Use Cases

In an increasingly consumer-driven landscape, an assortment mix that reflects accurate demand is the key to maximising profitability.

The Covid-19 pandemic left a huge financial strain on consumers. Cash-strapped consumers aren’t as willing to spend in this economy unless the products or services truly fit their needs. This mindset will likely remain for a period of time post-pandemic – placing greater importance on meeting demand accurately.

Buyers, merchandisers and designers typically research to gauge demand and understand consumer preferences. Designers for the most part, conduct conceptual or inspirational exploration to guide the design process. 

Meanwhile for buyers and merchandisers, trend forecasting is an integral part of assortment planning. Runway shows and street styles are observed closely to spot potential trends. Most fashion brands and retailers today are subscribed to trend forecasting services that provide trend reports for up to two years in the future. 

However, there are drawbacks to these methods. Inspirational or conceptual research is highly-dependent on intuition and poses risks that many brands can’t afford to foot. Accuracy becomes a major issue when it comes to trend forecasting. Products may launch at a time when the market is not ready to purchase.

Referring to historical performance is another common practice in the industry to forecast future demand. However, historical data is no longer a reliable indicator of the future, especially during the Covid-19 pandemic, when there are no past instances to benchmark.

With consumer preference constantly shifting, brands and retailers must innovate and adopt new methodology to gain accuracy and agility.

Accurately Meet Consumer Demand by Validating Category Trends

Trend forecasting on its own is unable to keep up with consumers’ increasingly fragmented interests. Brands and retailers should include data-backed analysis to validate true trends from short-lived fads.

Macy’s trend forecaster Abbey Samet highly recommends data-backed trend forecasting to add credibility and raise confidence in decision-making. She elaborated that in her position, analysing trend curves yield concrete insights on metrics such as YoY growth, which helps trend forecasters interpret trends better for buyers.

With a data-driven approach, you can track category trend movements against trade performance indicators over time for a holistic view on demand. In addition to substantiating trending categories, trend analysis also provides visibility on declining trends which allows for quick restructuring of assortment mix to minimise risk.

Let’s dive into validating category trends that meet consumer demand and create highly-commercial assortments with the following steps.

Step 1: Identify Uptrending Categories

The Omnilytics Trend Performance module surfaces trends from a selected market out of 47 available countries. These trends can be grouped by category, colour, patterns or materials to provide deep insights on understanding consumer demand.

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In the above chart, we’re looking at a high-level overview of the pants and leggings category in the UK for womenswear. From this perspective, it’s easy to spot trending subcategories in the market to guide assortment planning.

We can see that subcategories with fewer SKUs have fared better than top subcategories in the market, particularly joggers and leggings which are in line with current consumer demand for work-from-home essentials. From the overview, click on any uptrending subcategories and gather more in-depth information to further substantiate the trends.

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Studying a trend’s trajectory over time helps to assess risks. While the overall pants and leggings category’s trend line fluctuated sporadically, the demand for joggers is steady. Joggers also holds a higher trend score than other subcategories, which further corroborates its popularity. Trend scores are derived from a category’s performance across discount, replenishment and ageing over time.

Step 2: Validate Category Trends Against Performance

Analytics on most stocked colours, materials and patterns for joggers provides additional information on what is in demand in the market and also what is lacking. This is a great reference point for brands and retailers to seize opportunities and minimise risks.

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Examine key performance indicators like price positioning (median price and price spread) as well as discounted, replenished and sell-out rates to ascertain if the specific trend’s stock position accurately meets consumer demand. From the above chart, we conclude that joggers were able to drive high sell-out at 72.35%, with full price contribution at 52.74%.

We can also identify the top retailers and brands currently stocking joggers in the market – essentially a list of your potential competitors – which will come in handy in the next step.

Step 3: Compare Key Brands or Retailers

Compile a list of leading brands and analyse their category composition respectively. This is to better understand consumer demand and pinpoint gaps that you can fulfil ahead of your competitors.

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The above chart shows a breakdown of the pants and leggings subcategories by brand. For better context, observe the sell-out performance at full price by brand to work out the demand. Most brands recorded strong sell-out rates at full price for joggers, with the exception of Nasty Gal.

Nasty Gal’s failure to secure full price sell-outs for joggers signals a missed opportunity. The brand’s underperformance could be attributed to poor pricing or the wrong colour offering. Omnilytics’ new Competitor Benchmarking module allows for further performance-based validations to understand where you and your competitors fell short in assortment planning or brand positioning.

Once a category’s potential has been established, you can dive deeper to discern key patterns and colours that are in demand, with the Pattern and Colour Distribution and Analysis features in the Trend Performance module to identify style opportunities or risks.

Conclusion

Identifying and validating category trends with a data-driven approach helps brands and retailers outline a blueprint for building a demand-driven assortment. The process enhances trend forecasting by minimising the guesswork when crafting the right assortment strategy with the right launch timing – reducing risks and maximising profitability