Neil Banerjee
As the e-commerce sector grows increasingly complex and more intricate by the day, maintaining profitability has become a huge challenge for companies. Spiralling marketing costs, fluctuating warehouse and fulfilment costs, and the mushrooming of marketplaces have hit brands’ profit margins and put them under significant pressure.
SME Futures spoke to Prem Bhatia, the Co-founder and CEO of Graas, about how the company is using predictive AI to integrate disparate data silos, automate decision-making and execution, and deliver direct bottom line impact.
Here are the excerpts from the interview:
Can you tell us more about how Graas uses predictive AI to turbo-charge the growth of e-commerce businesses?
The growing complexity of the e-commerce sector has made it progressively challenging for brands to maintain profitability. With the proliferation of marketplaces, rising marketing costs, and fluctuating warehouse and fulfilment costs, brands’ profit margins are under significant pressure.
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In such a scenario, relying on data can be the most effective way to make informed decisions. However, the fragmentation of data has made any meaningful analysis challenging and resource intensive.
Graas’ solution is to tackle this issue by integrating disparate data silos and leveraging machine learning algorithms. Simply put, Graas uses AI to automate decision-making and execution across the entire e-commerce spectrum, including inventory, advertising and content, to deliver direct bottom line impact.
What are some of the biggest challenges that e-commerce brands face when it comes to data analysis, and how can AI help to overcome these challenges?
Graas addresses the challenges that brands face in three ways.
First, Graas connects previously siloed business segments, creating a unified data pool. Bringing data on to one single platform helps streamline operations and reduces complexity. With this, brands can have a single source of truth and a single pane to view their entire business metrics on.
Second, Graas applies a proprietary AI engine to this data pool. This engine acts as an in-house data scientist, analysing vast amounts of information. It predicts trends and patterns, identifies anomalies and outliers, and based on this, generates real-time insights and actionable recommendations.
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Finally, Graas’ solution allows brands to seamlessly execute these data-driven recommendations across their entire e-commerce business ecosystems – from storefronts (marketplaces, brand.coms), to ads, to inventory management, warehousing and last mile logistics.
Acting on opportunities in real time enables brands to drive profitable growth and stay ahead of the competition.
Can you walk us through a case study where Graas helped a brand increase their profitability through its platform?
Let us take the case of a fashion brand with a modest e-commerce presence, of 50 SKUs, selling on three channels –Amazon, Flipkart and their own brand.com website. Let us assume that they advertise on Google, Instagram and Amazon. On an average, the brand owner is faced with taking three decisions every two minutes.
If they are selling t-shirts, they need to identify where the demand is coming from – Chennai, Kolkata or Mumbai? What sizes do they need to restock, and should it go on the flash sale? Should they stop the ad campaign targeting a certain colour since it is likely to go out of stock soon? This is just for one product.
The more variables we add to this equation, the greater the complexity. Further, the lack of data backed insights makes decision making very unreliable. Graas’ predictive AI engine helps to reduce this complexity – which will ultimately help brands to reduce their time to market and enable them to create a streamlined, informed approach to growth and profitability.
What sets Graas apart from other Growth-as-a-Service technology solution providers in the market?
We are the only AI engine that covers the entire e-commerce business, end-to-end, across advertising, storefront (content & promotions) and inventory. We believe that this is a multi-billion-dollar opportunity that is founded at the intersection of AI, e-commerce, adtech and fintech.
How does the company plan to expand its presence in the fast-growing e-commerce markets of Southeast Asia and India?
We currently already have 250 customers, with our platforms managing 4 million SKUs and 45 million data points every month. With Graas’ unique Growth-as-a-Service offering, our customers have seen significant revenue growth and bottom-line impact in their e-commerce businesses.
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While globally companies have been battling economic headwinds and recession, the India and Southeast Asia region has witnessed steady growth, especially when it comes to e-commerce growth. Right now, Graas is focused on accelerating our growth in the region and bringing our solution to more brands, big and small.
Can you share some more information about the acquisition of India’s leading D2C and data specialist, Shoptimize Inc and Southeast Asia’s marketplace specialist, SELLinALL by Grass and how this has impacted its growth trajectory?
As a part of its fast go-to-market strategy in the region, Graas fully acquired Southeast Asia’s marketplace specialist, SELLinALL and India’s leading D2C and data specialist, Shoptimize Inc. This has allowed Graas to integrate their technology, leverage their client base and benefit from the expertise and knowledge of their teams.
What are the future prospects of AI-enabled Growth-as-a-Service providers in the e-commerce industry, and how do you see this technology evolving in the coming years?
Graas’ vision is to help drive growth for e-commerce businesses, large and small. Our goal is to use our AI engine to deliver actionable recommendations across the entire e-commerce business, end-to-end, resulting in direct bottom line impact. Traditionally, this would take an entire data science team, resulting in very high infrastructure and people costs. With our plug-and-play model, Graas’ Growth-as-a-Service makes growth accessible to brands of all sizes, with minimal need to adjust their internal structures.
How does Graas ensure the security and privacy of its clients’ data while providing valuable insights and recommendations?
Data security and privacy are critical for us. When clients connect their data sources, Graas pulls that data using standard APIs, ensuring that it is direct from the source and accurate.
Graas’ AI engine uses anonymous metadata collectively for analyses, insights and recommendations. The system uses a feedback loop to learn continuously and keeps fine tuning the algorithms to deliver improved insights and recommendations.
All the data that we collect is stored on a cloud-based, private database with industry standard protections. We ensure that the privacy of our client data is maintained at all times, and we are also in the process of obtaining ISO27001 and SOC-2 certifications.
