ABCs of Marketing Measurement

Artificial intelligence is used in marketing analytics to automate data analysis. Using AI in marketing measurement enables marketers to find opportunities for optimization faster and typically in a more cost-effective way. But be careful to not get trapped by generic AI that hides the method behind measurement.

Brand building is a way to actively grow a company's brand through top of the sales funnel marketing efforts to broader audiences. The measurement of brand building can be difficult but is imperative to understand how to drive long-term business growth.

To remain successful, Direct to Consumer (DTC) brands must make the transitions to broader-reach, CPG (Consumer Packaged Goods) style marketing. This requires a shift from a sole sales activation strategy to one that mixes activation with larger brand-building.

In measuring impact of advertising channel, it is critical to account for the dynamics of the relationship of media to response, including time to action and the risk of over-exposure. Decay and Saturation are two primary components of any Marketing Mix Model, helping isolate the quantitative shape of those relationships. Decay accounts for the long tail of conversion after exposure, while saturation helps understand if an audience has seen an advertisement enough times (or too much). For every brand, and every tactic, there is a unique shape of impact that needs to be identified to ensure the most accurate measurement.

It’s no surprise, but Experimentation is a critical tool for any marketer to test strategies, learn, and then apply those learnings. In today’s cookieless world, experimentation, in the form of incrementality testing, is playing an even more critical role in helping marketers understand the true attribution of some of their tactics. And while it provides accurate and easy-to-understand results, it can be limited in its application across the mix and challenging to do at scale. Experimentation and modeling are extremely complimentary when applied correctly, and gives holistic and validated measurement and optimization opportunity.

Funnel analytics are a critical component of marketing measurement that allows businesses to track and analyze the customer journey from initial awareness to final purchase, to identify points of friction and optimize conversion rates. With the right tools and data, marketers can use funnel analytics to make informed decisions, drive growth, and achieve their business goals.

To get the most out of any analysis, it is important to break out variables into the appropriate level of granularity. Granularity is the level at which media placements and KPIs are grouped together or broken apart. Too granular and the results may be fragmented, unreliable, or paralyzing. Too abstract and the analysis is too general to act upon. It is important to find the right level of granularity for each measurement solution to get the most out of measurement.

Hypothesis testing is a statistical methodology that helps to identify patterns and relationships between variables, and to assess the significance of these relationships. By formulating and testing hypotheses, marketers can gain a deeper understanding of their target audience, their interests and behaviors, and the effectiveness of various marketing strategies.

Incrementality and attribution are two different approaches to measuring the impact of marketing efforts, with some key differences between them. Attribution quantifies the impact of multiple marketing touchpoints within a conversion, while incrementality can go as far as measuring the outcomes that would not have occurred without the marketing touchpoint.

In today's data-driven marketing landscape, several job roles are crucial for measuring and optimizing marketing efforts. These roles involve leveraging data to uncover insights, optimize strategies, and drive business growth.