Trend Forecasting Through Data Analytics

Updated on March 6, 2024 in Ecommerce by

Trend Forecasting Through Data Analytics

Traditionally, the fashion industry relied on artistic vision, but the digital age has changed things. It has transformed trend forecasting from a creative to a dynamic interplay between creativity and data. 

Every online action, from clicks to shares, leaves a digital footprint that offers valuable insights on evolving preferences. It’s crucial to decipher these trails of consumer data as it is moving at lightning speed. 

With technology, like social media and live-streamed events, fashion is now accessible to everyone. Trendsetters aren’t just designers anymore; they’re also Instagram influencers, fashion bloggers, and celebrities. Trend Forecasting is now a bigger challenge, requiring creativity and rapid response.

Join PixelPhant as we delve into how trend forecasting blend seamlessly with customer preferences, paving the way for data analytics to guide our future. 

Art + Science of Trend Forecasting

Trend forecasting is all about predicting future styles and trends in fashion. Fashion experts observe consumer behavior and analyze past sales data to predict ongoing styles, colors, and patterns. 

Fast forward to 2023, many brands and retailers have started using data analytics to predict future buying habits. It involves both long-term and short-term trend forecasting. 

Long Term Trend Forecasting

Long Term Trend Forecasting
Source: Heidi Abra & The New York Times

Long-term trend forecasting involves predicting macro trends like technological advancements or shifts in culture and economy that last for over two years or often years ahead.

Experts gather information from different sources and demographics to understand consumer lifestyles. They analyze global and domestic fashion industry changes to help create products with high demand potential.

For example, During the 1970s, Karl Lagerfeld and celebrities like Joni Mitchell pioneered a culture of fashion. 

Mitchell’s distinctive style, including tie-dye blouses and kaftans, defined the Bohemian era. Back then, fashion houses relied on observing style icons like Mitchell, as technology and data analysis tools were scarce, to decide consumer trends.

Short Term Trend Forecasting

Short Term Trend Forecasting
Source: Rhode

Short-term trend forecasting focuses on the quick-changing micro-trends in fashion. Experts track seasonal shifts influenced by pop culture, sports, and art. 

While it may seem less important than long-term forecasting, it’s crucial for attracting consumers and staying competitive. Brands use short-term forecasts to launch trendy products and stay ahead in line. 

For instance, The Rhode Lip Balm Phone Case by Hailey Rhode Beiber is storming the internet. It combines skincare and smartphone accessory, appealing to beauty lovers and tech enthusiasts. 

This clever trend forecasting idea merges daily essentials with our reliance on phones. The case features a built-in lip balm slot, making it convenient and doubling as a Rhode advertisement. It’s sparking conversations and spreading through word-of-mouth effortlessly.

So, what role do experts have, and what kind of data do they use to make these predictions and foresee the industry’s direction? 

Let’s dive into the specifics.

Fashion analytics covers everything from strategies and tactics to technology for analyzing fashion trends. Now, it’s also leveraging artificial intelligence and machine learning to move beyond just looking at the past.

They not only track sales but also reveal customer behaviors driving purchasing choices. It helps retailers decide on pricing, suggest related products, and stock the right items each season. 

Instead of relying solely on instinct, fashion analytics lets them use data for decisions on styles, colors, sizes, and inventory levels. As a former Neiman Marcus executive, Greg Flinn quotes, “It’s about adding science to retail art.” 

It’s not just about what’s in trend but about understanding the audience’s mind.

Bright Pink: Making a Statement in Fashion
Source: WSGN

While Millennial pink faded after being overexposed, it paved the way for a brighter, more intense shade in the pink palate. 

Since 2018, WGSN has been monitoring the rise of bright pinks, which draw on ’80s and ’00s nostalgia and metaverse influences. As the metaverse evolves and digital experiences become more prevalent, cut to A/W 23/24 bright pink is impacting, with luminous pink being a fundamental color. 

Pink is now expected to branch out into warm and cool shades for S/S 24. So, “What comes next?

Every season brings new fashion trends. 

Women’s fashion has recently seen the resurgence of mini bags, long denim skirts, sleek sunglasses, and low-rise pants. These trends were considered outdated but are now in the spotlight, like how moto jackets became popular in the 1950s due to the movie “The Wild One.” 

The question arises: What drives these shifts in popularity, and how can fashion trend forecasting be done across various categories? 

The answer lies in the power of data analytics. Let’s get into it. 

Power of Data Analytics in the Fashion Industry

Data analytics empowers fashion brands to improve inventory management, profitability, and consumer targeting, streamlining collection planning like never before. 

AI trend forecasting offers insights into future trends and market potential, guiding brands’ planning. It is, therefore, essential for making strategic decisions in the fashion industry.

Types of Data Analytics

Descriptive Analytics

Summarizes past data to understand what occurred, using insights to plan future actions, for example, analyzing business intelligence reports and consumer habits.

Diagnostic Analytics

Examines prior data to determine why something happened, employing techniques like drill-down, data discovery, data mining, and correlations.

