This article originally appeared on Data Quest
Covid-19 saw an explosion in online commerce. Established brick-and-mortar companies started selling online, and a slew of younger brands (also referred to as ‘d2c’) emerged. Sales happened through their website, search, and social media channel, such as Facebook/Instagram/Google, and marketplace storefronts, such as Amazon/Flipkart. This led to the creation of enormous amounts of first party data (data that belonged to the seller).
Data included average order value, ordering frequency, returns, location, demographics, cart abandonment or dropout instances, returning customers, and so on. However, despite the immense potential of this data to drive business intelligence, it remains underutilized because brands/companies do not have the budget or expertise to make effective use of data.
AI’s role in enabling brands to convert data into actionable insights
Let us take the case of a company that sells roughly 50 SKUs via three channels in two countries and uses at least three advertising platforms to generate demand. Even a simple setup like this will generate massive amounts of data. The more variables we add to this equation, the more complex the data gets. In addition, all of this data rests in siloes, within different units like the marketplaces or the advertising platforms.
The siloed nature of this data makes analysis difficult and labor-intensive. It is important to consolidate this data into a single data repository. Next, utilize AI and ML to analyze the data to generate actionable insights across the entire business, from storefronts to inventory to ads.
Why rely on data to drive decisions?
Costs are rising, and there is a continuous increase in variables, e.g., new channels. Managing profitability is a genuine challenge, for brands big and small.
Using data and insights derived from it can be an effective way for brands to identify opportunities for growth and take prompt action. For example, the type of purchases that happen online during a festive season sale, how timed notifications and messaging contribute to the online traffic, and how a particular SKU is performing in geography, are all important to know in real-time while planning marketing campaigns.
Using ML on integrated data, helps arrive at trends and patterns. Imagine this working like an in-house data scientist. The end output of this data scientist is an informed approach to gauging the demand for a product, planning its inventory, optimizing content for better performance, and improving the outcomes of marketing campaigns and ad budgets.
Algorithmic eCommerce puts available digital resources to smart use. It helps overcome the problems posed by data scattered among separate sources and puts it on a unified dashboard. This ultimately impacts the brand’s bottom line, driving profitable growth.
Today, the usage of a predictive AI engine in eCommerce massively reduces the dependency the brands had on other resources to get the same level of productivity. With algorithmic eCommerce, it is like brands getting access to a larger data science and operational team at a fraction of the price that it would cost to build similar capabilities in-house.
Authored By Prem Bhatia, Co-Founder and CEO, of Graas.
18 Apr 2023