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Complexity vs Capability: How brands can keep pace with the e-commerce revolution

Prem Bhatia
Prem Bhatia • 5 min read
Complexity vs Capability: How brands can keep pace with the e-commerce revolution
Through algorithmic e-commerce, businesses can consolidate and produce meaningful insights from data directly fuelling growth. Photo: Unsplash
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It is undeniable that e-commerce has revolutionised the global retail industry, and advances in all related functions – like social marketing and marketplace integration – have opened the door to new possibilities for brands.

However, this advancement has also created so much new complexity that many brands now struggle to fully leverage this technology, and many risk being left behind by competitors. How did technology-driven complexity out-pace capability?

Though this has been a prevailing trend for the past decade, advancements in technology and more recently, the COVID-19 pandemic, have catapulted this leap with e-commerce widening its lead on traditional retail. During the pandemic, online shopping became the norm out of necessity. Global e-commerce rose from 15% of total retail sales in 2019 to 21% in 2021. It now sits at an estimated 22% of sales. This solidified ecommerce’s status, and its growth represents a permanent change in how people shop.

Especially in regions like Southeast Asia with earlier-stage markets, e-commerce is poised to grow significantly with some estimates citing 17% over the next five years. While mature markets like China and the United States have incumbent giants like Alibaba and Amazon, younger markets across Asia and Southeast Asia are seeing aggressive competition as players big and small wrestle for market share.

In this wrestle, e-commerce businesses turn to marketplaces, cross-channel advertising, social media marketing, search engine optimisation and more to find an advantage. What we are seeing is more tools becoming available to businesses and competition, motivating businesses to leverage them. Inadvertently, the rise in demand for e-commerce from consumers ultimately translates to higher complexity for businesses.

Due to the nature of ecommerce, businesses often find themselves with massive amounts of data, in terms of volume, variety, velocity, veracity and value. This is otherwise known as the 5 “Vs” of big data.

Besides that, as the industry grows, more sophistication is introduced through more marketplaces, direct-to-consumer websites, marketing and logistics partners – all of which produce yet more data. The massive amount of data is only the tip of the issue, the fact that all this data is in silos is the rest of the iceberg.

See also: How to innovate with AI in a privacy-protective way

Take for example, a relatively modest brand with 100 stock keeping units (SKUs) that sell across three countries through five channels and across three advertising mediums. Through this permutation, such a brand would need to make 45,000 decisions per month, which makes it practically impossible to make insightful decisions every time.

Making sense of it all

As such, there is an increasing trend of "algorithmic e-commerce"— the systemic digitalisation of business functions using artificial intelligence (AI) and machine learning algorithms that would have previously been handled manually.

See also: Mature data management is critical in ensuring enterprises are AI-ready

Generally, algorithmic e-commerce works to solve the problem through a multi-step process. Firstly, by connecting the data silos across disparate systems and platforms into a unified data pool, brands vastly reduce operational complexity. More importantly, having a unified pool of data is critical for the next step of analysis.

Secondly, feeding this wealth of data through an AI engine to analyse the data helps create value in the form of predicting trends and delivering actionable recommendations. It’s important to note that it is critical to leverage an AI engine that is purpose built for e-commerce.

Lastly, with these insights, it is critical that brands look to streamline and automate their execution. Businesses should look to platforms that will allow them to act on analysis across marketplace storefronts, brand websites, social and conversational commerce, performance marketing, inventory management, warehousing and last-mile logistics.

If done effectively, the application of algorithmic e-commerce can directly impact the bottom-line to increase margins and bolster growth.


Through algorithmic e-commerce, businesses will be able to consolidate and produce meaningful insights from data directly fuelling growth. This has led to the dawn of a new model: Growth-as-a-Service (GraaS).

Traditionally, we had Infrastructure-as-a-Service (IaaS), Platforms-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Considering the importance for businesses to increase growth through developing better margins, GraaS was created to address that need.

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Previously, only the biggest brands were able to invest in the resources needed to efficiently leverage algorithmic AI to accelerate growth. GraaS levels the playing field by making growth accessible to brands both big and small. It can be seen as a solution that integrates and combines with AI-powered analytics to directly impact a brand’s bottom-line. With the existing plug-and-play model, all brands are able to tap into GraaS, the equivalent of having an in-house data scientist.

Because of the pandemic, businesses that previously thought of e-commerce as an extension of their physical stores must now view it as the core of their business. E-commerce growth shows no signs of slowing down. Instead, it is permeating new markets and growing rapidly. Heralding a new era of how e-commerce businesses should operate as more players realise the need to efficiently utilise the data they are privy to fuel growth.

Prem Bhatia is the co-founder and CEO of Graas

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