Strategies to Evaluate Customer Data Maturity

Enterprises of all sizes, industries, and verticals are raising their budgets to invest in digital platforms and customer data management tools. Many such organizations have leveraged customer data to make strategic changes to their marketing operations to scale their business.

According to a recent report by Treasure Data titled “Customer Data Maturity Study,” nearly 28% of the survey respondents have had various siloes in processing customer data for more than a quarter. The study also highlights that approximately 50% of organizations are predicting a strategy change in the next year.

The modern IT infrastructure today has become very robust and comprehensive, making it challenging for IT leaders to spot improvement opportunities. CMOs should consider embracing a customer data maturity model to analyze their data strategy to get actionable insights to make strategic changes to their overall operations.

Also Read: Customer Data Platform Market is Gaining Momentum

Here are a few ways that enterprises can consider evaluating customer data maturity:

Evaluate the data governance capabilities

Enterprises today leverage multiple tools for activation but are not able to have a scaling data strategy. It is because they lack comprehensive and efficient strategies to govern, integrate and manage data. Many enterprises do not have centralized process enforces to ensure data quality and privacy compliance; as a result, the teams have to execute the data governance policy in every tool. Organizations that do not have a unified approach to managing customer data will result in redundant and error-prone workflows developing low trust in the data throughout the organization. Because Dispersed data sets limit them to that tool, and the workforce does not have access to a holistic view of the customer.

Businesses that want to measure customer data maturity need to evaluate their data governance capabilities while ensuring compliance.

Marketing teams are utilizing customer data platforms with Artificial Intelligence (AI) and Machine Learning (ML) to make strategic data-driven changes to their presales, analytics, and after-sales team. Marketing teams that want to move their customer level model beyond the reactive level of customer data maturity should unify their client data infrastructure that enables cross-departmental collaboration to streamline the information flow.

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Analyze the data centralization approach

Once the marketing teams move ahead of the reactive stage, they need to develop an infrastructure that enables them to gather client data through a single collection point and ingest them into the downstream tools through a server-side integration. Designing the data management processes is a crucial step in customer data maturity. Organizations need to evaluate their data centralization capabilities and create a forum for different data channels and teams to interact on what customer data needs to be gathered to accomplish business goals.

Evaluate the businesses capabilities to detect data quality issues

Enterprises should be able to evaluate their capabilities to identify and rectify data quality errors will help them to determine the maturity of their customer data. Businesses with the most effective strategies to detect and mitigate data quality errors will have an efficient customer data maturity model. As businesses have started embracing more sophisticated customer data management approaches, they need to evaluate their maturity in real-time to optimize the overall customer experience.