Marketing 0

particular demographic.
Every dynamic dataset should be stored in a single data management
platform, which enables marketers to capture, store, manage, and
analyze the data comprehensively. Any new data-driven marketing
projects with new objectives should continue to use the same
platform, enabling a richer data ecosystem, which is beneficial if the
company decides to use machine learning to automate analysis.

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Summary: Building Data Ecosystem for Better
Targeting
The rise of big data has changed the face of market segmentation and
targeting. The breadth and depth of customer data are increasing
exponentially. Media data, social data, web data, POS data, IoT data,
and engagement data can all make up a rich profile of individual
customers, allowing marketers to perform segments-of-one
marketing.
In the digital era, the problem is no longer the lack of data but rather
identifying the ones that matter. That is why data-driven marketing
must always start by defining specific, narrow objectives. Based on
the goals, marketers acquire relevant datasets and integrate them
into a data management platform that is connected to an analytics or
machine learning engine. The resulting insights can lead to sharper
marketing offers and campaigns.
Data-driven marketing, however, should never be embarked on as an
IT initiative. A strong marketing leadership team should spearhead
the project and align the company's resources, including IT support.
The involvement of every marketer in the organization is imperative,
as data-driven marketing is not a silver bullet and will never be run
on autopilot.