Metrics Causing Pain? Could Be A People Problem

broken-analyticsThis is the third in a series on what’s broken in the analytics space. Part one addressed data ownership, part two focused on technology and this story is about people and processes.

Part two of this series looked at startups that are trying to unify disparate marketing metrics, but technology can only go so far in solving for analytics fragmentation.

Both on the client side and agency side, marketers need to get better at streamlining their processes and personnel to accommodate for big data aggregation and analysis methods that weren’t possible even a few years ago.

“The larger, more traditional organizations haven’t yet figured out how to use that technology and embed it within the fabric of how they approach marketing,” said Rick Greenberg, CEO of Kepler Group, a digital media agency.

Industry statistics tend to support that conclusion. A CMO survey released last month by the Fuqua School of Business at Duke showed that in spite of increasing marketing analytics budgets, the percentage of CMO-led projects using marketing analytics is still below 33%. And a survey released by the analysts at Demand Metric found that while 86% of brands have some sort of analytics process, spreadsheets are still the most prevalent tool.

On the agency side, many of the process issues stem from dependence on workflow designed prior to the big data era, requiring “a lot of labor and resources to manage,” Greenberg explained.

To pivot from these legacy processes will require a rethinking of roles and responsibilities at agencies, said Gayle Fuguitt, CEO of the Advertising Research Foundation (ARF).

“Within agencies, it isn’t clear who is in charge of integrating – the new account manager has to have analytical skills, synthesis skills and leadership skills to translate disparate inputs and facts into creative strategies to grow brands and not just focus on ‘counting,'” she said.

While marketers can push their agencies to do a better job integrating big data technology into their processes, internal organizations must also look in the mirror to consolidate their own processes and personnel skills to get the holistic value out of their metrics and data-driven technology investments.

“The struggle isn’t always clients vs. agencies. In some cases, the struggles happen internally,” said Mike Lempner, practice executive for customer intelligence at the consultancy Infinitive. “Like when individual marketing teams hoard data about their campaigns.”

Jay Stocki, VP of online marketing for Experian Marketing Services, has witnessed internal struggles firsthand, for example, seeing a client’s media-buying team battle with the CRM team for customer data.

“Unfortunately, the intersection of these two organizations is often at the CMO level and unless the edict is driven from the top down, very little sharing of data and learnings occurs,” he said.

Such internal strife leads to a “fragmented customer experience,” explained Lisa Arthur, CMO of Teradata Applications.

“If the left hand does not know what the right hand is doing, they will miss opportunities for meaningful and proactive customer engagement,” Arthur said. “It’s that simple.”

She cites the American Red Cross as an example of how to fix this. A Teradata Applications customer, the nonprofit is working on an initiative it calls One Red Cross. The goal is to take better advantage of data on donors and volunteers and streamline messaging and engagement across the organization.

“The Red Cross does more by having more donations – of time, of money and of blood,” explained Peggy Dyer, CMO of the American Red Cross. “You could be a volunteer one day and two months later you could be a blood donor and we have not had a way of understanding both who the supporters are and why they are supporting us. If we can use data to help us get more donations of time money and blood, we can do more to help people in need.”

The Red Cross case study highlights how the data science discipline needs to be injected into internal organizations. As part of the process, the organization opened up a new role, VP of data strategy, to bridge the gulf between IT and marketing, which Dyer said can “operate like they are on different planets.”

The experience of the Red Cross should be a guide for the industry.

“The dynamic media landscape and measurement mania is fueling new roles in both clients and agencies: chief data scientist, head of analytics, chief data officer,” said ARF’s Fuguitt. “Marketing roles are evolving to include analytics.”

Organizations that want to keep up must not only bring in highly qualified data scientists to fill new roles, but also improve existing employee analytical skill sets. That will come with its own set of kinks. The CMO study from Duke found that 83% of organizations today find it moderately to extremely challenging to find the right talent to get the most out of marketing analytics.

To make this transition, it may make sense to observe other lines of business to see how they’ve established their metrics discipline, said Jennifer Zeszut, CEO of Beckon. Marketers today frequently “let their data stay a total mess” and put big data deep-dive tools on top of that to “fish around in there and look for nuggets of insight,” she said.

The difference with other business functions is stark. “Does finance, weeks before earnings, dive in with analysts to sift through all of the people’s piles of receipts to see if we met our numbers?” she said.

Which is why marketers need to flip the data analysis model on its head so that, as with finance, there’s some sort of “marketing chart of accounts,” Zeszut said. Rather than letting random, easily available data drive reporting, marketers need to be more intentional and analyze specific data sets based on business drivers.

This has to be internally led, but it also brings marketers back full circle to the third-party role in the big data era. Brands do need to create data flows and processes to support multichannel vision, but they don’t necessarily have to do all of the analytical heavy lifting or hold all the data scientist resources. As scared as some agencies and service providers may be about data transparency and big data models threatening their business models, there is opportunity to go around.

“In the last couple of years there’s been a lot of discussion and debate over whether the proliferation of specialized technology means the death of the agency,” said Rick Greenberg, CEO of Kepler Group, a digital media agency. “I think the proliferation of specialized tech will mean the death of the traditional agency service model, but I actually think it increases the need for an expert intermediary and partner.”

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