SAN DIEGO — The message at Gartner's three-day digital marketing conference here this week can be distilled into four words: Stop guessing, start measuring.
In session after session here, in talks about everything from customer journeys and programmatic media to social and mobile, speakers reiterated the reality that data and analytics are the lynchpins of digital marketing.
That message echoed through the halls of the Manchester Grand Hyatt here, where more than 800 senior marketing executives gathered to learn about marketing trends, develop strategies and uncover actionable insights.
The Evolution of Data-Driven Marketing
Data-driven marketing is not new: Way back in 1991, the Harvard Business Review predicted the dawn of an era in which marketers would integrate data on historic sales and cost figures, competitive trends and consumer patterns, as well as create and test advertisements and promotions, evaluate media options and analyze viewer and readership data.
More recently, as marketers increasingly incorporated those ubiquitous four dimensions of big data (volume, variety, velocity and veracity) into everyday conversations, the potential of measurement and real-time insights gained traction.
Now, data and analytics are as much of a given as a catchy campaign slogan was in the 1980s.
Marketers who boast how they rely on gut instinct are fossils from the analog age. According to Gartner's 2015 survey of data-driven marketers, more than two-thirds intended to base most of their decisions on data and analytics within two years.
Creating a Data-Driven Culture
During a presentation Wednesday on cultivating a data-driven culture, Gartner Research Director Ewan McIntyre explained how to identify and overcome cultural hurdles, increase your marketing team's data fluency and partner across the organization to lead change.
He warned that old business ideas can get in the way of becoming a truly data-driven marketing organization, and directed marketers to develop a culture where the use of data is pervasive across all business processes.
"And this needs to start from the top down, building an understanding of current data-driven maturity, and developing a plan to step-up," McIntyre said.
Seismic Shift from 'Customers to Code'While no one seemed intent in arguing the merits of data and analytics, there were undercurrents of fear, curiosity and concern. As Research Director Christi Eubanks, who led a small group session on best practices in marketing analytics and data-driven marketing, noted, marketers generally prefer to "talk about customers than code."
“Some of you are here today because of FOMO, the fear of missing out,” she continued.
In a question and answer session, Eubanks offered insights on commonly asked questions about the challenges and opportunities of data-driven marketing. Machine learning is already a key part of marketing technologies and will grow more important as the volume of data grows in coming years, she said.
But more data does not necessarily correlate to greater insights. Today, about 90 percent of data collected is unused because there is no clear purpose behind it, she noted.
Marketers have to learn how to communicate to data scientists both the type of data they need and the purpose it is intended to address. “If you can’t identify the business problem (the data is intended to solve), you can’t get anywhere,” Eubanks said.
Eubanks said marketers should form close partnerships with data scientists so they don’t have to build models and code everything themselves. But they should know enough to talk to data scientists about the business problems, processes, models and technology they are using.
“We need more marketers who are comfortable with data science,” she said. To get started, Eubanks suggested marketers advance their data education by enrolling in a massive open online course (MOOC) and working more closely with colleagues in data science and IT. Master some data fundamentals and take on a small project to put them into practice within the next year, she advised.
Make Data the PriorityMarketing, Gartner analysts concur, is on a journey. And while the destination is full of probable greater return-on-investment and unimaginable insights about customers, the road itself can be difficult to navigate.
Many organizations still struggle to get buy-in from former leaders who "vacillated between selecting data to support foregone conclusions and expressing outright hostility toward the idea that quantitative analysis might trump intuition in the quest to motivate people to action."
It’s no longer an option but an imperative, Gartner maintains. The research organization argues that today's best marketers have a smart approach to harvesting, analyzing and activating data.
The bottom line, Gartner stressed, is that data is an opportunity that can jump-start digital marketing programs.
Data Drives Customer Experience
Michael Isaac, digital marketing analytics Lead at Avanade, a provider of business technology solutions and managed services, explained how marketers could harness unique insights to transform customer experiences. The key to differentiating your customer experiences requires understanding your clients in ways your competition does not and then delivering beyond their expectations, he explained.
Data, naturally, is the catalyst for that deeper understanding. Perceptive companies and brands are investing in options to connect marketing data to external data sources to unlock insights and enable real-time decisions.
Data from third-party sources can expand insight and enrich internal datasets, he explained, adding that in-house “dashboards and scorecards are not enough.”Gartner Research Director Jennifer Polk expanded on the theme of using data as a foundation for customer experience in a talk that focused on customer journey analytics.
"There are really four main steps to work through when using customer journey analytics to understand your customer journey: Collect customer data from different systems, start with what you have, like marketing analytics and build out," she said.
"Map datasets to individuals using a common ID. Use data visualization to analyze, measure, discover and explore patterns in the data. Activate insights: begin with a hypothesis, test, refine and repeat."
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