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Data strategy: the basis for customer-centric marketing
If you want to be successful, you need to know your customers. Market leaders such as Apple and Amazon make targeted use of their data to better understand their customers and learn more about their (purchasing) behavior. In this article, you can find out why developing a data strategy is also useful for your company and what prerequisites you need to create.
What is a data strategy?
A data strategy is a highly individualized plan to achieve your own goals with data-driven decisions. The design of a data strategy begins with the goals and ends at the starting point: the data. Based on the goal, the first step in developing the data strategy is to identify and prioritize the most relevant fields of action and use cases for data analytics.
Analytics maturity level and roadmap
In the next step, the analytical maturity level is examined with regard to the prioritized use case. This indicates whether and how "well" a company or department is already using data.
This involves looking,
- which systems and applications already exist and which data products (reports etc.) are available,
- which data products are specifically required for certain actions and whether they are already available,
- the dependencies between the data products,
- which systems are required,
- and in which step-by-step process the next higher applications are to be realized.
We at netzwerk P use the canvas tools from the data strategy design kit from Datentreiber under the Creative Commons license.
Data Strategy
Once the roadmap for the required data products has been drawn up, the next step in the strategy is to go one level deeper, to the level of detail and utilization: What data and what analytics (e.g. processes, links, models) do we need for the corresponding reports and recommendations for action, and how do we need to process them? We want to use data to answer questions. Consequently, the data that could provide answers to the questions must be identified:
- What specific data do we need (development) in order to obtain relevant information (utilization)?
- How can we merge the data and process it into valuable information (refinement)?
- What are the technical requirements? What tools, processes and personal resources and roles do I need?
We at netzwerk P use the canvas tools from the data strategy design kit from Datentreiber under the Creative Commons license.
Data landscape deep dive
Our marketing and communication data comes from various sources. Campaign data, for example, from social media channels and web analytics tools, but transaction data from CRM systems, online stores and ERP systems.
For this reason, we are now going one level deeper: data becomes information when individual data points from these different systems are linked together. In concrete terms, using data means converting it into information flows.
We therefore need to think about where we get the data we need and how entire data sources, storage systems or even just individual data tables can be linked together. Because we want to create information that provides answers and helps us achieve our goal.
We at netzwerk P use the canvas tools from the data strategy design kit from Datentreiber under the Creative Commons license.
Possible applications
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Acquisition opportunities
through lookalike audiences
Increase in customer lifetime value
through cross/upselling (next best experience/offer)
Automation
through scalable and structured processes
Action plan
Now that we have sketched out a detailed picture of the data structure required for our use case, the next step is to take a critical look. In other words, the examination of "desirability" and "feasibility" together with economic viability. Because: Of the many conceivable use cases, only a few are really desirable (accepted and used by users in the company), feasible (required data is accessible) and economical. The result may be a new iteration and reprioritization of the developed use cases.
If the data strategy for the use case passes the test, the next step is to create a concrete action plan. This serves as the basis for implementing the corresponding data and system infrastructure as well as data collection and processing.
Once the data has been collected and made available, reports and analysis processes can be used to generate the required knowledge (e.g. about customers, products, etc.). This knowledge forms the basis for customer-centric, targeted and budget-optimized communication, e.g. on the basis of customer segmentation and analyses and with the help of scalable, structured and automated processes such as marketing automation.
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