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How to perform a trend analysis (+ trends to watch in 2025)

Kirsten Lamb

What will the economy look like next year? Which social media platforms will be your customers’ favorite next? Which marketing channels will be the best for your brand next quarter? 

Trend analysis can help you answer all of these questions. 

In this article, I’ll give you a high-level definition of trend analysis, show you how to do a trend analysis, cover market trend analysis examples, and show you the best tools to use. 

Let’s take a look.

What is trend analysis?

Trend analysis is the process of using historical data to identify trends and patterns and make predictions about future developments. Trend analysis is used across several industries to predict future developments and help businesses make informed, data-driven decisions — including science, finance, healthcare, and marketing.

Many businesses use consumer trend analysis research to make predictions about consumers future behavior, attitudes, and preferences. The process can highlight things like consumers’ media consumption habits, purchasing behaviors, or views on social issues — helping companies gain deeper insights into what consumers may do, think, or feel next and what they want from them as a result. 

While cCompanies can use trend analysis in market research to spot patterns in market trends and identify potential areas of opportunity, — such as a gap for a particular product that may serve an unmet consumer need. 

The benefits of a trend analysis

Trend analysis has several benefits. Primarily, a trend analysis can help you make smarter business decisions. With the right data behind them, companies can more accurately predict future trends and align their strategies accordingly, more confidently identify and act on white-space opportunities and reduce the inherent risk involved in their business choices. 

Take consumer behavior. The quote, “The best prediction of future behavior is past behavior,” has been attributed to many people — from Mark Twain to B.F. Skinner. And psychological research backs up this idea. According to research, how frequently people perform a past behavior is an indication of habit strength — this directly impacts the likelihood of engaging in the same action in the future. 

So, if past trends show that Gen Z consumers more frequently use TikTok than Instagram, then it’s likely that they’ll also be more likely to be watching videos on TikTok than scrolling their feeds on Instagram over the next several quarters. Companies who have this data will likely put more of their marketing budget towards TikTok than Instagram — making a data-informed decision that’s likely to reach, engage, and convert the most consumers. 

Analyzing trends in consumers' use of social media platforms can also help businesses identify which platforms are on the outs with consumers. Take Snapchat, which was one of the biggest social media platforms with the Gen Z demographic throughout 2014-2018. As Faizaan Baig and Hirushan Thalayan note, if companies tracked Gen Z’s snapchat usage throughout 2018, this is what they’d see: 

“Despite its popularity among teens and young adults, Snapchat saw a decrease of daily active users (DAU) in the second quarter of 2018, followed by a further decrease of two million DAUs in the subsequent quarter. Snap Inc. CEO, Evan Spiegel, attributed the decline to negative reception of its January 2018 redesign. However, year-over-year DAU growth in 2017 was already 30% lower than in 2016, suggesting a deeper problem.”

Trend analysis is also a great tool for comparing your brand’s performance to that of your competitors’. Thanks to modern trend analysis tools, it’s easy to pool data from multiple competitor sources including their marketing channels, revenue information, industry news, reports, and what consumers are saying about them online. 

Great trend analysis tools can support comparative data analysis through comparative charts and graphs — making it easy to spot gaps in performance and get a deeper understanding of the strengths and weaknesses of your brand as well as those of your competitors. 

How to conduct trend analysis

Let’s explore how you can perform a trend analysis: 

Step 1: Identify historical trends

The first step in undertaking a trend analysis is to identify relevant historical trends. 

By identifying historical trends, you can provide a benchmark from which you can compare and analyze the data you collect going forward. You can choose to gather data on and identify trends across different areas — such as consumer behavior, technology, culture or marketing. 

You may also choose to look at historical trends within your own company data, such as customer retention levels, revenue growth patterns or omnichannel marketing performance. You can also contrast and compare this data with similar historical data from your competitors. 

