Trend forecasting is the process of analyzing and predicting future patterns and directions in industries, particularly fashion, based on data and insights. Trend forecasting identifies upcoming shifts in consumer preferences to guide businesses in product development and strategic planning.

In this blog, we explore the power of trend forecasting by using consumer insights to predict demand. Learn how to build a trend-ready organization, performing trend analysis on an ongoing basis to build a competitive edge.

Table of Contents

What is trend forecasting?

Trend forecasting is the key to unlocking consumer insights. Trend forecasting allows us to predict future consumer preferences by analyzing relevant data in the market. Trend forecasting data can include historical sales data, social media data and other indicators that correlate with consumer sales.

Trend Forecasting graph showing short-term forecasting adjustments based on consumer insights

What is long-term forecasting?

Long-term trend forecasting is forecasting for time horizons of 1+ years or more. It’s a method of trend analysis that means looking at larger shifts in consumer preferences. The trick with long-term forecasting is creating a baseline that can later be adjusted as market trends progress. Long-term forecasting is a critical component of strategic decision making.

What is short-term forecasting?

On the other hand, short-term forecasting focuses on operational decision making. Short-term forecasting is forecasting for time horizons within one year (within 2 to 3 months). Typically, short-term forecasting occurs in monthly planning meetings where forecasts are adjusted based on in-season trends and demand.

In our perspective, short-term forecasting can become a key competitive advantage if done right. Short-term forecasting involves catching market trends as they occur, and planning strategies to capitalize on them. We recommend acting on market trends unfolding in-season, rather than waiting 6-months and trying to catch them after. We wrote a blog talking about how AI in short-term forecasting can boost demand forecast accuracy by over 20%.

photo of woman holding white and black paper bags
Trend forecasting and fashion forecasting in the apparel industry

Why is trend forecasting critical in fashion and apparel?

Market trends impact the fashion industry at a higher rate and are critical to the sell-through of products. In this dynamic sector, consumer preferences fluctuate rapidly, making it essential for brands and retailers to align with the latest market trends.

Stocking up on items that miss the trend heavily impact margins, forcing retailers to markdown items or hold excess stock. Leveraging consumer insights and relevant market data is critical in this domain.

By accurately predicting upcoming trends, fashion businesses gain a competitive edge. They can introduce trend-aligned collections ahead of competitors, increasing the likelihood of attracting and retaining customers.

How can we build a trend ready organization?

How can we build a trend forecasting ready organization? Team. Trend Tools. Trend Slots.
  1. Team: Assign 1 or 2 team members responsible for monitoring trends and responding swiftly when relevant trends emerge. They should also be critical for dispersing trends data to relevant team members - creating alignment between demand planners and marketing teams.
  2. Trend Tools: use AI based tools that can predict and flag trends that are important in your category. These tools should use a variety of data sources to better understand the consumer market.
  3. Trend slots: Make sure your strategies are adaptable and have room for trends. For example, allocate 10% of your category budget to be able to adjust for trending colours or styles in your market.

From what we’ve seen, not every trend will be relevant for your strategy. Have a clear criteria set for assessing trends. Consider factors like alignment with brand values, potential for engagement, and resonance with your target audience.

What are 3 of the best trend forecasting methods?

From what we’ve seen working with thousands of products in different domains, it’s important to use a mix of forecasting approaches. We first cluster similar products together and apply forecasting methods to each group.

  1. Linear Trend Analysis: Linear trend analysis is a method used to identify a consistent, straight-line pattern in data over time. It assumes that the relationship between variables is linear, with data points following a linear progression. This method is valuable for forecasting when historical data shows a relatively stable and consistent trend.
  2. Exponential Trend Analysis: Exponential trend analysis is applied when data exhibits exponential growth or decay patterns over time. It assumes that data points increase or decrease at a consistent percentage rate. This method is useful for forecasting in scenarios where rapid growth or decay is observed, such as in technology adoption or disease outbreaks.
  3. Moving Average: Moving average is a smoothing technique that calculates the average of a set of data points within a moving window or time period. It is used to reduce noise and highlight underlying trends in data. Moving averages are commonly employed for short- to medium-term forecasting when dealing with fluctuating or seasonal data.
To dive into the details of the best trend analysis methods, we leverage resources from the Institute of Business Forecasting & Planning.
Trends forecasting materials for the Certified Professional Forecasting (CPF) designation

How to perform trend forecasting?

This section provides a step-by-step guide, highlighting the frequency of each step and the groups involved. From our perspective, the step on continuous adjustment and looking at micro trends is a critical adjustment to the process.



Groups Involved


Assess Available Data Sources & Manage Data Sources

Identify and evaluate the data sources that can provide relevant information for trend analysis.

Data Teams (Data Analysts, Data Engineers), Demand Planners

Periodic (e.g., quarterly)

Collect and Validate Data from a Large Range of Sources For Forecasting

Gather data from diverse sources, including historical sales data, inventory, social media, and more.

