Definition

data activation

Contributor(s): Corinne Bernstein

Data activation is a marketing approach that uses consumer information and data analytics to help companies gain real-time insight into target audience behavior and plan for future marketing initiatives. Instead of using assumptions, data activation employs quantitative, analytical means of measuring results based on historical customer activities, demographics and buying patterns to help organizations with both digital marketing and traditional marketing campaigns. The ability to act on data in real time helps companies make informed decisions and tailor messages based on insights.

Data activation enables the collection, storage, categorization and analysis of customer data at a time when some companies still use intuition rather than customer data to make marketing decisions. Many organizations have not advanced on this front because some marketers and salespeople may not have complete and accurate data. They may also lack mature data analytics capabilities. Additionally, companies may find that data quality across departments is inconsistent and untrustworthy.

In fact, a 2018 Forrester study found that only about half of B2B marketers and sellers said their organizations were effective at providing customer data they can trust to be complete and accurate. A few actions organizations can take for a successful data activation include engaging partners for data and analytics services, embracing advanced analytics technology and establishing systems of insight.

How does data activation work?

Data activation involves the aggregation of data that resides in point of sale (POS) systems, customer relationship management (CRM), data management platform (DMP) and other disparate database platforms to help guide a focused, unified sales and marketing strategy. For data activation to be effective, there should be a cohesive dataset based on available customer information, and the data must be accurate and up to date.

Stages of data activation

The basic process of data activation can be broken down into four stages:

  1. Collection- The first stage involves retrieving data from disparate locations and bringing it into a centralized platform within a single structure. That way, the data – which comes from various locations including websites, mobile apps, offline databases, CRM systems, media mentions or transactional data – can be used in aggregate.
  2. Analysis- Once all data is stored in one place to create a comprehensive view of the customer, analytics can be run on the data. This can be used to drive advertising and optimization for outbound marketing as well as discover new audiences that meet the target profile. Examples include audience analysis, sentiment analysis, act-alike modeling and data modeling.
  3. Execution- The next step is to act on the insights collected with data activation. This phase entails deep integrations throughout the ad and marketing ecosystem and might include sharing data segments that have been developed with marketing partners.
  4. Measurement- The final stage involves analyzing the success of data activation processes. Refining practices ensures that performance is high and results are the most accurate possible.

Data activation combines these stages. Since each company inputs its own data, uses different products to unlock the value and works with different partners, the result is a unique solution.

Examples of data activation use cases

A few applications that data activation can be used for include:

  • Creating audience categories for customer segmentation.
  • Implementing content personalization.
  • Recommending relevant products to customers.
  • Executing dynamic advertising campaigns.
  • Selling audience data, such as demographics, behavior, interests and device information.
  • Visualizing information by putting together graphs, charts and dashboards.
  • Uncovering a target audience.
This was last updated in August 2019

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