Customer Data Platform Operating model

The CDP Operating model allows organizations to ensure that end users understand the utility of the platform and leverage the same to derive anticipated benefits. Defining the right target operating is essential for successful implementation of CDPs.

There are 4 essential elements of a CDP Operating Model -

1. Define the governance framework and team structure –

Defining the governance framework and the team structure along with the key roles and responsibilities helps the stakeholders in understanding who is responsible for any operational activity that is associated with the implementation of a CDP and lays down a decision making framework that is robust to govern the CDP implementation. There are three key roles in a CDP operating model -

2. Outlining the data strategy –

Organizations must outline their data strategy and align CDP goals with it to ensure successful implementation. There are two types of Data Strategies –

· Offensive — Such organizations focus on increasing profitability through data analytics, modelling and enrichment

· Defensive — Such organizations focus more on data security, compliance and governance through optimized data extraction, standardization and access [1]

There are several types of CDPs that are available, and each has attributes that are suited towards specific goals. To effectively use a CDP, organizations needs to clearly define the strategic priorities that govern the data that is collected. In order to exploit the full potential of a CDP, organizations must select business use cases that are in alignment with its data strategy. [2]

3. Specify the tools and processes to be used

CDPs provide marketers the necessary tools to perform critical marketing processes like customer segmentation, consumer lifecycle orchestration and modelling and analytics. — processes that are likely being performed by other teams and resources.

Once the data strategy has been outlined, it is important for organizations to clearly specify what use cases will be adopted and the corresponding tools and processes that will be used. Taking an incremental approach and proceeding with one use case at a time can ensure organizations to minimize the resistance to CDP implementation.

Specifying the tools and processes that will be used can also help businesses in defining the target to-be state post the CDP implementation and ensure easy tracking of business impact against stated objectives. [3]

4. Define the value measurement framework to track business impacts

Finally, a robust value measurement framework is required to track the business impact of a CDP. This framework will convert the stated objectives to key metrics that can be measured to validate the impact of CDP implementation. Some essentials of a value measurement framework are as follows –

· Defining the key KPIs

· Establishing measurement process and frequency to track benefits

· Defining the benefits reporting mechanism

Guiding Principles for CDP Operating Model

The addition of a CDP fundamentally changes the functioning style of organizations. Therefore, defining the right target operating model for a CDP is necessary to ensure that the envisioned business objectives can be realised. Here are some of the guiding principles which organizations must keep in mind while defining the CDP operating model –

1. Ensure customer centricity

2. Focus on real-time customer engagement

3. Hyper-personalize offerings to redefine customer relationships

4. Leverage the power of data

5. Focus on ensuring fast decision-making and improving business agility

CDP implementation is pivotal for its success. The right CDP can drive cross-device and omnichannel personalization campaigns for each individual user and thereby increase conversions and the overall digital revenue.

References -

[1] https://blog.treasuredata.com/blog/2018/09/06/how-to-implement-a-customer-data-platform/

[2] https://www.cmswire.com/customer-experience/4-best-practices-for-implementing-a-cdp/

[3] https://martechtoday.com/7-steps-to-cdp-heaven-a-maturity-model-for-data-management-242143