By Sandeep Bhargava
In recent times, each customer has unique characteristics, making broad categorisation obsolete. Mass demographic segmentation is no longer effective. Thanks to digital transformation and data modernisation, retailers can now look forward to a successful future by providing personalised offers and tailored communications through data analytics and micro-segmentation.
According to the EY report, by 2025, Gen AI could potentially elevate the retail sector’s profitability by 20%. The technology does not just reduce overheads but can significantly increase sales through tailored consumer experiences. Even local retailers can provide ‘hyper-local’ personalised shopping experiences by integrating Gen AI tools. This along with its IT prowess, India could emerge as a global nucleus for Gen AI retail solutions.
Meanwhile, Rackspace Technology’s 2024 AI Report found that there is a continued surge in AI investment, with companies anticipating doubling their budgets in 2024 as compared to 2023. The survey also reported their investments are yielding significant returns, with 86% reporting that their companies have seen tangible benefits from the implementation of AI
However, achieving those numbers can be a challenge, especially when businesses are faced with inaccurate data locked in multiple silos.
Even with AI-based analytics enabling businesses to harness vast amounts of data to simplify the process of creating customer micro-segments and deliver unparalleled personalisation, many are still struggling to maximise the value of their data.
Therefore, to boost the effectiveness of their marketing programs, companies must first understand how to extract value from their data and use AI to address the challenges associated with micro-segmentation.
Eliminating data silos
The primary requirements for effective micro-segmentation are accurate and complete data. For retailers that often have several brands, with each storing siloed and disparate data, achieving this objective becomes a significant challenge. These data silos not only limit their comprehension of customers but also hinder their ability to provide personalised experiences through micro-segmentation.
To overcome this problem, retailers need to consolidate their internal and external data sources into a unified customer data platform and ensure that the data is readily accessible to marketers in an easy-to-digest format.
The role of AI in personalised marketing
For retailers that face challenges in configuring their data for personalisation, it’s very likely that they simply need to modernise their data storage.
By utilising AI to help micro-segment their target audiences, retailers stand to reap a range of benefits, including the ability to:
- Share data across all departments of the business to ensure everyone has access to comprehensive insights.
- Implement visual data transformation tools to enable easy understanding of data without the need for coding.
- Offer highly personalised customer experiences driven by in-depth data analysis.
- Suggest products based on factors such as consumer purchase history, social media engagement, and current trends.
- Provide contextual product recommendations, such as, “It’s raining today: keep dry with our top-rated umbrellas.”
Retailers are increasingly embracing personalisation as the cornerstone of their success. However, the ability to fully harness its potential relies heavily on the availability of fast and convenient access to precise data. Nonetheless, embarking on a journey of data modernisation and artificial intelligence will yield remarkable results. This transformative step will empower marketers to establish deeper connections with consumers, elevate engagement levels, and ultimately drive substantial growth in the company’s revenue.
The key to succeeding in today’s retail environment is leveraging AI to convert customer data into actionable insights. By adopting a data-driven approach to personalisation, retailers can deliver exceptional customer experiences and achieve significant returns on their marketing investments.
The author is SVP, global services and solutions, Public Cloud Business Unit
(Views expressed are the author’s own and not necessarily those of financialexpress.com)
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