Validating Trend Forecasts to Unveil True Demand

Validating Trend Forecasts to Unveil True Demand

Validating Trend Forecasts to Unveil True Demand

Introduction

Key Insights

  • Validate, Validate, Validate: A data-driven approach provides concrete insights to trends when measured against performance.
  • Enhance Trend Forecasting: Data analytics work hand-in-hand with trend forecasting in building the right assortment strategy.
  • Full Price Opportunities: Brands can drive full price sell-out for specific trends or categories when examined at a granular level.

In assortment planning, merchandisers, buyers and designers typically have to conduct their research to gauge market demand. To guide the design and buying processes, trends and inspirations can come from various sources:

  • Buying trips and trade fairs
  • Fashion runways and social media
  • Trend forecasting services
  • Historical sales performance

However, each of these methods poses its own risks. For starters, runway styles are risky if they fail to translate to commercial pieces or when the trend is short-lived. Trend forecasting reports are directional and aspirational-focused, with no geographical relevance. They fall short of translating a trend in the context of local markets, which leads to failure in meeting demand.

Historical performance, a reliable indicator in the past, can no longer solely be referred to, especially during the Covid-19 pandemic.

With the current climate where consumer preference shifts regularly, how do brands and retailers determine what consumers want?

Layer Trend Forecasting with Analytics

Despite the risks entailing runway trends, social media, and trend forecasting services, they remain the key sources in assortment curation for brands.

To accurately meet consumer demand, brands need to be able to differentiate between promising trends and fads. This can be made possible by including data-backed analysis to trend forecasting to identify true demand.

A data-driven approach not only enables greater visibility on trend lifecycles but also identifies opportunities and weaknesses in assortments. Fashion brands Marni and Miu Miu have adopted data analytics for this purpose.

Chart 1: Comparisons of Fashion Data Providers
*Extracted from Omnilytics’ Fashion Trends: Analytics vs. Forecasting report

To demonstrate how to conduct fashion trend analysis, let’s start by diving into the Tops category on fashion market insights platform, Omnilytics Dashboard.

How to Identify What’s Trending?

Omnilytics’s Trend Performance module can surface trends from 44 different countries by category, colour, pattern and material.

Step 1: Determine Uptrending Subcategories
Having a high-level view of the key subcategories in the market is important in guiding assortment planning, before diving into trends. By filtering ‘Group by: Category’, the subcategories of Tops are displayed.
Chart 2 shows an overview of the Women Tops’ subcategories of five fast fashion retailers in the UK, namely Asos, H&M, Topshop, Zara and River Island.

Chart 2: Trend Performance Overview for Tops

Analytics: From this view, we can see that some subcategories with bigger contributions in the market were uptrending, particularly T-Shirts and Shirts. T-Shirts had a higher trend score, which was in line with the current demand for casual wear in the work-from-home era. The trend score takes into account the subcategory’s performance across replenishment, discount rate and ageing over time.

Insights: Looking at the trend’s trajectory over time helps brands to assess risks. By referring to Chart 3, the demand for T-Shirts showed an upward movement (green line), exceeding the total category’s trend line from January to June 2020 (grey dotted line), justifying its popularity. You can also filter the date range to extend the period of analysis for up to 18 months for year-on-year comparison.

Chart 3: Trend Scorecard for T-Shirts

Next, dive deeper into the styles of uptrending subcategories to identify opportunities or risks.

Step 2: Establish Silhouettes
Category as broad as T-Shirts would need a further deep dive on specific styles to get a clearer view of what is in demand. Looking at the product attributes or fit is one of the ways to achieve this.

The surge in relaxed and oversized silhouettes due to the rise of loungewear pinpoints a potential trend to look out for. In Chart 4, this fit was mainly trending in T-Shirts and Shirts.

Chart 4: Trend Performance Overview for Oversized/ Relaxed Silhouettes

Step 3: Discern Key Patterns
To find trending patterns specific to T-Shirts, toggle the view to Pattern in the ‘Group by’ filter. We can see that Graphic, which contributed to 32% of patterned T-Shirts, was trending (Chart 5). The print continued to follow an upward trajectory throughout the period analysed, indicating a staple pattern for the subcategory.

Chart 5: Pattern Contribution for T-Shirts

Based on the findings from Chart 2-5, we have uncovered a trend –Graphic T-Shirts.

