Competitive businesses operate with an eye on what’s next, tapping into rising trends to better understand the future. But trend forecasting is downright impossible without first knowing that a trend is there. Enter trend detection, every successful organization’s strategic secret weapon.
New to the particulars of trend detection? Let this be your guide. In this blog, we’ll take a look at what trend detection is, how it’s done, and how algorithms make it more sophisticated than ever before.
What is a trend?
What kinds of trends are there?
Trend detection definition
Why is trend detection important?
What are trend detection methods?
What is a trend detection algorithm?
Using trend detection algorithms to solve business problems
What is a trend?
Trends are shifts in patterns that develop in a particular direction. They can increase, decrease, or move horizontally.
You’ve most likely heard the word trend used to talk about temporary shifts in cultural preferences or behaviors, like fashion trends, marketing trends, social media trends, PR trends, Instagram trends, etc. Think bell bottoms in the 1960s or acid-wash jeans in the 1980s. However, trends can more broadly refer to patterns or shifts in sets of data, such as those gained via social listening, and they aren’t necessarily temporary.
In business, organizations analyze data trends to extract information about what may happen in the future. Detection is the first step in understanding and analyzing trends.
What kinds of trends are there?
There are many kinds of trends in data, but some of the common trend lines are:
- Linear, which increase or decrease steadily
- Exponential, which increase or decrease dramatically
- Seasonal or cyclical, which develop in a pattern or on a particular, repeating timeline
Additionally, trends come in different magnitudes, from microtrends (like the short-lived cottagecore aesthetic) to megatrends (like the post-World War II baby boom or the rise of AI technology). They can also be at different stages of development, such as emerging or declining trends. The way trends are categorized, in business and beyond, depends on context and objective.
Trend detection definition
What is trend detection? Before you can analyze a trend you have to detect it to know that it is there.
Trend detection refers to the process of identifying those patterns, shifts, and developments.
In some cases, trends are so dramatic that detecting them is simple. Often, however, trend detection takes much closer examination and analysis.
Why is trend detection important?
In business, trend detection is the first step in trend analysis, in which organizations work to understand shifts. They may want to detect trends, like a particular product becoming more popular or growing customer service complaints on social media, so they can make better business decisions moving forward.
Organizations that don’t actively detect trends will, sooner than later, be negatively caught by surprise. They’re unable to keep up with evolving business demands and economic forces as a result.
What are trend detection methods?
In general, there are two main trend detection methods:
- Visual analysis
- Statistical analysis
Trend detection can take many forms, but in general, they look at deviations from regular data patterns. These deviations can include:
- Ramp-ups or -downs, in which data trends up or down over a sustained period (like the initial rise or drop of a roller coaster)
- Abrupt shifts, in which data goes from one steady trend line to another, maintaining that new value over a sustained period of time (like the abrupt rise or drop of a plateau)
- Pulses or dips, in which the data values increase or decrease significantly before going back to the original value (like the heartbeat lines on an EKG machine screen)
These and other types of deviations and anomalies can point to emerging, or past, trends.
Methods of detecting trends range from manual to automated, but all rely on careful, complete, and accurate data collection and reporting.
Visual trend analysis
The most basic way to detect trends is with a visual analysis.
For example, someone looking at an energy usage bar graph on their electric bills over three years may notice seasonal trends. The bar graph of their monthly usage may go up in summer when they use their air conditioner more, and down in winter when they use it less. This basic method may work well for situations like analyzing personal finances, but it becomes much more prone to error for larger, more complex data sets used in business, such as examining sales, media exposure, or social engagement over longer and more segmented periods of time.
Statistical trend analysis
One more advanced method of trend detection involves statistical analyses, which use complex mathematical equations and models to detect anomalies in data patterns. They find these anomalies in numerical values, such as percentiles, averages, and means. Those statistical analyses can then be used to create projections of what future data values might look like. Scientists have long used statistical analyses to predict things like extreme weather events and global warming.
And while statistical analyses, when done right, can produce reliable insights, they are still manual processes that aren’t always feasible in business operations. Instead, organizations can use algorithms to detect data anomalies and trends for them.
What is a trend detection algorithm?
Trend detection algorithms are tools for automating and refining the process of identifying data trends.
Commonly used via other complex systems or technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Learning (NLL), trend detection algorithms often operate within platforms and software that present the results in easy-to-understand formats.
For example, our social listening and analytics suite leverages AI-powered trend detection algorithms to detect spikes in online conversations and send out real-time alerts. Algorithms within our consumer intelligence suite also use AI to extract consumer insights that uncover the context around trends so you can forecast where they are headed next. Both translate that data into visualizations with shareable dashboards.
In business, using trend detection algorithms saves time, turning complicated analytical processes into something any organization can achieve.
Using trend detection algorithms to solve business problems
Businesses use trend detection algorithms across industries in many different use cases. Let’s look at a few examples.
- Supply and demand: Trend detection algorithms help retailers forecast demand for particular products so they can adequately prepare their inventories. Fashion brands use them to spot and track the rise of emerging style trends (trendspotting).
- Labor: Trend detection algorithms can help anticipate staffing needs. Healthcare clinics can use them to track seasonal illnesses and plan their hiring cycles around them.
- Customer service: Trend detection algorithms within social intelligence solutions can track spikes in customer service complaints. Airlines can use them to anticipate service delays and proactively accommodate travelers, while tech companies can use them to identify bugs and needed software updates.
- Reputation management: Organizations across industries use trend detection algorithms for brand tracking to keep track of developments in the reputations of their brands. For example, chain restaurants can use them to evaluate their reputations in different markets over time.
With clear objectives, the applications of trend detection algorithms are infinite. This is especially true when used with other advanced technologies that can break through the data noise.
Leading organizations around the world are leveraging insight-driven intelligence to solve their business problems — yours should be one of them. Bring your organization to the cutting edge of trend detection technology with Meltwater’s data-rich platforms. Learn more about what our consumer intelligence solution can offer by scheduling a demo today.