Predictive Analytics

Predicts future events using statistical methods such as machine learning and modeling.

Prescriptive Analytics

Makes predictions and recommends actions based on those predictions. It focuses on future outcomes and proposes actions and their business implications.

The pandemic underscored the significance of data analytics for the fashion industry. It became essential to adapt and plan quickly, especially regarding future collections. 

Fashion brands and retailers suddenly dealt with issues like understocking, overstocking, outdated products, and misaligned marketing campaigns with changing consumer values. 

So, how can data analytics help brands and retailers at times like this?

Data Analytics Throughout Production Cycle

Previously, fashion departments used descriptive analytics to guide collection planning. For example, sales data from previous winters helped determine product designs, quantities, and marketing strategies for upcoming winter collections. 

However, today, predictive analytics is the preferred method for choosing trends for future collections. AI trend forecasting predicts from demographics to market potential. 

Stage 1: Design & Ideation 

Shein, Zara, and other fast Fashion brands use data analytics from social media and consumer purchases to understand trends and boost profits. 

For instance, if data shows growing interest in sustainability, designers can integrate eco-friendly materials to meet market demand.

Stage 2: Line Planning

Manufacturers use data analytics to determine customer responses to samples, removing unpopular designs and reviewing every product aspect, from colors to sizes.

It helps create a product mix that maximizes value and optimizes pricing by considering factors like materials and transportation costs. 

For instance, data analytics can pinpoint coming colors and styles, enabling manufacturers to produce more items and boost brand sales. 

Stage 3: Sourcing

Sustainable brands like Nike, Zara use ethically sourced, eco-friendly materials. Data analytics help assess third-party salespeople with sustainability standards using specific metrics. 

For instance, Brands can utilize data analytics to locate suppliers that match their sustainability objectives, reducing environmental harm and boosting brand image.

Stage 4: Production 

Sharing production updates with stakeholders through data visualization is highly effective. It helps supervisors spot potential issues like low productivity, lost targets so that they can address them promptly. 

For instance, it can locate backups in production, allowing managers to allocate resources wisely and shorten lead times.

Stage 5: Inventory Tracking 

Data analytics is crucial for analyzing inventory data, assessing product and channel performance, and understanding customer preferences. 

For instance, it can identify slow-selling inventory, prompting businesses to take action like discounts or promotions to clear it.

Stage 6: Quality Check

Fashion brands are embracing prescriptive analytics and AI trend forecasting to maintain top quality. It helps supervisors tackle quality issues swiftly, avoiding major production disruptions. 

Quality analytics also offers insights into defect counts and targets, helping process optimization.

For instance, a fashion brand can use analytics to check the quality of a batch of garments during production, enabling quick adjustments to prevent recalls and save time and money.

The Evolving Trends of the Fashion Industry

When you shop the latest trend backed by data, you ensure confidence in your purchases. This season, classic styles and accessories are making a comeback. 

As per the reports of Data, But Make It Fashion, “Hair ribbons have surged by 20%, while red nail polish popularity has skyrocketed by 160%, possibly influenced by the viral Red Nail Theory on TikTok. 

The timeless button-down is back, featured in 17% of runway looks, highlighting its enduring appeal. Maison Margiela Tabis, known for avant-garde designs, has seen a 30% increase in popularity, reflecting a growing interest in unique footwear choices.”

Heuritech did a brief AI-based trend forecasting experiment comparing a pair of sneakers through data analytics.

An Experiment By Heuritech for Product Positioning

Source: Heuritech

Sneakers are a perennial trend, with their shape, color, and texture constantly evolving yearly. 

Aim: To focus on the latest trendy collection, they asked their client to compare two sneaker models to determine future best sellers in the market. 

And with the help of data analytics, they can prioritize or avoid based on changing customer preferences, ultimately aiming to meet demand and boost sales.

Experiment: The client was interested in two sneaker styles: Lug soles and Slip-on sneakers. Both styles matched the client’s brand identity and positioning. However, they were at different stages of adoption and targeted different consumer segments.

Result: The lug sole sneaker is forecasted to rise by 15% in the US during Fall 2021. Conversely, the slip-on sneaker is expected to decline by 2% during the same period compared to the previous year.

These figures indicate that compared to the previous year’s report, a trend forecasting experiences a change in visibility by the specified percentage during the current season. The trend’s potential market demand is also crucial to consider to avoid understock or overstock when determining assortment quantity. 

Conclusion: Furthermore, it’s essential to take note of the high season as it indicates the best time to launch a particular trend.

Wrapping Up

After reading this blog, we hope you grasp the significance of data analytics in trend forecasting. 

As we know, fashion has constantly evolved throughout history, from narrow-brimmed hats in the 17th century to low-rise jeans in the 2000s. Men and women have swapped styles, like trading top hats for baseball caps. 

For that, data analytics plays a part in fashion design and production strategies, from planning collections to targeting consumers and predicting demand. 

Especially for fashion brands aiming to thrive in today’s digital landscape, PixelPhant, a professional photo editing company, can help in your revenue growth and keep you ahead in the competitive curve of the fashion industry. 


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