arierap / case_airmet_echo

Airmet & London Cycling Case Study

Anna Riera

June 2020, Barcelona

Content

• Airmet
• Brief
• Facts & Constraints
• Deliverables
• Assets
• Organization
• Links

AIRMET

The best helmet is the one that's with you

Airmet is a London based company that manufactures and resell an inflatable helmet of the same name specially designed for those regular users of cycle and other 2 wheels hire schemes. The Airmet uses cutting edge technology to provide best in class protection to the rider and once deflated takes the shape of a credit card to fit in your wallet so you always have it with you.

Brief

The Marketing Director at Airmet wants to launch a new marketing campaign in order to raise awareness and boost acquisition.

His idea, in order to target a relevant audience, is to take over some cycle scheme docking stations and turn them into an ad.

The Director has found a free dataset online detailing historical traffic for all the cycle scheme docking stations in London. As Airmet's go to data guru, you are asked to come up with a recommendation to optimise chances for success of the campaign.

Facts & Constraints

• After a year of existence, 5,000 Airmet™️ have been sold, all through their online store
• The Airmet™️ retail price is £179
• The cost to take over one full docking station (disregarding the number of docks) will be of £200 per station per week + a £100 one off per station for print and set-up
• Overall budget for the campaign is £50,000
• There are no constraints around the timing of the campaign

Deliverables

You are expected to present your findings and recommendation including :

• Duration of the campaign
• Number of docking stations and specific locations to take over
• Detailed assumptions made in the process

You will also be asked to share the code used to analyse the data

Assets

You are expected to present your findings and recommendation including :

• Duration of the campaign
• Number of docking stations and specific locations to take over
• Detailed assumptions made in the process

You will also be asked to share the code used to analyse the data

Organization

My repo is straight forward with the following structure:

• your-project folder:

• code: 2 jupyter notebooks with "Campaign timing prediction" & "Campaign Key Stations"
• data: data used for visualization purposes

Links

Repository
Slides
Tableau