To identify historical trends, ask yourself these questions: 

  1. What patterns, cycles or key moments of acceleration can I see in the trends in my historical data?

  2. What were the biggest influencing factors in each of these trends? 

  3. What were their main inflection points?

  4. What impact did these trends have on other areas (if any)? 

Step 2: Collect trend data

The second step is to choose your data collection methods. You can choose from primary or secondary data collection methods or enrich and get a deeper perspective on your data by using both methods. Primary data collection methods involve collecting data from research subjects directly. While secondary data collection methods involve collecting data from pre-existing sources. 

Good sources of secondary data include:

  1. Industry or government reports and statistics 

  2. Data analytics 

  3. Academic papers 

  4. Social media listening 

  5. Internal business resources like invoices and receipts 

Primary data collection methods include:

  1. Surveys — send digital or physical surveys out to consumers, prospects, or customers. 

  2. Interviews — one-on-one interviews are great sources of in-depth insights into interviewees’ behaviors, emotions, and perspectives. 

  3. Focus groups — focus groups involve bringing together a small group of participants and supporting conversation that supports the deeper explorations of opinions, feelings, and perspectives. 

Step 3: Analyze trend data 

Once you’ve collected your data, it’s time to analyze it. Use tools and software to centralize your findings and more easily extract patterns and insights with data analytic capabilities (more on the best tools below). 

For your data to be usable, it must be accurate, reliable and complete. To prep your data for analysis, you need to make sure your data meets quality standards by preparing and cleaning your data. Review your data and correct any errors, duplicates or missing values. 

To help make sure your data is well organized and easy to pull key insights from, use AI-based tools to automatically organize it into easy-to-review charts, graphs and spreadsheets. Once you’ve organized your data, review it for any interesting or noteworthy patterns and trends. 

To enrich your analysis, bring up your historic data and analyze the changes you can observe. If you’re using visual tools to help analyze your new data, you can centralize and synthesize the insights from your historic data alongside charts and graphs with the same scales and axes to highlight the correlations and differences between both sets of data.

Tools and software for trend research and analysis

Here are some the best tools to use to help support your trend analysis research:

Social listening tools

Brand24 and Sprout Social are two great social listening tools you can use to track online brand, product or competitor mentions.  

Survey tools 

Use survey tools like Zappi to make user-friendly surveys served to your target consumers to get their take on your brand, or ad or innovation concepts long before too much time and money have been invested in their production. The high response rates help support your data analysis through features like automated analytics and AI Agents for optimization. 

Data analytics and visualization tools 

Make it easy to spot patterns in your data with data visualization tools like Excel, Zappi and Tableau. Excel organizes your data into easy-to-read spreadsheets. While you can use tools like Zappi and Tableau to analyze and synthesize your data and turn these insights into tailored reports, charts and graphs.  

Statistical analysis tools 

Statistical analysis software, such as tools like Stata and IMB’s SPSS, can be an essential tool for helping you to organize and analyze your quantitative data. Use these tools to help you interpret complex data and draw insights from your research. 

Trend analysis statistical techniques

Here are some of the main statistical techniques you can use to analyze trends:

  1. Moving averages: A moving average gives you an overall notion of trends in a set of data, providing you with a series of averages of different selections of your dataset. You can use moving averages to capture the trend of data for a specific period of time — such as seven days or three months. 

  2. Regression analysis: This technique tracks the relationship between a dependent variable and one or more independent variables. 

  3. Comparative analysis: This approach involves comparing two or more sets of data to find differences and similarities. One common form of comparative analysis is the t-test, which you can use to compare the differences between the means of two groups. 

  4. Cluster analysis: This technique groups together similar data points based on their similarities — charting multiple clusters across a graph. 

Trend analysis examples in action

Let’s look at some examples of trend analysis in action.

Oatly

Oatly campaign encourages consumers to, “Call their local oat dealer.”
Source: Campaign Brief

As we note above, trend analysis is a great technique for helping companies identify gaps in the market and move towards potential opportunities. 

