The Trend Performance Module enhances your adaptability to changing market landscape and consumer demand.

The reimagined Trend Performance has four new feature tabs, coupled with smarter visualisations and deeper granularity.

Brands and retailers can easily identify the top patterns, category, materials, and colours across 47 markets with these tabs.

Trend Performance is the only module that does not require selecting a brand or retailer in the main filter (on your left).

Overview

The Overview tab provides an aggregated view of your market trends, with an immediate view into the top five patterns, colours and materials. And with this new update, you can also group by:

For categories and colours, you can further drill down to the sub-categories and shades by selecting the category or colour of your choice on the main filter (on your left).

The Overview tab also provides a breakdown of top patterns, colours and materials within the market. This changes depending on the ‘Group by’ filter on the top. For example, if Patterns was selected in the Group by filter, the composition below would change to top categories, colours and materials.

You can click into ‘View Analysis’ at the bottom for a deeper dive into product composition by patterns, colours, materials or categories.

Currently, the default date range of 6 months is shown when you click into the module. The date range can be edited from the ‘Date Range’ dropdown menu at the top right corner of the page.

On the main Overview tab, you will see the total product count and trend score at the bottom. 

To drill into a trend, you can click on any one of the cards at the top, and the Trend Scorecard will slide out from the right. In this example, we selected ‘Activewear’ for further analysis. 

The Trend Scorecard contains new analytics to help you have a deeper dive into the trend. In the chart below, we can see that the category Activewear carried a positive trend score of 4.2. In the chart, there is a solid and dotted line.

If the overall trend score is positive (ie. up trend), the solid line will be in green. Conversely, if the trend score is negative (ie. down trend), the solid line will be shown in red. 

To derive the trend score, the trend line for activewear will be compared against the trend line for overall categories (ie. shown as the dotted line) for the date range selected. The trend line itself is calculated based on the popularity score.

Popularity score is assigned for every category based on the category’s performance across discount, replenishment and ageing over time.

In the above example, the solid line is consistently above the dotted line, which means the category has a solid performance compared to the others in the last 6 months (ie. Macro trend). With this information, brands and retailers can adjust their category mix accordingly.

However, this should be cross analysed against internal data to ensure that the selected category is aligned with brand positioning.

Similar to the main page, there is also a composition of top patterns, colours and materials available for the selected category trend. Again, this would change depending on the ‘Group by’ filter selected on the main page.

You can hover over the contribution breakdown and click in to view visuals for the selected patterns, colour or materials. In this example, here’s what appears when we clicked into the ‘Graphic’ contribution under Top Patterns.

Easily identify which retailers or brands stocked the most dresses, and in what trending patterns, colours and materials.

This refers to the median price and price spread of the selected trend.

Price positioning (median price and price spread) along with performance metrics on discounted, replenished and sell-out rates are important indicators to inform if the specific trend is meeting consumer demand against its stock position.

There are currently 3 performance metrics available for analysis.

  • % of Discounted – % of total product count on discount and the average discount for products currently discounted. In this example, we can see that out of total product count of 149,601 (as seen on the trend card for Activewear on the main Overview tab), 58.58% of the product count (ie. 87,635) is discounted. Of these discounted products, the total average discount % is 35.8%
  • Replenished – % of total products replenished within the selected date range for the activewear trend.
  • Sell-Out at Full Price – The current total sell-out for Activewear is 63.45%. That is, of the total product count of 149,601, approximately 94,922 product counts registered a sell-out during the date range selected. Of these 94,922 products, 50.89% of the products registered a sell-out at full price.

The above performance metrics are best analysed by comparing two different date ranges to see changes in performance.

There is also a demand analysis below, showing the seasonality and correlation of intake against first time discount for this trend.

Interestingly, we see a spike of intake in December 2019 as well as a peak in total first discounts for activewear products in the same month. This signifies that while the demand is high for activewear in December 2019, there is also a lot of competition from end-of-season clearances.

With the view on the quantum of first time discounted products by month, you can plan and predict the lifecycle of a specific trend to invest appropriately. 

Further down on the page is a list of assortment for the selected trend. There is a ‘Sort By’ feature above the assortment list which allows the retailer and brand to sort the products based on their goals.

Key Takeaways

  1. What are the key category/sub-category trends in the market?
  2. What are the top patterns, colours and materials adopted for this trend?
  3. What is the median and price spread for this trend?
  4. What is the performance of this trend?
  5. What is the demand of the trend in terms of new in vs first time discount?
  6. What are the most replenished/restock products in the trend?

Pattern & Colour Distribution

This tab shows the breakdown of product count by pattern and colour in two separate cards: 

  1. Pattern Distribution
  2. Colour Distribution

The objective of this analysis is to understand which top retailers or brands have stocked key patterns or colours

Pattern Distribution

Pattern Distribution card shows a breakdown of patterns sorted by product count. 

