CDP implementation: the collaboration model

CDP Implementation

From planning to implementation to evolution

Our series of valantic blog posts consists of eight parts and provides insights into the implementation of a customer data platform (CDP). In the planning phase, we established the project organization and defined requirements. Next, we progressed to the implementation phase, created the specifications, set up the basic configuration and data collection, configured use cases, and performed quality assurance before the go-live.

We can now move on to the operational and further development phase, which marks the final stage of our project. This phase will allow us to maximize the benefits of the CDP.

Working better together: the collaboration model

Once a CDP has been implemented, it’s crucial to ensure effective collaboration between team members to maximize the value of available data. Successful collaboration allows for the pooling of expertise from various departments, leading to a comprehensive understanding of customers and their needs. This, in turn, facilitates decision-making, promotes the creation of targeted marketing campaigns, and strengthens customer loyalty.

For the effective use of a CDP, existing processes must be adjusted and new ones introduced to promote collaboration between different departments. Equally important: providing education and training to employees to acquire the necessary knowledge and skills to use the CDP effectively. Adopting an agile, hypothesis-driven process within the operational model can help foster collaboration and derive maximum benefit from the CDP.

Agile processes for the operating models of a CDP

Agile methods are particularly well-suited for developing operating models in CDPs. Through regular iterations and close collaboration between teams, results can be achieved quickly, and feedback can be gathered to adjust the model accordingly. We recommend the following steps:

  • Hypothesis generation: The first step is to generate hypotheses. These hypotheses serve as the foundation for the CDP team’s work and include ideas for new automation use cases, personalization strategies, and segmentation approaches. Each hypothesis is tested for its impact on the goal of the CDP and measured by indicators.
  • Impact analysis: In the impact analysis step, the CDP team evaluates the impact of different measures based on the CDP data, which helps prioritize the hypotheses. This step is crucial because the team has limited resources and needs to select the most effective measures to achieve the desired results.
  • Feasibility analysis: The next step is a feasibility analysis, which aims to determine the technical feasibility of the prioritized hypotheses. The CDP team assesses whether the necessary data and resources are available to implement the proposed systems and processes to support the hypotheses. Only those hypotheses deemed technically feasible are pursued in the subsequent steps.
  • Implementation: The next step is to implement the measures that were developed based on the prioritized backlog. This includes building new automation processes, integrating new data, and configuring new activation channels. Agile processes such as SCRUM or Kanban can be used, depending on the organization, to enable continuous reflection and optimization of the results.
  • Activation: Once the technical preparation of the measures has been completed, they can be activated. It is important to monitor the success of the measures and collect sufficient data to enable meaningful evaluation. The time required for this varies depending on the type of measure, the target being studied, and the company. It is advisable to use professional evaluation tools to achieve meaningful results.
  • Review and optimization: In the last step, the implemented measures are analyzed and evaluated. However, the focus shouldn’t be exclusively on economic indicators but also on the insights gained. It’s important to check if the expected results have been achieved and to identify potential reasons for deviations, such as incorrect assumptions or factors that weren’t considered.

Further development after implementing the CDP

The collaboration model is a structured approach that can be effectively applied for process optimization. A thorough analysis, prioritization, and implementation process, along with ongoing review and optimization, ensure the success of various measures in the long term.

To make sure that the new work processes defined in the CDP project become firmly integrated into the structures of the organization, continuous development is necessary. We will delve into this topic in greater detail in the final part of our CDP blog series.

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