What Big Data Is Doing To The Online Marketing Space.

Back in the day, businesses had to do a lot of strategic guessing or bank on lady luck to see if certain campaigns would work out or not. Marketing teams struggled to keep up with trends and it was difficult to foresee or understand what the consumer wanted.

The digital era has enabled markers to finally have all the data they need to fully engage with the consumer.

The only problem – the sheer amount of data is overwhelming and incredibly extensive.

Big data, simply put, is extremely large and complex data sets, so voluminous that traditional data processing software cannot manage them.

But why is it so important?
Big data analytics has enabled millions of organizations to identify new opportunities using the insights harnessed from today’s huge data resources. This has led to more efficient operations, smarter use of resources, higher profits and of course, happier customers.

Big data can serve to deliver benefits in surprising areas. One example can be found in the fashion industry.

Data analytics may not be new to the industry, but the real revolution lies in the way data is now becoming available and utilized by these fashion companies. The emergence of COVID-19 has presented a major challenge to the fashion industry. Traditional brick and mortar stores have suffered from decreased footfall to shopping areas, and a consequent drop in sales as well, as a result of social distance measures/lockdowns. To counter this, many stores have set up online storefronts to ensure that customers can still reach them, coupled with sales and discounts.

Here are the case studies of 2 leading fashion brands and how they have used the data gleaned from online sales, or other digital strategies they have employed, to help them stay competitive in the market.

How ZARA uses market-based analysis to create higher turnover rates and demand from consumers.

Traditionally, retailers would do their best to estimate demand for the different Stock-Keeping units (SKU) based on industry experts’ opinions. Models that sold well would quickly be out of stock, and models that didn’t sell as well would have large inventories and later on be discounted.

To counter this, Zara has since adopted market-based analysis to find out what products appeal most to our customers and what they might buy in the future. This method employs unsupervised learning, meaning that it learns what customers like based on the frequency of their purchases. So instead of ordering the bulk of the quantity for the season, Zara only orders a small amount of merchandise. Once the merchandise hits the stores, Zara collects sales data and analyzes each SKU’s sales against supply. Zara uses these insights to guide their following orders, and design and manufacture models that have the most popular features to satisfy demand. Instead of replenishing products that get sold-out, Zara instead creates new styles and looks, reducing the need for markdown of prices.

How ASOS uses big data analytics to tailored clothing pieces to each individual consumer.

In the past few years, ASOS has launched a number of personalised recommendation services which use a machine learning technique known as clustering. Clustering discovers interesting patterns in data, such as groups of customers based on their behaviour. On ASOS’s website and mobile app, you can find

Style Match which shows customers, products that are similar to the ones they have bought or are currently browsing.

And Your Edit: which shows products that we thinks customers might like;

ASOS’s algorithm will recommend you with products that have been purchased earlier by other customers with similar market baskets, thereby providing customers with personalised recommendations. This is one of ASOS’s most important tools because it enables us to sieve out what a particular customer might like amongst our many products, reducing the chance of them leaving the site in frustration and improving the chance of a purchase happening.

ASOS has also recently launched their very own size recommendation tool called Fit assistance. The thing about the fashion industry is that there isn’t a universally-standardised size chart, so this makes finding the right size online even harder, because customers don’t have the luxury of trying it on. To solve this, ASOS has developed a sizing tool called fit assistance which was created in collaboration with the team at Fit Analytics. So ASOS shoppers take a small quiz which tells us their height, weight, age, general body proportions and also their fit preferences. ASOS then compares your data with the data of shoppers like you to generate a size recommendation.

We can all agree that there is nothing more frustrating when you receive a really cute but ill-fitting product. So Fit Assistance has allowed us to reduce the percentage of returned items per order due to incorrect sizing, thereby increasing customer satisfaction as well as sales revenue.

What does this all mean?
As the internet evolves, we must too. Big data has since become a potent tool when it comes to the online marketing world because it captures insights into our customers at a level of detail never thought possible before. As Geoffrey Moore, author of best-selling book Crossing the Chasm: Marketing, tweeted in 2012

“Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.”

It may have been an exaggeration back then, but a universal truth right now.


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