The number of plant-based food businesses has tripled since 2010. Alongside the rise in plant-based dietary options, research into plant-based foods has also tripled in just the last three years. 

Many consumers cite environmental concerns as the biggest reason for adopting a more plant-based diet, with scientists backing the idea that eating less meat and dairy is one of the best ways to limit the impact of climate change, reduce water scarcity and cut pollution. 

Swedish company Oatly is one of the biggest plant-based brands on the market — with an estimated market cap of $426.53 million.

But in the words of CNBC, Oatly, “Sat in relative obscurity,” for the first twenty years of its existence. 

That changed in 2017 when Toni Petersson took over as Oatly’s CEO. Petersson set out to revamp Oatly’s brand image and develop new product lines of plant-based creams and specialist milks. 

As part of their new strategy, Oatly created a unique third-blend barista edition of their oat milk to get the attention of high-end coffee shops around New York city. 

Rather than trying to get Oatly placed in grocery chains like their plant-based competitors were doing, they introduced Oatly Barista to cool, independent coffee shops across the state. The idea was to get trendy consumers to try their product in their favorite coffee shop — poured by a talented, knowledgeable barista who could pass on the Oatly recommendation. 

As a result, Oatly was positioned as a premium product — rather than just an everyday dairy alternative.

Let’s look behind the scenes at how Oalty’s successful move into the U.S. market was supported by trend analysis: 

1. Oatly identified a growing adoption of plant-based milks for both sustainability and health reasons by U.S. consumers. New York is one of the most health-conscious states in the U.S. — in 2012, when Oatly entered the U.S. market, New York had the fourth lowest levels of obesity. 

2. Rather than heading to grocery-store chains like their competitors, Oatly found their area of opportunity within the market by observing the coffee consumption habits of consumers and moving in on coffee-shop culture. In 2012, 75% of American adults said they drank coffee. 

In the same year, Americans had some of the highest coffee consumption habits of the past 20 years. By partnering with artisan coffee shops, Oatly could differentiate its plant-based milk and position itself as a high-end, premium product to both coffee shop owners and employees and trend-conscious consumers. 

3. Oatly saw a gap in the market for a premium, oat-based barista blend. By upping the fat content and cultivating a foamy-texture ideal for a latte or frappacino, Oatly helped support the needs of baristas while providing an environmentally friendly and health-conscious alternative for consumers. 

McDonald’s 

Matt Cahill, Senior Director of Consumer Insights Activation at McDonald’s underlines the importance of learning from historical data to McDonald’s innovation strategy — rather than falling into the trap of attempting to come up with the next “great brand new idea”: 

“I think in our innovation system, the first thing we realized was, because we're in a lot of the same places, the easiest way for us to get better is to learn from what we've done historically...learn from where you've been and force that step. If you don't force that step, it won't get done. It's easy to skip because everybody wants to have the greatest new idea ever and you don't want to acknowledge that someone has probably had a similar idea before.”

Matt refers to learning from historical data as “getting a head start,” he says: 

“I always call that getting a head start. Why wouldn't you want a head start with all this knowledge that we had before? It's just going to make your outcome better.”

Matt says that mining what you know is always the first step in McDonald’s innovation process — the team uses data to review and learn from their past experiences — including both the successes and failures. 

By using historical data as their starting point, Matt and his team can move forward to testing new concepts — giving them a benchmark to compare their new data against. Here’s how he approaches this when developing a new shake flavor: 

“For a new shake flavor, I analyze the drivers of interest & purchase in all the shakes we’ve tested before. I can see how consumers play those concepts back, & what they want us to do differently. There’s a lot I can do easily with the data set.”

The team uses Zappi to streamline their research with a custom domain that gives them access to the brand’s typical audiences, markets and recommended solutions. They use the platform to tag and classify concepts and track trends in the metrics against their benchmark norms. As a result, the team finds it easier to undertake a comparative analysis of how each concept performed — seamlessly moving through the innovation process and testing a range of alternatives. 

Read the full case study here. 

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