Assess Your Organization’s Customer Data Maturity Now


Successful personalization at scale requires intentional planning around customer data management.

Investing in customer data is a top priority for marketing leaders. Research found that marketers who reported using customer data to heavily influence business decisions are 1.6 times more likely to see greater organizational revenue growth.

But not all organizations have been able to use customer data to improve business results. Many marketing departments are still trying to make sense of the customer data they’re collecting, often struggling to manage data quality, assemble 360 customer profiles, and activate data in real time. To prevent your customer data set from becoming a data swamp, it’s important to lay a solid, scalable customer data infrastructure at the heart of your martech stack.

Several characteristics separate organizations with high customer data maturity from their less data-mature counterparts. These include:

  • Speed: How quickly can you move data through the data lifecycle? How easily can non-technical stakeholders access the data they need and activate it in a timely manner?
  • Adaptability: How easily can you adjust to changes, both inside the company and in the market ecosystem?
  • Trust: How confident are data consumers in the accuracy, consistency, completeness, and privacy compliance of your data?
  • Collaboration: Do you have cross-functional processes in place to ensure the infrastructure supports the needs of all data stakeholders?

By establishing data processes and tooling that optimize for these characteristics throughout the data lifecycle, you’ll be better situated to increase your data maturity and use customer data to improve business results.

Data Maturity Level 1: Reactive

At Level 1: Reactive, organizations utilize numerous tools for activation, but are unable to scale their data strategy due to lack of a comprehensive process for data management, ownership and data integration.

As teams begin to leverage customer data to support better marketing, analytics and customer service, they often start by implementing tools to support each of these functions independently. This is a huge step forward from not being able to use customer data at all. But this reactive workflow leads to several challenges. Vendor implementation requests increase in direct proportion to marketing and product programs, making it difficult to scale. When tools need to be replaced, or when fundamental platform shifts occur, manual development is required to keep the tech stack up to date.

To progress beyond the reactive level of data maturity, teams need to centralize their customer data infrastructure in a way that relieves engineering workload and facilitates cross-departmental collaboration around customer data.To progress beyond the reactive level of data maturity, teams need to centralize their customer data infrastructure in a way that relieves engineering workload and facilitates cross-departmental collaboration around customer data.To progress beyond the reactive level of data maturity, teams need to centralize their customer data infrastructure in a way that relieves engineering workload and facilitates cross-departmental collaboration around customer data.

Data Maturity Level 2: Centralized

Teams progress in customer data maturity when they move from a series of disparate data pipelines to a centralized customer data infrastructure. At Level 2: Centralized, teams implement an infrastructure that allows them to collect customer data through a single point of collection and forward it out to downstream tools via server-side integrations, akin to a hub-and-spoke model. Marketing and product are able to access the data they need in their tools without having to depend on engineering for data requests and SDK implementations, executing tool-level use cases such as advertising, email and product analytics with greater independence.

The functions of the centralized customer data infrastructure at this stage, however, are limited to data collection and connection. Teams are still forced to perform tasks related to data governance manually, and there is no packaged solution for rule-based segmentation or filtering. To continue to improve speed and trust, teams need to find a way to automate cross-channel identity resolution, data privacy management, and data quality management

Data Maturity Level 3: Advanced

At Level 3: Advanced, teams continue to collect customer data into a central customer data infrastructure, but they do more within their central infrastructure than simply route customer data. Specifically, teams automate how channel-level identities are resolved to 360 customer profiles, identify data quality errors and block them from polluting downstream systems and control data flows based on customer consent state in accordance with data privacy regulations. Automating these processes saves engineering from having to support these functions manually, and also helps data consuming teams have more trust in the validity of the data they are working with.



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