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8.2.1 Statistical and Other Quantitative Methods
8.2.2 Observation
8.2.3 Surveys
8.2.4 Analogy
8.2.5 Judgement
8.2.6. Market Tests


Content


References

  1. ^ two mathematical approaches to determine the ultimate numbers: the chain ratio calculation or the use of indices.
  2. ^ begin with an estimate of market potential
  3. ^ Chain ratio forecast: trial of fresh pasta
  4. ^ The Buying Power Index (BPI) is a weighted sum of a geographical area’s percentage of national buying power for the area, based on census income data (weight .5), plus the percentage of national retail sales for the area (weight .3), plus the percentage of national population located in the area (weight .2).
  5. ^ Category development indices (CDIs) are similar indices that report the ratio of consumption in a certain category (say, restaurant sales) to population in a defined geographical area.




Details


Regardless of the method used, the ultimate purpose of the forecasting exercise is to end up with numbers that reflect what the forecaster believes is the most likely outcome, or sometimes a range of outcomes under different assumptions, in terms of future market potential or for the sales of a product or product line.

The combination of judgement and other methods often leads to the use of either of [1] two mathematical approaches to determine the ultimate numbers: the chain ratio calculation or the use of indices. See Exhibit 8.3 and Exhibit 8.4 for examples applying these mathematical calculations to arrive at sales forecasts. Both mathematical approaches [2] begin with an estimate of market potential (the number of households in the target market in Exhibit 8.3; the national market potential for a product category in Exhibit 8.4). The market potential is then multiplied by various fractional factors that, taken together, predict the portion of the overall market potential that one firm or product can expect to obtain. In Exhibit 8.3, which shows the more detailed of the two approaches, the factors reflect the appeal of the product to consumers, as measured by marketing research data, and the company’s planned marketing programme.
Exhibit 8.3 [3] Chain ratio forecast: trial of fresh pasta
Once Nestlé’s research on fresh pasta had been completed (see Exhibit 8.2), it used the chain ratio method to calculate the total number of households who would try their fresh pasta. The chain ratio calculation went like this:





Research results for:

Data from research
Chain ratio calculation
Result
Number of households in
target market

77.4 million


Concept purchase intent: adjusted figure from Exhibit 8.2

34.5% will try the product
77.4 million × 34.5%
26.7 million households will tryif aware
Awareness adjustment: based on planned advertising level

48% will be aware of the product
26.7 million × 48%
12.8 million households will tryif they find product at their store
Distribution adjustment: based on likely extent of distribution in supermarkets, given the introductory trade promotion plan

The product will obtain distribution reaching 70% of US households
12.8 million × 70%
9.0 million will try the product
Similar chain ratio logic is useful in a variety of forecasting settings.
Source: Marie Bell and V. Kasturi Rangan, Nestlé Refrigerated Foods: Contadina Pasta and Pizza (Boston: Harvard Business School Publishing, 1995).

Exhibit 8.4 Estimating market potential using indices
There are several published indices of buying behaviour, including the ‘Annual Survey of Buying Power’ published by Sales and Marketing Management.[4] The Buying Power Index (BPI) is a weighted sum of a geographical area’s percentage of national buying power for the area, based on census income data (weight .5), plus the percentage of national retail sales for the area (weight .3), plus the percentage of national population located in the area (weight .2). If this calculation comes to 3.50 for a given state or region, one might expect 3.5 per cent of sales in a given category (toys, power tools, or whatever) to come from that geographical area.
[5] Category development indices (CDIs) are similar indices that report the ratio of consumption in a certain category (say, restaurant sales) to population in a defined geographical area. Trade associations or trade magazines relevant to the category typically publish such indices. Ratios greater than 1.0 for a particular geographic area, say metropolitan Chicago, indicate that the area does more business than average (compared to the country as a whole) in that category. Brand development indices (BDIs) compare sales for a given brand (say, Macaroni Grill restaurants) to population. Companies that use BDI indices typically calculate them for their own use. The ratio of the BDI to the CDI for a given area is an indicator of how well a brand is doing, compared to its category overall, in that area. These various indices are useful for estimating market potential in defined geographic areas. They are, however, crude numbers, in that they do not consider differences in consumer behaviour from region to region. The CDI or BDI for snowmobiles in Minnesota is far higher than in Texas, for example. Attempting to rectify this imbalance by increasing the snowmobile advertising budget in Texas would be difficult!








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  1. client contact systems
  2. collector bias
  3. competitive advantage
  4. competitive intelligence
  5. computerised reorder system
  6. consumer behaviour
  7. data sources
  8. evidence based forecast
  9. experienced user
  10. internal records
  11. just in time
  12. logistical alliance
  13. market potential
  14. market segmentation
  15. market segments
  16. marketing program
  17. marketing research
  18. mass market
  19. mass market strategy
  20. michelin; us west;
  21. micro segmentation
  22. middleman
  23. modified rebuy
  24. multi-functional sales teams
  25. multilevel selling
  26. multiple buying
  27. multiple level relationships
  28. mutual trust
  29. narrow market segment
  30. narrow niche
  31. nationalisation of producers
  32. nerve center
  33. new task buy
  34. nine west group
  35. observation;direct observation' tanzania mobile;
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  37. opportunity; research
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  42. organizational customers
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  45. organizational purchasing behaviour
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  47. paperless exchange
  48. parity pricing
  49. personal selling
  50. personal use
  51. political risk
  52. potential market; penetrated market
  53. pre-delivery inspection
  54. pre-sale service
  55. prestige buyer
  56. pretender
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  60. qualitative data
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  64. quantitative data
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  67. retention programme
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  74. social construction
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  92. underlying consumer demand
  93. unethical demands
  94. unstated but implicit assumptions
  95. users
  96. value analysis
  97. value shopper
  98. vertical integration
  99. visceral thing that cannot be trained
  100. wild guess