Retailers Use Big Data to Inform Expansion into New Markets

By Karen Lowe, karen.a.lowe@us.ibm.com

India may be one of the most promising retail markets in the world. The economy is expected to grow anywhere from 6% this year to 8% over the next five years, based on recent projections.  And as an added incentive, the Indian government passed a suite of reforms last year that will make it easier for foreign-owned and businesses to open shop there, removing some barriers to entry.

Among the companies that have set their sights on India: Wal-Mart announced plans to open its first stores there over the next couple of years; and Ikea recently received approval to enter the market. Luxury brands are also coming fast and furious. Prada, Alexander McQueen and Dolce & Gabbana are all reportedly looking for local real estate in India.

Yet another reason why the billion-person market is so appealing: Mobile phone penetration is high. Ultimately, it’s easy to imagine that many Indian shoppers will bypass computers, opting instead to do their shopping on their mobile devices. (Given the potential size of the market, it’s not surprising that Amazon is lobbying to have the Indian government relax laws so that foreign businesses can sell directly to consumers online.)

For retailers, there is tremendous opportunity, but entering a foreign market is much easier said than done. The retail experience — including everything from advertising to merchandising — must cater to the local customer base. Understanding their needs is not a trivial task. Ikea, for example, sends thousands of researchers to people’s homes near their stores to see how people live and the types of products they might use. In developing nations where Ikea prices are not considered low, the store markets itself as an “international lifestyle” chain. In developed nations, it’s marketed as a low-price, mass market retailer.

Adapting and localizing brand strategies is critical to retailers' success. “Big Data” analytics can help them avoid embarrassing or expensive mistakes in established markets as well as ease retailers’ transition into a new market. Most major department stores and chains already harvest a variety of data and apply analytics to determine where to locate and how to stock stores, what advertising and promotions are appropriate, how to display goods and so on.

Big Data analytics could do even more in markets where consumers are diverse. Sending researchers into consumers’ homes is a labor-intensive and expensive undertaking. Analytics could prove a cost-effective alternative. Retailers could look, for example, at demographic data to help determine the types of products to carry. They can look at live tweets or comments made on social media to immediately identify market trends and individual preferences; they can use location-based data generated by mobile phones to gain a better understanding of local geography; and, after stores are up and running, retailers can use live, streaming sales data coupled with historical data to predict how trends will evolve.

Undoubtedly, there are risks involved with global expansion, but for retailers who take the time to understand the needs and preferences of local markets the investment could pay off for decades to come. Succeeding in a new market isn’t just a matter of making a splashy, expensive introduction; it’s about making informed strategic decisions — and data analytics can help retailers make that happen.

Karen Lowe, IBM general manager, Global Retail Industry. She can be reached at karen.a.lowe@us.ibm.com.


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