 $145,434 $106,971 $154,428 $436,048 Actual -$22,888 $6,378 $41,309 $144,049 $111,468 $146,648 $426,964 55
  53. 53. Summary Focus on what matters, Take advantage of swings and roundabouts: • Aggregate • Forecast by quarters instead of months (take a page from the Rolling Forecasting book) 56
  54. 54. Was that over-simple? We do some some analysis on what’s going on in sales. We are concerned about the role of price and mix play. Maybe growth based on volume increase is too crude? What are some scenarios where this may be the case? 57
  55. 55. Synthesised KPIs This business sells a range of products in four categories with consistent margins. There is seasonality and some underlying trend growth. 58
  56. 56. Synthesised KPIs A synthesised KPI is an indicator created form data you already have. Accounting ratios like days sales outstanding, or inventory stock cover, are examples. We will explore a measure of sale mix. 59
  57. 57. Sales KPIs: An example of a synthesised KPI Price effect Mix effect Voume effect A small technique that may help give some insight into sales trends of product categories 60
  58. 58. When may it be useful? If innovation or competition is affecting products differently The concept can be extended to anywhere there is ‘something going on’ at a level of detail which is partially or substantially influenced by outside factors 61
  59. 59. 2009 2010 2011 2012 2013 Sales $21,135,799 $19,381,347 $21,006,389 $22,907,390 $19,363,575 CM $7,001,739 $6,268,051 $6,925,696 $7,554,288 $5,551,022 CM% 33% 32% 33% 33% 28% YOY Sales -7% 9% 7% -24% YOY Margin -10% 11% 7% -36% Introduction to Sleepy Pty Ltd 62
  60. 60. Minor spreadsheet point To multiply one block of numbers by another, can use SUMPRODUCT(block1, block2) A block from f1 to f5 is written F1:F5 63
  61. 61. 2011 2012 PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales Category 0 32824 $107 $3,512,230 21474 $109 $2,342,974 Category 1 21474 $161 $3,446,639 27037 $163 $4,417,849 Category 2 32256 $211 $6,814,688 30409 $218 $6,628,260 Category 3 28602 $263 $7,513,273 34483 $270 $9,302,344 TOTAL 115156 $185 $21,286,830 113403 $200 $22,691,427 Total sales value increase is $1,404,597 which is growth of 6.6% we will explore how the change in sales is due to changes in quantity sold, price, and mix 64
  62. 62. C D E F G H SIMPLE PRICE AND MIX EFFECT 2011 2012 PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales 5 Category 0 32824 $107 $3,512,230 21639 $109 $2,360,833 6 Category 1 21474 $161 $3,446,639 27247 $163 $4,452,914 7 Category 2 32256 $211 $6,814,688 30741 $218 $6,699,094 8 Category 3 28602 $263 $7,513,273 34822 $270 $9,394,549 TOTAL 115156 $185 $21,286,830 114449 $200 $22,907,390 Total sales value increase is $1,620,560 which is growth of 7.6% Price effect if only the quantity changed, sales would have been $22,346,677 (new qty * old prices) = SUMPRODUCT(F5:F8,D5:D8 ) which is growth of ... 2.5% (new qty * new prices) / (new qty * old prices) -1 Quantity effect If we sold an average product (no mix)... Due to quantity -0.6% Change in Qty =F10/C10-1 Mix effect The remaining change explained 5.7% 65
  63. 63. 2012 2013 PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales Category 0 21639 $109 $2,360,833 28616 $111 $3,188,955 Category 1 27247 $163 $4,452,914 55583 $168 $9,347,540 Category 2 30741 $218 $6,699,094 23021 $225 $5,175,647 Category 3 34822 $270 $9,394,549 5912 $279 $1,651,433 TOTAL 114449 $200 $22,907,390 113132 $171 $19,363,575 Total sales value increase is which is change of % Price effect if only the quantity changed, sales would have been (new qty * old prices) = SUMPRODUCT(F5:F8,D5:D8 ) ie new qty * old price, per line This means price explains a change of (new qty * new prices) / (new qty * old prices) -1 TIP! New qty * new prices is 2013 Sales Quantity effect If we sold an average product (no mix)... Due to quantity -1.2% Change in Qty =F10/C10-1 Mix effect The remaining change 66
