The unbearable uncertainty of machine learning (ML)
Surveys on the topic tend to revolve around three factors:
- Lack of know-how
In an international survey on the topic, more than 50% of marketing professionals say they themselves are only beginning to understand how to utilize machine learning. A mere 30% of respondents believe they are average in their understanding. It is hardly surprising then, that experimentation remains low, if people don’t feel comfortable with the general principles.
This personal lack of familiarity may also contribute to the difficulty of finding partners with the needed skills - also a recurring theme. It is hard to validate the level of skills of someone claiming to be an expert in something you yourself don’t fully understand. In addition to this, high quality data scientists are quite few compared to the potential amount of work it would take to bring ML to marketing across the board.
While machine learning is no longer as mysterious as it once was, it does still require non-ubiquitous, specialized skills.
- The lack of reliable data
The lack of reliable data is a problem somewhat particular to marketing, and this tends to cause some key issues for the use of ML.
Cookie based analytics causes data quality issues, and gaps in the data you have, even when best practices are being followed. But while well known, this is not the only big issue with reliable data in marketing.
The available toolset for digital marketing is overabundant, and combining the use of various tools for a common goal will inevitably lead to some issues with using the resulting data. Additionally the lack of individualizable data from the two largest players, Google and Facebook (Alphabet and Meta) is an issue for marketers who want to tap into all available data to run machine learning with.
- Uncertainty of profits
The third major challenge in developing ML solutions for marketing is the uncertainty inherent in calculating returns on the investment. Problems that will benefit from machine learning are almost necessarily complex, and thus difficult to predict in advance. Both results, and the time needed are difficult to predict. This uncertainty is a large roadblock between large scale use of machine learning in marketing.
This issue isn’t, however, entirely unavoidable. Part of the problem of profitability for machine learning projects is the typical approach through large scale projects, and big investments. Starting out with large projects is inherently even more risky, as failure in large projects may be impactful for the company as a whole. This limits the amount of companies that are willing to participate in the implementation of such projects.