Brand Marketers: Personalization at Scale Requires Effective Data Orchestration

Brand marketers have access to extensive customer data, but using it to drive results remains a stumbling block for many. In fact, most B2C marketing organizations face a similar problem: their data is siloed in separate platforms, creating partial views of consumers for different tasks. Incomplete data inevitably leads to inconsistent results.

The pervasiveness of this problem has been one reason for the growing adoption of customer data platforms. By bridging data from disparate sources into a single platform, marketing teams can take a big step towards a unified customer view and stronger performance across KPIs.

Centralize and Orchestrate Your Data

Getting all the data in one place is important, but it’s only the first phase of the challenge. Once data is centralized, organizations need capabilities to work with their data in custom ways to support specific use case requirements. This aspect of data use is called “orchestration,” and it’s a critical competency — involving both technology and ways of working — that precedes other initiatives such as segmentation, activation and measurement. Without effective data orchestration, your business may struggle to achieve lift across core initiatives.

In today’s age of the empowered consumer, personalization is the key to effective digital marketing. While it’s your content that makes an impact on the consumer, it’s your data that determines which consumers you’re reaching, how you’re reaching them, and when — all of which contribute to a personalized experience. If your business has personalization-related goals it’s trying to achieve, you need to be able to integrate and customize your data for different purposes. Otherwise, it may prove difficult to deliver personalization across all the consumer-facing functions in which your organization is engaged.  

Create Custom Datasets

With the Tapad CDP, brand marketers and data scientists gain the ability to create custom datasets for marketing use cases by bridging first, second and third party data with Tapad’s big data and other data stored in the customer’s data mart. The resulting “derived datasets” are both dynamic and persistent, enabling teams to implement data assets curated for each initiative. Orchestrating data in this way empowers your organization to use datasets that are purpose-built rather than generic.

This approach also has a democratizing effect by enabling marketing and data science teams to collaborate in an agile framework. While custom data isn’t new to data scientists, the Tapad CDP streamlines workflows that used to be time-consuming, and makes the resulting data assets exposable to different teams in an automated fashion. Data scientists can produce custom datasets for various functions, while each team can utilize precise data — all with less manual work and tighter team coordination.

Key Benefits of Derived Data

Many data science and analytics teams already work with custom data, but Tapad’s Derived Datasets provides new benefits. The tool saves time by automating manual processes, opens up new methods of data access, and enables the creation of persistent objects and workflows. For organizations using HDFS, Derived Datasets also helps by providing SQL-layer access to data resources -- enabling users to query data without the need for deep coding.

Here are three ways your organization can benefit:

  • Data Loadability: There are no standards imposed on schema for data loaded into the Tapad CDP. Datasets don't have to be formatted the same way, saving data scientists the time and headaches of standardizing disparate datasets.
  • Easy Access to Tapad's Big Data: Tapad data is a great resource, but can be challenging to access in raw format given its scale and size. Derived Datasets makes it easy to access Tapad data and combine it with other data sources.
  • SQL-Layer Access: Data science and analytics teams can use SQL to query datasets, a huge advantage over manually querying HDFS via Spark, Python, R or other coding languages. This gives analytics teams advanced data science capabilities without sacrificing ease of use.

Drive Personalization at Scale

Since personalization depends on customizing the way you communicate with each consumer, providing custom data to each team is a key ingredient in meeting that goal. Brands leveraging the Tapad CDP can do this systematically, applying their own business rules and governance principles to the production and use of custom data. The result is the ability to personalize across all consumer-facing initiatives with data optimized for each use case.