There are many challenges to measuring marketing effectiveness. Factors include:

• Types of Data Available: The type, richness and quality of data vary widely from channel to channel. For example, with programmatic display advertising, marketers can get user-level and impression-level data to track the user all the way to a conversion, regardless of whether a conversion occurs or not. However on TV, marketers would only get reporting of when their ads were flighted on air. On Facebook, marketers collect rich aggregate data reporting about their targeted audience, but can obtain almost no user-level data.
• Frequency of Data: Some media channels offer data on a real-time basis, whereas others only offer data post-campaign and reporting may only be delivered weekly or monthly.
• Attribution and Incrementality Methods: Not all attribution models and incrementality studies are created equal. Depending on the type of methodologies used, the channels under test and a host of other factors can lead to limited inputs for a media measurement framework. Gauging the contributions of each media channel, tactic, or audience without an always-on test and control experimentation practice in place is very difficult to execute in an apples to apples way.

 

There are three main challenges to measuring marketing effectiveness: Types of data, frequency of data availability and choosing the right approach.

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For years marketers have been searching for the holy grail of media measurement, one that could address the challenges stated above. A single framework that ingests all online & offline data and automatically generates reports and insights to guide budget reallocation decisions in real-time with no friction. At one time we thought MTA was the answer to this desired state, however it failed to fulfill its promise due to many factors like - the collapse of the third-party cookie, identity resolution gaps, severe data reconciliation issues and perhaps the biggest blindspot of all, no access to the walled gardens. For all intents and purposes MTA is no longer a viable exercise. The types, frequency and availability of user-level data limits the universe of advanced measurement techniques that can be used to measure marketing effectiveness. The types of decisions that the measurement informs also plays a big role in how useful marketing measurement itself is. Marketers looking to make tactical decisions, like optimizing between creative A vs creative B running on a specific audience within a specific media channel, can use data made available by that channel. There are advanced measurement methods available that can answer the cross-channel attribution problem statement but are not subject to the limitations of MTA. For more strategic decisions, like budget allocation across multiple media tactics based on incremental sales and incremental ROI, you would have to use advanced marketing measurement techniques such as incrementality measurement to get to an answer. For other big strategic questions, like the impact of weather, competition, interest rates, or government policy on sales would need to apply a marketing measurement technique like MMM (Marketing Mix Modeling) for long-term planning. Here are additional resources for reference:

• Incrementality Testing
• Marketing Mix Modeling (MMM)
• Multi Touch Attribution (MTA)
• Pros and Cons of Incrementality Testing vs MMM vs MTA