Marketers’ confidence in their ability to accurately measure marketing is at an all-time high. This is good news since proving the value of marketing has never been more important. But a gap exists between those who are most confident and everyone else, driven primarily by inconsistent data quality, too many metrics to measure, and too many disconnected data sources. All three of these top challenges can be addressed with a thoughtful, coherent data strategy for measurement.

A solid measurement data strategy unites five key actions:

  1. Identifying the right data. Selecting the right data means being thoughtful about which data you are collecting. Collect only the data that matches the metrics and measurement objectives you are tracking.
  2. Building reliable data access. Whether you use a data lake, customer data platform, cloud storage solution, or centralized source collecting data from APIs, it is important that you can always access the data required to support your metrics and measurement objectives.
  3. Establishing strong data standardization practices. No matter what measurement methods you are using, you will be combining data from various internal and external sources, and each will have a different taxonomy and format. In order to make the different datasets compatible with one another, standardization is required.
  4. Refreshing the data at regular intervals. Effective measurement relies on continually updated data, so make sure you have recent data flowing into your system on a predictable basis. Minimum data granularity varies between the different measurement methodologies, but it’s a good goal to have media and revenue data refreshing at least weekly.
  5. Maintaining a robust data history. Marketing mix modeling (MMM) requires at least two years of historical data to run effectively. Even if you’re running other methodologies, calculating the impact of seasonality requires multiple years of data. So start collecting this data now even if you’re not yet ready for advanced marketing measurement.

Whether you are planning to run your own marketing measurement or partner with an outside provider, thinking critically about all aspects of your data (selection, collection, standardization, refresh cadence, and history) is fundamental to your success. Activating a thoughtful data strategy reinforces investment in measurement talent and tools to ignite a virtuous cycle where measurement contributes to new business goals, in turn creating new data requirements and strategy:

This graphic shows the process by which business goals like revenue growth, customer acquisition, and loyalty; data strategies including centralize and taxonomize, implement identity solution, and acquire third-party customer data; and measurement such as launch MMM, attribution modeling, and loyalty-offer incrementality continuously feed one another to create an ever-expanding knowledge base and measurement practice.

 

For deeper insights and instructions on how to improve your marketing data strategy and increase your measurement confidence, Forrester clients can read my new report, Must-Have Data For Marketing Measurement.

If you’d like to discuss your measurement data strategy in depth, please schedule a guidance session with me.