Wonderkind case

Enabling Machine Learning for the #3 fastest-growing Dutch Tech Company



How much online job advertisement budget is needed to find your new PHP developer in Amsterdam, your new Sales Manager in Zwolle or even in London? This is the question Customer Success Managers of Wonderkind are facing every day. Using data of all their previous job advertisement campaigns, external API’s, machine learning and the knowledge of the Wonderkind employees, Enjins developed the Wonderkind Predictive Learning Engine. This helps Wonderkind to reach their ultimate goal: make their complete system self-learning.

Being #3 in the list of fastest-growing Dutch tech companies,

the scaleup Wonderkind is rapidly expanding their number of customers and online campaigns. Their promise is to reach talented people wherever they are. They use highly-targeted job ads on a variety of channels (like Youtube, Instagram or in-app advertisements) to find talent for their clients. And they do this completely automated.

The rapid expansion of Wonderkind required even further automation of the process where a customer can set up their job ads. Whenever a customer comes to Wonderkind and wants to hire a PHP Developer in Amsterdam or an account manager in Zwolle, the question that sales and customer success managers of Wonderkind were facing was; how much budget do we need to spend to deliver the right amount of talent that will ultimately convert to the right hire? This is where Enjins comes in.

Based on data of previous campaigns and API connections with their partners (Google, Facebook), Enjins developed the Wonderkind Predictive Learning Engine. This engine predicts the needed budget for a new campaign, such that the required amount of leads is reached. Moreover, we realized the data science infrastructure of Wonderkind so that future data scientists are able to their work efficiently and are enabled to put models in production. We kickstarted the data science team of Wonderkind and helped them to reach their ultimate goal: Make their complete system self-learning.


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