Future Savvy: Identifying Trends to Make Better Decisions, Manage Uncertainty, and Profit from Change

Citation

Lillis, M.P. (2010), "Future Savvy: Identifying Trends to Make Better Decisions, Manage Uncertainty, and Profit from Change", Journal of Consumer Marketing, Vol. 27 No. 3, pp. 296-297. https://doi.org/10.1108/07363761011038400

In light of the dynamic nature of today's competitive environment, organizational survival frequently rests on the ability of an organization to adjust, proactively responding to changing market conditions. To be effective, managers must be able to predict how future circumstances are likely to impact business operations. In his book Future Savvy, Adam Gordon focuses on a vitally important mechanism for increasing an organization's capacity to adapt and meet new strategic challenges: business forecasting. In this introspective and highly compelling manuscript, Gordon identifies the essential building blocks of a good forecast and offers useful advice on how organizations can predict trends and capitalize on market opportunities. He underscores the significance of a good forecast:

All enterprises benefit from narrowing down what they must adapt to and plan for – all effort spent preparing for a future that will not emerge is a waste of personal or organizational resources. Good forecasts are a key ingredient in limiting the vagaries of uncertainty, and therein working smarter not harder, avoiding surprises, exploiting new opportunities and plugging weaknesses in fitting in with the future, and where possible influencing the future to suit the organization. This is true not only of business. People and institutions of all types position themselves for success by anticipating and adapting to events, or shaping them… (p. 6).

Organizations are finding it increasingly necessary to change their business model to meet emerging trends in consumer demand. Furthermore, organizations are continually bombarded with a plethora of advice from consultants and industry experts, each with their own vision of emerging markets. Who should they listen to? A central premise of the book is that the success of an organization hinges upon its ability to “anticipate the important trends in the world” (p. 12) by readily evaluating the quality and validity of strategically relevant forecasts. To that end, throughout his 11 chapters, Gordon offers key insights on how to distinguish good predictions from bad, arming readers with vitally important tools that help to improve their ability to effectively evaluate predictions.

The first chapter begins with a discussion of a forecast's time frame. Given that the competitive environment changes more quickly in some industries as opposed to others, the length of a forecast represents an important criterion when making value judgments about a forecast. Next, Gordon suggests that when judging the merit of a forecast, it is important to understand the motives of those who research and produce forecasts. He envisions two types of forecast intentions:

1. 1.

   Future‐aligning. Forecasters who seek to anticipate the future for the purpose of minimizing threats and capitalizing on opportunities.

2. 2.

   Future‐influencing. Iindividuals who try to influence the future course of events by creating expectations that influence the actual future that emerges.

The chapter offers suggestions for recognizing both forecast modes, thereby allowing the reader to better differentiate between what a forecaster “thinks will happen” and “what he or she would like to see happen” (p. 36).

The focus of Chapter 2 is on the quality of forecast data. In this chapter, Gordon identifies eight issues that are believed to undermine the quality and reliability of data:

1. 1.

   Secondary sources. Data are collected and obtained from someone other than the forecaster himself/herself.

2. 2.

   Old data. Inappropriate reliance on outdated information.

3. 3.

   Projected data. Faulty bases for extrapolating past data to future values.

4. 4.

   Sample validity. Questions regarding the representativeness of a sample.

5. 5.

   Definition validity. Lack of standardization of measures.

6. 6.

   Skewed questions. Question selection and design introduces bias into a question set.

7. 7.

   Missing data. Vital data are unknown or not reported.

8. 8.

   Innumeracy. Tendency to misunderstand probability.

Whereas the prior chapter addresses the quality of the forecast data itself, Chapters 3 and 4 explore two issues that impact the quality of the interpretation of that data. First, Gordon identifies a number of forecast contexts that represent typical bias traps ‐ for example, when a forecast is needed to: assert one's success/failure, motivate organizational stakeholders, safeguard the relevance of an organization's work, appease a sponsoring organization, serve a political agenda, obtain publicity, maintain the status quo, or promote the cause of a particular media outlet. Secondly, interpretation quality is also influenced by cognitive biases brought on by our learned cognitive frameworks and judgmental heuristics. To overcome these limitations Gordon suggests:

If we reverse engineer the forecast to identify its assumptions, test their validity, consider alternative assumptions, and see how these lead to alternative inferences about the future, we can escape the forecaster's cognitive and paradigm biases (p. 102).

The next two chapters identify a variety of forces that either promote or resist change, and they address how forecasts need to account for their potential effects. Chapter 5 considers the role of consumer utility in change and the importance of the cost‐benefit equation in forecasting:

There is a ruthless selection process at work with every innovation, as consumers match them up to what utility they already possess in that area, and what else they have and need, and relentlessly weed out options that do no move them to greater utility. (p. 117).

Other drivers/enablers and friction/blockers are considered in Chapter 6. Specific attention is given to the impact of four variables on the forecasting process:

1. 1.

   technology;

2. 2.

   powerful individuals/organizations;

3. 3.

   ideas and ideologies; and

4. 4.

   social/moral values.

Chapters 7, 8, and 9 consider forecasting scenarios that involve the simultaneous interaction of multiple variables. Chapter 7 discusses the value of quantitative modeling, and it identifies conditions that make computer‐driven projections more reliable. Chapter 8 explores systems dynamics, revealing the potential for unexpected or counterintuitive effects in forecasting. Lastly, Chapter 9 describes scenario planning; a qualitative forecasting method that:

seeks to create a set of scenarios that broadly captures the spectrum of plausible outcomes (p. 210).

Taken together, all three chapters provide insight on a variety of analytical tools that are appropriate for the range of uncertainties and complexities that prevail in organizational environments.

The book concludes with two chapters that attempt to integrate material from prior chapters. The intention of Chapter 10 is to provide real‐world projections that are then critiqued in accordance with the author's own forecast assessment methods. These examples provide practical illustrations of the forecast filtering process. Finally, in Chapter 11, the author lays out a forecasting template, intended to guide the reader through key insights previously offered in the book. As such, it represents a useful tool for navigating through the overabundance of forecast‐related data that managers are continuously inundated with on a daily basis.

According to Gordon, in a world of rapid change, “a higher‐quality reading of the future operating environment” (p. 7) is what differentiates the winners from the losers. The business world is replete with examples of how one company grabbed new product and market opportunities while an industry rival stagnated. For example, how could Kmart go bankrupt while at the same time a small retailer in Arkansas (Wal Mart) becomes the world's largest retailer? Similar questions might be raised when making comparisons between other competitors like Samsung and Nokia, Texas Instruments and Motorola, Matsushita and Sony, etc. … Although there are a variety of factors that allow one competitor to prevail over another, Gordon maintains that the ability and inability of firms to anticipate future events and adapt to meet changing market conditions play a key role.