This short document describes how sophisticated and careful our long-term predictive models are and allows readers to glean under the hood. Each step may look easy, but in combination we are unique in the world to make long-term models like these. How we do it in practice is proprietary to us. What you see here are some of the building blocks, at a high level. We do not build models for customers, per se. We use them to support our strategic planning products into which we integrate predictive models. Our focus is on helping improve decision making, not delivering models. Having said this, our predictive models are as good or better than anything else in the world. The latest example is PACE (Pricing Aligned with Consumer Economics), which builds on what we describe here, and more.

1. CREATING ROBUST
LONG-TERM FORECASTS
The Tellusant 7-Step View

2. PREDICTIVE ANCHORS
Independent variables
Economic
• Gross domestic product (growth)
• Disposable income (growth)
• Income distribution (growth)
Demographic
• Population (growth)
• Age bracket (growth)
Antecedent categories
Antecedent countries
1. Long-term predictions (3-10 years out) need to be
anchored in a view of the future. There are only a few
such data series available.
To predict only using historical data is a fool’s errand.
We know more about the future than such an
approach suggests.
Source: Tellusant thought
Usage terms: CC BY-NC-ND 4.0

3. Medium to long term
3-10 years out
Short term
<18 months
Tactical
uses
e.g., promotions
Strategic
uses
e.g., resource
allocation
decisions
Income
elasticity
dominates
Price
elasticity
dominates
ELASTICITY USES
Revenue
management
Strategic
planning
2. The tools for long-term predictions are different than
those for short-term predictions. Income elasticity is
of fundamental importance. Price elasticity less so.
Often, analysts extend existing short-term models
when senior management requests a long-term
perspective. This is usually a bad idea.
Source: Tellusant thought
Usage terms: CC BY-NC-ND 4.0

4. 0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Personal
income
per
capita
Cumulative share of population
INCOME DISTRIBUTION
U.S. Example
Bottom 99 percent
0M
5M
10M
15M
20M
25M
30M
99.0% 99.2% 99.4% 99.6% 99.8% 100.0%
Top 1 percent
3. Income distribution says much more about the future
than using averages. This is called distributional
economics and enhances accuracy significantly.
Too often, analysts use antiquated concepts like
averages in their models, thereby reducing
believability and precision.
Source: IRS; Tellusant thought and analysis
Usage terms: CC BY-NC-ND 4.0

5. Consumption
per
capita
0
10
20
30
40
50
60
70
80
90
100
110
0 50 100 150 200 250 300
Product
demand
per
capita
Work minutes (effort) required to buy product
Country
TELLUSANT’S LAW OF EFFORT
4. The work effort required to buy a product is of
fundamental importance in long-term models. This
effort is usually measured in work minutes.
Most demand models do not take the work effort into
account, statically or dynamically. It helps explain
why some markets suddenly take off.
Source: Tellusant thought and analysis
Usage terms: CC BY-NC-ND 4.0

6. 0
100
200
300
400
500
600
700
800
900
0
0.5
1
1.5
2
2.5
250 2500 25000
Demand
per
capita
Income
elasticity
Income per capita
S-CURVE WITH CORRESPONDING INCOME ELASTICITY
5. Nonlinear models are more powerful than standard
linear regressions. S-curves typically depict demand
well and are mathematically related to elasticities.
Income elasticity usually declines over the predicted
horizon. Using linear models therefore lead to
exaggerated views of future demand.
Source: BEA; Tellusant thought and analysis
Usage terms: CC BY-NC-ND 4.0

7. 6. Purchasing power parity-adjusted prices are a better
predictor than standard exchange rate-based prices.
This is especially true in less affluent countries.
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
100 1000 10000 100000 1000000
Price
level
relative
to
USA
Disposable income / capita (log)
COUNTRY PRICE LEVELS
Source: ICP; Tellusant thought and analysis
Usage terms: CC BY-NC-ND 4.0
Many companies underestimate market opportunities
in emerging countries based on flawed exchange rate
assumptions in demand models.

8. 7. Pooling of data between countries is a powerful
technique. It also applies to logical thinking. Use as
many countries as possible in the analyses.
Source: ICP; Tellusant thought and analysis
Usage terms: CC BY-NC-ND 4.0
ILLUSTRATIVE POOLING OF DATA
Comparison countries chosen to be spread out on map
Often, analysts in ther home country model only their
home country. Bad idea. Much insight applicable to
the home country comes from comparison countries.

9. Tellusant Website
Contact Us
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