In the world of digital marketing, managing Internet privacy and brand perception requires a careful balancing act. Striking that balance requires more than just a “do no evil” mentality—it’s about actively offering value in ways that consumers deem worthy of transacting their personal information. This means providing accurate and timely messaging that feels more like personalized customer service and less like spam. One might describe this holy grail of digital marketing as “providing decision support by getting the right information to the right people, at the right time, via the right channel” (save that thought for later). Taking into account the new breed of data-hungry consumers armed with mobile devices, Resource sees this as the perfect opportunity for introducing “Responsive Experience Design”, or RxD. The goal of creating responsive experiences is to put the consumer in the information driver’s seat, leveraging content on their own terms for their personal decision making.
From a data-driven marketing perspective, this move toward RxD is the next logical step in a progression that started almost 20 years ago. Back in the early days of the Internet, brands simply followed the traditional paradigm of spewing out high volumes of “one size fits all” messaging though email, websites, and ad networks and counted on some of those impressions making their way through the funnel to interested consumers. Gradually, CRM and ecommerce technology enabled marketers to create a closed loop through which they could learn more about individual consumers and their responses—allowing those broadcast messages to be tuned in to specific market segments. However, channels and campaigns were often analyzed separately, limiting efforts to reach more holistic business goals.
The growth of Business Intelligence
While this approach to data-driven marketing was slowly maturing, another interesting development was happening within the enterprise IT world. In the late ‘90s, Business Intelligence technologies became more widespread, as their track record for improving business decision making began to grow. In a Business Intelligence (or “BI”) practice, data analysts collect disparate information from around the organization into “data warehouses”, then apply analytical processing tools to extract insights and predictions from previously hidden relationships within the data. According to Wikipedia, “Business intelligence (BI) is defined as the ability for an organization to take all its capabilities and convert them into knowledge, ultimately, getting the right information to the right people, at the right time, via the right channel.” Sound familiar?
Today, BI systems have become a staple within Fortune 100 companies, typically aimed at marketing (customer and sales data) and pricing (the supply chain) to help drive growth and profitability. Marketers tend to use statistical analysis systems to mine out prospects, but often these systems are still focused on reporting a “rear-view mirror” of the business, or on simply managing the cost of marketing efforts. Newer tools like predictive analytics are needed to drive marketing efforts because customer preference is a target that keeps moving and changing shape.
Enter “Big Data”
New social listening and data-mining tools have helped to surface more forward-facing insights like brand sentiment and purchase intent from Tweet streams, blogs, ratings and reviews and other consumer-generated content. Classified as “Big Data”—massively scaled, unstructured and rapidly changing—this type of information is not yet widely welcomed in the data warehouse, leaving marketers to perform offline analysis and correlation to arrive at actionable insights.
Fast forward to today, where the BI industry is starting to talk about a new breed of “Consumer Intelligence” tools that will shorten the marketing cycle even further, applying advanced statistical analysis and machine-learning to offer larger-scale and real-time personalized content. Just as ecommerce turned the traditional point-of-sale system around to face the consumer, Consumer Intelligence (or “CI”) aims to turn the BI console around to give the consumer a more direct look at their own relationship with the brand. When this can be provided in real time, contextual cues such as location, current click-stream, day-part and local weather and social cues like check-ins in can be correlated with more persistent data such as past interactions and purchase history and affinity with other consumers.
With the help of new tools like social listening and CI, RxD will provide the ability to layer more personal, meaningful messaging and content on top of continuous, ubiquitous digital reach. But to avoid the “big brother” perception, brands will need to provide the appropriate opt-in and opt-out flows for their consumers and help them understand how and where their private information is being put to work on their behalf. In doing this, they will create new opportunities by guiding with the trusted, familiar voice of a “big sister” that their customers are happy to confide in.