M&E Journal: Making Big Data Work for You to Create Contextualized Engagement

By Philip Paisnel, VP Client Partner, Softtek

Both business users and consumers desire an intuitive content delivery experience that is as frictionless as possible. The name I use to reference this type of engagement is “frictionless contextualized experience” and today’s technologists are faced with the challenge of how to deliver the right message at the right time, to the right business user or customer through the right channel.

Meeting this challenge is supported by two tenets: deploy a modern software platform to enable this functionality, and make meaningful use of your data to execute the desired engagement. The data tenet is typically the tougher one to solve.

The problem with data

Data sits in many different forms within many different platforms; some of these you control, and others you do not. Nonetheless, you need to gather all of this disparate data so that your platform can leverage it to provide a frictionless contextualized experience. Additionally, because all of this data resides within different platforms, there could be duplicate records throughout your data ecosystem.

These data lakes leave you with a monumental task that begins with a data hygiene exercise before you can start to build models that your platform can leverage to provide the desired engagement model.

As it relates to the consumer, their access to content is becoming key. Moreover, the “where” part of the equation involves geographic location as well as the context of how the consumer engages with the content — all of which can impact consumer behavior and interests. For marketers, therein lies the challenge.

Specifically, where do you begin to look for insights from customer and market data? How do you organize various data sources and draw meaningful connections between them? What outputs can be created that drive informed and measurable decisions?

To solve these issues, marketers are increasingly teaming up with their technology teams and technology service partners to develop solutions and execute strategies aimed at organizing data infrastructure and enabling data migration.

Additionally, by utilizing artificial intelligence (AI)- fueled modeling and leveraging predictive analytics for audience insight, media companies can deliver a relevant contextualized content experience.

The opportunities are significant. Machine learning systems can run hundreds of “what if” models in real-time and deploy pattern recognition capabilities to identify links and correlations between seemingly random data sets. Such analyses shed light on audience preferences and how consumers respond to different types of content, delivery channels and external factors.

For example, audience interest can be impacted by variables such as the weather, the time of day and events ranging from the Super Bowl to terrorist attacks.

Machine learning systems can gauge the impact of these variables, as well as assess the relative significance of a given variable’s level of influence. The obstacles to achieving this vision, meanwhile, are daunting.

Organizational issues perpetuate the existence of operational silos that prevent data sharing and integration. Inadequate infrastructures further hamper data transparency and hobble the effectiveness of machine learning tools. Up to 80 percent of all big data is unstructured and unusable.

As a result, media marketing teams today struggle to get their arms around available structured data, as well as access and utilize potentially valuable unstructured data.

Existing analytical tools that lack predictive and machine learning capabilities are typically based on limited slices of data and are unable to provide the critically needed real-time 360-degree views of an audience.

The problem with platforms

Problems with disparate platforms are another issue. According to, almost 5,000 solution providers are today creating silos of data, often with no single source of truth.

So how can you overcome these obstacles and realize the potential of predictive models to get smarter with your audiences? How do you address your organizational and operational constraints, as well as define critical success factors and outcomes related to deploying machine learning?

Softtek’s CMET analytics team can help you make sense of these data silos and how best to leverage them to make your platforms provide a contextualized experience to your consumers, as well as, help your business leaders make the right business decision to drive your business forward.


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