Why It Matters For Go-To-Market And Technology Teams Alike

Jim McHugh is the CEO of Mperativ, the revenue marketing platform that connects go-to-market strategy to the revenue operations engine.

Today, there is greater demand than ever for better and more quantifiable connections between marketing budgets and how they drive business outcomes. Even with thousands of marketing technology products available, it has been difficult for them to clearly show the impact of marketing initiatives and spend on revenue outcomes, paving the way for new technologies to rise up to this challenge and create the future of marketing science.

This year, we’ll begin to see go-to-market teams establish a new realm of marketing science that brings together siloed data to quantify how marketing drives results, setting the stage for continuous, bitemporal data models that will unlock the true benefits of AI and ML for marketing. Here’s what’s in store for the rest of 2022.

Connect marketing to revenue and predict business outcomes.

When businesses cut back on marketing budgets earlier in the pandemic, the pipeline for some companies collapsed, while for other companies, it didn’t move in quite the way executives might have expected. This brought into question current marketing metrics.

This year, we will see the emergence of a new marketing science realm that is focused on connecting marketing to revenue and predicting business outcomes. We are already seeing marketing working more closely with data scientists and financial engineers to unleash a new level of marketing science, and this will evolve even more next year with the drive for better and more quantifiable connections to how marketing budgets drive business outcomes.

Utilize bitemporal data to help reap the true benefits of AI and ML for marketing.

Organizations are going to realize that simply stuffing data into a data warehouse is not the path to AI and ML for business. Marketing teams will need to capture bitemporal data.

Whereas a temporal database captures only what happened, a bitemporal database captures what was planned versus what actually happened. Bitemporal data grants organizations the opportunity to maintain a complete and accurate picture of who knew what, what happened and when—ultimately providing insight into how data evolves over time and the context necessary for AI predictions.

Unfortunately, most marketers are currently relying on snapshots in time from CRM and marketing automation systems. Increased reliance on these systems in the pursuit of quantifiable results has narrowed the focus of marketing leaders to reactive and tactical decisions.

Next year, this will no longer be enough, as higher-level, strategic thinking will be key to success. Capturing and analyzing bitemporal data will allow CMOs to implement AI and demonstrate their contribution beyond pipeline metrics, quantifiably proving how marketing is making an impact on the entire customer journey, not just the top of the funnel.

What does this mean for go-to-market and technology teams?

It’s time for marketing science to tell the story of how marketing initiatives and marketing spend generate revenue outcomes. Many companies have started revenue operations models centered around sales forecasting, but this is missing the entire intention of RevOps to connect systems and processes across functions. As long as the current structure keeps the different departments siloed, CMOs will not be able to get a unified view of their company, and the company will continue to lose out on a portion of its revenue because of departmental misalignment about the most effective ways to generate growth. CMOs must make sure they have the tech and infrastructure to critically examine every stage of the customer journey.

Marketing leaders and technologists must team up and establish a RevOps approach that incorporates the entire demand engine to provide meaningful trend data needed to analyze and predict business outcomes. Through this collaboration, marketers can eliminate the complexities of building RevOps strategies in a silo, focus on more strategic efforts and eradicate the large costs associated with making constant customizations to marketing platforms.

Marketing and technology teams should consider these three best practices to foster better team collaboration.

1. Capture the entire customer lifecycle. Work together to create and focus on a data model that captures the entire customer lifecycle across every touchpoint. When data is collected as time series data in a bitemporal manner rather than as snapshots of time, trend analysis is possible.

2. Align metrics. Marketers currently base their decisions on results obtained from siloed data. In order to expand the purview and obtain the full picture, marketing and tech teams must pursue alignment of marketing metrics and systems with sales. In doing so, the two teams can work together to paint an end-to-end picture that allows the visualization of relevant data to extract and identify what’s working to drive revenue.

3. Help each other avoid distractions. Technology teams should protect marketing teams from being distracted from growth by not making them worry about the intricacies of data infrastructure. Marketing leaders must work alongside data engineers and technologists or seek alternative solutions that eliminate the complexity of building it on their own. Ideally, marketing should consume the value of analytics without the constant distraction of customizing reports and operational infrastructure.

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