In the era of artificial intelligence (AI), data has emerged as the most valuable currency for marketers. Machine learning (ML) and generative AI (GenAI) have the potential to revolutionise marketing practices, but their effectiveness depends on the quality and accessibility of data. The more comprehensive and accurate the data, the better AI systems can provide insights, recommendations, and informed decision-making.
However, a significant challenge arises from the fragmented nature of data across numerous marketing management tools.
This fragmentation leads to data silos, each with its own unique schema. When siloed data is fed into ML models for AI-guided marketing, it can often result in biases and inaccuracies.
These biases can hinder marketers’ ability to act on data effectively, leading to missed opportunities and lost sales. While siloed data might be helpful for some tasks, it creates a blind spot when viewed holistically.
How to limit data fragmentation
Traditionally, marketing teams have relied on isolated pockets of data stored in separate systems. Disconnected customer data poses significant challenges for marketers, particularly in creating accurate consumer profiles. When a customer interacts with a brand through multiple channels (e.g., website, social media, in-store), fragmented data hinders a comprehensive understanding of their preferences and needs.
This lack of a unified customer view makes it difficult to personalise marketing messages and offers effectively, potentially leading to customer disengagement and decreased loyalty. Marketers may struggle to analyse customer segments and identify trends due to limited understanding of target demographics. Additionally, inaccurate consumer profiles hinder the ability to tailor marketing efforts effectively.
How minimal data fragmentation improves insights
The ability to analyse data across various marketing channels provides a comprehensive view of the customer journey, offering valuable insights for improvement. For example, by analysing data from supply chain management, CRM, social media, inventory, website traffic, and sales, marketers can gain a deeper understanding of how these factors interact and affect overall business performance.
Simultaneously, a unified view of marketing campaign performance across different channels empowers businesses to measure ROI and identify areas for improvement.
Every aspect of the business can have a ripple effect on the others, and marketers might not understand the change in patterns unless viewed in a larger context. For instance, delays in the supply chain can lead to longer delivery times, which can negatively impact customer experience.
This, in turn, can affect social media sentiment, as dissatisfied customers may express their frustration online. By analysing, for instance, negative ratings online alone, marketers might not read much into the problem. However, when viewed alongside sales or inventory data, marketers can identify patterns and correlations between supply chain issues and customer behaviour.
Using AI for data-driven marketing
Similarly, AI chatbots can serve as a valuable tool for gathering insights into customer inquiries and interests. When integrated with other marketing management tools, AI can analyse interactions and identify common questions, pain points, and emerging trends. This can then be used to refine marketing strategies, tailor offerings, and improve overall customer satisfaction.
“Businesses remain trapped in outdated marketing structures, hindered by data silos and fragmented teams.’’
Real-time data and insights are crucial for success in today’s competitive environment. Without them, businesses are flying blind and risk making decisions that could negatively affect profitability. The fragmented approach to data management extends beyond marketing. It creates inefficiencies throughout the entire business.
Curing fragmentation to support data-driven marketing
The causes of data fragmentation often lie in the use of disparate systems that don’t effectively communicate with each other. For example, a company may have separate databases for email marketing, social media, and CRM. This siloed approach makes it difficult to consolidate customer data into a single view.
The solution lies in breaking down data silos with unified platforms. This can encourage businesses to adopt a first-party data strategy. By leveraging their own customer data, brands can gain a deeper understanding of their audience. By collecting and analysing this data, they can tailor marketing efforts for a more personalised and effective customer experience.
First-party data offers several advantages. It’s often more accurate and complete than third-party data, ensuring a reliable information source. Customers must also explicitly consent to its use, ensuring privacy compliance. Since it’s directly relevant to customers and their interactions, first-party data is highly valuable for personalisation.
Data-driven marketing is the future, and those who fail to adapt are falling behind. Companies that leverage technology and analytics are consistently outperforming their competitors. However, many businesses remain trapped in outdated marketing structures, hindered by data silos and fragmented teams. As the volume and complexity of data continues to increase, the importance of unified data will only grow.
By Hyther Nizam, President Middle East and Africa (MEA) and VP of Products, Zoho
