How can modern marketers adopt better data cleaning practices? Alon Goren, CEO at AnswerRocket weighs in with a few tips:

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Hi Alon, tell us about yourself and more about AnswerRocket.

Thanks, happy to! My career has been centered around applying innovation to solving complex enterprise problems for businesses. The concept behind AnswerRocket was to address the challenges many organizations face in turning raw data into meaningful insights. We’ve developed a platform that leverages AI, particularly generative AI, to streamline the data analysis process, enabling all departments like marketing to quickly derive insights that can answer questions such as “Why are certain marketing tactics not performing in the SouthEast region?” or “Which channel is most effective in reaching our target audience” in real-time, based on the company’s specific results. AnswerRocket is about simplifying complex analytics so that decision-makers can focus on strategy rather than getting bogged down in data processing. This allows businesses to increase sales from campaigns, act on new opportunities quickly, double down on their return on advertising spend, and predict future performance – all through an AI assistant that understands the questions they need answered.

Can you share a few observations on the recent trends redefining the dependence on better data for modern marketers?

In today’s market, speed is everything—marketers need to identify trends and respond to them as they happen. This shift is driving interest in AI tools that can process data quickly and accurately. We’re also seeing an increased emphasis on integrating various data sources, from structured data like sales figures to unstructured data such as social media interactions. These integrated insights are crucial for understanding the full picture of consumer behavior and staying ahead of competitors. Additionally, there’s a move towards more personalized marketing, where data-driven insights play a critical role in tailoring messages and strategies to specific audiences.

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How can today’s marketers optimize their data cleaning processes to enable better internal cycles and campaigns and to ensure their martech stack always has clean data?

Data cleaning is foundational to effective marketing. To optimize these processes, it’s important to establish clear data governance practices that ensure consistency and accuracy across all data sources. Automating aspects of data cleaning can also significantly enhance efficiency—AI tools can help identify and correct errors, as long as the base data is reliable. Regular audits are crucial to maintaining data integrity over time, which in turn supports more effective campaigns and internal processes. In our experience, investing in data quality upfront not only streamlines operations but also ensures that the insights you generate are trustworthy and actionable.

How are new age AI and GenAI enhancements enabling better data cycles and processes for modern marketers?

GenAI is fundamentally reshaping the way marketers interact with and understand their data. One of the most transformative changes is how these AI systems can contextualize data across various dimensions simultaneously. Today’s marketing technology can handle complex queries that require synthesizing information across departments, which have often been handled in silos. For instance, if a marketer wants to understand how a specific campaign is performing across different regions and demographics, AI can rapidly pull together insights from CRM systems, ad platforms, and customer feedback channels. This holistic approach means that marketers can now make decisions based on a comprehensive view of their data landscape, something that was much harder to achieve with traditional tools.

Additionally, these AI systems are increasingly user-friendly, allowing marketers with varying technical skills to interact with complex data models. The rise in conversational analytics has really been a proof point here. In short, new-age AI and GenAI are not just speeding up data processes; they’re enhancing the depth, accuracy, and accessibility of them.

When using AI to drive marketing cycles and data processes: what pointers should brands keep in mind?

When incorporating AI into your marketing strategies, it’s crucial to recognize that while AI can dramatically enhance efficiency, it works best when paired with thoughtful human oversight. Start with clean, well-organized data—AI’s effectiveness is heavily dependent on the quality of the data it analyzes. Equally important is ensuring that AI is used to complement, not replace, human expertise. AI can automate repetitive tasks, uncover patterns, and provide predictive insights, but it’s the human element that interprets these findings, adds context, and makes strategic decisions based on the insights generated.

Additionally, maintain a flexible approach. The AI landscape is rapidly evolving, and staying adaptable ensures that your processes remain cutting-edge. Regularly reviewing and refining how AI is integrated into your workflows helps to maximize its benefits while mitigating potential risks. By combining AI’s analytical power with human judgment and adaptability, brands can achieve a more balanced and effective marketing strategy.

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