The ‘Split by’ view options will auto-display without having to filter in the main filter. This includes a breakdown of information on Retailer, Brand, and Category.

From the data insights, a breakdown of respective pattern product count and contribution is reflected accordingly. For instance, Stripes pattern accounted for 180,000 product counts, which contributes a 14.45% towards the total pattern of the UK market in the first half of 2020.

Data insights from Pattern Distribution provides a focused analysis of pattern distribution and contribution to the market. Pairing with insights gained from the Overview analysis tab, Pattern Distribution redirects you in spotting further patterns opportunities to venture into. 

This analysis is particularly useful in identifying pattern opportunities across categories.

Some recommended filters analysis point include:-

  1. New-in Stock Status, split by Category

In this example, we are analysing the new-in patterns launched in the first half of 2020 in the UK market. This is useful to identify the new pattern launches. Further insights on category breakdown provide deeper data points to identify which are the core categories adopting these patterns to gauge market preferences. 

  1. New-in & Out-of-stock Stock Status, split by Category

Using the same example from above, now adding on the ‘Out-of-stock’ stock filter. The analysis goal is to identify the demand and preference of the market towards uptrending patterns.

Comparing both data insights, Checks outperformed Stripes, registering 16.58% sell-out contribution vs. 13.52% stock contribution. Interestingly, Graphic pattern, as the third top pattern contributor, showed a promising performance, with 12.32% sell-out contribution over 10.6% stock contribution.

Cross analyse pattern distribution by category view (under split by card filter) to identify the key patterns adopted by categories. For instance, Bags heavily adopted Animal prints at 34.5% contribution. 

  1. Category filter + ‘New-in/New-in & Out-of-stock’ stock status (split by Brand)

Deeper analysis to understand the adoption of patterns for Bags is possible by including the ‘Bags’ category in the main filter bar.

The breakdown by patterns for the category selected is reflected and enables you to identify the core pattern adopted by each category. For instance, Animal, Graphic & Conventional prints were the top 3 patterns for Bags in the first half of 2020. The breakdown by Brands gives further insights into the designs launched. 

In summary, Pattern Distribution provides a deeper understanding of the relationship between pattern and category to facilitate effective product design and development relevant to market demand.

Key Takeaways

  1. Which are the core patterns newly launched for the specific period (year/quarter/month)?
  2. What are the differences and similarities in market preferences towards the patterns? 
  3. What are the key patterns adopted for respective categories
  4. What are the pattern opportunities and risks in relation to category/segment? 

Colour Distribution

Similarly to Pattern Distribution, Colour Distribution card shows a breakdown of colour sort by product count. The ‘Show Colours’ card filter enables a focused analysis with a breakdown by Seasonal or Grayscale Colours

The ‘Split by’ view options will auto-display without having to filter in the main filter. This includes a breakdown of information on Retailer, Brand, and Category.

Similarly, a breakdown of colour product count and contribution is reflected within the card. 

This can be cross-analysed with data insights gained from the ‘Colour Composition’ card under the Competitor Benchmarking module to analyse the relationship between colour, category, brands and retailers

Data insights from Colour Distribution provides a focused analysis on colour distribution and contribution in the market. Pairing with insights gained from the Overview analysis tab, Colour Distribution redirects you in spotting further colour opportunities to venture into. 

This analysis is particularly useful in identifying colour opportunities across categories.

Some recommended filters analysis point include:-

  1. Stock Status: New-in & Out-of-stock 

In this example, we are analysing the bestselling evergreen products colour distribution in the first half of 2020 for the UK market.

This is useful to identify the core seasonal colours most preferred by market, retailers or brands and spot further colour opportunities in newly launched colourways.

Here, the colour Blue was the top seasonal colour across key categories like Outerwear and Tops. This shows its potential as an all-time offering across collections.

  1. Category filter + ‘New-in/New-in & Out-of-stock’ stock status (split by Brand)

A deeper analysis to identify key colours adopted by each category is possible by including the category under the main filter bar.

In this example, ‘Pants and Leggings’ is selected to further understand the colour distribution of this category.

A specific dive into seasonal colours would be relevant in identifying the colour adoption for these categories. In this case, Blue, Green and Brown were the top 3 seasonal colours adopted for Pants and Leggings.

In summary, Pattern Distribution provides a deeper understanding of the relationship between colour and category to facilitate effective product design and development relevant to market demand. 

Key Takeaways

  1. Which are the core colours newly launched for the specific period (year/quarter/month)?
  2. What are the differences and similarities in market preferences towards the colours
  3. What are the key colours adopted for respective categories
  4. What are the colour opportunities and risks in relation to category/segment?

Pattern Analysis

Pattern Analysis further validates Pattern performance by Retailer/Brand. To kickstart the analysis, at least a Market and a Retailer or Brand has to be selected. This analysis allows the tracking of pattern changes by retailer or brand and sell-out contribution % by selecting the ‘Sell-Out Contribution %’ option within the card. 

