Even AI — generative or general — cannot help businesses improve customer experiences if all the crucial data is siloed

Every customer is unique these days: broad categories — it is all about personalized marketing instead of mass demographic-based segmentation.

So, with advances in digital transformation and data modernization, retailers now deliver highly personalized offers and targeted communications using data analytics and customer micro-segmentation. Soon, this technology will change how retailers interact with customers, oversee operations, and enhance their supply chain efficiency.

However, businesses faced with inaccurate data that is embedded in multiple storage silos will encounter challenges. Even with AI-based analytics enabling businesses to harness vast amounts of data to simplify the process of creating customer micro-segments and deliver better personalization, many firms are still struggling to maximize the value of their data.

Breaking down data silos  

So, with this in mind, how can firms extract value from their data and use AI to address the challenges associated with micro-segmentation?

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 personalized experiences through micro-segmentation.

To overcome this problem, retailers must consolidate all internal and external data sources into a unified customer data platform, and ensure that the data is readily accessible to teams in an easy-to-digest format.

Using AI to tap data value

For retailers that face challenges in configuring their data for personalization, it is very likely that they simply need to modernize their data storage.

By utilizing 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 personalized 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 heavily today: keep drier than others — with our technologically-advanced umbrellas.”
  • Optimize data utilization to facilitate streamlined delivery of intelligent applications and services across the organization

While retailers know that personalization is a key to success, unlocking its potential hinges on having fast and easy access to accurate, complete data.

For organizations that are ready to put their data to work, one fast solution is to engage a “cloud computing services” provider. With the right external expertise, retailers will be tapping into cloud-native AI development processes and machine-learning algorithms that empower businesses to work faster and smarter.