As marketers, we constantly look for ways we can become more effective and efficient in what we do.
But beyond just thinking up creative ideas, any marketing campaign needs a sense of structure so that any decisions made have the proper foresight behind them.
One such technique is model validation. In short, model validation looks to measure the impact that each marketing method has both individually and collectively on the overall sales performance.
It’s fair to say that model validation has lots of facets to it making it a complex topic. However, here is a general overview of how the concept can be used to strengthen your marketing campaigns.
Model Validation In Marketing: An Overview
In recent years, we’ve all become familiar with using digital analytics tools to tell us about the performance of our marketing campaigns.
But before the internet was a thing, model validation was how businesses including retailers were able to understand the impact of their marketing campaigns. Specifically, in terms of actual sales generated.
Now, model validation is slowly being picked up again. That’s because although digital analytics tools can tell us a lot about customers, something they can’t do is pinpoint exactly which marketing method resulted in the purchase.
In addition, recent data privacy changes have made it more difficult for marketers to effectively track their customers. For example the phase out of cookies as well as new Apple privacy requirements.
As a result, marketers are beginning to return to model validation to be able to effectively analyse their marketing spend and ultimately make better decisions.
Methods Of Model Validation
Backtesting – When the dataset compares past results to see if it could have predicted the known outcomes. In other words, backtesting allows a model to predict outcomes but with events that have already happened, to see if the method of testing is accurate or not.
Bootstrapping – Bootstrapping is a method of understanding how the model might perform on different samples, including when working with limited data. It is used to assess the accuracy of predictive models by repeatedly sampling from the original dataset with replacement data.
Cross-validation – In cross-validation, the training dataset is categorised as multiple folds. The goal is to prevent overfitting so that a more accurate estimate of model performance can be achieved.
Out-of-sample testing – Assesses how well the model will work on unseen data by including data that was not used throughout the training phase.
Beyond common model techniques, some new open-source model validation libraries including LightweightMMM from Google have recently been released. Therefore, there are lots of ways to tap into potential datasets when looking to perform model validation.
How Can Model Validation Can Be Used In Marketing?
The challenge for marketers is choosing the right model validation, since as noted at the start the topic can be incredibly complex.
So while caution is needed to choose the right model for your business, there are lots of ways in which the data can be used for the greater good.
An obvious place to start is measuring the effectiveness of marketing campaigns to optimise budgets. For instance, looking at whether PPC or organic search traffic is capable of generating the best ROIs based on past as well as predictive datasets.
Getting more granular, and aspects such as segmentation and churn prevention can also be enhanced. This is because such aspects rely on having precise data about customers along with the type of behaviours that would make someone a churn risk.
Beyond marketing, model validation can also be used across your wider sales strategy including forecasting future demand.
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