A customer data strategy for your customer data platform

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Do you want to prevent your customer data platform from infiltrating useless data that no one has an overview of, let alone enjoy? So read on here and learn more about why a data strategy is alpha and omega. And how to get started drafting it.

The article is written for the purpose of establishing a customer data platform (CDP), but much of the guide will also be able to be used in connection with new CRM or other similar systems. CDP is used here because with the many source systems behind a CDP, there is an even greater risk that the system will become unmanageable with many different data inputs.

What is a Customer Data Platform?

A customer data platform is a data tool that gathers the entire organization’s customer data into one system and displays each customer as one single profile regardless of which source system the data comes from. Identification of the customer takes place through data consolidation, whereby data from a number of different systems are uniform, matched to customer profiles and gathered in the same platform.

It is precisely the automated alignment of data from different source systems and matching of relevant customer profile and behavioral data that is the unique thing about a customer data platform. It enables the platform to identify the same customer across systems (whether data is about online or offline behavior) and consolidate them with master data and transaction data. Customer insight will exceed anything you are used to. Imagine, for example, the possibilities for preventive churn measures, in-depth product development analyzes, better and faster customer service. Or what about personalized communication and present sales?

If the CDP can do all this, then why do I need a data strategy?

It sounds almost too good to be true, and the trees do not grow into the sky either. The challenge is that a CDP can quickly become a mess with a wealth of irrelevant information and randomly logged customer behavior. Information that at best simply makes the system unmanageable, but at worst can create bad customer experiences or illegal data logging.

Therefore, you need a data strategy.

What does a data strategy look like?

If you want to get the most out of your customer data platform, you need to do some prior work. This is the work that will lay the foundation for merging your customer data. Without these rules, the system will quickly turn into confusing information and become virtually useless.

You can choose to see the set of rules as a constitution for your new data government. Without a constitution, your data landscape will quickly degenerate into anarchy because the right institutions are not in place to secure the government.

Data Governance Description

Governance means governance. And in this context, it means that some general rules must be designated for how data is collected, aligned and entered into the customer data platform. The set of rules must be in place before the first data is entered into the system. Otherwise, the foundation will rot as you build upstairs. It’s never a good starting point.

It must be a simple set of rules, which must be known by the employees who use the system.

To spread the knowledge of the set of rules, you can make a training session for the users of the organization. In some systems, you can insert small notes into the user interface of the various sections to specify naming standards or formats. Alternatively, you can share them from a document management tool like templafy or sharepoint. The most important thing is that you can easily change the rules centrally and thus keep the guides updated and top of mind.

We divide the data governance set of rules into 3 small sections which together form the basis for the good data governance description.

1. Data tracking

Data tracking is a simple overview of:

  • what data you collect
  • who owns the collection process
  • the source system behind the collection
  • why data is collected.

2. Data validation

Data validation is a description of who owns the process of unifying data. This is also where you need to describe whether you load data into the CDP’s own formats or feed the system with raw data, which must subsequently be aligned with tools in the customer data platform, for example Segment Protocols. The data validation also contains rules for which unique master data, customer profiles are established on the basis of (eg email, phone number, credit card token, customer number) and rules for when data must be written to, overwritten or deleted on a given customer profile.

3 Data Enforcement

Data enforcement is about role management in CDP or in the source systems. These are the administrator handles that define access to user-level data and features. The role management ensures that changes go through the validation process above and are approved by the right stakeholders.

The role management also includes the division of responsibilities for clean-up tasks in the system. For let’s be completely honest. Even with the best governance, outdated data will emerge that needs to be cleaned up. Do a cleanup cycle and put those in charge on the tasks.

Decide on each individual data input

It is important to ensure that the platform only collects the data necessary to conduct business. Redundant data can, as previously mentioned, create confusion and in the worst case, bring you into conflict with the law.

To avoid this, each data input must be assessed on the basis of 3 simple questions:

  1. Who needs this data?
  2. What purpose does this data serve?
  3. If we did not collect this data, could we still run the business?

If you cannot answer the first two questions, do not save data. If you answer yes to question 3, then do not save data either.

Should historical data be migrated to the new platform?

It can be tempting to migrate all historical data to get some volume on the customer data platform in a hurry. But that is not necessarily a good idea. Follow steps 1 and 2 above and stick to the rules you set up for the system. Again, a poor foundation with old data and poor structure creates a mistrust among the users of the organization and can ruin the adoption of the system and thus the value of the investment.

Understand the organization’s data silos

It is called data silos when data is collected by different departments in the same company and stored separately from data collected by other departments. It creates blind spots in your data and means that you do not gain full insight into your customers.

If you want to avoid data silos, or want to remove the existing data silos in your company, then it requires cooperation between the departments. You must gain a common understanding of what data is collected and what it is used for. Start by finding out what tools are used to collect customer data in your organization. Build from there upstairs with what information the tools collect. Maybe you find out that data is locked inside a silo for legal reasons. Or maybe the data fence is inside alone due to lack of communication between the departments which could also benefit from them. Fortunately, something can be done about that. And a CDP is a great opportunity to bring data to life throughout the organization.

A data strategy is a simple plan that secures the future of your systems

With a good and simple data strategy, you create the framework for proper data hygiene. And you are well on your way to ensuring that a customer data platform meets the basic requirements of the organization:

  • To phase out data silos by merging and storing data in one central location
  • To increase data accuracy by minimizing redundant data and overlapping customer profiles
  • To gather all customer data across the organization so that all parties only have to search one place
  • To enable the business to bring customer data effectively into play for analytics, product development, market research, customer service, up, cross and additional sales, win-back campaigns and much more.

Good work ethic

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