Marketers need to learn insights from companies that have successfully worked with academics to manage the two-way knowledge transfer, adding non-research skills to the mix.

The fundamentals of research are based on statistics, observation, surveys, and anthropology derived from social sciences. With the environment constantly changing, it is difficult to ensure that academic-based researchers speak the language of business and marketing. The most excellent strategy of such interplay of academics and practitioners is witnessed in the Multitouch attribution (MTA).

MTA is a newer class of tools borne of digital data where marketers can track their ads and content consumption events. These tools statistically analyze the patterns vs. business outcomes ratio effectively. Academic exposure clarifies that assigning credit for a conversion to different advertising tactics does not follow the same logic. Similar experiences are recorded while comparing actual results to the counterfactual of what would have happened if a particular tactic were not used.  Marketers need to be cautious of inferring causality from correlation during any statistical analysis.

The best strategies are the ones seasoned with proper experimentation of quality standards, with a treatment variable applied to the test cell. However, it is crucial to be cautious that the experiment creates a clean data set analysis, such as statistically controlling for covariates that might not be ideally matched. This starts blending into practical issues of what variables are essential to monitoring the available data.

However, the purity of experiments is offset by practicality.  MTA delivers an analysis of multiple media tactics, like – creative asset, segment targeted combinations in one study. It is not practically easy to create an experiment plan that can isolate the effects of all of these combinations using the design of experiments. In addition, the human factor plays the ace card.  An experiment is actually a debate that analyses the most critical factors like – which media strategy to test, and the right time to launch it. MTA also analyzes media that is running to support the brand, without the need for particular experiments that need running incremental media. In the case of technologies like MTA, interacting with academics brings in more clarity and value addition. The ultimate goal is to create a positive business impact that requires practical compromises.

Most marketing problems require a hybrid of advice and suggestions from academics and practitioners. It is essential to have alternative marketing plans, the current plan vs. one guided by the results of the MTA model, and test them using proper experimental design. Validation of the set of marketing tactic recommendations from MTA is essential to build confidence in the tool.  For example, the marketing mix regression models of weekly or monthly sales and MTA models will provide conflicting findings 80% of the time. Therefore, pure, well-designed experiment bake-offs become the only definitive way to resolve the conflict.

Academics use knowledge and experience to bring rigor and advanced methods.  Practitioners have real and robust data sets and real-world problems that academics need to test hypotheses in the context of important marketing issues. Also, what the marketing industry needs currently is the perfect mix of both.