Leveraging the Analytics for Fashion eCommerce

Sure you need to spend some money on analytics (technology and people), but it's going to be less than the price you'll pay if you don't know how your business is performing.
Leveraging the Analytics for Fashion eCommerce

eCommerce companies are using analytic tools to address industry concerns like increasing competition, consumer retention, optimizing supply chain, merchandising, innovating marketing techniques, customer satisfaction, variation in margins and seasonal impact on revenues. Analytics is also helping eCommerce companies in informed decision making, enhancing user experience and maximizing profitability to achieve ROI.

“Sure you need to spend some money on analytics (technology and people), but it’s going to be less than the price you’ll pay if you don’t know how your business is performing. Consider, the missed revenue opportunities and potential cost savings you would be giving up.” was said by Arthur Charles Nielsen, Founder, ACNielsen.

[Image Source: http://eshopbox.com/]

Importance of Analytics in Fashion eCommerce

A well defined Analytics process can lead to positive impact across various aspects of Fashion eCommerce. These are as follows:

●       Insights over enhancing customer buying experience and better conversion of e-Commerce store.

●       Department wise, Process Wise monitoring and improvement.

●       Focus on Key Performance Indicator on a constant basis.

●       Getting an extra edge over competition by taking more well informed and targeted decisions.

●       Bridging gap between Qualitative and Quantitative performance. 

Data sources and its implication

[Image Source: http://eshopbox.com/]

As we understand the importance of data analytics, it is equally important to follow the sources from where this data is collected and its implications on the functioning.

Transactional data from different functions

Sources to assimilate transactional data could be:

A.    Historical sales

B.     Location wise sales trend

C.     Product returns

D.    Discounting pattern for  products

E.     Trending fashion in different seasons, etc.

How could these help

Analysing these would give an insight on planning the fashion collection on the basis of consumer preferences, seasonal trends and locational preferences.

Basis the insights it would be conducive to determine:

●       Category wise  split of collections (i.e in terms of what percentage of the items should be shirts, dresses, tops, skirts, and so on.)

●       Trends across geographies for price points, color and pattern.

●       Inventory levels  for easy replenishment and reducing “out of stock” time period.

●       Avoiding ageing of the products.

●       Products which need more discounts for liquidation

●       Marketplaces and locations where products would sell with lesser discounting and time when discounting strategy should be implemented.

In a recent observation, it was understood that at a higher average selling price it is possible to tap the market on myntra and flipkart but on snapdeal it sells with higher discounts. Comprehensive analysis of the sales pattern and consumer behavior would also help to depict the channels that should be chosen for maximizing profit.

 

Web and Mobile Analytics

 

Sources to collect Web and Mobile analytics data:

A.    Data from own website like frequently visited categories, open rates, click through rates, input data on user queries while accessing the website, etc.

B.     Inputs from Mobile applications like type of mobile device model, manufacturer, screen resolution, device capabilities, preferred user language and many more.

How does this help

To understand the ease of accessibility and effective communication between the buyer and sellers, it is pertinent to improvise on the user interface across web and mobile platform. Using insights from web and mobile analytics would help to evolve the user experience in sync with the user behavior. Some of the popularly used tools are Google Analytics, Clicky, Mint, KISSmetrics to name a few.

Competitive data collected by web crawlers

Competitive information is a vital requirement in today’s business scenario and staying up to date on competitor’s actions over the Web, is inevitable.

Sourcing of data:

Web crawler programs are being used to browse websites of competing fashion labels in a scripted pattern and collate data on available product details like color options, patterns, pricing, discounting information, etc.

How does this help:

These data are saved to analyse and create meaningful information for strategic decision making.

Data collected from Promotional activities

Promotional activities give quite a lot of insight on the consumer perspective. Being able to quantify the marketing activities in terms of user responses and feedback could give data and information on:

A.    Consumer behavior

B.     User engagement

C.     Effectiveness of promotional communication

How does it help

Analytics make marketing and promotions lot more quantitative. By utilising the available information on previous campaigns and competitor’s campaign, retailers could formulate better strategies to focus their marketing activities to get more traction.

Considering a case where, if a fashion collection receives a lot of response and engagement from Tier II cities, then the marketing activities should shift focus on to that market to capitalise on the response and generate more revenue out of it. The approach towards the other locations should be in sync with the responses as perceived from the data.

Social Signals

Social media channels are the most prominent consumer engagement platform today.

Source of insights on Social Media:

A.    Data of social sites like Facebook, Twitter, Pinterest, Instagram, etc, providing their own intelligence to generate consumer insights.

B.     Analysing the consumer engagement on these sites in terms of participation, increase in follower list, feedbacks are crucial inputs to be considered.

How does it help

Staying active on social platforms would instill a strong faith on consumers by staying in touch with them directly, resolving issues proactively and responding to feedbacks.

“In God, we trust; all others must bring data” - William Edwards Deming

According to Quora, there's an estimation that only 3% of the Internet-generated data could be considered valuable for businesses. However, only 0.05% of the 3% of data is being analysed to generate guided insights for business. That leaves about 99.95% of useful digital data not being utilized to find new opportunities or to add additional value to the business.

When deciding which pieces of data are the most meaningful, start from the end and work backwards. Without analytics, a company could be vulnerable. Extracting value from analytics requires perceptive staff, well-defined processes, a clear business strategy, and leadership support. With the advent of this competitive scenario, eShopbox, has started an initiative to set up a data sciences team to mine the available data on Fashion eCommerce. They would be creating the information with the help of their product eShopbox Insights, a business intelligence platform for fashion brands  to monitor product performance, optimize promotions, plan assortment and replenishments.

About the Author:

Ankush Karwa, Co-Founder, eShopbox, Gurgaon

Ankush oversees business strategy and technology roadmap of the company. With in-depth experience of developing strategy and technology for online media, he guides his team in building innovative eCommerce solution and delivering valuable service to brands.

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