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How can data analytics benefit customers in era of e-commerce 4.0?

Analyzing massive data sets will not only benefit e-commerce companies, but also customers, given that e-commerce companies will use data-driven insights to improve the way they cater to customer needs.

Johan Sulaiman (The Jakarta Post)
Jakarta
Thu, October 10, 2019

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How can data analytics benefit customers in era of e-commerce 4.0? Analyzing massive data sets will not only benefit e-commerce companies, but also customers, given that e-commerce companies will use data-driven insights to improve the way they cater to customer needs. (Shutterstock.com/one photo)

Data analytics has played an increasingly crucial role in the e-commerce ecosystem.

Within a short period and by necessity, the use of data analytics has spread across all business departments, from IT and marketing to operations and logistics, to help process valuable customer and market data that will shape the strategies e-commerce companies adopt to provide their services successfully.

Indonesia’s e-commerce industry is currently transitioning into the era of e-commerce 4.0, in which offline merchants will integrate themselves into the online sphere and contribute new data into the existing pool. Additionally, the deployment of the Internet of Things (IoT) in warehouses and logistics fleets will further drive up the volume of data and the need to refine them higher on the analytics chain.

Analyzing these massive data sets will not only benefit e-commerce companies, but also customers, given that e-commerce companies will use these data-driven insights to improve the way they cater to customer needs. And as the technology behind data analytics goes through the course of technological transformation, the advantages that customers receive will surely increase.

From the universe of technology-driven transformations, three will have a significant impact on e-commerce and, therefore, on customers.

The first transformation is the growth of omnichannel technologies as offline merchants start using web/mobile applications, tablet point-of-sales (POS) and QR codes to integrate themselves into e-commerce platforms. Raw data from these new sales points will contribute to the additional surge of individual and aggregate data emanating from mobile devices that customers use to access e-commerce platforms.

This data explosion is as much a game changer for online and offline retailers as it is for customers. With more data, retailers can discover fresh ways to optimize their services to customers. For example, knowing the types of groceries customers like to buy online and the ability to pick them up directly at offline stores will help retailers ensure the stock availability of such items so that customers can always find the products they need.

The second transformation is the use of faster, on-demand processors at scale powered by cloud-based server virtualization. These powerful processors enable the capturing and analyzing of massive data sets generated by smartphones, personal computers and POS and QR codes in real time. Additionally, online retailers can adjust computing power as needed, spending significantly less during the first quarter of the year but rapidly ramping up server processing capacity during key 10.10 and 11.11 dates, also known as National Online Shopping Day (Harbolnas).

For customers, this translates into several benefits, one of them being deeper consumer micro-segmentation. Previously, consumers were lumped into basic categories of age, gender and income. Now, greater processing power means e-commerce companies can then use this data to design and analyze more granular segmentations that could more accurately determine customers’ tastes and moods, which would translate to better product recommendations. A common example of this is face care; two customers from the same demographic can have different preferences, such as with customers who love K-pop – some have a greater tendency to prefer Korean cosmetics. Coincidentally, this creative process in designing, evaluating and testing these segments is evidence that human creativity is still at the forefront of e-commerce 4.0, despite the heavy involvement of technology.

The last area of technological transformation that will enhance e-commerce data analytics is the continual rise of social media. Indonesians are avid social media users. Data from We Are Social shows that 150 million Indonesians are active on social media. E-commerce players have leveraged the popularity of social media by providing customer services over Facebook, Instagram and Twitter. On the marketing and branding sides, e-commerce companies are heavily collaborating with key opinion leaders (KOLs) with a substantial amount of social media fans. These two approaches have boosted customer engagement, with customers interacting with e-commerce companies through comments, likes and shares. E-commerce companies are keen to leverage this new media further for deeper interactions.

All these interactions on social media are essentially data. E-commerce players can intertwine this openly available data with those generated by machines – including web checkout carts and offline POS tablets – to truly understand what makes customers tick. Of course, the processing of data that customers publish on social media should fall within the perimeters of user agreements. It is also important for customers to determine the right level of sharing of their online activities.

What do improvements in data analytics mean to the industry? The answer is steeper growth of a retail and healthy consumer-based economy driven by rising customer satisfaction. A study by Morgan Stanley suggests that online sales would expand to contribute 18 percent to national retail sales by 2023, and as customers become increasingly satisfied with their online retail experience, they will be more inclined to transact online and, therefore, drive the economy. (kes)

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Johan Sulaiman is the vice president of analytics at Blibli.com. He is a seasoned data technology leader with experience in designing and leading large-scale information systems and data analytics programs for the United States’ Fortune 500 companies.



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