What can CMOs do with Traditional AI?
I was a bit taken aback when I recently heard a bank's CDO use the term "traditional AI." He was simply distinguishing it from Gen AI and LLMs. Fair enough, but with the rise of Gen AI, marketers are buzzing about how it's reinventing copy and content. However, that’s like using a nuclear reactor just to charge your phone!
Traditional AI offers far more when it comes to marketing effectiveness. Gen AI, at the moment, addresses mostly tactical marketing problems on a surface level. This blog focuses on how Machine Learning (ML), a specific subset of AI, can be leveraged to filter irrelevant customer data.
Irrelevant customer data refers to information that doesn’t contribute to accurate insights or effective targeting. This includes outdated or incomplete data—such as customer profiles with missing, obsolete, or inaccurate information—low-engagement users who rarely interact with your brand, leading to inefficient ad spend, and noisy data, such as random behaviors that don’t indicate purchase intent (like accidental clicks or brief page visits). At the campaign level, irrelevant demographic data may also include variables that have no bearing on campaign goals, such as age groups or locations that don’t align with your target audience.
How can ML help marketers?
Machine learning plays a crucial role in filtering out irrelevant customer data by analyzing vast amounts of information and identifying patterns that truly matter for targeting. It sifts through large and noisy datasets, eliminating incomplete or inconsistent data, and highlights high-quality leads.
Algorithms focus on customer behaviors, engagement levels, and purchase histories to refine targeting precision. Over time, machine learning models learn and automatically improve their filtering processes, ensuring marketing efforts are concentrated on high-potential customers rather than wasting resources on irrelevant segments.
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ML brings multiple solutions to marketers' desks:
Behavioral Clustering: ML segments customers based on browsing patterns on apps or websites, offering personalized product recommendations. Behavioral clustering also enhances upselling efforts, as seen with the “people also bought” feature on e-commerce sites, which suggests additional products in a contextually relevant manner.
Predictive Retargeting: ML models track real-time purchase intent, filtering out low-engagement users from retargeting ads to maximize ROI. When combined with human insights, such as recognizing when tax-saving investment products are purchased before the fiscal year-end or travel bookings spike in pre-summer months, campaign efficiency can almost double.
Anomaly Detection: ML detects abnormal data points in large customer datasets, isolating valuable leads from irrelevant data noise. It reduces the cost of targeting wrong or irrelevant customers through media channels. For example, when marketing travel, someone searching for “best time to visit Bali” is less valuable than someone searching “flights to Bali in February.” The former is in the early planning stage, while the latter is ready to book now.
In conclusion, while Gen AI is making waves in the creative side of marketing, the real strength of machine learning lies in its ability to filter out irrelevant data and enhance marketing precision. By leveraging ML’s capabilities, marketers can focus resources on high-potential customers, improving efficiency, cutting costs, and achieving better results.
- Sreeraman Thiagarajan is CEO of Agrahyah Technologies and Adjunct Professor of Digital Transformation at IIM Trichy
Digital Transformation to Experiential Farming | Founder at Bokeh Ventures and Dhwaj.
1 个月I really hope many more Matketeers read this post. I still feel that people are struggling with basics, noisy data can be a masterclass for most! Loved this read. So simple, evidently real yet so distant in many busineses.