Terrain is Going International!

Why We Chose Toronto As Our Next Location

07 Apr 2022
Photo Credits: Jaimie Harmsen on Unsplash

Site selection for businesses has come a long way thanks to the evolutions in analytics technology. Historically, companies were challenged by a myriad of pain points when trying to build a meaningful site selection strategy from scratch. Oftentimes there was insufficient access to data needed to offer a holistic perspective of all the considerations the business cared about when thinking about economic, political, and social factors. Instead, reports consisted of fragmented data manually stitched together using common data sets, lacked in-depth industry-specific insights, and only considered former definitions of technical talent that were too broad and all-encompassing.

Today site selection no longer relies upon talent centrality within major tech hubs to create “pull” for your business. There is more data available than ever about talent pools in different cities, but these data sets are highly fragmented and siloed making it challenging for businesses to harness actionable insights that help them understand talent markets. As companies think about scale, expansion & density, it’s critical to know where talent is located now, how big that market is, the depth & cost of the talent pool, and whether or not your company can truly compete effectively for that talent. Human networks can’t do this alone. Businesses need sound actionable insights derived from systems built to handle vast amounts of data to make it reportable, enabling them to enter new markets with confidence.

At Terrain, our mission is to reinvent how companies identify the best markets so they can hire the best talent anywhere in the world. By leveraging data and machine learning to understand how talent influences markets, our analytics stack surface objective and critical business insights to support long-term planning, drive business growth, and reduce financial risk. Ultimately, we’re using cutting-edge technology to disrupt the way business leaders analyze markets, hire the best talent, and manage risk more effectively.

In this article, we’re sharing our own account of how Terrain selected a new market to expand into by evaluating multiple markets using our own data. To understand the viability of establishing offices in Toronto, our framework builds on initial research we conducted on understanding software talent in various cities like Waterloo and Vancouver. We’ll also share some best practices and tools customers can leverage for their own site selection initiatives.

Analysis

Let’s take a closer look at some of the considerations we accounted for when evaluating city locations for our next office.

Talent Quality & Talent Pool Size

Terrain leverages its own TalentRank* tool to take a deeper dive into talent quality by developing a Talent Quality Depth score, a numerical score based on a scale of 100. By using the Talent Quality Depth score as a reflection of the combination of the quality and quantity of talent available in a market, it allows us to compare the talent quality of metropolitan areas of all sizes. Toronto scored 45 on its TalentRank metric, while Vancouver scored 53 indicating the city offers a slightly higher quality talent. Though Toronto scored a few points lower than Vancouver, a score of 45 on our scale demonstrates that Toronto has very good quality talent among software engineers.

But talent quality is only part of the full picture when comparing the talent markets of these cities. It’s also imperative to understand the size of potential talent that can be tapped into. Therefore, we evaluate the talent markets of each of our prospect cities as a ratio of a city's tech talent quality rating against its target talent population.

Given that Vancouver scored higher than Toronto in its TalentRank metric and has a much smaller target talent population (about 12,757 employees), it’s no surprise that Vancouver surpasses Toronto with respect to the quality of its talent pool. However, Toronto’s target software engineering talent pool is almost twice as large as that of Vancouver, a notable discrepancy in the sheer size of potential employees for hire.

Affordability

For site selection planning, understanding the cost of talent is also paramount in gauging the affordability of different cities. Since Terrain is targeting technical talent among our shortlist of cities, we compared the average compensation of these workers.

Our data indicates that Toronto and Vancouver have very similar averages for the average annual compensation of software engineers. However, the difference here has significant implications on the affordability of talent associated with these two cities. A company can hire the same quality of talent as Vancouver for 9% less in Toronto and almost 50% less in Toronto when compared to San Francisco. When taking into account talent size and quality, we see that a company can hire good software engineering talent at a more reasonable price point in Toronto.

