How Online Fashion Brands are Using Analytics to Personalize Customer Experience

Brands use data and analytics to fine-tune their merchandise, something that attracts repeat visits from users.
How Online Fashion Brands are Using Analytics to Personalize Customer Experience

These days it is not unusual to hear words like PDP views, ATC and BTD ratios, checkout drop-offs, etc. in the corridors of a fashion start-up. The impact of data and analytics has been profound in how new-age fashion D2C brands have scaled up leaving traditional apparel companies behind in the digital race. Embracing data-driven decision-making over human intuition has become key in building a ‘creative’ business like fashion.

When online fashion commerce had started, it was said that the level of ‘personalization’ available in an offline retail store could never get matched online. Well, how wrong were we? 

Long gone are the days when human merchandisers and retail store owners decided what a customer could buy when they walked into a store. Customers don’t need to keep requesting harangued salesmen to show the next best piece. And salesmen don’t need to guess what the customer would really like using their years of experience. Now, simple taps on a phone screen bring alive the entire catalog of these brands. And the magic of data and algorithms is able to show the right products to the right customers at the right time.

The gains to a brand like these are quite easy to see. Based on millions of online user journeys over the years, algorithms are able to predict the best product for any customer as they navigate through the brand. The recommendations are based on complex patterns that are based on hundreds of paraments like the search keywords user has used on the website, the products they have added or removed from their cart, the time they have spent on the product display pages (PDPs), so on and so forth. More personalized is the user experience, more is the time spent by the user on the website and higher are the chances of the user converting into a customer.

Data also helps brands to tailor-made deals and discounts for customers as they shop online. What deal or discount to give, what should be the value, when should it be given, are all automated via data and analytics around user journeys. The key metric is ‘conversion’ (or in common parlance, ‘customer order’), and conversion is dependent on the customer having a great browsing experience that ultimately leads to a sale.

But it is not just limited to it. Brands use data and analytics to fine-tune their merchandise, something that attracts repeat visits from users. Unlike traditional brands, customers do not have to wait for ‘seasons’ to see new designs. No human merchandisers decide what collections are to be launched when to launch them and where. 

Further, the age-old problem of not having enough inventory for what is selling and having too much inventory of what is not is also getting solved using data. Forecasting tools give brands the edge to fine-tune their production and sourcing strategies. Using data brands have been able to predict what to make when to make and how much to make. Vastly improved inventory planning helps brands offer great prices to their customers.

Last but not the least, fashion belongs to an impulse buying category. A user abandoning a cart is not unusual and hence data-driven retargeting helps to bring back the potential customer. And for customers, this is great as they are able to complete their pending orders without fuss or delay whenever they are reminded about the pending order later via email or social media.


Indubitably we can say that data analytics has become an integral part of the fashion industry to address the various problems that it faces, such as the lack of unified sizes and the increasing number of customers wanting to shop for clothes online. Data-driven decisions will allow you to take the competitive advantage that will come with being in the fashion industry. Data-driven marketing techniques can help fashion players improve their inventory management, profitability, and consumer targeting. Through artificial intelligence, trend forecasts can predict future trends and provide actionable information on how those trends will play out. To implement AI-powered data analysis in existing fashion programs, collaborations with technology partners are needed. 

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