Executives are usually taught that data is an objective and critical input for strategic planning and operations. Applying this, however, is much easier said than done — especially among companies operating in emerging markets.
Emerging-market data can be challenging to work with due to significant data gaps, biased data, and outdated or incorrect numbers. Of course, these issues can cause a headache for any company, in any market. But because they are so prevalent when it comes to emerging-market data, the challenges are exacerbated. They can lead executives to make misguided investment decisions, and put a company’s reputation and jobs at risk.
As multinational companies seek to strengthen their emerging-market portfolios, they must have a better understanding of what their data can and cannot do, and when to trust it. The margin for error in business has become substantially smaller as global growth slows.
Based on our research at Frontier Strategy Group, we’ve found that companies can avoid the pitfalls of evaluating emerging-market data, and successfully use it to support strategic decisions, if they follow a few key do’s and don’ts.
Emerging markets’ data have considerable gaps. To understand a product’s sales potential, executives want industry-specific indicators. For example, if a company sells toothbrushes, it uses toothbrush sales forecasts to determine yearly targets. However, the more specific the information, the less likely it is that data will exist, especially in less developed markets.
Yet companies still expect their data providers to offer this industry-specific information. In order to bridge the gap between what companies need and what data are available, data providers sometimes use models to create the data, which can lead to serious data quality issues. And executives are often not cautioned to take those models with a grain of salt.
For example, the alcoholic beverage company Beta decided to prioritize Iran as an opportunity market for its drinks. According to available data, Iran’s alcohol consumption was comparable to that in India. However, alcohol consumption is illegal in Iran. The numbers were based on outdated statistics from the 1970s, when Iranians, prior to the Iranian Revolution, were emulating Western lifestyles. Growth forecasts were placed on top of these outdated numbers, without taking decades of conservative rule into consideration. As a result, Beta had to restart its market prioritization process without the erroneous industry-specific data.
The solution is to be creative in gathering data. Finding the right proxy indicators can help fill in gaps. For instance, in order to understand potential demand for cables in Kenya, companies should not try to look for an indicator that specifically quantifies cable sales; instead, they should draw on macroeconomic numbers for government spending and investment in infrastructure, where such cables could be in demand.
Remember:
- Don’t expect your approach to developed-market data to be applicable in emerging markets
- Do be creative and critical about your use of industry-specific indicators
Data is biased, particularly in emerging markets. Data can be biased for a variety of reasons. First, organizations tend to use local governments’ historical data, which typically reflects whatever agenda the government may have. While developed markets have stronger institutions that can challenge government estimates, this often is not the case in emerging markets, leaving data more susceptible to political interference.
Furthermore, many organizations have stakeholder-driven agendas that occasionally add political bias. For example, as Greece returned to financial crisis in 2015, the IMF forecasted 2.5% YOY GDP growth in its April World Economic Outlook. (Greece’s actual 2015 growth was -.8%.) While it was clear that the Greek economy would not grow, the IMF was involved with the country’s bailout program, giving it a disincentive to forecast recession. The IMF’s intentions were good — a negative growth outlook would have made Greece’s circumstances worse —but the forecasting process was not objective, undermining the ability to devise realistic business plans.
Companies must ensure that their data have been collected by sources with no interest in dressing up the numbers. Find out whether the numbers were reviewed by analysts with in-depth market knowledge. And when looking at forecast data, consider that it faces the same challenges as historical data, with regards to bias, gaps, and lack of comparability, but there are even more assumptions built on top, and therefore more opportunities to make mistakes.
- Don’t blindly trust data, even when it comes from a reputable source
- Do question potential motivations behind numbers
Data in emerging markets can be incorrect. Erroneous data, often based on missing information, creates various planning difficulties for companies. It can lead to executives to miss investment opportunities or to develop faulty strategies.
One case we came across was logistics company Omega. Their China team conducted an analysis to understand which provinces in China might offer the best opportunities. Examining provincial data from the Chinese government, Omega’s China team felt that growth was reported at higher levels than it saw on the ground. The team learned that because provincial governments were evaluated on their economic activity, they overstated the economic figures. The official national figure was actually only 7% YOY for 2014, while the combined official provincial statistics figure added up to nearly 10% YOY GDP growth. So not only was the data biased, it was also wrong. Had Omega’s China team used the provincial data, the company’s targets would have been far overstated.
If official numbers contradict your understanding of the market and actual sales performance, do not ignore it. Wrong data is likely responsible for the misrepresentation of realities on the ground. Base sales expectations on local market dynamics and sales performance numbers, and adjust your strategy and targets if you notice something amiss.
- Don’t take numbers at face value if they make no sense
- Do investigate further by checking data sources against local market observations and expectations
Data is used and interpreted in different ways across an organization. Many different individuals and teams require data for different reasons. For example, local executives use data to make the case for a good opportunity, while corporate executives frequently use data to pressure local teams to deliver on expectations.
One illustrating case of this was technology company Alpha, whose industry-specific data provider predicted computer sales growth of 90% in Ukraine for Q1 2015. Alpha’s U.S. office set aggressive sales targets for Ukraine, based on a data source that was reliable for most Western countries. However, on the ground, Ukraine was facing a war, a devalued currency, and a drop in spending across all industries. Alpha’s local team faced an impossible target.
While the use case for data can vary substantially by stakeholder, so do the skills of the individuals interpreting that data. Many executives understand this but do not appreciate the extent to which it happens. The result is often a culture that ignores data and relies on hunches and guesses that contradict the data focus at headquarters.
To avoid bias and analyze data objectively, executives have to provide clear guidelines and training for data collection, usage, and interpretation across the organization—not only for market monitoring staff but also for the executives regularly using data to make business decisions.
- Don’t assume your team uses data consistently
- Do establish clear organizational standards for gathering and interpreting data
Companies often use outdated data to make forward-looking decisions. Many companies are turning to macroeconomic indicators, such as consumer spending or industrial production, to get an idea of how demand for their products may evolve over time. These data points are key for understanding how external economic developments could affect business plans and targets.
However, companies frequently misinterpret indicators as “leading” when they are actually lagging, as most official data are released on a lag. It’s important to understand the timeline you’re working with to get a sense of how macroeconomic changes, such as slowing growth, and industry changes, such as regulations, can impact your bottom line.
For example, consider consumer goods company Vega, which had long used consumer spending as a leading indicator to understand consumer demand. This was not helping Vega set targets and deploy resources across markets because it was outdated. To solve this problem, Vega engaged in a predictive analytics exercise to understand the real demand drivers for its products. The results showed that while some countries’ demand was driven by income and other spending factors, others countries’ demand was driven by larger factors that impact consumer purchasing power, such as inflation. By identifying the true leading indicators of demand in key markets Vega was able to rightsize targets and reapportion its marketing spend to the most attractive growth opportunities.
- Don’t focus on outdated, lagging indicators
- Do identify forward-looking demand drivers
Understanding data and using it correctly is essential for a company’s success in emerging markets. There will be several practical challenges to doing so, but they are not insurmountable. On the contrary, with the right mix of critical thinking, questioning, and creativity, most data problems can be overcome.