Within the Trend Breakdown by Retailer card, the ‘Sort by Product Count’ filter is available for selection:- 

  1. Default – Patterns are arranged in alphabetical order.
  2. Highest – Patterns are arranged from the highest to the lowest product count of All Retailers.
  3. Lowest – Patterns are arranged from the lowest to the highest product count of All Retailers

Within the Trend Breakdown by Retailer card, there are deeper analysis points for pattern:-

  1. Pattern Sell-Out & Product Contribution % breakdown by retailer or brand
  2. Pattern vs Plain ratio breakdown

The vertical axis is dynamic to display up to the highest % contribution (not necessarily 100%) for visibility on Patterns with low contributions.

Pattern Sell-Out & Product Contribution % breakdown by retailer or brand

In the chart above, cross-analysis with the inclusion of ‘Sell-Out Contribution %’ filter facilitates opportunities spotting should the specific pattern register a higher sell-out contribution.

In this example, a potential in Checks patterns is seen, registering a higher sell-out contribution at 16.98% vs. product contribution at 12.59%.

The stacked analysis here facilitates comparison among patterns from retailers/brands level. Further white spots can be identified, as well as understanding retailers/brands design direction and focus.

Pattern vs Plain ratio breakdown

The breakdown of Pattern vs Plain ratio is useful to understand the design focus and direction of the retailer or brand. It can be compared among retailers or brands to identify the similarities and differences in pattern offerings. 

For example, Boohoo registered a higher Plain product contribution at 43% vs average of 23% among all retailers selected. This provides a first level interpretation of simple, minimalist design preference by the Boohoo target customer. 

A deeper analysis of pattern performance is possible by sorting the product count by ‘Highest’ to identify the underlying reason.

By diving further into Boohoo analysis, you can see a high sell-out contribution and product contribution % on Graphic pattern which contradicted vs. all retailers. This data insights further reflects Boohoo customer buying preference for Casual products. 

In summary, retailers and brands customer behaviour and preference could be unleashed by layering the analysis of sell-out contribution and product contribution by pattern.

Key Takeaways

  1. How are the sell-out contribution % performed against product contribution %?
  2. What are the core patterns for each retailer/brands
  3. What are the differences between pattern performance between retailers/brands?
  4. What are the pattern opportunities in the market potential for further investment?
  5. What are your customer preferences for patterns?
  6. What does the pattern and print ratio imply for each retailer/brand?

Colour Analysis

Colour Analysis further validates colours performance. In order to kick start the analysis, at least a Market and a Retailer or Brand has to be selected.

This analysis allows the tracking of colour breakdown by retailer or brand and sell-out contribution % by selecting the ‘Sell-Out Contribution %’ option within the card. The colour breakdown can be viewed by All colours, Seasonal Colours, or Grayscale Colours.

Within the Trend Breakdown by Retailer card, the ‘Sort by Product Count’ filter is available for selection:- 

  1. Default – Colours are arranged in alphabetical order.
  2. Highest – Colours are arranged from the highest to the lowest product count of All Retailers.
  3. Lowest – Colours are arranged from the lowest to the highest product count of All Retailers.

Within the Trend Breakdown by Retailer card, you can deep dive into Colour Sell-Out & Product Contribution % breakdown by retailer or brand.

The vertical axis is dynamic to display up to the highest % contribution (not necessarily 100%) for visibility on Colours with low contributions.

Colour Sell-Out & Product Contribution % breakdown by retailer or brand

In the chart above, cross analysis with the inclusion of ‘Sell-Out Contribution %’ filter facilitates opportunities spotting should the specific colour register a higher sell-out contribution. In this example, observed a potential in Blue pattern from Boohoo specifically registering a higher sell-out contribution at 8.09% vs. product contribution at 7.99%.

Further analysis can be done by filtering down to a specific colour. In this case, it is possible to dive deeper into the performance of different shades of the colour blue by filtering in the main filters.

The stacked analysis here facilitates comparison among patterns from retailers/brands level. Further white spots can be identified, as well as understanding retailers/brands design direction and focus.

In the chart above, data has been broken down into subcategories of Blue Colour with the inclusion of sell-out and product contribution which gives a better understanding for users to view should the specific shade of colour register a higher sell-out contribution among other shades of colour.

In this example, high product and sell-out contribution can be seen for Dark Purplish Blue, Pale Blue and Very Pale Blue.

Key Takeaways

  1. How are the sell-out contribution % performed against product contribution %?
  2. What are the core colours for each retailer/brands
  3. What are the differences between colour performance between retailers/brands?
  4. What are the colour opportunities in the market potential for further investment?
  5. What are your customer preferences in relevance to core and seasonal colours?
  6. What are the differences in the colour performance between different categories?

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