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The results of the ranking exercise of previous step help identifying the axes along which the scenarios can be constructed, therefore find out the scenario logics. Determining the axes of the scenarios is the crucial step in the entire scenario-generating process. The main goal is to end-up with just few scenarios that whose difference make a difference for the decision-maker. If the scenarios have to be used as useful learning tools the lessons they teach must be based on issues basic to the sources of the focal decision. Basically, the scenario logics can be seen as organizing dimension around which the scenarios are structured.

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Framing a Futures & Foresight Project: A Detailed Account

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The next step consists in the ranking of the driving forces on the basis of two criteria: the degree of ‘importance’ of the focal issue identified in Step 1, and the degree of ‘uncertainty’ surrounding those factors and trends.
One suggestion is to use an impact/ uncertainty matrix with a simple ‘High-Medium-Low’ scoring system. The aim is to identify the two or three factors or trends that are the most important and the most uncertain.

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Identification and analysis of the drivers

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Identification and analysis of the drivers

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The next step is to identify the key drivers that will influence the listed key forces at macro and micro-level. Micro-environmental key forces are those that have a direct influence on the issue you are dealing with. Macro-environmental key forces are broader and possibly are global. They relate to social, technological, political, economic and environmental forces that might have an impact on the issue considered. Research may cover markets, understanding new technology, political factors, economic forces, and so on. The list of the driving factors should include Social, Technological, Economic , Environmental, Political and Values .

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Driver analysis, which is also known as key driver analysis, importance analysis, and relative importance analysis, quantifies the importance of a series of predictor variables in predicting an outcome variable. Each of the predictors is commonly referred to as a driver. It is used to answer questions such as:

What is the best way to improve the preference for a brand?

Should a firm concentrate on reducing the price or improving quality?

Should a brand focus on being positioned as being cool, or competent?

Outputs from driver analysis

The key output from driver analysis is a measure of the relative importance of each of the predictor variables in predicting the outcome variable. These importance scores are also known as importance weights. Typically, they will either add up to 100% or the R-squared statistic.

Data required for driver analysis

Driver analysis is usually performed using data from surveys, where data has been collected for one or multiple brands. For each of the brands included in the survey, there is typically an overall rating of performance, as well as ratings on performance on various aspects of that overall performance (i.e., the drivers of overall performance).

Such data is typically collected from one or more grid questions, such as the example below ("Hilton"). The last row collects data on the overall level of performance. This is an outcome of interest to the Hilton. The other lines measure the Hilton's performance on various attributes. Each of these attributes is a driver of the outcome of overall service delivery.

How are driver importance scores computed?

There are two technical challenges that need to be resolved when performing driver analysis. One is to ensure that all the predictors are on the same scale and the other is to address correlations between predictors.

If one predictor is on a scale of 0 to 100, and another on a scale of 0 to 1, the first predictor will end up with an importance of 1/100 th of the first, all else being equal. This can be resolved by either rescaling the data to make it comparable (e.g., making all predictors have a range of 1 or a standard deviation of 1), or by using statistics that ignore scale, such as correlations, beta scores, Shapley Regression, and Johnson's Relative Weights.

The more challenging problem with driver analysis is dealing with correlations between predictor variables, which make it hard to obtain reliable conclusions about relative importance. This is addressed by using methods specifically developed for this purpose, such as Shapley Regression and Johnson's Relative Weights.

Analyzing data for multiple brands

Often a survey will collect data on multiple brands, and the goal of driver analysis is to quantify the average importance of the predictors across all the brands. This is performed in the same way as described above, except that the data needs to first be stacked.

Typically, the data will initially be in a wide format, such as shown below.

Stacking when conducting driver analysis involves rearranging the data, so that it instead has a single outcome variable column, and a single column for each predictor, as shown below. Typically a new data file is created that contains the stacked data.

Acknowledgments

The Hilton grid comes from http://blog.clientheartbeat.com/customer-survey-examples/

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Driver analysis, which is also known as key driver analysis, importance analysis, and relative importance analysis, quantifies the importance of a series of predictor variables in predicting an outcome variable. Each of the predictors is commonly referred to as a driver.

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A narrow focus will prevent the scenarios from drifting into broad generalizations about the future of society or the global economy. When determining the focal issue it is important to consider the appropriate time-horizon for the scenarios, because it will affect the range of issues to be considered within the scenario development process. It is important when deciding on the focal issue, to deal explicitly with the range of uncertainties that might characterize the long-term future.

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First we have to do an investigation and analyze the future results

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Scenarios are not predictions about the future but rather similar to simulations of some possible futures. Scenarios were first used by corporations. Scenarios have since been used by the financial services industry, banks and insurance companies, given their value as a tool for analyzing and understanding key competitive decisions. Both public and private sector organizations have implemented scenarios for a wide array of functions.

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There are many different possible ways of developing scenarios. We suggest here one possible approach that we could define as a ‘walk through the process’ divided in six steps with two important elements. One element is the ‘decision-focus’ of the scenarios, which means that the process begins and ends not with vision of the future, but with agreement on the strategic decision which the scenarios should be designed to illuminate. The second key element is the ‘scenario logic’ which constitutes the core of the process.

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From Scenarios to Strategy: Top Three Methods

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Almost all industries use scenarios to develop their business strategies. At national, regional and local level scenarios can be used to improve planning capacity, to enrich strategic public policy decisions and to guide major capital investments. For example, the development of scenarios allows new insights into the opportunities and risks involved in making decisions about public transport that would have major consequences for the development of a region over the next few decades. To be effective, scenarios must be plausible, consistent and offer insights into the future.

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One suggestion is to use an impact/ uncertainty matrix with a simple ‘High-Medium-Low’ scoring system. The aim is to identify the two or three factors or trends that are the most important and the most uncertain. As outcome of this sorting, it is then possible to focus the attention and the selection of the scenario logics in the next step. High importance/ low-uncertainties forces.

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Determining the axes of the scenarios is the crucial step in the entire scenario-generating process. The main goal is to end-up with just few scenarios that whose difference make a difference for the decision-maker. If the scenarios have to be used as useful learning tools the lessons they teach must be based on issues basic to the sources of the focal decision. Basically, the scenario logics can be seen as organising dimension around which the scenarios are structured.

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4 Archetypes, Shell, 2x2: Top Three Scenario Planning Methods Explained and Compared