Nearly four out of five organizations will increase their annual martech expenditures during the next five years, according to McKinsey & Company. In fact, 34% will boost their annual martech spend by more than 10%.
Yet when the consulting firm interviewed roughly 50 senior marketing officers at Fortune 500 organizations, not one could quantify the ROI of their martech investments. “Lots of companies are spending lots of money around it but not investing in training or even tracking the return,” says Robert Tas, Partner at McKinsey & Company.
In its report “Rewiring Martech: From Cost Engine to Growth Center,” McKinsey diagnoses the causes of organizations’ lack of insight into the value of their marketing tech and prescribes a cure that includes thoughtful application of AI.
Four Reasons Martech Underperforms
Among the 233 Fortune 500 respondents to the McKinsey Martech Buyer and Decision-Maker Survey, 34% blamed underskilled employees for at least some of their organization’s inability to reap value from their marketing technology. “The competency and skills training to use those tools is way behind where they should be,” Tas agrees.
Beyond lack of training, McKinsey cites three other primary reasons for companies’ failure to make the most of their martech investments:
- Lack of executive sponsorship and understanding. The C-suite often doesn’t grasp martech’s potential beyond basic functionality, tasks and use cases. This goes hand in hand with a lack of executive ownership, especially when the technology falls under IT’s purview. This results in martech being considered an expense rather than a profit center, Tas says. “It’s really important that marketing tech be repackaged as a growth engine. Martech’s got to be a business driver.”
- Overcomplexity and fragmentation. “The infrastructure of this stuff has gotten so out of control,” Tas says. “Every martech tool is available to marketers, so there’s so much overlap.” Nearly half (47%) of the decision-makers surveyed admitted that the complexity of their stacks, in conjunction with organizational silos and integration challenges, prevents them from getting the most from the technology.
- Lack of effective measurement. Organizations generally measure tactical results, such as email clickthrough rates and campaign conversion rates by channel. But they typically fail to align those metrics, along with strategic outcomes and goals, to each applicable marketing tool.
Four Steps to an Improved Stack
McKinsey outlines a four-step plan for maximizing martech effectiveness and efficiency:
Step 1: Set the North Star. Determine the organization’s marketing and overall business goals. “Start with focusing on how you can help your customers” — internal and external alike, says Tas. Executive sponsorship is most critical here, to ensure that the goals are aligned with leadership’s holistic vision for the business. “We want someone who really owns it and gets it and activates it across the enterprise, because that’s where the real value is,” Tas says.
Step 2: Map your future functionality — and how AI can assist. Given that streamlining and simplifying the organization’s tech portfolio is key to optimization, introducing a layer of agentic AI might seem counterintuitive. Yet “AI is a pretty interesting option to reboot, to reimagine things we really haven’t been able to do today,” Tas explains. “You need to leverage AI to orchestrate across all your platforms. You need a layer across to connect the dots and all those things that don’t talk to each other.”
Determine which workflows and outcomes AI is most likely to benefit, then map how humans and AI agents will work together in each workflow for optimal effect. By automating the interaction of data and functions across channels, business units and locations, agentic AI should “knock down the silos and figure out how to operate far more efficiently than we do,” Tas notes.
Step 3: Audit and assess. Now’s the time to detail the technical and data requirements of your AI-optimized workflows. While you may discover the need for new tech, you’re just as likely to discover that some of your existing tools are redundant, outdated and otherwise surplus. In this step as well as the previous one, be sure the major stakeholders, including IT and department heads — “people who can give a clear and guided view of how the function can be more productive,” Tas says — have their say. You should also determine which employees need to be upskilled and whether new hires are necessary.
Step 4: Build. Identify a few preliminary use cases that can be quickly deployed with an MVP toolkit so that you can demonstrate the value of the AI-optimized tech stack from the start: “Get some wins on the board,” Tas says. Then iterate and scale while training and driving user adoption.
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