Step 4: Spot Opportunities
Studying the trend trajectory over time is critical in uncovering emerging trends. Despite not being in the top 5 key patterns for Tops, Polka Dot print was promising as it registered a high trend score. This is demonstrated in Chart 6, by toggling the ‘Group by’ filter to Patterns.

Chart 6: Trend Performance Overview for Patterns in Tops

The print showed an upward trend movement, exceeding all patterns throughout the period analysed. It was also trending across major subcategories in Tops, which included Blouses, Shirts and T-Shirts.

Chart 7: Trends Performance for Dots in Tops

By combining the result from Chart 2 and 7, this reveals an opportunity for another trend – Polka Dot Shirts.

How to Validate Trends?

Based on the previous sections, we have successfully identified two trends:

  1. Oversized/Relaxed Graphic T-Shirts
  2. Polka Dot Shirts

Before investing in these styles, you need to have a complete overview of the trend by understanding its current demand in the market.

Assess Competitors and Performance in a Snapshot
In the same Trends Performance module, the Trend Scorecard page (Chart 8) gives you an overview of competitors, price positioning and performance of the subcategory.

Chart 8: Key Performance Indicators for Trends

Oversized Graphic T-Shirts: Asos contributed largely to the style, with a whopping 88% contribution. With a low median price of GBP 15.81, the style was able to drive high sell-out at 81.56%, with full price contribution at 54.79%.

Polka Dot Shirts: While Asos was the top retailer, Zara had the most products followed by New Look and Warehouse (Top Brands). Looking at the performance metrics, a sizeable portion of the style was discounted with 80.95% contribution. Although the high discounted products led to strong total sell-out at 71.42%, developing products in this trend should account for calculated risk such as markdown cost.

Having a trend’s performance validated increases confidence in decision-making. You can begin by reviewing the trade performance at subcategory level on Competitor Benchmarking module, before pulling up the trends performance metrics at Trend Scorecard page for a final validation.

Step 1: Analyse the Subcategory Performance
Chart 9 shows the breakdown of performance for Tops. We can see that T-Shirts, with the biggest contribution for Tops (30%) performed well at full price. It exceeded the category average of 31%, indicating an opportunity to drive full-price sell-outs. While Shirts’ performance was not as strong as T-Shirts, it closely matched the category average.

Chart 9: Trade Analysis for Tops Subcategories

Step 2: Benchmark Trend Against Subcategory Performance
To assess whether the trends were not short-lived fads, they should record sell-out performance that were at least the same level as the overall subcategory performance.

Here in Chart 10, Oversized Graphic T-Shirts performed strongly at full price (54.79%), exceeding the total T-Shirt subcategory rate of 43.36%. The number of discounted items was also well below the subcategory average, further backing the popularity and stability of this trend.

The performance of Polka Dot Shirts marginally exceeded the subcategory average. Its sell-out at full price recorded 31.11% against the total Shirts subcategory at 29.87%, despite a high percentage of discounted items.

Chart 10: Key Performance Indicators for Trends

How to Identify Bestsellers?

Now that we have established the performance of the two identified trends, how do the bestsellers look like for design or buying inspirations?

Step 1: Use Sell-Out at Full Price as Indicator
To identify the bestsellers, you can start by inputting the right filters on Competitor Benchmarking module. Then, click through the Sell-Out at Full Price metric on the Trade Analysis card under Trade Performance. A new page with SKU images will be displayed.

Chart 11: Trade Analysis for Polka Dot Shirts

Step 2: Sort by Most Replenished
You should toggle for the SKUs to be sorted by Most Replenished to get a better view of the popular items.

Referring to Polka Dots Shirts, we can see that most of the bestsellers were comprised of basic long-sleeved style in small dots and in core colours. Besides polyester, organza material was also popular for this style.

Chart 12: Bestselling SKUs for Polka Dot Shirts

How to Identify Trending Colours?

There are two methods that can be used to identify trending colours.

Method A: Trend Performance Module

Using Trend Performance module, the trending colours can be identified using the similar steps outlined in determining the trending subcategories on page 4.

Step 1: Filter by Colour
To find out the trending colours for T-Shirts, simply select the subcategory on the side filter and toggle the view to ‘Colour’ in the ‘Group by’ top filter.

