Hack:
Empower, Relax and Perform by Letting Your Stakeholders Decide

The solution can be found in a broad, ambitious three-phased approach stretching over several years:

1.    Remove mental blocks

2.    Reform survey analytics

3.    Redesign the management processes

These three phases are summarized below.

The mental blocks go back to people’s experiences from school: High scores are good scores.

This is no absolute truth in business life.

Survey scores are often used to measure how well a company is doing in the eyes of its stakeholders. However, if you link survey scores to measures of companies’ actual financial performance, you are likely to be surprised:

• Customer scores are typically unlinked to customer profitability
• Employee scores are typically negatively linked to ambition and willingness to help the company succeed
• Investor ratings are typically unlinked to all survey scores

This list of surprises can be made much longer. Employees tend to appreciate CSR if it means taking good care of the employees, otherwise they would rather see the money stay within the company. Local politicians may like CSR if they themselves get to decide where to use the resources, and appear as the ones who made the efforts possible. (See my MIX contribution called 'CSR - Loved by Employees, Ignored by Customers, a Headache for Investors' for further discussions and supporting evidence regarding this topic.) Members of a pro bono organization may not be particularly interested in the results they achieve but enjoy the feeling of doing good together with other people.

To overcome the mental blocks, you need first to manage top management’s expectations by showing them examples from other companies. Then you should re-analyze their own existing surveys, linking the scores to actual performance measures, and stress how poorly they relate.

The second phase is to reform survey analytics, a topic which I describe in further detail on the MIX site under the title “Capture Preferences, Manage Attitudes for Success in an Ever-Changing World”.

Survey providers excel at producing cross-tabulated scores in colorful graphs and complex tables. However, when the mental blocks have been removed, such deliveries add little managerial value. See the attached document 'Observations of Absence of Linkages Between Margins and Scores' for a discouraging series of graphs that show no linkages whatsoever between survey scores and financial value, measured as actual contribution margins, for a consulting firm.

Instead you need to simulate the human decision process and translate it into quantitative terms. You want to know how much value a certain action would add, and this can be estimated by combining the scores from purpose-built surveys with actual performance facts using factor analysis and structural equations.

This approach is generic and valid for all categories of stakeholders, albeit with fundamental differences in content.

For most categories, the main traits of the decision process are obvious. We can all imagine what drives people’s willingness to buy a certain product or service, what makes people accept a certain job, or what makes pressure groups vividly express their opinion about a specific circumstance.

The least understood category is the investors, yet they are the ones who ultimately determine the value of an action.

The investors base their decisions about what to buy and what to sell on likely future profitability balanced against the risk that this profitability doesn’t materialize. The net effect is that two companies with identical profitability prospects may be differently valued, and that actions may create value although profitability prospects deteriorate.

Thus, the expressions “value creation” and “financial performance” are used intermittently in this text and imply that the value of actions is a function not only of revenue and costs but also of risk.

Conventional surveys often miss the most important piece needed to estimate linkages to financial performance, namely questions and facts about the behavior that you would like to influence. When reforming survey analytics, the desired behavior is the starting point for new questionnaires.

Once the desired behavior has been defined, you need to specify an overall evaluation that predicts the desired behavior. Tentatively this may be “Satisfaction” but just as well some other abstract concept that appears to be relevant, such as “Attractiveness” for customers, “Motivation” for employees, “Fair Share Price” for investors, and “Responsibility” for indirect stakeholders.

The human brain hates chaos, so we tend to systemize details into broader driving factors, but we are not very selective when we do this. Typically there are only three to five driving factors behind people’s overall evaluation of a company.

Having defined tentative driving factors, it is time to convert them, the desired behavior and the overall evaluation into specific questions to be used in questionnaires.

You should also determine which demographic and behavioral facts to collect directly from the respondents and which facts you can extract from existing databases.

When data have been collected from both respondents and databases, factor analysis is used to confirm that the driving factors are meaningful not only businesswise but also statistically. If need be, the questions may be moved from one factor to another, or create a new factor, or be excluded from the cause-and-effect analysis.

The factor analysis is important as most survey responses in most surveys tend to correlate more or less strongly, see the attached documents called 'Observations of Correlations in Consumer Survey' and 'Observations of Correlations in Employee Survey'.

Many times analysts have erroneously claimed causality by finding correlations between two individual variables. This has led to exaggerated efforts to rely on point solutions rather than addressing the full complexity of the challenge – “We just need to answer the phone faster” or “We just need to better inform our colleagues about our decisions” when the real problem may have been the entire customer service function or the way management trusts the employees.

As a consequence the quantitative linkages between financial performance and survey scores must be done relative statistically verified factors, not individual questions.

This is done in structural equations describing the stakeholder category’s decision process, i.e. the expected linkages between its driving factors, overall evaluation and desired behavior.

Here the calculations can be set up in such a way that they show by how much a certain change in a driving factor is likely to impact on the overall evaluation, and by how much a certain change in overall evaluation is expected to impact on the desired behavior.

Quality measures are used to determine how reliable the links are. No analysis based on survey scores is as exact as the figures may appear. There are confidence intervals, explanatory power and several other quality indicators to consider before basing decisions about resource allocation on the conclusions from the analyses.

The concluding estimates of the value of actions are made outside the structural equations using Excel spreadsheets or equivalent tools.

First you estimate the costs associated with changing a specific score by a specific amount. Then you use the impact estimates from the structural equation, adding facts about the size of the stakeholder group affected - and if relevant also associated profit margins – to arrive at a value that this action could generate.

If you expect the risk level to change, this value has to be adjusted accordingly, for example by using present price/earnings ratios as a starting point.

Finally you repeat the value estimates for alternative actions to get several alternatives to discuss and chose from.

Making such analyses with factor analysis and structural equations may seem complicated, but it is like riding a bicycle – hard before you know how to do it, easy once you know it.

In fact, with proper training and tools, the time for executing this kind of survey analytics from start to end, including generating tailored reports and integrated value simulators, is a matter of hours.

And compared with gut feel, the reformed survey analytics are a quantum leap towards better value estimates, thereby enabling more reliable strategies and better resource allocation.

The third phase, redesigned management processes, is characterized by more teambuilding, new possibilities for proactive decisions, and increased empowerment.

There are two main reasons why teambuilding is strengthened:

• Stakeholders perceive companies in terms of driving factors, not details, so the performance of an organizational unit depends on how well it works in total and together with the other parts of the company, not on the achievements of individuals
• Planning and budgeting needs to be done based on how specific actions are perceived by all stakeholders and for all driving factors, particularly if resources would be reallocated from one area to another area where they are likely to create more value

The possibilities for proactive decisions stem from the fact that changes in attitudes precede changes in behavior by several months. Thus, a company that has taken planned actions but without registering changes in attitudes knows that there will be no impact for the time being on the desired behavior.

This gives the company an early warning, saying that they either need to inform better, take additional actions in high-impact areas, cut costs in low-impact areas, or review the financial forecast.

The empowerment possibility is the logical consequence of better survey analytics, increased involvement during the planning and budgeting processes, and better early warning system giving management a good chance to take early corrective actions and remedy weaknesses in the strategy before they reach the end-of-year P&L.