There are several approaches to identifying and analyzing trends. One involves continually scanning for signals of possible changes and using methods like interviews to dig deeper into current developments as pointers toward future transformations. Based on trend research experience, patterns are identified to anticipate relevant developments specific to your context. Each trend has explanations, associated signals, and near-future probabilities to look out for on your radar.

An alternative method employs data, insights, frameworks, and expertise from both within and outside the organization to craft unique and significant trends. This approach also involves scanning the environment for elements that could drive change, identifying emerging future possibilities, and constructing and engaging with potential future scenarios and narratives. This approach helps anticipate and shape positive human-centric futures in an uncertain world.

There are also different methods depending on whether you’re looking for curated descriptions of trends or spotting ingredients for trends. For someone just getting started with foresight, plenty of tools are available for trend identification and analysis, such as AI-based trendspotting services or trend curation platforms. These platforms contain thousands of trends – from mega to micro trends – and their description.

Here are some of the commonly used methodologies in analyzing trends:

Trends research

Trends research involves identifying and analyzing patterns in data to predict future events. It’s a critical tool for understanding consumer behavior, business performance, and the potential trajectory of various industries.

There are two key methodological approaches to trend research:

• Primary research: This involves direct interaction with various groups such as customers, lead users, experts from scientific and industrial fields, regular users, employees, and suppliers. Techniques, such as expert and stakeholder workshops or a Delphi study, can be employed effectively in this approach.

• Secondary research: This approach leverages existing information. Numerous trend reports exist for various industries, which can serve as valuable resources and foundations for trend identification.

Trend analysis

Trend analysis identifies prevalent patterns within a specific user group and understands how these patterns have evolved or may evolve over time. This process is crucial in uncovering new opportunities and generating ideas for potential concepts or products, making it a valuable strategy to employ during the initial stages of the design phase.

As a research methodology in design, trend analysis involves gathering data related to and from users. This data is then scrutinized to identify a trend, which is subsequently analyzed to understand its progression over time.

There are instances, however, when a researcher needs to examine an existing trend within a user group. In such scenarios, specific data is collected from the user groups by meticulously observing the trend to pinpoint its cause. Identifying the cause of a trend is often more challenging than identifying the trend itself. It’s important to note that various factors, such as time of day, season, and geographic location, could influence the trend, and these factors should be documented during the trend monitoring process.

Trend extrapolation

Trend extrapolation involves identifying patterns in data over a certain period and using that information to predict future trends. This technique is often done by extending a linear or exponential curve for short-term forecasts, but other factors, such as limits to growth, will be considered for longer-term predictions. These limits can include how much a technology or cultural practice can spread, and different trend curves may be used to fit the data, such as the logistic curve. However, it is recommended to use extrapolation with other forecasting methods, such as expert judgment and scenario analysis.

Trend extrapolation may be used to predict future trends in various areas, such as sales, finance, marketing, operations, risk management, human resources, and more. By identifying patterns in historical data, organizations can use trend extrapolation to anticipate future events and make informed decisions to optimize their operations and achieve their goals.

Various methods are employed to predict future trends using historical data, which are known to be robust forecasting techniques; however, they may not always consider unexpected events.

Trends can be visualized using line graphs, where the dependent variable is plotted on the y-axis and the period on the x-axis. Trends can be grouped into several categories:

• Constant – there is no overall growth or decline. There could still be regular fluctuations, such as during different seasons.

• Linear – characterized by a steady and consistent increase or decrease in the data. A straight line represents these trends, and the slope can be either steep or gradual, depending on how fast the data changes.

• Exponential – the growth or decline in the data occurs at an accelerating rate. Instead of a steady progression, this type of trend is represented by a curve, where the x-value (plotted horizontally) is used as an exponent in the trendline formula to calculate the y-value.

• Damped – approaches a horizontal asymptote or a horizontal line that the graph of the function gets increasingly close to but never touches.