AI & ML in Marketing Analytics: Unlocking ROI & Stronger Customer Relationships

Marketing analytics stands out as an ideal starting point for businesses to integrate machine learning (ML) and artificial intelligence (AI). This is due to a confluence of complementary factors: their impact on revenue generation and the ability to build stronger customer relationships; a strong automation landscape in martech that already can deliver results at scale; and the latest wave in AI and ML tools to break down the data silos that have come to define customer data.

Businesses are increasingly investing in AI and ML, placing it as a top opportunity for their businesses. ROI remains at the top of their minds, and the Marketing Week survey fingered it as the number one effectiveness metric teams and boards care about. The clear adoption benefits have met their moment, and savvy enterprises prioritize the cross-functional collaboration necessary to achieve acceleration.

The use of AI and ML in marketing analytics is aligned with a powerful and durable trend, can drive evolutionary value out of martech ecosystems, and has the potential to deliver on face value against a core set of basic, high-impact use cases. It can help build stronger customer relationships, optimise customer acquisition and retention, and enable dynamic and relevant marketing outputs, meeting evolving customer expectations.

By resolving the execution strategy to the individual level using advanced martech fed by AI/ML inputs, businesses can run virtually unlimited tests to satisfy the preferences of each consumer as an individual. AI and ML can also circumvent the need to implement explicit data creation and testing strategies, reduce systems integration costs, improve performance, and lower licensing and storage costs.

A centralised strategy for developing marketing analytics is distributed and amplified to downstream connected applications. Companies can use ML algorithms to identify patterns within vast datasets, and AI to contextualise recommendations in the vernacular demanded by the marketer. AI and ML in a composable setting can scale first-party data in a way previously unimaginable, and predictive analytics powered by ML can forecast trends and customer preferences.

Experts in the enterprise are already good at doing what AI and ML can do. The paradigm has already shifted – if businesses aren’t started up, they are behind. A solid majority of CMOs expect “significant” or “very significant” estimated impact from AI use cases, and more than 70% of companies are already using some capability today. Embedding AI and ML as a centralized data management function within marketing analytics will amplify the results that can be delivered.

Originally reported by Martech: https://martech.org/ai-and-maching-learning-in-marketing-analytics-a-revenue-driven-approach/
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