Understanding Global Customers in a Digital World: New Rules and Tools for Success

With the ubiquitous connectivity of the internet, the troves of data collected by social media and the Internet of Things, and analytic tools of unprecedented sophistication, global companies have more information than ever for understanding the people they do business with. Yet organizations are still encountering surprises in terms of who their customers are, buying motivations and product usage.

For example, several NGOs working in the developing world over the past decade created smartphone applications for practical tasks such as taking courses and tracking crops. Yet recent research on internet usage showed that developing world smartphone users tend to spend time on their devices in the same ways as their counterparts in industrialized nations: playing games, streaming entertainment and connecting on social media. In cryptocurrency, male, tech-savvy millennials have become the stereotypical investor. Yet institutional investors and corporations are the ones buying in high volumes.

“Despite having access to heaps of data about consumers, many companies rely too heavily on the blunt instrument of demographics,” according to PricewaterhouseCoopers.

New technologies can be a powerful tool in understanding international customers. But digital data may only tell only part of the customer story, even with established metrics. For instance, digital marketing leader HubSpot examined net promoter scores cross regions. Latin America respondents tended to have a higher survey participant rate, with higher net promoter scores, than respondents in Japan, whose lower scores may stem from higher expectations for customer service.

The takeaway: Global companies need both high tech and high touch tactics for understanding their customers today. Examples follow that highlight emerging innovations—and the importance of cultural knowledge.

Leveraging innovation for a sharper focus

With the rise of AI, machine learning, and analytics, technology offers powerful solutions. Machine learning is helping UK-based retailer ASOS identify how higher-value customers look at different products at different times and market research firm Borderless Access determine the best times for targeting survey respondents. In fact, over half (56.5%) of U.S. chief marketing officers are using predictive analytics for customer insights.

The NikeID co-creation platform blends data analytics with product development. Online “Nike and You” customers pick the shoe style of their choice, then go in and edit the colors of the lining, the sole, the Nike “swoosh” and beyond. Shoppers get a highly customized product in two to three weeks, and Nike gains valuable troves of data on customer preferences.

Bolstering data with localized intelligence

At the same time, there’s often no substitute for first-hand conversations and observations.

With a presence across Latin America, McDonald’s expanded into Bolivia, then left two years later. Part of the reason might be found in the data: at the time, 63% of Bolivians lived in poverty and 38% in extreme poverty, according to the IMF. On-the-ground intelligence could have yielded insight into the robust street vendor culture—there are 60,000 street vendors in La Paz alone)—and Bolivians’ pride in their local food.

Another global leader, Walmart, once had 558 storefronts in Brazil which met a similar fate. What went wrong? The one-stop shop business model and everyday low prices valued by U.S. customers did not appeal to Brazilians, who prefer intermittent promotions and hunting for the best bargain.

“Large firms like Wal-Mart have gone to countries like Brazil and failed—the same way they’ve gone to countries like Korea and failed, the same way they’ve gone to countries like Germany and failed—mainly because of not understanding the local culture,” according to Uruguay-born Columbia Business School professor Nelson Fraiman, who helped design the school’s Entrepreneurship and Competitiveness in Latin America program.

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