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Unravelling the impact of AI and Machine Learning on the future of eCommerce

This article originally appeared on Hindustan Times.

The last few years have witnessed an intense shift towards cutting-edge technologies, with Artificial Intelligence (AI) and machine learning (ML) at the forefront. Every industry is betting big on these technologies because of their potential to revolutionise operations and decision-making processes.

The driving force behind this trend is the unprecedented access to vast data that businesses now possess. And as the saying goes, "data is the new oil," and with data abundance, AI becomes an invaluable ally.

Among the sectors riding the wave of AI and ML advancements, e-commerce has emerged as a key player, using innovative technologies to gain a competitive advantage. Large-scale adoption of AI in the e-commerce industry is not only inevitable, but also essential.

Despite the wealth of data at their disposal, e-commerce businesses have challenges to deal with. Managing multiple platforms such as online storefronts, marketplaces, marketing channels, and advertising platforms has led to the creation of data silos. These isolated pockets of information don’t give a complete picture of how their business is going.

To make sense of their data, e-commerce brands have to unite, clean, and organise their information. While the volume of data is substantial, the lack of integration renders it less potent. Many companies engage in manual efforts to consolidate data, but this traditional approach has proved to be inefficient. The real-time factor, a critical element in e-commerce, is compromised when relying solely on manual processes.

With such vast volumes of data, it is a mammoth task to perform quick analyses to identify trends, patterns and anomalies in the data. It lies beyond human capacity and is also very error prone, when done manually. AI and ML allow e-commerce brands to automate this process of analysis and arrive at answers for ‘what is happening’ and ‘how the business is doing’. Further, it can also help them get deeper into the ‘why’ behind the fluctuations in metrics.

Let’s say you own an e-commerce business in the clothing industry. Traditionally, for doing something like inventory analysis, you would have to manually scrutinise historical sales data, accounting for different genders you sell to, styles, colours, sizes, seasonal trends and more. However, with advanced ML models, this process becomes easier. AI models can ingest data in bulk, analyse trends over time, and precisely indicate why there is a change in the inventory movement or performance & recommend the next steps, which empowers your brand to optimise inventory. These insights mitigate the risks of overstocking (which causes loss) and stock-outs (that put a limit on your profits).

Ads are another area where a company invests a significant cost component, and you need all the insights you can get to generate maximum return on investment (ROI). Every ad campaign has many unknowns and variables. During peak sale days, with these advanced algorithms, you can deal with the complexities of audience targeting, budget allocation, and bid optimisation.

For example, your e-commerce business gearing up for the holiday season can use ML models to fine-tune your advertising strategy. The algorithms quickly analyse and detect patterns in historical data, current market trends, and competitor actions, providing real-time recommendations. This ensures optimised ad performance, reduced acquisition costs, and a maximised return on investment.

The other key area in e-commerce is conversions. In this regard, the customer journey is a crucial focal point and the online storefront is where most customer interactions happen. Analysis of customer behaviour, preferences, and historical interactions can help to create more impactful user experiences resulting in better conversions. From personalised product recommendations to targeted marketing messages, brands can make real-time adjustments in the customer journey, ensuring that every step a user takes is optimised for immediate satisfaction and is strategically aligned with long-term customer retention objectives.

You can also optimise your product listings based on algorithms that consider user engagement, keywords, and trends. This enhances the visibility of products and maximises conversions. By continuously refining content strategies, e-commerce brands can stay ahead of trends, resonate with their audience, and create compelling, high-converting content that stands out in the crowded digital market.

Automation is another key element to consider. It takes away the burden of repetitive tasks within e-commerce operations. From data entry to product categorisation and order processing, AI-driven automation streamlines workflows, optimising efficiency and freeing up human resources for more complex and strategic responsibilities. This accelerates operational processes.

Let’s say your ML algorithm gives you a key recommendation related to inventory or ads. If it is not executed on time, the recommendation might not be as effective. With automation, the recommendations can be executed automatically based on the rules you set. Once the recommendations are executed, the algorithm can measure the performance and note the feedback too. The more this process is repeated, the more accurate recommendations you’ll get in the future.

Beyond streamlining operations and optimising your decision-making processes, AI has permeated other aspects of the e-commerce ecosystem – whether it is AI powered chatbots and virtual assistants or fraud detection, it is shaping the ways businesses engage with customers. As we look ahead, the two areas which will redefine the e-commerce landscape will be prescriptive analytics and generative AI. While there are a lot of tech companies today working on these cutting-edge technologies, they are still some way off from providing off the rack solutions.

Finally, as AI and ML gain traction in the e-commerce landscape, it is imperative for brands to consider four key factors for effective implementation: the quality and timeliness of data, the evolution of data science models, the integration of automation for seamless execution, and strategic alignment with business goals.

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.

Prem Bhatia

5 Mar 2024

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