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Today’s consumer can access an increasingly wide range of media and online information in addition to their traditional offline shopping behavior. Due to the falling costs and increasing availability of smartphones and mobile devices, they are also exploring digital channels and mobile apps. This is driving multi-channel experiences and diversified media usage – often in parallel with higher expectations regarding their personal needs and preferences.  As a result, consumer marketing is becoming far more complex and time-dependent. The structures, processes, and systems currently in place in many companies are not able to deal with this omni-channel phenomenon. Valuable information is either lost not fully exploited – primarily due to the absence of central data management and control.

Digital Intelligence Enables Predictive Marketing

We are living in the age of the consumer, where consumer obsession is the new frontier of competitive differentiation — scaled and fueled by insights. Traditional web analytic practices cannot deliver the necessary digital insights to optimize the experiences of the newly empowered consumer. Today’s data-driven marketers must extend their digital analytic practices far beyond the limitations of web analytic practices to address:

  • Fragmentation of channels – Consumers who cross touch-points insist on harmonized outreach across these channels, raising the bar for marketing execution and analytics. Although the Web remains important, it no longer tells the entire consumer interaction story. Mobile, social, and video engagement continue to grow at a significant rate.
  • Multi-device consumers – In the United States, half of all online adults are “always addressable,” meaning they own and personally use at least three connected devices, accessing the Internet from various locations multiple times per day. To understand these consumers and the context of each interaction, marketers require analytics that provide transparency on users’ devices, locations, usage patterns, and preferences.
  • Predictive marketing analytics – With the increasing velocity of consumer interactions, marketers must complement traditional strategic analysis capabilities with advanced predictive analytics. Current web analytic methods produce isolated, backward-looking reports and dashboards, often delivering insights that are too late and lack clear actionability. In today’s digital world, you must be able to keep pace with your consumers and react to trend-shifts in consumer behavior.

As defined by Forrester Research, the term “Digital Intelligence” means:

“The capture, management, and analysis of data to provide a holistic view of the digital customer experience that drives the measurement, optimization, and execution of marketing tactics and business strategies.”

If you notice, I underlined the word digital within the quote above. If you remove it, doesn’t this definition look very similar to any marketing department’s analytic mission statement over the last ten years? The only thing that has changed is the digitization of society. As marketing organizations become more familiar with the opportunity of digital intelligence, senior business leaders will direct web & customer analytic teams to work together. However, in many cases, these projects will struggle to get off the ground due to a clash of approaches & culture. Obstacles will include:

  • Data types – Structured vs. unstructured data streams, known vs. anonymous audiences
  • Skills – Data scientist/data miner vs. web geek/digital analyst
  • Analysis – Advanced analytics vs. “good enough” analytics

This cannot be understated. The intersection of advanced analytics and digital analytics has arrived, and the resources who support both of these areas will need to work together. It is long overdue, but change is not easy.

Web (and social) analytics have typically supported descriptive and diagnostic analysis (i.e. What happened?). Digital intelligence aims to address predictive and prescriptive analysis (i.e. What will happen? How can we make it happen?). To begin on this journey, organizations will need to rethink how they collect data from digital sources (first party vs. third party), normalize digital data for the downstream purpose of predictive analytics (and not simply summary reports and dashboards), and subsequently execute on the promise of prescriptive marketing processes through optimized outbound and inbound interactions.

Interactions with consumers cannot be dictated by silos, but with an integrated decision-centric approach that enables the understanding of constantly-changing consumer behavior to bring insights in line with the structure of the corporate mission. By balancing analytic insights, business rules, and data-driven actions, modern marketers can be more agile operationally in consumer contact situations. Ultimately, the customer experience is the priority, and our ability to be relevant and adaptive at the pace of the consumer will differentiate us from our competition.


So do you want to predict your customer's next move? What I gathered from Suneel is that it's all about using the digital intelligence available to you and addressing the 3 major obstacles of data types, skills sets and caliber of analysis.

Suneel will share these insights and more at the DMA Annual Conference this year, presenting in two separate sessions:

  1. The Analytics, Digital Intelligence, & Experience Management Pre-Conference Workshop, and
  2. The Marketing Analytics, Business Communication, and the Art of Interpretability Breakout Session.

SAS is proud to sponsor and participate on the program advisory committee of the DMA Annual Conference, the global event for data-driven marketers. As always, we'll capture content at the show and make it available for you in different formats.  Thanks for following!

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People are funny. They’re often fickle, choosy, demanding and impatient. At times, they’ll say one thing and then do another. So when they become customers, how can you possibly predict what they’ll do? Well it turns out there are ways to do it quite effectively using data and analytics. One advocate of such approaches is SAS Senior Solutions Architect, Suneel Grover. Suneel posted a blog recently about this topic on the Direct Marketing Association....

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