These days, one would think that the terms “marketing” and “analytics” would be stuck together like “peanut butter” and “jelly.” After all, data-informed decisions are a marketer’s best friend; there’s safety, job security, and promotions in quantifiable marketing results. Few would argue the critical role data should play in making better decisions in cross-channel marketing efforts. But many marketers struggle to use analytics outside of traditional sources, such as web analytics and email. In this blog post, we will explore a simple and effective approach to marketing analytics that can provide a core foundation for every marketing organization.

A 3-Step Methodology for Marketing Analytics

NMX%20graphic%20%281%29 A 3 Step Approach to Marketing AnalyticsAt the heart of every data-driven decision lie three essential elements that contextualize data and allow real business decisions to surface from the sea of numbers that are produced from marketing activity.

  1. Understand
  2. Execute
  3. Monitor

Each of these steps requires a different application of data and marketing analytics:

1. Understand: Based on the source of the data, what type of business decisions can be extracted from the data? It’s just like Lewis Carroll said in the 1800’s “If you don’t know where you are going, any road will get you there.” Analysis for the sake of analysis provides little value and consumes precious time in a marketer’s busy day. Develop a basic understanding of what you plan to accomplish with marketing analytics—improved customer knowledge, new segments, channel effectiveness, etc. It might even be appropriate to back into data requirements by defining a core business challenge or key question that the organization would like to answer with the analysis. This will help determine exactly what data is required and possible even identify new opportunities for capturing critical data in the future.

2. Execute: After developing a core understanding of the data itself and translating that into business strategy, it’s time to execute and act. Often involving some additional refinement of the information produced in the Understand phase, execution may be done through reporting engines, analytical models, testing, and even spreadsheets. For example, if the goal of an initiative is to improve online targeting, you may start with a series of tests (accessible in most digital marketing platforms) such as A/B testing, multi-variant testing, visual targeting, or predictive targeting. Testing offers an automated means to use physical data, rather than intuition or gut feel, to define exactly how you should act to maximize marketing results.

3. Monitor: It’s critical for marketers to close the loop on analytical exercises through ongoing monitoring of a standard set of KPIs. This might be accomplished via a dashboard or periodic report, but the goal should be to standardize metrics over time so you can establish a benchmark for significant changes in the data that may alert you to opportunities to optimize marketing results. This information should also be available for marketers to better understand and initiate new campaign cycles.

Each of these three simple steps forms a perpetually-optimized process for analyzing marketing data. Naturally, it’s much easier to apply marketing analytics when a core system of record exists to capture, aggregate, and analyze customer data from a variety of marketing channels. Yes, marketing analytics is marketers’ new best friend. It’s crucial to performing and optimizing essential daily tasks—but only if the tools are user-friendly, putting these three steps in the hands of marketers so they have full control over the marketing strategy. For more information on analytic accessible tools for marketers, check out the blog post “The Evolution of the Empowered Marketer: Actionable Analytics Shouldn’t Require a PhD.”

In my next blog post, we will explore another layer to the virtuous marketing analytics cycle by identifying strategies for enriching customer data for conversational marketing.

Read more: Cooking Up A Web Analytics-Driven Marketing Campaign?