'Assessing Your Organization's Customer Data Maturity' customerdata customerdatamanagement
for support experience. Once these tools are successfully implemented, teams are able to leverage the customer data available in each tool to support their respective goals.
This reactive workflow leads to several challenges. Vendor implementation requests increase in direct proportion to marketing and product programs, making it difficult to scale.
At level two of customer data maturity, 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. At Level 2: Centralized, marketing and product are able to access the data they need in their favorite 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.
Organizationally, teams progress from operating with a cross-functional group collaborating on customer data processes to having a clear owner of the customer data infrastructure. This individual is responsible for liaising between data implementing teams, such as data engineers, web developers, and app developers; and data consuming teams, such as marketing and product management.
In addition to resolving cross-device identities through deterministic identity resolution, teams can enrich customer profiles with data ingested from external data sources, such as customer support information. With a foundation of high-quality, deterministic customer profiles, brands may choose to layer on a probabilistic matching strategy to increase advertising reach.
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