  64. 64. C D E F G H 2012 2013 PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales Category 0 21639 $109 $2,360,833 28616 $111 $3,188,955 Category 1 27247 $163 $4,452,914 55583 $168 $9,347,540 Category 2 30741 $218 $6,699,094 23021 $225 $5,175,647 Category 3 34822 $270 $9,394,549 5912 $279 $1,651,433 TOTAL 114449 $200 $22,907,390 113132 $171 $19,363,575 Total sales value increase is -$3,543,815 which is change of -15.5% Price effect if only the quantity changed, sales would have been $18,793,991 (new qty * old prices) = SUMPRODUCT(F5:F8,D5:D8) This means price explains a change of 3.0% (new qty * new prices) / (new qty * old prices) -1 TIP! New qty * new prices is col H Quantity effect If we sold an average product (no mix)... Due to quantity -1.2% Change in Qty =F10/C10-1 Mix effect The remaining change explained -17.4% 67
  65. 65. 2012 2013 PRODUCT GROUPS Qty Avg Margin CM Qty Avg Margin CM Category 0 21639 $39 $852,302 28616 $40 $1,139,776 Category 1 27247 $38 $1,030,982 55583 $39 $2,177,463 Category 2 30741 $67 $2,059,618 23021 $70 $1,601,269 Category 3 34822 $104 $3,611,386 5912 $107 $632,514 TOTAL 114449 $66 $7,554,288 113132 $49 $5,551,022 Total margin value increase is -$2,003,266 which is change of -26.5% Margin per product effect if only the quantity changed, margin would have been $5,385,795 (new qty * old CM) = SUMPRODUCT(F5:F8,D5:D8 ) This means margin per prod explains a change of 3.1% (new qty * new CM) / (new qty * old CM) -1 Quantity effect If we sold an average product (no mix)... Due to quantity -1.2% Change in Qty =F10/C10-1 Mix effect The remaining change explained -28.4% 68
  66. 66. If price erosion and mix changes are important developments: new sales = (old_sales*price_effect% + old_sales*mix_effect% + old_sales*qty_effect%) Note: this is only an approximation and when the effects start getting > 10% more accurate approaches should be considered. http://www.growthpath.com.au/index.php/Business-Growth/separating-price-and-mix-in-changes-to-sales-and-margin.html Applying these effects 69
  67. 67. How to use Price and mix effect are good KPIs for sales and marketing. Influencing price, mix and units sold take different steps. These measurements may capture performance and trade-offs. Easy to measure. Meaningful. Data is based on invoices so it is already available with little investment in new systems or integration. Can give effective early warning of trends. For forecasting: new sales = old sales + old_sales * price_effect + old_sales*qty_effect + old_sales*mix_effect For business cases: using with margins not price can justify marketing spend based on mix improvements 70
  68. 68. Synthesised KPIs Synthesised KPIs can’t reveal new information, but they can reveal new perspectives in facts you already have. Since we have lots of accounting data points, this is best place to look for them. Traditional accounting ratios are synthesised KPIs. 71
  69. 69. Next topic: Making the forecast influential 73
  70. 70. Making the forecast influential Traditional forecasting aims to predict future accounting reports mostly using old accounting reports, and the outcome of forecasting is a P&L But the decisions and the plans for the business are probably focused on customers and innovations. 74
  71. 71. 75 STRATEGY Points of difference Competitive Advantage Barriers to entry Plan, Objectives Operations Business Control Are we on track? Corrective action. Are there surprises and opportunities? Traditional forecast Influential forecasting
  72. 72. Influence and Diagnose The important decisions for a business are mostly based on Customers, Competitors, Innovation. So the closer finance gets to that, the more useful we become. Too often finance is merely helping to manage the consequences of those decisions. 76
  73. 73. But we also need ... Simplicity Numbers Focus 77
  74. 74. A solution 1. Use models but use them properly 2. Base the forecast on “business drivers” 78
  75. 75. What is the default model of traditional forecasting? 79
  76. 76. Models are great. Are they perfect? Models work because they let us freeze most real world complexity and focus on what matters. If the stuff we freeze is just noise, this is very helpful. 80
  77. 77. When models go bad 1. Black Swans 2. When the “noise” actually becomes important. E.g. you may ignore a stable cost but if it turns out to be currency driven (supplier offshored) and the AUD crashes... 81
  78. 78. When models go bad 3. When business drivers change (competitor, M&A, innovation, market shift, new management) 82
  79. 79. Black Swans So named after the Australian Black Swan. Other examples: GFC Rise of iPhone Sept 11 83
