
AI is set to transform the way we work, yet its full potential remains untapped. In marketing analytics, AI holds the promise of revolutionizing the field by:
Given the potential for transformational gains, broad AI adoption should be the norm in marketing analytics. Why isn’t it? What barriers prevent this shift? More importantly, what can organizations and their teams do to change this? Here, we provide practical answers to these questions.
Let’s begin with the blockers, as highlighted in IBM’s 2023 AI Adoption Index. They identify five key obstacles:
These challenges are significant, but we view them more as hurdles than insurmountable barriers — hurdles that can be overcome with a use-case-driven approach to AI deployment.
Over the past year, we’ve applied this approach with nearly a dozen brands, achieving rapid time-to-value and substantial performance improvements. Here’s how.
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Sometimes, use cases are self-evident. For instance, a large retailer we work with faces a customer churn problem, where an AI-driven approach to predicting churn could deliver significant business value.
Other times, the most relevant use case isn’t as obvious. In these cases, building a use-case catalog helps prioritize opportunities. This catalog lists potential AI-enhanced use cases and scores them based on impact, scale and effort required.
Here are some core AI use cases in marketing analytics we’ve encountered:
These examples illustrate how AI can drive substantial business value. Once the use cases are defined, the focus should shift to overcoming the barriers to implementation.
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The first hurdle can be cleared by focusing on a high-value, low-effort use case, as highlighted in the use-case catalog approach. For instance, our churn prevention strategy for one client involved using AI-driven customer intelligence to trigger email messages for high-risk customers. This solution was seamlessly integrated into existing workflows, demonstrating how targeted use cases simplify scaling efforts.
Complexity in underlying data is the most common hurdle we encounter. The aphorism, “Don’t let the good be the enemy of the great,” is fitting. Data is never perfect. The best approach is to set aside the quest for perfection and focus on the data that matters.
Website interaction data and customer transaction data are two types of data commonly available in most enterprises. They are especially powerful for building AI-driven segmentation models for propensity, engagement, loyalty and churn. Moreover, AI-enabled data preparation and cleaning can automate tedious tasks, enabling faster and more comprehensive data accessibility.
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Expense issues often stem from a fundamental misunderstanding of value creation. Implementing AI in marketing analytics does require investment. This can range from a modest $50,000 to start, to seven-figure sums for more ambitious projects. However, this spending is an investment, not just an expense.
ROI can be forecasted, quantified and measured. By focusing on specific use cases, it’s easier to build a strong business case for ROI to justify the investment. For example, AI-driven segmentation and scoring typically yield improvements of 10%-15%. A brand investing $20 million in outbound marketing could see an annual return of $2 million to $3 million, making a compelling case for AI investment.
Expanding the pool of available expertise can address limited skills. While few professionals have both the technical skills and subject knowledge to deploy AI for marketing analytics, this issue is primarily internal to the enterprise. The solution is to outsource the expertise.
In a fast-changing environment where specialized skills are both rare and necessary, it’s often impractical for enterprises to develop these capabilities in-house. Partnering with a specialist to create tailored AI marketing analytics applications is the most effective and low-risk approach. These efforts can eventually become owned assets, but without the immediate burden of building and implementing them internally.
The final blocker, ethical concerns, stands apart from the previous four. While ethical considerations in AI are serious and impactful, we have not seen them act as a significant barrier to AI adoption in marketing analytics. The more common blocker is practical: legal and compliance issues.
Legal and compliance teams are particularly concerned with generative AI, where fears of inappropriate or off-brand content, as well as copyright and intellectual property risks, can significantly slow down or even halt AI initiatives.
Ultimately, every organization must establish its own governance and controls for AI adoption. To get started, focusing on high-impact, low-risk use cases has proven successful. For example:
AI is transformational and will revolutionize marketing analytics. A use-case-driven approach provides a clear roadmap to overcome barriers to AI adoption in marketing analytics. This measured strategy paves the way for sustainable AI integration, boosts internal team confidence and fosters AI expertise within the organization.
Marketing analytics leaders who adopt these strategies will be well-positioned to enhance performance, streamline operations and cultivate a responsive, data-driven culture ready to harness AI’s potential.
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