From Chart 13, we can see that most of the uptrending colours consisted of core colours. This was in line with the colour palettes that leaned towards basic shades amidst the Covid-19 pandemic. The top three core trending colours were Grey, followed by White and Black.

Chart 13: Trend Performance Overview for Colours in T-Shirts

Step 2: Filter by Seasonal Colours
While core colours contributed to most of T-Shirts, fashion colours are most important to get right to avoid overstocking. To get a clear view of fashion colour distribution, you can filter by ‘Seasonal Only’ in ‘Show Colours’ and sort by the highest product count.

Pink, blue and green were the top seasonal colours as seen here, with pink and blue in line with the trending colours in Chart 13.

Step 3: Analyse Performance
Layering the sell-out contribution by colour validates the trending colours found in the previous steps. In Chart 14, pink led the fashion colours, followed by blue. As depicted by the yellow line, pink contributed to 5.7% of total sell-out. The colour appeared in various shades on the Spring/Summer 2020 runways, as seen at Pyer Moss, Molly Goddard and Salvatore Ferragamo.

Blue followed closely with a 4.7% sell-out contribution. The colour was especially popular this year after Pantone announced Classic Blue as 2020 Colour of the Year.

Chart 14: Seasonal Colour Breakdown with Sell-Out Contribution

Method B: Competitor Benchmarking Module

An alternative way to identify trending colours is through the Colour Composition tab under Competitor Benchmarking module.

Step 1: Filter and Compare Against Last Year
You can easily spot colours in-demand in the Colour Composition tab as it provides a comparative view against last year. Filtering by ‘Out of Stock’ gives insights into the current colours in demand.

Chart 15: Colour Composition Sell-Out

Step 2: Compile and Compare Colour Shades
The Colour Composition tab further validates the popularity of colours with greater visibility on the shades. By comparing against the same period last year, we can see that core colours were in higher demand this year.

Fashion colours saw significant reduction in sell-out contribution across all retailers, especially at Topshop and Asos. However, lighter shades of blue, lavender and nude-pink saw an increase in popularity this year.

Chart 16: Colour Composition Sell-Out TY vs. LY

How to Spot Opportunity in Materials?

In addition to colours, brands can also spot trending materials used for T-Shirts in the same Trend Performance module by following the same steps.

With sustainable materials becoming more commercialised leading to lower costs, brands can seize the opportunity to develop capsule ranges, starting with identifying popular materials.

Step 1: Narrow Results Using Keywords
The eco-friendly material, lyocell was uptrending across several key subcategories in Tops, such as Shirts, T-Shirts and Tank Tops (Chart 17). This indicated a growing adoption of sustainable products among consumers.

Chart 17: Trade Movement of Opportunity Subcategories in Materials

Step 2: Analyse Performance of Material
The performance of lyocell material in T-Shirts further signifies the growing consumer demand for sustainable materials. The sell-out at full price matched the subcategory average, despite a high proportion of products on discount (Chart 18).

Step 3: Observe Stock Movement
The stock movement provides visibility on brands that are actively investing in sustainability to meet the current consumer demand. This can be done by using the Trade Movement chart in the Competitor Benchmarking module.

Based on Chart 19, Zara and H&M had consistently produced tops in lyocell, despite a slowdown during the peak pandemic period.

Chart 19: New-In Movement for Tops Made of Lyocell

Though some brands’ sustainability plans are threatened by the crisis, the ones that continue to incorporate responsible practices are in a better position to resonate with consumers.

Key Next Steps

Incorporate Analytics: As trend forecasting alone is unable to keep up with consumers’ changing demand in different markets, a data-driven approach adds credibility and confidence in decision-making. By consistently tracking and validating trend movements at a granular level, brands can create commercial assortments that accurately meet demand.

Balancing the Art & Science of Fashion: There is no denying the significance of trends coming from runways and social media. However, building an assortment by merely referring to what’s ‘trending’ on those sources can be challenging and risky, especially in the current climate.

While brands can draw trends from various sources, validating them with data-backed insights minimises the guesswork and increases accuracy in meeting demand. This will result in improvements in sales and inventory management.

New Category Expansion: With activewear and loungewear gaining newfound popularity amidst the pandemic, brands should validate the performance of these segments by market before development using fashion market insights tools.

With Omnilytics, brands have the visibility on true demand as different markets react to trends differently. Additionally, brands get to identify threats and opportunities by monitoring the product offerings of the key players in the segments.

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