By Jose Ferreira, VP of CXM and Data Management, CMI/Compas.

If you were working in the marketing or advertising industries a few years ago, then there’s a pretty good chance you heard the term ‘big data’ get thrown around. If you were close to the technology end of the business you probably heard it more than once a day. It was easily the industry’s favorite buzz-word for at least two years, if not more. Basically, data became a valuable commodity and everyone was in the business of acquiring as much as possible in hopes of gaining a strategic market advantage over direct competitors. For publishers and content owners these developments disrupted their industries when the focus turned largely away from content to customers. In other words, the product that most publishers and content owners monetized and sold became active customers. The value of those customers was dependent on the “big data” used to keep them engaged and attributes that could be learned about them to then package them to advertisers. You may have noticed that a lot of newspapers went out of business in the last decade. This one of the major reasons why that happened.

This disruption had a similar effect on marketers. Companies spent large sums of money implementing technology stacks and acquiring data to better understand existing customers and prospects. In fact, an entire industry of marketing and advertising technologies has seemingly sprung up overnight to manage “big data” environments and leverage those data to make better informed decisions about creative, channel, tactic, and audience investments. Of course, the introduction of more and more data plus technology also introduced more complexity, as companies began to develop strategies to turn their large datasets into meaningful and actionable insights that make an impact in the market. Those efforts have taken many forms. In large customer facing organizations it has required the development of rules and practices to create better experiences by ensuring every customer touchpoint is imbued with the knowledge of touchpoints that have come before.

The customer and industry knowledge that have been gained from big data, and the customer-centric relationship management technologies that have been developed to leverage data in the marketplace are now being applied to the healthcare professional marketing sector. There are a lot of big and somewhat vague terms being used in the industry to describe this trend, like triggering, interaction management, next-best action, experience management, and countless others. These terms relate to specific things, some are synonyms for the same thing, but all fall under the rubric of marketing automation, so let’s start there. Marketing automation, simply put, is the act of leveraging audience knowledge and insights to develop marketing campaigns that are automated based on business rules, artificial intelligence or a combination of both. These kinds of campaigns are always on and largely dependent on how customers interact with media or what kind of business results they deliver as a result of combinations of media arrayed to provide high-quality customer experiences. In practice, this requires the development of a customer map, a bank of creative assets and content, and a deep understanding of where a customer is in their relationship with the brand being marketed. Those components can then be orchestrated to manage customer experiences across a multitude of marketing channels, and tested in real-time to determine the best sequencing and triggering of messages that is required to meet a brand’s objectives.

Triggering is a component of marketing automation and where the industry is largely focusing as a first step towards more holistic marketing automation. In fact, the idea of triggering messaging or essentially reacting to an activity with another activity has been around for some time. For example, re-targeting – targeting website visitors with ads related to their engagement with an owned digital property – is a kind of proto-triggering. We just didn’t call it that when it first became prevalent. Today, marketers are pushing beyond that more narrow triggering use case to include more communication channels and react to more events. Some oncology brands, for example, are pushing targeted messages to healthcare professionals based on the applicability of their patient pool as determined by claims datasets and/or using predictive algorithms to determine which healthcare professionals are likely to have applicable patients in the near future. In other markets, brands are using claims data to trigger messaging based on formulary rejection and other factors that present switching opportunities. Those are isolated cases of brand’s identifying an opportunity where a key decision about their brand will be made and then acting to strengthen the odds that their brand will be the one selected to treat an applicable patient. They are closed-loop examples in very specific scenarios, but they are indicative and illustrative of the way the industry is moving forward in applying data and technology to automate marketing to customers. The natural evolution of that will be to incorporate machine learning and artificial intelligence to determine not only when a message needs to be triggered, but also the content that message needs to contain. That is real-time interaction management, one of the buzz-terms I previously mentioned. It’s part of triggering and marketing automation more generally. In the near future, marketing automation will go beyond what I’ve already described to encompass entire campaigns, across all media channels and include owned, third-party paid, and content marketing components.

This is a pivotal moment in the industry where a lot of the work in building technology stacks to manage holistic marketing automation campaigns, integrating the most actionable data sources, onboarding a network of partners, and increasing the agility of creative teams will be tested. The ultimate objective is a seamless customer experience to the point that a brand is seen as providing services to healthcare professionals and not selling a product, which is a paradigm shift that will occur slowly. The first step will be applying technologies developed for closed-loop marketing and re-appropriating them to be always on and powered by clearly defined and data supported business rules. That includes creating the necessary connectivity with third-party content providers to fully integrate experiences across channels and media. Once those things are in place brands and marketers will be truly able to leverage the power of machine learning and artificial intelligence to deliver exceptional experiences. The industry is essentially in place where it is looking at campaigns, determining what kinds of interactions will take place across tactics, building in reactive communications, and building that out to its logical conclusion. Soon it will be a matter of setting guidelines and objectives, and allowing big data insights to drive outreach and engagement. One important thing to keep in mind is that this goes beyond engagement. Marketing automation, as defined here as the future of our industry, will need to harness exposure and reach as much as engagement. Broad reach and targeted media will need to work together. It will be interesting to see the case studies and ROIs related to marketing automation in our industry that have yet to be written. But one thing is clear, customers expect personalization and service from brands. If the old adage that the customer is always right is true then this new wave of marketing automation can’t be wrong.