How online fashion brands are using analytics to personalize the customer experience

These days, it is not uncommon to hear words like PDP views, ATC and BTD ratios, cash crashes, etc. in the halls of a fashion start-up. The impact of data and analytics has been profound in how the new era D2C fashion brands have developed, leaving traditional clothing companies behind in the digital race. Embracing data-driven decision making rather than human intuition has become the key to building a “creative” business like fashion.

When the online fashion business first started, it was said that the level of “personalization” available in an offline retail store could never be matched online. Well how wrong were we?

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

The payoffs for a brand like this are pretty 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 the brand. Recommendations are based on complex patterns based on hundreds of parameters such as the search keywords the user used on the website, the products they added or removed from their cart, the time that he spent on product display pages (PDP) etc. etc. The more personalized the user experience, the more time the user spends on the website and the greater the chance that the user will turn into a customer.

The data also helps brands create tailored offers and discounts for customers when they shop online. What deal or discount to offer, what should be the value, when should it be given, are all automated through data and analytics around user journeys. The key metric is “conversion” (or, in common parlance, “sales order”), and conversion depends on the quality of the customer’s browsing experience that ultimately leads to a sale.

But it is not limited to that. Brands use data and analytics to refine their merchandise, which attracts repeat visits from users. Unlike traditional brands, customers don’t have to wait for “seasons” to see new models. No human merchandiser decides which collections should be released, when and where to launch.

Additionally, the age-old problem of not having enough inventory for what sells and having too much inventory for what isn’t is also tackled with the help of data. Forecasting tools give brands the advantage of refining their production and sourcing strategies. Using the data, brands were able to predict what to do, when to do, and how much to do. Dramatically improved inventory planning helps brands deliver great prices to their customers.

Finally, fashion belongs to a category of impulse purchases. A user who abandons a cart is not unusual and therefore data-driven retargeting helps bring the potential customer back. And for customers, this is great because they can complete their pending orders without any issues or delays whenever they receive a subsequent pending order reminder via email or social media.

Conclusion

Undoubtedly, we can say that data analytics has become an integral part of the fashion industry to solve the various problems it faces, such as the lack of unified sizes and the growing number of customers wanting to buy clothes. in line. Data-driven decisions will allow you to gain the competitive advantage that will come from being in the fashion industry. Data-driven marketing techniques can help fashion players improve their inventory management, profitability, and consumer targeting. Using artificial intelligence, trend forecasts can predict future trends and provide actionable insight into how those trends will unfold. To implement AI-based data analysis in existing fashion programs, collaborations with technology partners are needed.

About Nunnally Maurice

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