  80. 80. Black Swans • "Black swans" are highly consequential but unlikely events that are easily explainable – but only in retrospect. • Black swans have shaped the history of technology, science, business and culture. • As the world gets more connected, black swans are becoming more consequential. 84
  81. 81. Black Swans • The human mind is subject to numerous blind spots, illusions and biases. • One of the most pernicious biases is misusing standard statistical tools • Expert advice is often useless and overvalued. 85
  82. 82. Black Swans • Most forecasting is pseudoscience. • You can retrain yourself to overcome your cognitive biases and to appreciate randomness. But it's not easy. 86
  83. 83. My Black Swans Indonesia 96/97 Sept 11, 2001 Rise of China 2000s EU Anti-dumping Shift to consumer for CFLi CO2 abatement programs GFC 2009 87
  84. 84. One of the biggest black swans North American shale oil (fracking) Remember peak oil? Now, oil is $50 a barrel and the US no longer needs to import. It has so much gas that cheap energy is now a competitive advantage. 88
  85. 85. How to deal with black swans? Don’t be over-confident in your forecasts. Be robust and highly responsive to opportunities. We should value forecasting approaches which can quickly adapt to new circumstance. An agile, responsive business will use simple forecasting methods, but simple forecasting methods won’t make a business agile. Extremely expensive and sophisticated models are not better. They can be worse because they encourage over-confidence. Google Long-Term Capital Management. 89
  86. 86. When models get over-extended A model which loses its simplicity is a half-way place, the worst of both worlds. 90
  87. 87. One of the worst examples… Until 15th century, Europeans had the earth at the centre of the universe. 91
  88. 88. Epicycles to explain phases of Venus The model kept getting more and more complex. 92
  89. 89. What was the “Black Swan” for models of the solar system? 93
  90. 90. Coping with the weakness of models Be prepared to change models when business focus changes. As a habit, diagnose both favourable and unfavourable deviations from your forecast. 94
  91. 91. What to model? 95
  92. 92. The P&L as a forecast model: Pros and Cons PROS • You are going to do it anyway • Historical data in this format is free CONS • Bad at focus • Says little or nothing about how the business wins customers • Poor for diagnostics • Extremely limited at decision support • Very complex (mix of cash and imaginary numbers, and technical considerations) • Serves too many masters 96
  93. 93. Business Drivers A business driver is ●Influenceable by the business ●Measurable ●Linked to sales (or costs) via a simple formula 97
  94. 94. Model built on business drivers Connect drivers to a cashflow event with linear formulas: cash_event = driver_score * something1 + something2 You can initially guess something1 and something2. Measuring it means you will get more accurate. 98
  95. 95. The Perfect Customer A business should have a “perfect customer” in mind. This customer should recognise herself in the selected handful of business drivers. 99
  96. 96. Which of these could be business drivers for a mid-sized residential construction business operating in Melbourne’s north? • National home lending approvals • Number of visitors to our display homes • Number of VCAT modifications and rejections of our plans • Percentage of modifications per job to our standard home designs • Avg percentage of total home project spend we capture • Percentage of clients who take our landscape gardening bundled service • RBA interest rate settings • Number of days lost to injury on site 100
  97. 97. Strategic plan says “We target customers looking for fast, simple and cheaper builds”. Choose two drivers. 101
  98. 98. Or Strategic plan says “We will capture high value clients by providing a complete, custom approach to the entire family home project including integrating green space with the home design”. Choose best two. 102
  99. 99. Account for most, not all A small range of drivers will not capture every variation in the business, but they should capture most. 103
  100. 100. Recap Business Drivers A P&L is a generic report. But what is the fingerprint, the DNA of a business? 104
  101. 101. Business Drivers and business DNA The DNA of a business is its competitive advantage. How it is different from competitors in a way that adds value to customers. The selection of the top five or ten business drivers reflects how the business plans to build on its competitive advantages. 105
