For as long as digital marketers can remember, data analysts have held the position of data prophets—they were the ones who could peer into data reports as if they were tea leaves and divine the secrets everyone knew were there but few knew how to access. End users like sales and marketing teams waited for analysts to offer insights before determining whether they could even act on them. And the insights weren’t necessarily timely. The process of analyzing data was slow, cumbersome, and expensive.
Giles Richardson, head of analytics at the Royal Bank of Scotland (RBS), calls this traditional approach to data a “fireworks culture.”2 There would be a big launch of a new part or redesign of the digital experience. We would literally light it up everyone would run away and then people would clap. We were disconnected from the data of what was actually happening. Had we fixed a struggle for customers? Was it even a problem in the first place? The data was there, but it wasn’t used effectively. Decisions were made by gut feeling, and the wish list of digital improvements was often hopelessly backlogged. If we wanted to become an experience business we needed a new vision.
Fortunately, technology is catching up. Analytics is seeing a renaissance that’s changing everything about the way you’ll use your data. It’s being democratized, so everyone’s empowered to use data in extraordinary ways to solve business and marketing problems.
The buzz about data democratization began about a decade ago but could arguably be traced back to the early days of the Internet. With the widening access brought by the World Wide Web, information that was once available to only a select few became available to everyone. But when the data democratization conversation began in earnest, businesses didn’t have a clear sense of what democratization actually meant. Business owners and marketing teams were thrilled with the possibilities of big data. And our ability to capture intimate information about our customer’s wants and needs was greater than ever before. So we started sharing. For a while, this was our answer to the question of how to democratize the data.
But democratization isn’t just about sharing. And it isn’t just about access. Having more tabular reports, graphs, and charts in front of you isn’t going to give you the power of insight. And this is where the conversation gets really interesting—for data democratization to truly work, we have to have tools that allow us to act on the information we have. We need customizable analytics that everyone can use.
As with any movement toward democratic principles, the dream is about empowerment—giving individuals greater freedom. In the world of data democratization, breaking down information silos is the first step toward user empowerment. The next step, which is more like a giant leap, is giving people the power to do something intelligent with that information. This is analytics democratization. It’s the ability for everyone to make data-driven decisions directly related to their jobs, whether they’re managing budgets, selling products, or creating content. It’s an environment where silos are gone and data is connected, but where permissions are customized and controlled for individual users, so that people get the exact data they need, without getting overwhelmed with unnecessary information.
This is a new concept for marketing departments and the organizations they serve. The challenge is no longer simply about connecting and disseminating information. It’s about insight empowerment for all. It’s about self-service analytics that fuel data-driven decision-making at all levels in an organization.
And it’s getting easier. Data that was previously siloed and segregated can now be connected and managed from a single place. Analytics helps make sense of that data, with simple tools to help spot trends, correlations, and insights into customer behaviors. These can then immediately be acted on by marketers, product developers, and other business decision-makers. And these actions can be continuously measured and adjusted based on real, deep data. Best of all, new analytics tools are making simple what was once complex—and when anyone can use the tools, democratization can truly revolutionize your organization.
Part of this new era of analytics is its turn away from the esoteric and toward the accessible—so that a common good can be created by and for all people, and everyone can be a participant in the process. New analytics tools are enhancing our ability to dive into data, making it easier than ever for anyone to turn mounds of information into actionable insights. Even better, new advancements in analytics workspaces can now be curated for each employee’s role or the unique jobs they’re trying to accomplish, from a marketing manager analyzing customer segments to build a new campaign to a CMO analyzing marketing ROI to build next year’s budgets. This is what data democratization has been reaching for all along—a way for every player on your team to unleash the potential of data.
Imagine analytics as a mountain with both technical ascents and easy hikes. Near the summit, where the data scientists and hardcore analysts will undoubtedly climb, you’ll have machine learning, predictive algorithms, and data science. At the base, where the analytically green will be ambling around, you’ll find easy-to-interpret, drag-and-drop visualizations, dashboards, and breakdown tables. With this kind of workspace, Jack, the data newbie, will be able to run optimization tests alongside Jill, the data pro. Teams will become better, stronger, and faster at understanding what customers do in real time and over time—all while making the most of each individual’s unique skills and perspectives.
As one of the leading women’s lifestyle brands, POPSUGAR faces a mountain of data daily. To keep it moving internally so every team member is empowered to provide customers with a continuously exceptional brand experience, they’ve made analytics democratization a priority.
POPSUGAR started its journey to democratization by shifting the architecture of its organization, bringing analysts and content strategists together in a content studio “bakery.” Analysts surfaced insights and delivered them instantly to the brand strategy team. The strategists then started cooking on new kinds of content to develop based on these insights. Collaboration flourished as strategists developed highly targeted campaigns and editorial based on the analysts’ insights. And in an environment where everyone was free to learn together and act on their discoveries, the innovation happened on the spot, often in real time.
