Marketing Measurement: Lost In Translation

According to David Lewis, President and CEO Americas of DemandGen, addressing this measurement gap is now a top priority for B2B marketers. "One of the biggest investments a company typically makes is in its marketing organization," said Lewis. "The pressure on marketers to say how these investments are paying off is enormous, and it's going to keep growing."

Experts agree that a solution to this problem involves more than just technology. It also requires marketers to think about how to define useful KPIs and what processes are required to capture that data.

Moving From Tactical To Strategic Metrics

First, said Russo, marketing organizations need to rethink what they're tracking and reporting. "A lot of them continue to produce metrics that only measure activity," such as inquiries or leads generated, he said. "That's meaningless at the executive level; it needs to be translated into revenue impact."

This "translation process," Russo stated, should make use of the "revenue language" already associated with most sales organizations. "Don't talk about inquiries, leads and MQLs; leave that language at the door."

Lewis elaborated on this theme: "Sales can measure revenue, the performance of individual reps, pipeline opportunities, revenue per customer – very revenue-focused measurement. Marketers need to focus on the same concepts, looking at how marketing-sourced leads convert to opportunities and eventually to revenue."

Lewis said he recommends that his clients rethink their reporting goals in terms of three categories: executive KPIs, including revenue-focused metrics; demand funnel KPIs that shed light on contributions to the pipeline and deal velocity; and marketing effectiveness metrics that focus on more conventional campaign tracking.

"The issue is that marketers are still struggling to identify and pursue strategic versus tactical metrics," Lewis said. "Sales and finance organizations mastered this decades ago, but marketing is just beginning to make this journey."

Closing The Loop With Sales Is Still A Problem

Once marketers are focused on asking the right questions, Russo said, they can turn to the task of where to get the data they need for the answers. This is the fundamental premise of closed-loop marketing reporting and metrics – a process that allows marketers to see exactly what happened to leads once they enter the sales pipeline.

"One of the first questions I ask [on a consulting engagement] is whether a marketing organization has visibility into the pipeline," he explained. "Marketing needs full pipeline visibility to compare the performance of marketing-sourced and sales-sourced deals. Marketing may only see the upper end of the funnel, and that obviously isn't enough."

Lewis pointed out that this isn't just a matter of getting visibility into revenue-focused marketing metrics. It's also relevant to the question of whether a marketing organization is even getting the credit it deserves when process issues impede the flow of data.

"The clients I work with often see a 'data cliff' when it comes to closed-loop reporting," he said. "The processes and systems to align sales and marketing aren't being designed correctly, and the data fails to flow from click to close. The lead gets handed off to sales, they engage, and they create a corresponding opportunity in their CRM system. But all of the historical marketing data associated with that opportunity doesn't follow it, and it ends up getting lost.

"This is a huge process and technology challenge," Lewis added.

Analytics Tools: Pros And Cons

Clearly, while process and organizational issues are important, companies must still make decisions about marketing analytics tools. This generally means choosing from three options: analytics tools built into marketing automation solutions, mainstream stand-alone analytics, and more advanced predictive analytics offerings.

Analytics associated with marketing automation offerings have at least two major advantages: They're relatively easy to implement, and they represent a big step up for organizations that still rely on spreadsheets.

According to Russo, however, turnkey functionality is a double-edged sword.

"Marketing automation toolsets are built around very specific use cases – for example, how they track inquiry-to-close processes," he said. "If you're a good match with the use case, their [analytics] modules are solid. But if you deviate, you might end up back in Excel, adding assumptions and making estimates."

Another option, especially for bigger firms, is to implement a stand-alone business intelligence solution that can address revenue-focused marketing KPIs. This includes offerings like GoodData, Cognizant, SAP and TIBCO, all of which might be suitable for enterprise marketing organizations. One strength of these solutions is their ability to span multiple enterprise applications, including marketing automation, sales automation, CRM and ERP systems, giving decision-makers a richer set of performance metrics.

A solution such as Marseli, which emphasizes its ability to give both sales and marketing teams extensive visibility into the pipeline, can also satisfy marketing teams that want to emphasize closed-loop reporting and revenue-performance KPIs.

In order to take advantage of such a solution, however, Russo again emphasized the importance of addressing organizational issues first.

"You have to give somebody within the organization the ability to see things from end to end," he said. "That sounds like a simple thing, but it's two different organizations, each used to doing its own thing. The companies that succeed at this today generally have sophisticated approaches, and honestly, the majority of companies just aren't there yet."

What About Predictive Analytics?

Earlier this year, Demand Gen Report offered a fairly optimistic assessment of a third analytics option: predictive tools that would allow sales and marketing teams to make accurate forecasts and plan resource allocation for more efficiently. According to Russo, the promise is real, but it's still largely unrealized.

"When [predictive analytics vendors] get where they really want to be, it's going to be nirvana," he said. "But most companies aren't there yet."

Lewis said that he's not yet persuaded by empirical evidence that predictive analytics deliver ROI. "There's lots of buzz here, but I'm still waiting on the case studies that show success," he said. "The companies that I see experimenting with the technology are still struggling to understand how the data being reported back to them is actionable."