The same technology that generates video suggestions on Netflix and filters suspicious emails is now used by a broad range of businesses to improve customer conversion rates, automate labour-intensive processes and predict workflows.
Once a buzzword mostly of interest to academics, machine learning is an increasingly mainstream technology used by law firms, accountants and in a range of tech sectors to accelerate business growth and gain a competitive advantage.
To help local executives understand the opportunities that machine learning presents to their business and the ease with which these solutions can be implemented, OBJ, CENGN, AuditMap and sponsor the University of Ottawa Professional Development Institute teamed up to explore how machine learning is changing industries.
This is an edited transcript of a discussion between Peter Heath, senior manager at CENGN, AuditMap CEO Matt Lemay and OBJ’s Peter Kovessy. To hear the full interview, please watch the video above. Prefer an audio version of this podcast? Listen to it on SoundCloud or Spotify.
KOVESSY: Peter, there are a lot of practical use cases for machine learning in business. Let’s dive into one. How can machine learning help businesses with customer acquisition?
HEATH: When potential customers are reaching your website, your company is collecting all types of useful information. What you can do is use techniques in machine learning, such as clustering, to classify different demographics and understand how they are using the website. Once you gather those data trends, you can fine-tune your website to transition people from visitors to actual customers by creating a more tailored user experience.
KOVESSY: Matt, you’ve identified a very important use case for machine learning. How can businesses apply this technology to document classification?
LEMAY: The entire field of natural language processing gives us the ability to navigate data the same way humans navigate language. If I have a pile of documents, I can classify them by type of document, and am able to contextualize that data in a way that is quite useful. I can also identify if there are names, addresses or credit card information within those documents that require privacy. Machine learning can also help identify key sentences in the documents that could be useful in various reports.
KOVESSY: Peter, how complicated is it to implement machine learning in a business?
HEATH: It is definitely technical, but it’s accessible. It’s not restricted to the realm of PhDs or academics. If you have technical skills in programming or data analysis there are tools that can help you move forward. There are also great courses, such as the CENGN courses offered through the University of Ottawa Professional Development Institute that can help business leaders better understand and use the technology effectively.
