How Fashion E-commerce Data Transforms Strategies for Physical Stores

How Fashion E-commerce Data Transforms Strategies for Physical Stores

Written by Amelia TehAugust 14, 2020

How Fashion E-commerce Data Transforms Strategies for Physical Stores

With trends increasingly established by consumers as opposed to brands, retailers are referring to multiple sources of data to gain visibility on demand and the competition in the market.

Historical data, trend reports (from WGSN, Pantone, Vogue and the likes) and social media feeds are common data sources that are analysed concurrently to predict what consumers want.

This approach, however, is challenged to undergo transformation to adapt to the ‘new normal’. Here’s why:

The Need for Greater Visibility

As Covid-19 continues to impact consumer behaviour in unexpected ways, historical data and trend reports are now rendered unreliable, while social media trends are scrutinised by cautious retailers to avoid inventory glut.

Physical stores have reopened, but sales continue to suffer as the numbers are nowhere near pre-crisis levels.

The existing data sources are limited in validating demand and decisions.

Retailers are also missing external data benchmarking for further verification. Social distancing compounds the issue as it hinders effective physical comp shopping.

The need for greater visibility on demand to increase confidence in decision-making becomes critical and urgent.

A Reliable Data Source for the Fashion Industry

E-commerce data – information relating to the performance of an online shop, widely used by marketers in understanding consumer behaviour and enhancing conversion funnels – has been gaining strong adoption in the practice of merchandising and buying. 

Fashion market insights platform, Omnilytics, utilises a combination of machine learning, image recognition and keyword analysis to process e-commerce data from over 400 retailers across 49 countries, and develops benchmarks for brand positioning, trade performance and product taxonomy.

Merchandisers and buyers use the platform to identify demand signals, validate trends and understand competitiveness in the market for decisions on assortment planning, pricing optimisation and distribution expansion. 

This approach of benchmarking external data also completes business analysis through verifying decisions and ensuring investment is backing the right growth opportunities.

Opportunity in Crop Tops: Zara outperformed Fashion Nova on sell-out by 2.5 times with 189 SKUs. Source: 104,912 data points were analysed on products retailing online from Zara and Fashion Nova in the US from Jan 1st to Jun 30th 2020, as tracked on the Omnilytics Dashboard.

Ways E-commerce Data can Transform Retail Physical Stores

Driving business growth for both online and physical spaces is in more ways similar than assumed otherwise. Common growth drivers include product newness, category expansion, pricing optimisation and tactical promotions.

While online retailers will find the benefits of analysing e-commerce data an obvious solution, many others are unsure of ways it can impact the brick-and-mortar stores and revolutionise processes.

To help fashion retailers better understand the potential resources and opportunities for business growth driven by e-commerce data, we have identified 3 major ways how this can be achieved:

#1 Gain Competitive Advantage in the Physical Space

Brick-and-mortar stores typically carry product assortments that differ from the websites, due to differences in consumer demographics and their readiness to purchase.

This scenario presents opportunities for emerging online trends, validated with e-commerce analytics (new-in rate, full-price sell-out, replenished rate, etc.), to be explored and then introduced in the physical stores ahead of the competition. 

By layering a deep understanding of consumers who shop offline with e-commerce data insights, retailers can adjust the colour options, depth quantities and sizing of new products developed, to tailor to the specific stores.

#2 Minimise Risk by Experimenting Before Scaling

With e-commerce data unveiling emerging trends and demand-validated products, retailers can trial them starting with minimal quantities and across just a few selected physical stores before rolling out to all stores. 

In a similar approach, retailers can leverage e-commerce data insights on pricing and promotion mechanics for physical stores by experimentation before full implementation.

Validated demand: Stripe or floral patterns can add interest to a series of plain Crop Tops. Source: 104,912 data points were analysed on products retailing online from Zara and Fashion Nova in the US from Jan 1st to Jun 30th 2020, as tracked on the Omnilytics Dashboard.

#3 Maximise Omnichannel Productivity and Efficiency

Through routine monitoring of e-commerce data, retailers can ensure core online assortment and bestsellers are also represented in the physical stores.

This improves customer retention by giving customers a familiar experience every time, whether that means browsing on the website or collecting purchased items at the store. 

Concurrently, retailers can increase engagement by offering an online catalogue in physical stores, allowing shoppers to always find availability of styles, colours and sizes.

Integrating stocks on the e-commerce platform and physical stores offer a seamless and convenient shopping journey which ultimately enhances the brand experience.

Key Takeaways

Analysing internal data, trend reports and social media feeds is not just insufficient to predict demand, but also dangerous when applied without validation.

With the right data, both internal and external, visibility is raised to prompt and validate demand signals, leading to the discovery of actionable insights and informed decision-making.

With e-commerce data, opportunities in an omnichannel environment can be seized and risks minimised with confidence.

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