Using democratized analytics tools—including customizable data dashboards that made the right data available to the right people at the right time—the teams were able to create a continuous flow of social media content, native advertising videos, and events. With this shift to a democratized workspace, both physically and digitally, POPSUGAR has been able to stay nimble, predictive, and digitally savvy with their content. In June 2015, for instance, analysts and strategists determined that Halloween was already a huge trending topic. At the same time, they uncovered that the summer’s top celebrity trend was Taylor Swift. Insight moved immediately to action as POPSUGAR’s editorial team created a piece of content called “17 Ways to be Taylor Swift This Halloween.” Over one billion page views later, POPSUGAR showed that democratized analytics and real-time insights could turn audience interest into brand devotion.5
In an Adobe survey, two-thirds of respondents (67 percent) reported being highly dependent on analysts to answer even basic questions. Only 13 percent felt empowered to run with the data entirely on their own.7 Not surprisingly, a lack of training seems to be an important factor. Seventy-two percent of the high dependency group were offered no analytics training whatsoever, compared to 36 percent of all respondents. With this kind of gap, the power of data remains unharnessed.
of marketers felt their organization was not accountable to digital metrics
of marketers are highly dependent on analysts
of marketers received no web analytics training
received only ad hoc training
And let’s not forget about the high-level data analysts. Despite being named the sexiest job of the 21st century, being one of a few data scientists in an organization can be a heavy burden.9 They can field innumerable ad hoc requests on any given day and are expected to pull insight at increasing speeds from ever-increasing channels of information. Democratizing analytics allows business-side users to self-serve their own tests. This frees up the data scientists to sink their teeth into the ultra-juicy bits of data that can lead to predictive insights and competitive intelligence.
To shift the paradigm of how data was used at RBS, Richardson created a new program for digital journey managers called “Superstar DJs,” which emphasized creating new content second by second. DJs make music in real time. They’re responsive to their audience and make small or large adjustments to their set based on their audience’s feedback. Superstar DJs at RBS have learned to do the same. “Within seconds—within milliseconds—our DJs know if their content’s working or not,” says Richardson. With this instant insight, the DJs can make constant, up-to-the second adjustments to their sets all the time.10
Data analysts got a new role as well, becoming “Producers.” RBS provided support and training—teaching the Superstar DJs how to harness the power of analytics and encouraging the producers to let go of data control—and everyone began working collaboratively on the marketing cloud. This was a significant step. As the Producers relinquished control, allowing the DJs to try their hands at analytics, they discovered they were free to do more complex analysis and work at the top of their skill level. With more training and access, the DJs stepped up. Now there weren’t just a few guys in the corner making magic. There were 50 DJs doing constant optimization testing and evolving exponentially.
Richardson knew that to get data out to everyone, he needed more than analytics tools—he needed to further break down the organizational silos that had become part of the RBS workspace. So he made it fun. There were no more unproductive meetings, no more confusing email chains—everyone worked collaboratively in the cloud and capitalized on the music theme. Billboard charts were set up at the end of each week to get friendly rivalry going. There were gold disk presentations for genius optimization work and Diamante headphones when employees reached the Superstar DJ level.
Another radical move of Richardson’s was to insist that every insight had to have an action attached to it. This may seem obvious at first, but even the best tests and insights don’t always translate into plans and actions. With more team members running tests and actually using the results, individuals at every level of the organization were empowered to make changes that were likely to improve results. For instance, if a web manager saw that loan applications were falling off two minutes into the application, she could test for an optimal time frame and improve the application so that applicants finished the process before they ever reached the drop-off danger zone. Soon it wasn’t just the DJs who were running tests and seeing optimization results. After training, RBS saw 80 percent of its bank board members logging into their analytics program, running live tests, and using the insights to make changes in real time.
With more people acting on their data-driven insights, the RBS team worked together to create consistent, real-time, cross-device stitching of their customers’ experiences. The web page would interact with a customer and immediately send personalized campaign information to the customer’s mobile app. With easy analytics becoming so available, insights from these cross-channel experiences were made available to everyone. With this came easy evangelizing and executive buy-in, which is crucial to keep the momentum of change moving forward.11
2 Tests Completed
400 Tests Completed
With all this optimization going around, RBS was able to exponentially increase the number of tests conducted. Insights came quicker and easier—all with just a small increase in the number of analysts.12
With everyone in on the game, RBS saw mobile conversions explode. This was data-driven marketing at its best.13
30% used mobile devices to apply for loans
Conversion jumped 20% on loan applications
Loan applications were completed in minutes rather than days.
Evolving your digital analytics program for a shift toward democratization requires foundational work. It’s crucial that you evaluate the current state of your analytics program. Know that building confidence for the analytics newbies and their managers will take time and investment, but it will set the stage for providing exceptional and consistent customer experiences across channels.
To get this moving, here are five strategies to help you successfully transition into an organization where everyone gets in on amazing analytics action.
In an online survey conducted by Adobe Customer Analytics Evangelist Brent Dykes, only 51 percent of respondents reported having either a C-level or a VP-level executive sponsor for their digital analytics program. Without executive leverage, any program is doomed to stagnation. And if the VP hasn’t persuaded key stakeholders to support the program, confidence (especially for those new to analytics) plummets. Take every opportunity to evangelize the value of the program. Demonstrate how democratizing analytics leads to cross-pollination of ideas and action across marketing, finance, and other areas of the company. Share empowerment successes vertically and with enthusiasm. As with any move toward democratization, it’s important to clearly show how the aims of a particular team (editorial, for example) give leverage to the overall goal of the organization.
