Data Engineering for Data-Driven Marketing

Technology   |   Mark Sarbiewski   |   Jul 6, 2021

Marketing departments know the value of data. After all, Marketing has been on the forefront of gleaning insights from data to foster positive relationships with new and existing customers and ultimately increase conversion rates and revenue.

Harnessing data to more accurately target potential customers, personalize customer engagement in real time, and improve customer loyalty is an intrinsic part of the role of the modern marketer. And as Big Data technologies become more refined, so have the capabilities of marketers.

Many organizations are inundated with data emanating from disparate sources, including transactional records, CRM and marketing automation data, interaction data, as well as public data sources, such as census demographics, weather records, and social media posts.

Moreover, the flood of data from the Internet of Things (IoT)—Internet-connected devices—is just over the horizon. Some 30.9 billion devices will be connected to the Internet by 2025, up from 13.8 billion in 2021, IoT Analytics, published Q2, 2020.

With the surge in IoT devices, the subsequent spike in data, and the availability of data wrangling (data engineering) tools, the possibilities for Marketing are bountiful.

Although diverse and complex data holds the promise of robust insights, the sheer volume and variety can create a bottleneck in the data analysis process, which becomes increasingly labor-intensive.

Before data can be analyzed, it must be wrangled into the requisite format for the analysis you want to run, especially if you need to blend data from various datasets or sources. This step creates a bottleneck or, worse, an impasse that prevents organizations from harnessing and using the potential of their data to enable data-driven decisions.

Data wrangling has historically been the exclusive domain of skilled data scientists—though, even for trained data scientists, this step can take as much of 80% of an analysis cycle. And because the demand for technical talent far outweighs the supply, the bottleneck in marketing data analytics is compounded.

But help is on the way. A recent influx of emerging data technologies has democratized the process of data wrangling, making it easier for marketers to get hands-on with the data they need to drive results.

Data Engineering Facilitates Marketing Analytics

To ease the bottleneck in the analytics process, data engineering (wrangling) tools have entered the market. Building on decades of work in human-computer interaction, scalable data management, and machine learning, data wrangling solutions significantly enhance the value of marketing data.

Regardless of how unmanageable the data may appear, these solutions can help alleviate the manual work of cleaning and preparing data, which usually requires laborious hand-coding. As a result, the analysis process is drastically accelerated, and data that might have been untouchable becomes accessible.

Understanding Existing Customers and Identifying New Ones

Customer data from various devices, touchpoints, and applications are used by marketers to increase the sophistication of audience segmentation and gain a more accurate understanding of their target audience by revealing customers’ changing preferences and behaviors at internet speed.

Data wrangling tools quickly format siloed data from transactional, social media, and other sources to turn customer interactions into insights that can be used to optimize marketing efforts. (For example, offering relevant services and products based on a 360-degree view of customers and their touchpoints—a capability that Amazon excels at.)

A similar analysis can be done on data from social media and Web browsers to identify new customer prospects based on insights gathered about their lifestyle, preferences, and purchasing habits. And when that’s combined with an examination of data from CRM systems, marketers can identify attributes of existing customers and target similar groups in the wider market.

Increase Customer Loyalty

Marketers also use customer data to cultivate deeper relationships with new and existing customers.

By analyzing data from every touchpoint a customer has had with a brand, marketers can provide highly personalized customer experiences based on real-time intelligence. Marketers can also increase customer loyalty by proactively identifying customer complaints and remediating the issues as they arise.

Organizations can build deeper relationships with their customers by turning their data into valuable insights they are able to share with customers (for example, repurposing data from a sleep or exercise tracker and sharing insights into how to improve their health or lifestyle).

Measure Customer Engagement Across Channels

Customers engage with brands via a variety of channels and devices, and it has become increasingly important for organizations to provide a cohesive experience across touchpoints. In a world where, increasingly, one size does not fit all, marketers must tailor their messages and offers at a much more granular level, by channel and by audience segment.

By analyzing available data sources, marketers are able to examine the level of engagement across channels and audiences, and adjust marketing efforts accordingly, based on the insights gleaned.

* * *

Marketing continues to evolve as technology innovation leads to its further digitization, creating new possibilities for data-driven campaigns. At the same time, technology innovation has resulted in a sea of new data that today’s marketer must contend with.

With data as a driving force within marketing, the need to apply all available data becomes paramount. New data wrangling technologies have opened doors for the modern marketer to harness data and analysis techniques that were previously available only to data scientists.


Originally published on Updated July 2021.

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