• Kevin Duggan

alooki: Starting with "Why"*

Updated: Jun 22, 2018

A local news story inspires a new way of thinking about recycling.

Recycling Heatmap
Recycling Heatmap

It all started with a local news article about the state of recycling in my hometown of St. John’s, NL on the east coast of Canada. In reading that recycling rates in St. John’s are disappointingly low, I was surprised to learn just how low... it was only 10 percent! The key realization for me was that the city doesn’t have a very good understanding of who recycles and who doesn’t. It got me thinking that there has to be a better way to measure recycling rates. As the saying goes, you can't manage what you don’t measure.

After mulling this over for a while, I started thinking that the answer to the measurement problem is related to the visibility that the individual workers have. The people collecting the recycling and garbage every week have a great line of sight into which neighbourhoods are recycling and which are not. They see first hand the number of bags going into the trucks, and can likely predict before their shift begins how much recycling they will collect based on their experience week over week for a particular route.

Taking a step back, I quickly realized it’s unrealistic to think that these folks could manually track by household/neighbourhood the bags collected each and every week in addition to their other responsibilities. The time and effort of doing so would be prohibitive on a number of levels.

However, in following recent advancements in machine learning and image-based pattern recognition it occurred to me that, taken together, these technologies could bridge the gap. Combined, I believe these technologies can provide a way to measure recycling activities at a more granular level (think household data from a census versus aggregate country data in an atlas) so that recycling rates by route, neighbourhood and maybe even by household could be understood.

Tying this back to the concept of measurement as the bedrock of management, it became clear to me that insight into recycling rates at the neighbourhood or household level provides insight into the underlying behaviours impacting those recycling rates. Following on from that, providing specific incentives and targeted campaigns to promote recycling becomes possible based on analysis of this type of data. Tracking the effectiveness of these campaigns week over week/month over month also becomes possible - again based on this enhanced granular data.

In short, that’s how the idea for alooki was born: One brief but impactful article and I knew I had to do something to leverage my experience as a tech entrepreneur to protect the place I call home. I firmly believe that solutions to low recycling rates lies hidden in the data. I intend to gather the data and find some of those solutions along with two other experienced entrepreneurs (Danny and Steve) who helped me grow and sell two previous companies. With a little luck, and some patience, I’m confident that alooki can fundamentally alter the way we approach recycling to make a big, big difference in helping towns and cities find recycling solutions that match their particular needs.

*Inspired by Simon Sinek’s book “Start with Why

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