Marketing Analytics Strategy: A Hierarchy of Measurement

How does one go about crafting a marketing analytics strategy? With so many moving parts and moving data points, it may seem impossible to get a grasp on it all.

The key is to hone in on the KPIs of your client and then tailor the aggregation of data to reveal insights that inform the next round of marketing spend.

Typically, we find that this approach fits the data and its analysis snugly into a hierarchy of measurement; it’s the kind of approach that leads from data to insights. That data reveal audience behavior, and the insights can lead to better-performing campaigns.

Here’s the hierarchy, which we’ve developed over many business quarters of refinement:

  • Delivery – Did you get what you paid for?
  • Success – Did the campaign achieve its goals?
  • Optimize – Can you improve upon the insights gleaned from the last go-around?
  • Learn – Can we utilize these insights to inform future marketing strategies?
  • Know – Do all stakeholders agree on what is best and on what the next steps should be?

Brand growth depends on consideration of the long-term impact of marketing efforts. This requires a suitably long-term approach to measurement that builds upon institutional knowledge and streamlines success.

That’s why our marketing analytics strategies include explicit measurement plans around each of those buckets listed above.

This approach to measurement and the crafting of an overall marketing analytics strategy is in many ways foundational to Leavened’s marketing measurement technology. Our platform increases marketing ROI precisely because it enables the planning, measurement, and optimization of efforts based on data gleaned from consumer behavior.

Agencies can develop strategic marketing plans and guide the design and development of marketing infrastructures for companies both large and small with marketing data collaboration tools. 

These analytical tools generate insights into ways in which a client can more effectively and efficiently expand their advertising efforts to achieve exponential growth.

Attribution models are designed to harness the power of data analytics derived from marketing campaigns in real-time. Data-driven and data-informed marketing strategies can then be implemented by marketing teams focused on more than just conversion rates and social media marketing activities. It’s a holistic approach to marketing investments.

A digital marketing analytics strategy, therefore, requires the aggregation and analysis of data and the identification of outcomes and trends. The strategy can be developed and modified based on the outcomes and the return on investment.

Bottom line: a marketing analytics strategy that works for the client and informs the next round of marketing spend, with each round more refined and perfectly suited to the client, their KPIs, and their business goals.