The CMO’s guide to AI-powered marketing

While many organizations collect and store first-party data (and may combine it with second- or third-party data) to use in marketing campaigns, a lack of connectivity and interoperability between the data can be problematic and limit the impact of marketing analytics or activation efforts. Although not the only culprit, siloed or independent data systems often contribute to an organization’s lack of data unification and sharing, especially as the volume of collected data grows.

Based on our research, the top challenge organizations face when sharing data internally mirrors a top challenge associated with organizations using AI/ML for their marketing use cases: ensuring sufficient integrations or interoperability across data platforms.

There are many opportunities for marketers that are properly prepared to collect, process, and unify their data, particularly as the volume of gathered data grows. The ability to organize, access, and act on data is critical. Organizations that fail to address data management and unification requirements may find future data influxes to be more of a challenge than a chance for innovation.

Consumers have seemingly endless ways to research their interests, be inspired, or make a purchase. Conversely, marketers are dealing with a larger and more fragmented network of content channels to reach and engage people. This only amplifies the challenges that organizations and marketers must navigate—especially since, as research shows, people expect to be consistently treated as individuals across all of these channels. Organizations need to adapt to consumers’ changing interests in real time and serve up personalized content no matter where they are.

The slow and oftentimes complex orchestration of technologies within organizations can hinder the development of a project before it even starts.

In recent years, marketing teams have advanced their tech fluency. Since 2015, the fastest-rising skills mentioned in chief marketing officer (CMO) job postings are “key performance indicators (KPIs)” and “cloud solutions”—two areas that fit comfortably in the world of data and technology. Additionally, our research revealed that 72% of marketers possess either primary decision-maker or executive approver authority within their organizations when it comes to choosing marketing or data technology solutions.

This shift in skill set and responsibility has moved the focus of IT more toward system integration support and away from its traditional role as the primary enterprise technology decision-maker. For a while, this worked out, but over time and with the growth of first-party data and AI, conflicting priorities and perspectives between IT and marketing teams surfaced.

On the IT side, there’s a strong will to lead enterprise data and AI projects. It makes sense: This team oversees the organization’s data governance and security policies and often builds and trains AI/ML models. From the marketing team’s side, the desire to spearhead these data and AI projects stems from the fact that they are the ultimate consumers of these data assets (for instance, AI/ML model scores). Marketers need them to understand customers and build better marketing activation tactics, such as personalized content generation or an optimized media buying strategy.

Ultimately, this can lead to misalignment on the core objective(s) for an AI project, which can encourage both teams to continue to work independently. This can create an environment for AI adoption projects to sputter along or fail altogether as both teams struggle to agree on important project components, such as the business case, technical and financial requirements, and evaluation criteria.

Through our research, we continue to see that today in how teams within an organization believe first-party data is used to support an organization’s marketing use cases.

Ever-changing data privacy regulations require privacy leaders to constantly update and conform their organization’s data compliance and governance rules. Additionally, the expansive but often fragmented collection of data privacy laws propels many organizations to adopt internal policies that apply regulations, such as the California Consumer Privacy Act of 2018 (CCPA) or the General Data Protection Regulation (GDPR), across any consumer that interacts with the organization, regardless of that individual’s location. Additionally, heightened controls among internet browsers and hardware companies are impacting traditional data collection, and with these disruptions in gathering valuable data, marketers are facing significant disruption in how they drive business impact and measure ROI.

These changes can impact everything from campaign planning to post-campaign reporting, and they can affect the accuracy of third-party ad platforms and brand measurement systems. A marketer’s ability to target, measure, and understand its consumers is directly affected, which makes it vital that organizations align on and establish a privacy-centric approach to marketing.

This can be especially challenging for global brands that must untangle a web of regional, national, and international data privacy laws. Regardless of the complexity, owning and building a foundation of consented first-party data is crucial to AI-powered marketing. Our research indicates that high-growth companies are focused on using AI to support their marketing processes because AI is the business multiplier that can help organizations keep up with shifting consumer demands and gain necessary insights in an efficient and privacy-centric way.

As our research pointed out, many marketers are already cognizant of AI capabilities and the need to protect their customers’ data, but they may lack the necessary infrastructure, internal policies, or personnel to effectively use AI to support their marketing processes.