6 Reasons Measuring Marketing Signals Is Difficult


Brands are executing marketing activities at a pace like never before — display ads, videos, social messages, the list goes on. As a result, the volume of interactions a brand has with its audiences on a daily basis has never been greater, as people click on ads, open emails, watch videos, “like” and share messages, etc. Each of these interactions is a “marketing signal,” and the challenge marketers face today is measuring all of the signals to understand what is happening and how it is impacting their marketing performance.

One of the reasons why measuring marketing signals is so challenging is because of the dependency most marketing organizations have today on a variety of third-party platforms and services. Each of the platforms generate its own set of signals, which is made up of a lot of raw data. This dependency on heterogenous data creates problems when all of the raw data is brought together for measurement purposes.

The top six trouble-makers are:

1. Data is varied: The activities executed by marketing organizations generate a wide variety of data at a couple different levels. First, there are a number of different types of data, including: performance data, activity data, spend data, audience data and customer data. Then, for each data type, third-party platforms and services have their own set of data elements.

2. Data is siloed: Marketing’s dependence on third-party platforms and services results in a natural fragmentation of data. What is surprising, however, is that data is often fragmented even within a single platform. For example, Facebook and Twitter each have an API to access data related to advertising activity and a different API to access non-advertising data.

3. Similar data is defined inconsistently:
Third-party platforms and services use different terminology and definitions for the same data. A classic example of this is “time.” Different platforms define time in different ways. The start of the day is not defined consistently among platforms so the definition of “today” varies. Also, some platforms store time based on GMT (Greenwich Mean Time) and some don’t.

4. Platforms use different data dimensions:
Third-party platforms and services each have their own framework for the dimensions users use to view and analyze data. For example, in Google Analytics, website visits can be scrutinized by the dimensions: audience, acquisition, behavior and conversions. The problem is that not all platforms define their dimensions the same.

5. Platforms have different hierarchical structures: Third-party platforms and services organize their data along different levels, from a hierarchical perspective. For example, the hierarchy within Google AdWords starts with an Account, which has Campaigns, which has Ad Groups, which has Ads and Keywords. Similar to the dimensions issue above, the hierarchical structure across platforms is not consistent.

6. Data can be incomplete:
Having a complete, detailed set of data is important in order to be able to analyze and pinpoint the reason why something exceptional happened. Unfortunately, third-party platforms and services don’t always provide all of their data. Sometimes there are gaps in the data due to operational issues, such as an API becoming inaccessible. In addition, there are certain metrics that are not provided by the third-party platform and service providers, for various reasons.

The bottom line is that each of the platforms and services handle raw data and the conversion into signals differently. This makes it very difficult to process all of the marketing signals that are being sent and to understand how they are actually impacting the effectiveness of campaigns and the business results. Marketers must adopt a standardized, platform-agnostic approach to measurement — marketing signal measurement.

A key aspect of marketing signal measurement is having a process that transforms the raw data into a high-quality, integrated set of marketing signals from which insights can be derived and acted upon. Going through such a process when data is coming from so many different sources — each with its own nuances — is challenging but necessary if marketers want to get a handle on how all of the marketing signals are impacting their business.



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