General

The idea is to have a granulated understanding of data, and statistical impact patterns with the leverage of ML algorithms. This includes analysis of the statistical impact of marketing spend grouped by campaign, job type (job function), location and across different performance marketing channels such as Google Ads, Meta Ads manager, e-mail marketing and so on.

Data

The model is trained on data of job openings, job applications and Google Analytics 4. Ingesting more and more data does not necessarily have a positive impact on the model’s accuracy.

There is a general misconception that the more data is used, the better the outcome of the model will be. This is not always the case and we managed to find a sweet spot in the number of months of data to ingest from both business and machine learning perspectives.

The model is then used to predict the number of job applications for the job openings of the current week (i.e. the week of the prediction). The model is retrained every week using the new data of the previous week.

When building any model, data engineering is obviously a fundamental aspect. For the project, we utilise a data set stored in the data warehouse which was built together with Relevant Online over the past few years. The dataset consists of job openings, categories, locations, applications and marketing related info from, among others, Google Analytics 4. It enables tracking of candidates through YoungCapital’s systems, from the initial acquisition channel up until the final moment the candidate signs his or her contract.

Training the model

For training the model (Decision-making in ML infrastructure) we don’t use all of the features of the data set. Instead, we extract the features that are most relevant for the prediction of the number of job applications. This so-called “feature extraction” implies adjusting the data in a manner that provides better prediction results.

For the sake of transparency and explainability, Relevant Online and YoungCapital prefer a method, which enables tracing back which part of the data has affected the outcome of the machine learning product. Unlike some known and fancy neural networks, which are basically black box solutions, traceability and transparency of the ML are key factors continuously guiding Relevant Online’s development.

If you want to learn in more detail about the (open source) model, the metric and the algorithm which are used, you can read further after the results and conclusions of this case study.

Business implications and costs

Building and running a predictive model like the Predictive Automated Matchmaking Model has business implications and accompanying costs because it requires:

• cloud infrastructure on Google Cloud Platform (GCP);
• space for storing data in BigQuery and storing `metadata` of the pipeline process in Google Cloud Storage (GCS);
• hosting for the `Dataproc` cluster for data preprocessing training the model;
• time spent for maintenance of the pipeline, testing and adding new features.