  102. 102. A test Business Drivers should tell an observer what the business is doing, where it wants to go and how it wants to get there. A P&L + balance sheet is much less clear 106
  103. 103. Business drivers vs leading indicators Leading indicators may include GDP growth or birth rates. But we can’t affect them. Your forecast model should lead to choices and decisions. So the role of external leading indicators should be limited. 107
  104. 104. Business drivers and the sales funnel Business drivers often make a sales funnel Sale funnel is often the most important thing to forecast. 108
  105. 105. A partial real world example In this business, overheads are quite stable over a three year period, except for marketing and to some extent wage-bill. Project spending (e.g. expansion) is excluded. Contribution margin and spend per visit are also stable. 109
  106. 106. Note These numbers are disguised and distorted to protect client confidentiality, but not in a way that diminished the conclusions 110
  107. 107. Business drivers This is a high-end consumer business. The management team has a few common KPIs on their bonus, including • New customer acquisition • Repeat rate 111
  108. 108. Growth has rapidly escalated 112
  109. 109. Recent marketing spend 113
  110. 110. # customers is a leading indicator 114 Active Customers vs MAT SalesCust Cust
  111. 111. Building a simple forecast model Use management KPIs Focus on the issue at hand: marketing spend This is a ”disposable model”: how much has sustained marketing spend helped? Should it continue? 115
  112. 112. In this business: A customer repeats X times per year (X =2) The avg spend is $Y per visit (say Y = $500) There is a relationship between marketing and new customers 116
  113. 113. 117
  114. 114. Do a simple time shift (cut and paste in Google Sheets) 118 → 2 month lag
  115. 115. Linear correlation mkt vs customer (after time shift) 119 Active patients 4500 + (spend-150000)/300000*400
  116. 116. Simple revenue model 120 Example, obscured data Business Driver: Marketing spend (monthly avg) [2 month lag] $400,000 Active patients 4833 Business Driver: Annual visits (Repeat rate =2) 9667 Monthly visits 806 Business Driver: Spend per visit $500.00 Monthly revenue $403,000 Done! Tools: Google Sheets Time: 1.5 hours
  117. 117. Technical forecasting 121
  118. 118. Example: Excel 2016 Excel 2016 has a new forecasting model which defaults to “AAA version of the Exponential Smoothing (ETS) algorithm” I googled it and came to a PDF with pages that look like this:… 122
  119. 119. 123 This is 90 slides of explanation for this feature (in R, which I assume Microsoft has ported to Excel 2016 (Windows, not mac))
  120. 120. Technical forecasting Technical forecasting which uses advanced statistics on historical accounting is looking for hard to see connections in historical data you already have. But the most important relationships are easy to see 124
  121. 121. Simple Forecasting and Big Data I will extend meaning of ‘big data’ to include “integrated data”. This could mean: web stats, social media engagement, detailed job progress data and real time (sales, competitor, location…) 125
  122. 122. Predictive analytics means finding hidden connections between data and cash-flow events with neural networks and other advanced methods. This could be very awesome even for SMEs. Unlike traditional technical forecasting, big data introduces new information to the business. . 126 Predictive analytics (Big data)
  123. 123. Definition (Forrester) “Using patterns discovered in heterogeneous [mixed] sources of data, business analysts and data scientists can create predictive models to improve business outcomes.” Obviously, this is very interesting to my vision of business forecasting 127
  124. 124. Keep it Simple • By discovering and ranking business drivers, predictive analytics can help simple models work better. • But the value of simplicity is not washed away by neural nets and super-computers. 128
  125. 125. Examples of simplicity visualised 129 A sales model based on non- accounting leading-indicator drivers can provide continually refreshed forecasts. Simple forecasts and open-source technology make this cheap and easy.
