The new expectations redefining the modern CMO

The need to show ROI represents a shift from the traditional brand-building role of the CMO. In a time of economic uncertainty and rapid technological innovation, the sharper focus on ROI underlines the importance of investing in new marketing technologies (including AI) and the need to clearly understand which investments deliver the most value.

The CMO role and marketing overall have been moving toward a more data-driven approach in recent years. What’s new now is the infusion of AI into process automation and creative production, to orchestrate end-to-end, personalized customer experiences in real time. The goal of value creation with AI is to accelerate the shift toward analytics and data-centricity while enabling a holistic, end-to-end approach to customer experience. Using AI for complete customer journeys can generate data that can pinpoint what drives engagement, conversions, and revenue. First, though, CMOs need to solve challenges such as measurement and attribution, existing data unification, platform usage, and workflow optimization.

Measurement and attribution

Attributing marketing results and measuring the impact of marketing spend have always been challenges. Now, the demand to prove marketing ROI is running into a new challenge: the proliferation of channels and campaigns with variable elements. The rise of increasingly complex offline-to-online customer journeys and limited visibility into the full marketing funnel makes it harder to understand what’s really driving results.

For example, consider a customer who sees a pair of shoes featured in an email campaign, a printed retail flyer, a banner ad on a website, and perhaps in an app ‘nudge’ as well. After all these touchpoints, the customer orders the shoes on their mobile phone. Should that sale be attributed to the mobile channel? Were other elements of the journey more influential in getting the customer to buy? How can the CMO understand which channel and campaign spend generated the most ROI in this case?

Add AI’s role in generating custom campaign content and journey optimization, and the measurement scenario gets even more complex. It’s not surprising, then, that the market for attribution software is expected to nearly triple in size by 2032. Marketing leaders are searching for the right tools to measure and attribute results so they can make spending decisions based on data rather than an incomplete view of the customer decision-making process.

Data fragmentation and leverage challenges

Solving the attribution and measurement problem requires unifying existing customer data to stitch together the entire customer journey, from end to end and across all channels. This is fundamentally a data engineering challenge, but the responsibility lies with the CMO. IT and marketing can work together to pull data out of silos and unify it. Data unification is also critical for the success of AI initiatives. Without clean, well-organized data, AI can’t deliver effective campaign or efficiency outcomes – or generate useful data for calculating AI ROI.

There’s another data challenge for marketers that’s cultural. Despite the majority of marketers agreeing that data-driven strategies can improve efficiency, according to Adobe Business, a quarter of us: “don’t use data monthly to drive that improvement.” As data becomes more unified and more useful, it will be wise for CMOs to change their teams’ habits to ensure that people regularly use data to guide decisions.

Structuring for better AI ROI

CMOs know they need to leverage AI in their workflows, but so far, the discussion has been mostly about using AI for specific tasks such as content generation. Using AI in creative services is a popular early use case that can help control costs, accelerate campaign timelines, and improve personalization at scale. But AI can also orchestrate the processes needed to move campaigns from brief to content creation to reviews, compliance, and approvals.

To achieve this, campaign workflow steps should be integrated into a workflow that the AI can optimize using unified data. The workflow and AI activities should be defined with specific outcomes in mind. For example, the goal for AI-powered content generation might be to reduce turnaround time from three days to one day. That’s a clear goal that makes it easy to set KPIs to measure performance, up to and including ROI.

Today’s marketing channels, campaigns, technology options, and data silos are complex. But they offer CMOs the opportunity to create unified, AI-enabled marketing operations that can be measured against business outcomes. The CMOs who meet this ROI challenge won’t be looking for a ‘magic bullet’ solution. Instead, they’ll be using their creativity, their data and analytics skills, and their ability to problem-solve with IT to unify data, orchestrate workflows, and apply AI to those workflows. The result can be measurable results and, at last, a clear demonstration of where marketing spend delivers the most impact.