Plinc hosts its first hackathon

Last week, Plinc hosted its first staff-wide hackathon in the London office. The event created a collaborative environment for employees to learn new skills, offer their unique perspectives and work together to build innovative predictive analytics models.

15 October 2021



Last week, Planning-inc hosted its first staff-wide hackathon in the London office. The event created a collaborative environment for employees to learn new skills, offer their unique perspectives and work together to build innovative predictive analytics models.

Though the atmosphere of the hackathon was a comradely one, we couldn’t let the day go by without a bit of friendly competition. Staff were given a large, complex data set, then split into four teams to identify, code and profile potential attributes for later inputting into the model build. Then, using the insights gained, the teams were tasked with suggesting marketing targets and tactics based on their findings.

Following the attribution exercise, each team set to work building and optimising a predictive model and constructed contact strategies using the outputs of their final model.

At the end of the day, the teams presented their models and contact strategies and were evaluated on both their model effectiveness and suggested CRM strategies. The winning team, Team 1, was selected by our expert judges, Head of Analytics Leah-ann Grey and Chief Technology Officer Graham Burton.

“We were really thrilled with everyone’s enthusiasm for the day, the atmosphere in the office was buzzing. It was interesting to see how all the teams tackled the challenge differently and we were so impressed by everyone’s efforts. The resulting models and presentations were all fantastic, so it was hard to pick a winner,” said Leah-ann.

By the hackathon’s conclusion, the Planning-inc hackers had created more than 20 models using a selection of the 60+ attributes profiled and developed numerous campaign recommendations based on data-driven insights. Perhaps more importantly, staff who don’t get to work on modelling in their typical day-to-day functions walked away with a greater understanding of predictive analytics and contributed novel new approaches to get even more value from customer data.

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