Data Driven Decision Making

Originating during my Master’s of Science where I scored thousands of phenotypes and analyzed the severity of oncogenic mutations, data analysis and generating insights is a huge passion and driver for decision making. I’ve since taken my skills in advanced excel and applied it to real life consulting projects, deriving insights that ultimately direct corporate growth. Here are a few projects I’ve either led or been a part of.

Hamilton Tiger Cats Football

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After the launch of a new mobile app “Ti-Cats All Access” in 2016, the football association was left with an incredible amount of user data - tens of thousands of data points captured from users interacting with the app and its content. This included seat locations, season ticket holders, concession purchases, interactions with online quizes and surveys, family size, age, gender, # games attended etc. From this data, myself and team were able to:

  • Conducted in-depth analysis on users - segment users based on engagement to perform segmented marketing techniques.

  • Studied interaction data and trends to identify an engagement strategy with the users.

  • Using gamification theory, devised an online game that would drive engagement, concession sales and ultimately drive repeat ticket sales.

Yacht sales industry insights

As a broker, I’m permitted access to the backend of the MLS, presenting a wealth of data including all boats sold, age of vessel, time on market, location, brokerage, listing and selling prices. After a thorough analysis, I was able to conclude a number of industry insights used to steer company strategy.

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  • I led company direction with supporting data on geographic “hot spots” leading to the expansion to the West Coast market.

  • Competitor analysis demonstrating trends in competitor performance as well as broker “niche” sales

After adopting a high-end CRM, a large data set was generated providing insight into company and individual broker performance: lead conversion, deals closed, sales prices, boats by age/value, time to first action, # emails and calls made. I dissected this data and presented it at quarterly sales meetings:

  • Provided detailed individual broker performance: pipeline development and leads, # of actions as an indicator of successful deals, revenue tracking

  • Identified and qualified seasonal trends providing revenue projections

  • Generated stats used to quantify the sales process: # of inquires/deal, # days on the market contrasted to age of the vessel and price - used this to formulate the “ideal” client based on company expenditure, broker resources and projected revenue.