Less than 10 percent of respondents to Dykes’ online survey said they felt like their organization was accountable to digital metrics. Without this accountability, democratized analytics is an illusion. Transparency keeps people up to speed on strategy, and knowing who is working on what prevents testing overlap. And accountability creates a culture of shared responsibility, where it’s not just the people at the top who are making things happen. With this in mind, make certain that your organization sets clear expectations for actionable tests and campaigns with measureable results. Clarify roles and responsibilities within your analytics program, and empower each individual to use data to make decisions and put their ideas into action.
Think of strategy as your car’s headlights. If you don’t have them, you can drive all night, but it’s likely you won’t arrive where you want to go. When you’re democratizing analytics across your business, everyone needs to be clear on strategy so they can build their tests in alignment with KPIs and take actions to help reach business goals. Keep in mind that it can go the other way, too—strategy may need to be adjusted when the data clearly points in a different direction.
A crucial component in creating the shift toward a democratized analytics program involves aligning business needs and analytics reporting. Only 15 percent in the Adobe survey felt their business needs were matched by their digital analytics report, while 10 percent viewed their current digital data as irrelevant. When your analytics reports align with your business needs, everyone on your team will be able to find opportunities for testing and optimization that truly help your business build stronger results.
More than a third of organizations reported that their digital analytics users received no training at all. Another 31 percent stated that some ad hoc training occurred, depending on the manager. It’s important to clearly evaluate the maturity of your democratized analytics program. This will require that you train to all levels and give your team room to grow. Identify those who need to start at the base of the mountain and those who are ready to climb to the summit.
Part of the tectonic shift happening in organizations is a return to the origin of what sparks human emotion—stories. With the massive influx of data, and with democratized analytics allowing anyone to pull insight from that data, comes the need to translate this information into something meaningful. Slapping a dashboard or a series of graphs up during a team presentation doesn’t get to the heart of the story. This is where trends are shifting. Anyone in your organization who holds raw data must now step into the challenge of storytelling. The reason for this is that in this new era of insight, the way in which we share data internally will transform our organizations as well as our relationships with our customers.
Data storytelling is all about being boldly creative. This means moving out of the descriptive, logical mode and into one where character and emotion are the explanatory features. Creating a story to wrap around the data will give everyone that hears it that aha moment. This is the challenge—moving data past the dashboard and embedding it in meaningful, visual stories that drive action.
Do you know which web navigation, content, and offers will drive higher conversion and sales? Have you discovered that the landing page has confusing navigation? Good. Make sure your story drives toward your point.
Don’t expect your audience to know what to look for. This is where you’ll use data and visuals together to bring insight home. The intent of the story is to bring the analytics alive. As you’re telling your COO about the off-the-charts sales of the newly repackaged Barbie, particularly among thirtysomething mothers in the Bay Area, show your COO the data in creative ways. The story should reflect and inform the data and vice versa.
Know what happened at what time and when. How many touchpoints were there before a customer jumped off? Where were they? What was the last thing they did before they vanished?
Know your audience. Are you speaking to the head of business development or a merchandising specialist? Know their beliefs, biases, and preferences and know how familiar they are with the topic. We know this when we profile the ideal customer avatar, but it’s just as important internally, when presenting insights to your company.
Find your hero. Don’t just present your customers as a series of data points. Give them a voice. Your hero could be a single dad trying to find the best stroller for his son. She could be a millennial hunting around for outfits that will deck her out like Taylor Swift for Halloween. Either way, make customers real with telling details.
Build structure. All stories have a beginning, middle, and end, but you don’t necessarily have to build them that way. You can begin with an aha moment that’s deep in your data insights, add with a surprising detail right in the middle of your report, or lead with a hook and move forward from there. Play. Experiment. See which version of the “What? So what? What now?” narrative prompts work with your story.
Make sure the visuals match and highlight the message. Use color and creativity to draw people’s eyes to the data you’re discussing.
As organizations make the shift from old-school data dictatorship to true analytics democracy, they’re moving from top-down, intuition-driven marketing to data-driven decision-making based on real-time insights. These are today’s analytics pioneers, the ones who are able to take advantage of more cross-channel marketing opportunities, customizing campaigns across platforms with automated and predictive intelligence.
They’re doing it so well precisely because they’re focused on building a democracy, where no one works in isolation. By creating environments where people can learn and act on their insights, teams can come together to solve age-old problems in innovative new ways. And as they see their efforts translate into increased ROI, it’s easy to evangelize everyone’s efforts.
For companies that get this right, the conversation that began with data dissemination will turn into one about easy analytics for all. We’ve known about the power of data for years but are only now learning to harness it. And as the conversation continues, the harnessing will no longer be relegated to the technical elite. We’re catching up with data. Our collective fluency and ability to move from insight to action is getting easier. And with people empowered across organizations comes a consistent intimacy of customer experiences, putting extraordinary possibilities into the hands of anyone, anywhere, anytime.
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