Predictive Analytics can yield amazing results. The improvement that can be achieved by basing future decisions on observed patterns in historical events can far outweigh anything that can be achieved by relying on gut-feel or being guided by anecdotal events. In a recent test in the retail sector, we observed a fivefold increase in product uptake when applying stable predictive models compared to a random sample. Let’s face it, there would not be so much focus on Predictive Analytics, and in particular Machine Learning, if it didn’t yield impressive results.
But predictive models are not bulletproof. They can be a bit like racehorses: Somewhat sensitive to changes and with a propensity to leave the rider on the ground, wondering what on earth just happened.
The commoditising of Machine Learning is making data science a lot more accessible to non-data scientists than ever before. With this in mind, we compiled the following list of pitfalls to avoid to keep your predictive analytics models performing as expected:
1. Making too many assumptions about the underlying training data. Rushing in and making too many assumptions about the underlying training data can often lead to egg on the proverbial face. Take time to understand the data and trends in the distributions, missing values, outliers, etc.
2. Not using enough data. Low volumes of data can lead to statistically weak, unstable, and unreliable models.
3. The over-fitting chestnut. In other words, creating a model that has many branches and therefore seems to provide better discrimination of the target variable, but falls over in the real world as it has introduced too much noise into the model.
4. Bias in the training data. For example, you only offered a particular product to the Millennials. So, guess what? The Millennials are going to come through strongly in the model.
5. Including test data in the training data. There have been a few significant fails where the test data has been included in the training data, giving the impression that the model will perform well, but in reality, it results in a broken model. In the predictive analytics world, if the results are too good to be true, it is worth spending more time on your validations and even seeking a second opinion to review your work.
6. Working solely with provided data. Predictive models can be significantly improved by creating clever characteristics or features that better explain the trends in the data. Too often, data scientists work with what has been provided and do not spend enough time considering more creative features from the underlying data that can strengthen the models in ways an improved algorithm cannot achieve.
7. Expecting machines to understand business. Machines cannot yet figure out what the business problem is and how best to tackle it. This is not always straightforward and can require some careful thought, involving wholesome discussions with the business stakeholders.
8. Using the wrong metric to measure the performance of a model. For example, out of 10,000 cases, you have only two that are fraudulent and 9,998 that aren’t. If the performance metric used in the model training was just straightforward accuracy, the model would attempt to maximise accuracy. So, if it predicts all 10,000 cases not to be fraud, the model would have an accuracy of 99,98% which is seemingly amazing, but it does not serve any purpose in identifying fraud. It simply identifies 99,98% percent of the non-fraud instances correctly. Therefore, for rare event modelling (a good example of which is fraud), alternative approaches must be employed.
9. Using plain linear models on non-linear interaction. This happens commonly when, for example, building binary classifiers and logistic regression is chosen as the preferred method, when the relationship between the features is not, in fact, linear. Using tree-based models or support vector machines works better in such cases. Not knowing which methods apply to which problems results in poor models and subsequent predictions.
10. Forgetting about outliers. Outliers usually deserve special attention or should be ignored entirely; some methods of modelling are susceptible to outliers and forgetting to remove or cater for them can cause poor performance in your model.
11. Performing regularisation without standardisation. Many practitioners are unaware of the redundancy of applying regularisation to the model’s features without first standardising the data, ensuring all data is on the same scale. Regularisation would be biased, because it would penalise features that are on smaller scales more, for example, if there is a feature that is on a scale of 3,000 – 10,000, and another variable that is on the scale of 0 – 1, and another on the scale of 9,999 to 9,999.
12. Not taking into account the real-time scoring environment. Practitioners can sometimes get distracted by building the most perfect model, but when it comes to deployment, it is so complex that the model cannot be integrated into the operational system.
13. Using characteristics that will not be available in the future, due to operational reasons. One may identify a very predictive characteristic (like gender), but due to regulations, this field cannot be used in modelling, or the capturing of the field has been suspended and will be available in the future for use in the model.
14. Not considering the real–world implications and possible fallout of applying effective predictive analytics. American retailer Target made headlines in 2012 when New York Times reporter Charles Duhigg brought to the public’s attention the now–famous incident of Target’s analytics models predicted a teenager’s pregnancy before her father was aware before her father knew. As some have pointed out, just because you can, doesn’t mean you should.
Predictive analytics holds transformative potential but only when applied with care, clarity, and a deep understanding of both data and context. As powerful as the models may be, they’re only as strong as the data, assumptions, and design behind them. The pitfalls we’ve outlined aren’t theoretical; they’re the kinds of mistakes that derail otherwise promising projects and erode trust in analytics-driven decision-making.
At Principa, we know that intelligent decisions don’t come from black-box models alone. They come from a clear alignment between business objectives, human insight, and AI capability. That’s why our approach is both data-driven and expert-led, blending rigorous analytics with real-world know-how. Whether you’re optimising your credit lifecycle, reducing risk, or seeking smarter customer strategies, we’re here to help you harness the full power of predictive analytics and avoid the traps that waste time, money, and momentum.
In a world where the future belongs to intelligent decisions, we help ensure yours are informed, agile, and built to drive growth. Let’s talk: info@principa.co.za