One of the reasons why governments are interested in attracting FDI is because such investments are expected to upgrade technology and improve a host country's productivity.

Having first-mover advantage in the world of FDI attraction is considered paramount to success. Global investment promotion agencies (IPAs) spend considerable sums and use up a lot of energy attempting to be the first to identify companies considering expanding to their respective jurisdictions. The thinking is that the further upstream an investment project is identified, the more influence an IPA has throughout the company’s site selection and decision-making processes. The million-dollar question is: among the hundreds of thousands of viable target companies operating globally, how does an IPA identify those that are truly serious about greenfield investment?


This article proposes to address the convergence of traditional research techniques, in the form of 'executive interviews', and the emerging fields of big data, analytics and predictive modelling. The convergence of these fields has created exciting new areas for exploration relating to the early detection of companies in the plenary stages of international expansion.

A numbers game

The reasons why a company decides to expand outside of its home market vary widely, and have been the subject of extensive study. Contemporary research indicates that:

  • The economic, political, legal and cultural landscape of a host country is an important factor influencing the location decisions of foreign investors.
  • Cost, risk and opportunity converge to shape the expectations of foreign firms. More specifically, markets that are unstable, and impose excessive start-up costs or that fail to provide adequate factors of production at a competitive price, are less likely to attract FDI.
  • The two most significant factors affecting outward FDI are source country GDP and per capita GDP. This ties in with the FDI theory that economically prosperous countries are more likely to have highly productive firms with higher profit levels that allow for riskier FDI endeavours.
  • Destination country GDP growth is also a strong indication of FDI potential, as FDI typically leads to an increase in earnings from exports and/or investment. If a country experiences GDP growth over time, then FDI is likely to increase. This suggests a positive correlation between GDP growth and FDI.
  • Trade considerations, measured in terms of the openness of an economy, its free-trade agreements and the size of its domestic market, are also important factors.

A number of other variables also exist, including risk, political stability, effective country governance, trade agreements and quality of life.

Traditionally, IPAs have deployed investment attraction strategies to attract high-growth companies to set up operations in their respective jurisdictions. These strategies are most often based on targeting corporations in specific industries, which would be attracted by the assets already present in the region. Most often an IPA will start its lead generation process by performing a target industry analysis and asset audit to determine the specific sectors on which it should focus. In these scenarios, IPAs will be interested in reviewing shift share analysis and location quotients as key measurements for specific sector targeting. Ultimately, this allows IPAs to narrow their prospect search to a more manageable number, while leveraging local assets to attract these companies.

Needle in a haystack

Shift share is a standard regional analysis method that attempts to determine how much regional job growth can be attributed to national trends and how much can be attributed to regional factors. The shift share question that IPAs seek to answer is: why is employment growing or declining in my regional industry or cluster? Shift share is effective at highlighting sectors in which the region is outperforming or underperforming, making the calculation useful in identifying specific industry and sub-sectors to target.

Location quotient is a valuable way of quantifying how concentrated a particular industry, cluster, occupation or demographic group is in a region, compared with the country. It can reveal what makes a particular region 'unique'. In more exact terms, location quotient is a ratio that compares a region with a larger reference region, according to some characteristic or asset.

While location quotient and shift share studies are helpful, they do not reveal which companies to target. Now that it is known how an IPA may prioritise 'groups' or 'sub-sectors', what is really known about why companies expand and how this may help in selecting specific companies for lead generation purposes?

Armed with an understanding of how and why companies make their site-selection decision, along with updated target industry analyses, can IPAs effectively attract the next Google or Apple? Not quite yet. IPAs simply do not have the resources to scour the planet chasing the millions of companies that are considering international expansion. To effectively deploy limited resources, IPAs need to select specific companies to target for their FDI attraction strategy. This is a fairly sophisticated 'needle in a haystack' proposition.

IPAs must face some staggering realities. In the US alone (which is the largest source of FDI globally) there are approximately 5.7 million firms. Here is what is known about them:

  • Companies with a compound annual growth rate of 20%, doubling in size every five years (known as 'gazelles'), generate considerable job creation and R&D spending. Gazelles make up less than 1% of all businesses, yet account for approximately 10% of net job creation in any given year. The large majority of these companies end up with 20 to 249 employees. Several thousand of these fast-growing companies grow to a substantial size, employing up to 10,000 people and becoming the next generation of iconic companies. This fact is not lost on policymakers concerned with promoting economic dynamism. Approximately 0.8% of companies in the US (42,000 firms) are considered gazelles.
  • In the US, the top 5% of companies (measured by employment growth), or about 273,000 firms, create two-thirds of the new jobs in any given year. The top 1% of companies (about 55,000) generate 40% of new jobs in any given year.
  • In any given year, the top-performing 1% of companies account for some 40% of jobs. The 'average' company in the top 1% generates an astounding 88 new jobs annually, compared to the two to three new jobs generated by the country's average firm.

Something old, something new

Methods of identifying individual companies vary across IPAs. However, some of the most common techniques include push strategies where IPAs will offer information about their jurisdictions, via an email blast, cold calling and direct mail, to a large number of targeted companies, site selectors and other business multipliers. IPAs also count on participation at trade shows as another technique to build relationships with business stakeholders that may be considering expansion. More recently, IPAs have started to embrace more modern pull techniques, where marketing automation programmes and rich content can provide a scoring of a company's likelihood of expanding, based on a company’s level of activity on an IPA’s website and collaterals.

When these techniques are coupled with modern word recognition systems, media scan analysis, social media listening, sophisticated data on trade patterns and venture capital data, IPAs can develop a better picture of the specific companies they want to target. However, according to experts and research, the even best target company lists will only yield a 3% success rate. Therefore, if an IPA reaches out to 1000 companies, it may harvest only 30 viable leads that are considering crossborder expansion over a two to three-year period.

A truly disruptive product will involve combining the traditional method with the contemporary approach, using machine-based algorithms to detect the companies that are most likely to expand. The combination of machine and human work will always be necessary: machines can predict which companies are likely to expand, but only humans can verify this and identify the specific opportunity.

Signs of life

The disruption is not only impacting the volume of companies that can be examined. By the time companies have been identified by IPAs relying on traditional prospecting techniques, their technologically advanced colleagues may already have been nurturing those leads for three to six months.

For example, as identified in academic literature, English proficiency is one of the bigger known signs that a foreign company is ready for expansion (Jintae Froese, Hyemin Park and Si-young Lee, 2010). After identifying potential gazelles, for instance, at a trade show or a conference, it is possible to track their website for additions of new English content. Software allows this to be done for millions of companies at relatively low cost, while also capturing new job postings, office locations and executive team changes. 

There are many potential signals that can indicate that a company is getting ready to grow. Besides English, trade data may reveal that a company is increasing its exports to an IPA’s location, and may be ready to establish a physical presence. Again, using the website example, a company may list a local or toll-free phone number.

Many signals are anything but subtle: for example, a company may broadcast on social media website Twitter that it is going to a regional conference outside of its usual market.

The challenge is usually working with the large quantity and variety of data, followed by the task of ranking opportunities to focus on those most likely to expand to new regions. Is the company hiring for a regional director a better lead than another that has a sales person in the target region and growing exports to that location? How about a company that just raised series B financing versus one that translated its site into five new languages and hired 30% more employees?

IPAs that can navigate these big data and predictive challenges will be incredibly well positioned to outperform their peers and bring prosperity to their regions.

Steven Jast is president of lead generation company ROI Research on Investment.