A trend is a general direction into which something is changing, developing or veering. Novelties evolve into trends as they gain wider acceptance and appeal to consumers. A biofuel might be an example of a novelty that became a trend as engineers developed and manufactured fuels which included plant-based ingredients. However, as the global population continues to grow and food prices rise, the biofuel trend may run out of steam because producing enough plants to meet the demand would require the loss of precious food crop land that could be used for other purposes.
Trends can be identified from a variety of data sources, including market research, competitive intelligence and customer interviews. The most common tools for evaluating larger market trends are business intelligence software and market research websites that provide industry analysis reports, sales data and other benchmarking information.
The most common way to predict a trend is through analyzing market data over time, and identifying patterns that occur. Often, these patterns are consistent, such as a seasonal decrease in sales during the winter that is offset by an increase in the summer. This kind of trend prediction is useful to businesses that rely on a predictable revenue model, such as retail stores and restaurants.
Identifying a trend can also be done through qualitative data sources such as social customs and the target market for a specific product or service. These factors are more difficult to measure and quantify, but are critical to determining a trend’s potential success. These factors are also more important to consider if a new product or service is being launched without historical data to evaluate its performance.
The use of technology to gather and analyze market data is an increasingly popular method of predicting future trends. Data visualizations are a powerful tool that can help identify promising trends by highlighting growth patterns. They can be especially helpful in differentiating between fads and long-term trends. For example, Exploding Topics uses Google Search volume data to predict whether a topic is poised for success or if it is just another short-lived fad.
It is vital to make sure that the data you are examining is accurate, relevant and representative. The quality of the data can affect your ability to detect meaningful trends and to accurately predict their future trajectories. Thoroughly cleaning and preprocessing the data can address problems such as inconsistencies, missing values, and outliers, which can lead to inaccurate or misleading results. Also, ensuring that all the data is formatted consistently and in the same scale helps facilitate accurate comparisons and meaningful analysis. Finally, using data visualizations to present the results of your analysis can make it easier for stakeholders to understand and interpret your findings. These techniques can accelerate your trend discovery process and improve the accuracy of your forecasts. When it comes to trend, the quality of the data you use is just as important as the methodology you employ. Ultimately, the more rigorous your approach, the more reliable your results will be.