  126. 126. Forward drivers can be shown easily 130
  127. 127. Diagnosis 131
  128. 128. Communication and analysis 132 Measures which everyone understands. They get how to influence them. They are bonused on them. Result: very strong alignment and engagement and everyone is on the same page.
  129. 129. It’s simple It’s customer facing It’s aligned to relevant decisions and focus Is it diagnostic? So far, so good. 133
  130. 130. Growth-driven forecasting Simple forecasts can change fast. We are not committed to long complex processes and heavy IT. So, let’s take advantage ... 134
  131. 131. We sit in a discussion about growth We have done some “predictive analytics”. It shows that customers who buy a more diverse range of products are more likely to be repeat customers. What business drivers does this suggest? 135
  132. 132. Our aim is to reforecast the business in a few minutes Come up with a sketch of a new driver and try to include it in a forecast model. 136
  133. 133. What about cost drivers? Forecasting significant costs is the same. Influenceable, measurable drivers. Is currency influenceable? 137
  134. 134. What makes this more than just a basic spreadsheet? The selection and use of business drivers is the magic sauce. In terms of actually doing it, though, a spreadsheet is completely adequate. <Insert funny SAP R/3 story here> 138
  135. 135. Finding cost and/or sales drivers... Follow the points of difference (the competitive advantage) OR management plans. This will reveal where to focus. A business selling a commodity (iron ore): where are its points of difference? 139
  136. 136. Using simple forecasting for accountability The forecast should reflect the key decisions through the business and cost drivers in use. These should then be measured for diagnosis and accountability. 140
  137. 137. External factors beyond influence? Such as: Currency, economic growth, droughts, Chinese economic collapse … Two options 1: Be nimble, assume competitors are equally affected. React & forecast when it happens and otherwise ignore. 2. We can’t influence them, but we can include them in the simple forecast for sensitivity analysis Discussion … Which approach fits best with the “simple forecast” philosophy? 141
  138. 138. Simple forecast maturity model Level 1: Streamline the traditional forecast method. Use drivers or KPIs synthesised from accounting data such as price & mix effect, ratios like debtors days outstanding. 142
  139. 139. Simple forecast maturity model Level 2 Use non-accounting business drivers for sales funnel and/or key costs, feed this into traditional forecasting. Measure actual drivers and diagnose, several times per year but outside of the normal reporting and analysis 143
  140. 140. Simple forecast maturity model Level 3: Business drivers are aligned to strategic plan and incentive schemes. All business drivers meet the definition. They are the central discussion in forecasting and reviewing results. They are used for sensitivity analysis. Dashboard tools provide daily or real-time insight into business drivers and automatically updated forecasts. 144
  141. 141. Simple forecast maturity model Level 4. Business drivers are partly based on predictive analytics. Forecast models are revised to follow changes in management focus or market developments. 145
  142. 142. Conclusions Simplified forecasting is part of the new finance function. It brings finance closer to influencing profitable decision-making by aligning with the points of difference which lead to growth. 146
  143. 143. Conclusions Simplified forecasting swaps analysis of historical accounting data for a small set outward-looking, predictive measurements It is diagnostic. It uses simple models, and new models when needed 147
  144. 144. Conclusions Skilling up • Understand or suggest a small set of business drivers • The future added value of finance will be around numerical strengths. Consider CPD on statistics, predictive analytics and decision theory. 148
  145. 145. Books to read/sites to visit • Black Swan: Impact of the highly improbable (N. N. Taleb) • Visual Explanations (E. Tufte) • The Emperor’s New Clothes (H. C. Andersen) • Khan Academy: Probability and Statistics (website) • Superforecasting (Tetlock and Gardner) 149

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