Data is the New Fuel to Drive eCommerce Growth

During the post industrialization era, the world was more or less driven by fossil fuel, making oil the most valuable resource of all!

In today’s internet age, commercial communications and human interactions are equally dependent on information or data.

Thus, the often-used phrase ‘data is the new oil’ is highly justified in the cases of most industries and markets today.

Southeast Asia, where eCommerce market is growing at an unprecedented speed, customer data is truly as valuable as crude oil!

While almost all eCommerce businesses hold more or less consumer data, not all know how to use it to their advantage.

Here’s how online retailers can harness the power of data to scale their eCommerce business.


Understand customer behavior to optimize conversion

Consumer data, when analysed properly, gives amazing insight on how customers behave, from the time she lands on the website till she makes a purchase.

With proper analysis, businesses can easily identify customers into different segments based on geographic location, shopping patterns, product preferences, gender identities etc.

This segmentation can then help eCommerce businesses create targeted strategies focusing on a particular group at a specific time period.

For example, Priceza’s 2016 search history shows Fashion and Clothing as the leading product category for eCommerce in Indonesia.

With 24.3% of all eCommerce searches on Priceza Indonesia being conducted in this segment, a budding online retailer might want to focus on Fashion and Clothing while setting-up shop in the country.

Similarly, a Thailand eCommerce store might see more sales in Fashion and Clothing category during New Year and a surge in Electronics sales during Black Friday.

Taking intelligence from this data, the business might want to launch a Fashion and Clothing Sale during New Year and an Electronics Sale during Black Friday to optimize conversion.

Personalized product recommendations to boost cross-selling

As per a study conducted in 2014, 31% of total eCommerce revenue world-wide is attributed to personalized product recommendation.

The figure completely justifies Amazon, Alibaba and other leading eCommerce players’ huge investment in recommendation algorithms.

What do these recommendation algorithms do?

These algorithms extensively collect and analyse customer information to identify the products often brought in close proximity of time by the same user.

This way, the eCommerce stores can recommend a pair of headphones when a customer is placing the order for a smartphone.

Another way of using the power of personalized recommendation is to suggest products based on previous purchase history of a customer.

For example, if a user tends to purchase cosmetic items of a particular brand, the eCommerce store would display products of the said brand and may be the products that are very similar to it, every time she logs in.

Personalized product recommendations, not only helps online stores making cross-sales, but also add to the user experience of the customers in great way. By offering highly relevant products, the eCommerce store saves the user valuable time which she would otherwise spend looking for the product.

Create irresistible offers to drive campaign revenue

Southeast Asian eCommerce market is highly price sensitive and shows significant favors towards discounts, offers, price cuts etc.

However, offering an arbitrary discount amount on any product category can be counterproductive for generating revenue.

Here, data mining can be of great help.  With highly accurate customer segmentation (discussed in Point 1) and in-depth customer knowledge, online retailers can understand when to launch a campaign, what products to include in it and how much discounts to offer.

For example, an eCommerce store that sells fashion apparel may see a drop in the number of winter-wear sold during the end months of winter.

Data analysis may have shown that most consumers tend to make a purchase with the discounts are between 40%-50%.

Based on this data, the eCommerce store can now launch a full-fledged End of Season winter-wear sale with up to 50% discounts of product price.

Make near-accurate predictions for better inventory management

Inventory management is a huge concern for any retailer, be it online or offline. With sales pattern analysis, eCommerce businesses can almost get rid of the guesswork in the process.

By analyzing purchase patterns, purchase history, time of the year, any campaign that the business might be running at the moment, click through rates etc, algorithms can successfully offer near-accurate predictions on expected number of sales and inventory management issues.

This goes a long way in automating inventory management as well as making the retailer’s job easy.

Data is ready to revolutionize eCommerce industry like never before. According to industry experts, eCommerce is going to use data insight (with Big Data analytics) in more extensive way in the years to come.

How Alibaba Harnessed the Power of Data

The Chinese retail giant Alibaba which recently set foot in Southeast Asia is the perfect example of how to propel growth by utilizing data.

The company which is a brain child of Jack Ma has created an integrated ecosystem with eCommerce, Logistics, Marketing Tech, Cloud Computing, Payment and Entertainment over the years.

This enabled Alibaba to capture consumer data in unprecedented level. With the help of Big Data, the company used this intelligence to create personalized and integrated shopping experience for its users.

The result is for the world to see!

According to a case study by Goldman Sachs, Alibaba’s GMV is predicted to reach Rmb5.1trn in FY2019E.

Last Word..

Alibaba is the apt example of how data is ready to revolutionize the eCommerce industry like never before.

According to industry experts, eCommerce is going to use data insight (with Big Data analytics) in more extensive way in the years to come.

To survive and thrive in today’s data driven world, eCommerce entities, both big and small, must start data mining and use the same for creating more personalized and intimate retail experience to their users.