The State of Analytics 2018

Article ImageWouldn’t it be nice if technology development proceeded in an orderly, logical, and predictable manner? Once you finish laughing—or crying—at that sentence, recognize that especially when it comes to content and marketing analytics and the Big Data that underpins their viability, it was entirely unrealistic in 2017. “Technology didn’t wait for us to get our data act together,” observes Allison Snow, a senior analyst at Forrester.

As content and marketing analytics teams will attest, getting one’s data act together was made trickier by a number of factors last year. The explosive growth of Internet of Things (IoT) data—and the tantalizing insights it holds for predictive and prescriptive analytics—was accompanied by a rising prominence of unstructured data (such as video and audio) and a multiplying number of external databases that could uncover valuable relevance, if integrated properly. Add in the fact that much of an organization’s interaction with a customer could take place entirely on a third-party platform such as Facebook for a sense of the challenges in synthesizing and leveraging data for operational decision making in 2017.

Luckily, it was also a year in which artificial intelligence (AI) showed serious promise in helping to shoulder the workload reliably. Tools for decision makers to extract and interpret Big Data became easier to use and more powerful in their capabilities. More sophisticated attribution tools enabled marketers to pinpoint their role in creating value. And as analytics unlocked new insights, it spurred new creativity. Indeed, Forrester’s research showed that insights-driven firms were 69% more likely to report year-over-year revenue growth of 15% or more.  The big question for the year ahead: How fast must the tools evolve to keep up?


The Content Analytics Year in Review

The growing application of AI in content and marketing analytics had a profound influence in 2017. “Lots of marketers have been collecting data and doing analytics, for years,” says Snow. “But marketers fall down operationally because of bottlenecks like limited design resources and the approval process. So the extent to which we can use AI to automate responses to triggers—so it doesn’t take 2 weeks to process a buyer’s intent—is extremely important.” Snow gives the example of a customer who signs up for a company newsletter, at which point AI is used to assemble a personalized version from multiple content sources based on the customer’s known attributes, all within a fraction of the time it might have taken even a year ago.

Christina Noren, chief product officer of the behavioral analytics company Interana, says that in the publishing industry in 2017, she’s seeing evidence of increased analytics use to drive content development. “We’re learning that in an age where social media platforms own so much of the customer acquisition, there’s a shift in emphasis on analysis to re-engage with existing customers. The goal is to maximize the lifetime value of the customers you do have.” Noren also sees companies more efficiently layering in external data sources, looking for affinities across topics, and uncovering relationships (such as seasonality) that may influence buyer behavior.

Another major step forward in the application of marketing analytics in 2017, according to Snow, was in improved attribution, allowing marketers to not only track return on marketing dollars spent, but to better understand messaging impacts and the customer acquisition journey with increasing granularity. “Marketers have more insights than they used to, but they didn’t necessarily know how to claim credit as a business grows,” says Snow. The growing sophistication of attribution solutions means that marketing professionals are better prepared to define their department’s contribution to the bottom line.

What has underpinned all of these advancements, according to Stan Lequin, VP of consulting services for Insight, a technology provider of hardware, software, and service solutions to business and government clients, are analytics tools that are easier than ever to use, which are important at a time when qualified data scientists continue to be a rare commodity. “Six or 7 years ago, you had to know SQL and be strong in that area to do analysis,” he says. “But because it’s gotten easier to connect to external sources and use APIs, we write a lot less code than we did even a year ago.”


A Look Ahead Content Analytics

Given the continued dominance of distributed content and customer interactions on third-party platforms, Noren believes that publishers must come together to demand better raw data from those pipelines. “It needs to become a condition for getting on those platforms and needs to happen in a more consistent way.” She’d like to see publishers force third-party platforms to provide clickstream data and the tools to allow them to analyze it internally.

Expect continued evolution of the tools that will enable content and editorial teams to drill down into datasets. Addressing the need for easier visualization tools, Noren says that Interana has doubled down on user research to understand how its clients use its tools. “We’ve created flexible tools for content and editorial teams to explore data on their own terms.” Interana customer Bleacher Report uses behavioral data across departments. The content team prioritizes and plans new content based on customer engagement, the product team uses the data to streamline the onboarding process, and the marketing team uses it to build and test user personas.

As external datasets become easier to integrate, the ability to identify heretofore unseen relationships will spur new and powerful insights. Lequin gives an example, saying, “We’ve engaged with organizations in the healthcare field on staffing, and with some of the data that we can now pull back and test, we’ve enabled them to save $100 million in staffing costs over 5 years.” Forrester senior analyst Tina Moffett says, “The integration of different datasets, things like brand value, brand relevance, and customer identity, will allow marketers to understand more deeply the drivers of purchases.”

Snow is hopeful that the advances in attribution will mean a fresh look at the marketing budget approach in the coming year. “Don’t just replicate your 2017 budget; imagine you’re starting from zero. Are you poised to generate revenue from existing customers versus an acquisition-only focus? There’s a disconnect between what marketers are doing and what attribution analytics can tell us.” As Moffett says, regarding the gap between insight and operational activation, “Activation isn’t a technology issue. It’s a cultural and organizational issue.”

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