Fads vs. Trends, it is a thin line

Differentiating between a fad and a trend could prove to be a major challenge for company executives and other decision-makers in an organization. The crucial determinant of this process is the identification of customer needs. Using analogous historic data as inputs for a 70/30 machine learning model could provide a highly accurate prediction of whether an object is a fad or trend. Traditionally, fads share similar traits which makes the use of a predictive model a feasible approach. Fads are not inherently bad but detecting one could allow for an appropriate short-term plan and investment aimed at generating as much revenue as possible within the short fad cycle lifespan.

Fad vs. Trend Timeline

Much has been written on the topic of fads vs. trends, but the majority of the theories available are chronically subjective. In this article, I will present my personal take on fads vs. trends and how to best differentiate between the two phenomena.

The Oxford dictionary defines fads as: “An intense and widely shared enthusiasm for something, especially one that is short-lived; a craze”. The short-term nature of fads is its main character; a short burst of revenues is generated by feeding off an enthusiasm that is irrelevant to a product’s quality and functionality. Fad products usually have limited utility and low adaptability, but consumers initially tend to be drawn by an irrational perception of value. These consumers are often members of a niche market group who are attracted to the fad by the exclusive nature of the products due to their limited quantities. These adapters usually initiate the hype that is then boosted by media and “in the know” personalities. These factors lead to a bubble that typically lasts from 3 to 4 years before it bursts, leaving investors lamenting their losses.

As for trends, they are defined in the Oxford dictionary as: “A general direction in which something is developing or changing”. Trends are sources of steady revenue through meeting specific customer needs. Trends are often the result of general market direction and growth due to various political, economic, technological, social, environmental and legal factors (PESTEL) in the marketplace.

On paper, knowing the difference between fads and trends might not be a hard task. But in reality, decision makers end up making the wrong choices. Some reasons for this might be:

• Falling in love with an idea,
• Overestimating consumer goodwill,
• Long feedback loops, or
• Simply because of how blurry the lines are between fads and trends.

For decision makers not to fall into a trap of making major long-term investments into potential fads, they should consider the following:

Product: is the product meeting a specific customer need?

• Keep your hand on the pulse. Engage the consumer through surveys, focus groups, and one on one meetings. This exercise is ideally outsourced to specialized consulting companies with a proven track record of accurate market insights.

Google Trends

• Perform continuous market research and analyze the data. You could initiate this through analytical tools such as Google trends where it is possible to view audience interests both in the short and long-term basis. This research could then be furthered through other means of secondary and primary research approaches.
• Clearly articulate the market need and test your findings. Create a prototype, this could be a physical or virtual prototype to be tested through focus representative focus groups or other virtual tools such as social mention, Google Analytics or Facebook Audience Insight.
• Compare the data with analogous historic data through relevant predictive machine learning models. Python is a user-friendly programming language to use and creating a machine learning model could simply be achieved through tailoring a Scikit learning model to the scenario at hand.
• Utilize the predictive model’s results to qualify whether the need you have identified is both real and durable.

Adaptability: how functional is the product?

• Asses the value provided by the product. How are the consumers meeting their need today? Is what you are offering cheaper, more efficient, or provide a dramatically higher utilization rate?
• Allow access to the product through various entry points to your product. This could be done through a platform model that provides functionality and service to different categories of consumers and thus a bigger proportion of the population. Hulu and Netflix offer entertainment programs that are geared towards both adult and young viewers. Meaning that their platform could theoretically serve a bigger consumer pool.
• Enter licensing agreements with current proven value providers to enhance the customer experience. Potential partnerships are determined in the Business Opportunity Map model (BOM) that are discussed in the next section.

Evolution: what is the growth potential of the product?

Business Opportunity Map (BOM)

Fads tend to have small growth horizons. An example of such a fad is the pet rock that started in the year 1975 and lasted a mere 6 months. Finding a future potential of growth for this product a very challenging task indeed. Thus, for any object, the following measures could serve as a guideline to asses future growth potentials of an idea:

• Involve multiple points of views and initiate a future scenario to establish the opportunity insight.
• Map out the scenario on a business opportunity map (BOM). Analyze your product on the market, delivery, offering, competencies, and business model levels through your company, future, competitors, adjacencies and value chain’s scopes.
• Establish links between the different levels and scopes of the BOM through which you could identify growth gaps and how they could be met in both the short-term and long-term future.

The lines between fads and trends are blurry and decision makers find it hard to plan for the long term. The key approach to detect whether a new object is a trend or fad is through customer needs. Applying a 70/30 machine learning model with analogous data inputs could provide a highly accurate yes or no answer to the question: “is this a fad?”. Historically, fads have common characteristics making the use of predictive models a very feasible choice. Fads are not inherently bad but detecting one could allow for an appropriate short-term investment to generate as much revenue as possible within the short fad cycle lifespan. At the end of the day, decision makers are mainly after the wellbeing of their stakeholders’ and being able to draw up accurate short-term & long-term plans go a long way in facilitating this goal.