As someone who has spent over two decades driving growth and innovation in marketing, I’ve always believed in the power of data to unlock opportunities. But it wasn’t until I fully embraced Artificial Intelligence (AI) that I realized the true potential of a marketing transformation.
It reminds me of the early days of Telecom marketing when building and launching a product meant combining creativity with tech, think customers at all times and scaling results beyond expectations. We used to deal with millions of customers, any tweak in the product or services will immediately get a positive or negative feedback. The issue was the unpredictability of the results and impact on the revenues.
AI in marketing, today, feels like that moment again, but in a much better way. Today you can segment customers, run an A/B tests, check impact on revenues and user adoption in a few days, extrapolate the results, build predictions and budgets and see how you can reach your revenue expectations and all this without the unpredictability of the results user behaviour and revenues.
As marketers, we all think beyond the tools and focus on the strategy behind AI adoption. It’s not only about automating the mundane but also about amplifying creativity, and driving results with precision.
Some of the applications are great and the biggest one is exploring new business models and new products or innovation at speed. First let’s look at the usual use cases of marketing when we deploy Generative AI with marketing.
Customer Segmentation like never before
More than precise customer segmentation based on behaviour, demographics, and purchase history, one can now incorporate psychographics—factors like lifestyle, interests, and values—into our data models allowing for more targeted marketing strategies.
A recent report by McKinsey highlights that companies that extensively use AI in their marketing efforts see a 30% increase in conversion rates. This statistic alone illustrates why embracing AI isn’t a choice anymore.
Engaging Customer interactions
When AI + marketing comes to mind people think just chatbots or automation tools, however it is much more than that. It’s about understanding and predicting as well when a customer needs to interact with the brand and what services they may need.
According to a Gartner report, AI will manage 85% of customer interactions by 2025.
This shift means marketing teams need to rethink their strategies, moving away from traditional, repetitive targeting to creating meaningful, timely, and personalized engagements.
An Example is how IBM Watson is being used at Mall of America to create emotionally intelligent virtual assistants that can not only guide shoppers to stores or products but also engage emotionally.
The AI understands the shopper’s mood based on voice and language patterns and adapts its responses to improve the experience. For example, if a shopper is stressed or in a hurry, the AI offers concise, direct answers. If they are leisurely browsing, it can engage in more in-depth conversations.This emotionally adaptive AI interaction has resulted in a 15% increase in sales at the mall, as customers feel more understood and catered to.
From my experience at SCIKIQ ( a Generative AI-powered data platform), I’ve seen firsthand how AI not only improves customer engagements especially when the interactions is happening with voice. A noticeable jump comes immediately but grows faster after a few months of experiences.
Personalization at Scale: A Game Changer
Back in 2010, when I led app development in South Asia, personalization meant segmenting users based on basic demographics. Get the name right, the location verified and speak in the language customer if comfortable. Fast forward to today, AI allows us to offer true 1:1 personalization at scale and in ways we couldn’t have imagined before.
Reports from Forrester suggest that 77% of consumers choose, recommend, or pay more for brands that provide personalized services. But what is personalisation in the age of Generative AI .
A great example is how L’Oréal uses AI to generate thousands of creative variations of their ads—down to image selection, copy, and even the tone of voice—based on specific audience data.
For a new product launch, the AI generates different ad creatives for each target demographic, taking into account not just location and behavior but psychological preferences.
For instance, an ad seen by a 25-year-old skincare enthusiast may feature fresh, youthful imagery and informal language, while the same product will be advertised with luxurious imagery and sophisticated language to a 45-year-old consumer.
By tailoring thousands of ad variations through AI-generated creatives, L’Oréal saw a 40% higher engagement rate compared to traditionally crafted campaigns.
You have an idea now, how AI brings personalisation at scale.
Multi-Touch Attribution
The challenge with traditional multi-touch attribution is understanding which touchpoints drive the most value and how to assign the appropriate amount of credit to each.
AI Data models tracks the customer journey across touchpoints, helping marketers understand which channels or interactions are driving conversions to predict the most effective channels for spending, reducing wasted ad budgets and increasing ROI.
AI processes massive amounts of data, creating custom attribution models tailored to your specific business. Machine learning algorithms identify patterns in how users interact with different touchpoints, uncovering hidden interactions that might be undervalued in traditional models. AI excels at tracking customers across multiple devices and channels, a key challenge for marketers today.
Customer Lifetime Value (CLV):
In the past, calculating CLV was a static process, often based on historical data and assumptions. AI changes this by allowing for dynamic, real-time CLV tracking. The system can adjust a customer’s predicted lifetime value based on ongoing interactions and purchases, ensuring that the company always has an up-to-date understanding of each customer’s potential.
Walmart uses dynamic CLV models powered by AI to track customer value over time, adjusting its marketing and pricing strategies in real time to keep customers engaged throughout their lifecycle.
To boost Customer Lifetime Value (CLV), companies can use data models to profile customers based on their existing products and purchase behavior. AI then predicts the next likely purchase and offers personalized recommendations, loyalty rewards, and targeted ads. By anticipating life events, such as moving or family expansion, AI suggests timely products, enhancing relevance. Post-purchase, AI tracks product performance in real-time, offering predictive maintenance and tailored tips, increasing customer satisfaction and retention. This data-driven approach personalizes interactions, improves retention, and maximizes CLV.
New Business Models
AI helps you explore and leverage various revenue models. Companies that once focused on selling products are now becoming service providers by harnessing subscription-based, data-driven, and platform models.
A key driver of this evolution is the ability to leverage data and AI to create continuous customer value. Take Tesla as an example—it began as a car manufacturer but has evolved into a data and technology company that monetizes its data for autonomous driving, energy solutions, and software services.
This shift allows companies to move beyond a one-time transaction mindset, focusing instead on long-term customer engagement and recurring revenue. The moment a company begins this shift, everything changes. They can now evolve from product sellers into ecosystem creators by integrating multiple services—such as personalized content, predictive maintenance, and automated workflows—into a unified platform.
This helps companies enhance the customer CLTV, create a more every engaging relationships.
AI first marketing
As we look toward the future, AI isn’t just a tool—it’s the foundation for building blue sky possibilities that many businesses have yet to fully realize. The beauty of AI is that it enables companies to evolve continuously, adapt to shifting market demands, and create entirely new business models that didn’t exist before.
Imagine using AI to design completely new services, co-create products with customers in real-time, or even unlock business models centered around AI-powered experiences.
The only limit is how far we are willing to think. With AI, there are no ceilings—and the companies that recognize this will lead the next wave of innovation. So, why stop at what’s working now? Let’s think bigger, embrace AI as a transformative force, and explore the limitless potential that it offers to revolutionize industries.
After all, as someone who has always sought to push the boundaries of what marketing can achieve, I can confidently say AI is the key to unlocking limitless growth opportunities.
The Author is VP Marketing for a Data Analytics company
Further read:
https://scikiq.com
https://scikiq.com/supply-chain
https://scikiq.com/marketing-use-cases
https://scikiq.com/retail
https://scikiq.com/healthcare-analytics
https://scikiq.com/banking-and-finance
https://scikiq.com/telecom
https://scikiq.com/blog/how-scikiqs-emerging-business-package-can-help-you-transform-your-business/