Job Tenure

Understanding the likelihood of employee turnover at a company offers a perspective into the company’s longevity (or lack thereof) within a talent market. If employees are not inclined to stay, companies will more quickly churn through a city’s target talent pool. To get a sense of the rate of employee turnover, we took a look at our tenure data for each city.

As seen above, the averages for job tenure associated with Toronto and Vancouver are very similar. If employed for over 10 years, the average tenure in Toronto is slightly higher than the average for Vancouver.

Workforce Diversity

At Terrain, we’re avid advocates for fostering a diverse workforce and reaping the business benefits from it. Not only do DEI initiatives promote operational efficiency and workforce satisfaction, they also strengthen a company’s brand and position the organization as a desirable place to work.

As seen in our breakdown, Toronto offers a slightly more diverse workforce when it comes to gender and ethnicity of software engineers.

City Competitiveness & Price

As seen by companies such as Tesla opening offices outside of the Bay Area, businesses can grow their workforce by recruiting talent where their competitors are not. Terrain uses its own Competitive Index Score, which is a composite multiple score based on the concentration of talent in large employers (Herfindahl Hirschman Index), compensation relative to the national median, job openings relative to the population, and the average tenure of target talent.

Our data shows that with good talent, Toronto remains one of the least competitive markets  to recruit in. It also offers huge discounts for businesses compared to the other cities we considered.

Start-up Culture

Another critical aspect for a company relocating its offices is whether the talent market in the new city is accustomed to working in a similar workforce culture. For our evaluation of where to expand Terrain’s offices, we wanted to better understand each prospect city’s receptivity towards start-up cultures. For each city on our shortlist, we took measurements of the proportion of the talent pool that is accustomed to working in very small teams of 4-10 employees.

In Toronto, 46% of all employees work in very small teams of 4-10 people, the highest percentage out of all of our prospect cities. Vancouver has a higher concentration of big tech companies, such as Amazon, Microsoft, SAP, and Salesforce. The challenge associated with expanding offices in Kitchener is that our company would be competing directly with Google.

Conclusion

Our ultimate decision came down to determining which city offered the best talent quality at a reasonable price. Although our data shows that Vancouver offers the highest quality of software engineering talent, this aspect alone doesn’t make the city the most viable option for our office expansion. Given that Toronto’s talent pool is almost twice the size of Vancouver’s and doesn’t lag far behind in its overall talent quality rating, Toronto offers greater potential for finding technical talent of good quality at an attractive price point. Expanding to Toronto also offers other advantages for projected business costs.

Other considerations we took into account were time zone differences, commuting, and local English speakers. These initial considerations helped us narrow down our options from regions to specific countries and cities. Toronto and Vancouver appealed to us as prospects from the start because it would be easier to collaborate with teams located here than in the EMEA, APAC, or LATAM regions given the differences in time zones. Moreover, with a presence in New York, Terrain’s team could easily fly from New York to Toronto in under 2 hours; flights to Vancouver and Kitchener were longer and therefore were not as convenient of commutes for our employees. Our data indicated that LATAM scored well in talent quality, however it would be more difficult to recruit English speaking engineers of excellent technical talent quality.

Terrain’s breakthrough approach democratizes site selection capabilities by combining structured data and human intelligence into our analytics platform, leveling the playing field for customers of all sizes and industries. Gone are the days when only the richest tech companies had the budget to acquire the data and dedicate engineering resources to build bespoke analyses on talent and markets or had to commission large and expensive consulting firms to perform a similar analysis. Instead of letting your business incur huge search costs associated with opportunity identification and the potential risk in loss of value in picking the wrong market, let us manage our platform to bring you objective business critical insights harnessed by the power of data integration and machine learning.

Is your business ready to revamp its workforce planning? Contact our team to explore our service offerings and how our insights can support your businesses’ growth and site selection initiatives.

Footnotes:

* To offer a metric that gauges technical talent quality, Terrain’s TalentRank tool acts similarly to a resume by pulling data sets from the web to take into account things such as where employees have worked, how long they’ve worked at these companies, schools they’ve attended, open-source contributions, and other employee-specific online data.

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