Demand Planners, Data Teams (Data Analysts, Data Engineers)

Regularly (e.g. monthly)

Cleanse Data for Forecasting

Process and clean the collected data to remove inconsistencies, errors, and outliers.

Demand Planners, Category Managers

Regularly (e.g. monthly)

Test Various Forecasting Approaches

Experiment with different forecasting models, algorithms, and techniques to assess accuracy.

Data Scientists, Demand Planners

Periodic (e.g., quarterly)

Run Long-Term Forecast

Generate forecasts for longer time horizons to identify macro trends and strategic opportunities.

Strategic Planning Team, Category Managers

Annual or Semi-annual

Allocate Inventory

Allocate available inventory based on demand forecasts, ensuring optimal distribution.

Allocation Analysts

As needed based on inventory cycles

Continuously Adjust Short-Term Forecast

Refine short-term forecasts based on emerging trends and real-time data, adapting to market dynamics.

Demand Planners, Allocation Analysts

Regularly (e.g., weekly, monthly)

Identify Micro Trends to Capitalize On

Analyze data to detect micro trends or niche opportunities that can be leveraged for competitive advantage.

See our blog on leveraging micro trends before they are mainstream

Marketers, Category Managers

Regularly (e.g., weekly, monthly)

What data can enhance trend forecasting?

We surveyed experts from across the industry ask what data would help them improve their forecast accuracy. We summarized it here below and added additional insights from our work in the space.

  1. Market Data: External market data, including industry reports, competitor analysis, and market trends, can help identify broader market shifts and emerging opportunities.
  2. Social Media Data: Monitoring social media platforms for mentions, hashtags, and trends related to your industry or products can identify real-time shifts in consumer sentiment.
  3. Web Analytics: Data from website and e-commerce analytics, such as user traffic, click-through rates, and conversion rates, can reveal online consumer trends.
  4. Environmental Data: Data related to environmental factors, such as weather patterns or climate change, can impact trends in industries like fashion, agriculture, and energy.
  5. Economic Indicators: Economic data, such as GDP, inflation rates, and employment statistics, can help forecast trends in consumer spending and overall market health.
  6. Sentiment Analysis: Natural language processing and sentiment analysis tools can gauge public sentiment from news articles, social media posts, and customer reviews.
  7. Cultural and Event Data: Awareness of cultural events, holidays, and societal shifts can influence trends in various sectors, such as fashion, entertainment, and food.
  8. Customer Feedback: Real-time customer feedback, including complaints, suggestions, and reviews, offers insights into consumer preferences and concerns.

Trend forecasting using signals generated from Tiktok and other social media data

Tips for Trend Forecasting

Effective trend forecasting requires a strategic approach and the utilization of various techniques. Here are some key tips to enhance your trend forecasting efforts:

  • Utilize a diverse range of data sources:

To capture the full spectrum of emerging trends, make use of a wide range of data sources. Don't limit yourself to just one type of information; consider factors from multiple sources, such as market research, social media trends, consumer feedback, and industry reports. A holistic approach to data collection allows for a more comprehensive understanding of evolving trends.

  • Continuously adjust your forecast based on changes in the market:

Trend forecasting is not a one-time event but an ongoing process. Instead of setting a static baseline at the beginning of the year, incorporate real-time adjustments. Be agile and responsive to changing market dynamics and incorporate seasonal fluctuations and other temporal variations into your forecasting model.

  • Build step-by-step:

You don't need to have all the answers from the outset. Begin by systematically building your trend forecasting capabilities. Start by simply observing new data and trends as they emerge. As you gain insights and confidence, gradually incorporate these findings into your forecasting process. This incremental approach ensures that your forecasting methods evolve in sync with the changing landscape.


How do I choose the right data sources for trend forecasting?

Choosing the right data sources for trend forecasting depends on the product. Some data sources are more powerful for certain products. For example, YouTube works well for products like hair tools, where product reviews are posted. While TikTok & Instagram are better for items like shorts, where micro trends emerge.

What tools can enhance Trend Forecasting?

AI-based tools and predictive analytics software are pivotal for enhancing trend forecasting accuracy. They process large datasets, detect patterns, provide real-time insights, and adapt quickly to changing trends.

What are common challenges in trend forecasting? How can they be overcome?

Challenges in trend forecasting include data quality issues and unpredictable market shifts. Overcoming this involves data cleansing, continuous monitoring of market dynamics, and applying a flexible forecasting approach that adapts to unforeseen changes. We interviewed 87+ experts in the space, find our paper here.

About the Author

Tali Remennik is a Certified Professional Forecaster, certified by the Institute of Business Forecasting and Planning. She is a data scientist working at the intersection of AI, demand forecasting and retail strategy. She is the co-founder of Granularity. Granularity is an AI-powered trends platform AI and data from Tiktok, Instagram, Google and others to predict consumer trends as they emerge.

Learn more about Granularity - trend forecasting using data from Tiktok, Instagram, Amazon and others.