Big Data
A Big Difference for your Small Business
By Nick Kartsunes
July 21, 2017
Category: Business Perspectives
A data boom has been occurring over the past few decades. Large companies with the resources to analyze that data have undeniably been given a competitive edge. In an economy marked by interest rates and regulations that encourage banks to invest in existing successful companies rather than loan out to newcomers in a market, this data boom has a potential to endanger the livelihood of small businesses on a grand scale. But this inevitable disadvantage need only exist if a small business chooses to be left behind. A company which begins with an intention to build an infrastructure that is friendly to data analytics is a company with its finger on the pulse of the market today. Data analytics offer businesses the intelligence necessary to identify a product's target consumer, laser focus marketing efforts, and use predictive modeling to evaluate inventory restocks for the minimization of waste and missed sales. This capacity for increased performance allows new businesses to generate the buzz necessary for investments; investments which permit them to scale without sacrificing the appeal, intimacy, and pride typically characteristic of a small business.
In a 2013 article for the Harvard Business Review titled, "Small Businesses Need Big Data Too," Christina Donnelly details the findings of a longitudinal study for which seven small companies in Northern Ireland were given access to the data collected from a loyalty-card program at Tesco, a UK superstore. The companies were makers of food products sold in Tesco and Sainsbury's, another grocery chain. The companies ranged in size from seven to forty-five employees. The findings of the study were promising and two-fold. Analysis of card data allowed the small businesses to replace what was once a merely reactive approach to marketing, dictated by buyers' guidelines and initiatives taken by competitors in the market, with a more formalized and proactive approach. This change allowed for grand scheme strategizing and talks of long-range innovation. Further, upon gaining access to data ripe with extractable information, these small firms, which initially tended to defer decisions largely to their owner-managers, were able to find validation of prior decisions in the data analysis they did. This led to increased executive confidence and ultimately to a more vertically reciprocal workplace environment. Most of the owners and managers went on to share access to the data with their employees, encouraging the exchange of entrepreneurial ideas from all levels.
Small businesses should be jumping on the opportunity to take advantage of data analytics. In 2016, the Center on Budget and Policy Priorities published a report stating that 87% of job growth within states comes from startups or intrastate expansion from existing companies, rather than out-of-state companies bringing business to new states. In a FiveThirtyEight article titled "The Rent-Seeking Is Too Damn High," Chris Wasselman uses this revelation to refute the misconception that small businesses are driving job growth. But he does not embrace the platitude that the biggest companies are the ones making new jobs, either. Rather, Wasselman suggests that the driver of job growth is "new businesses, and in particular the small subset of new businesses that rapidly grow into big companies." The CBPP report advocates state policies that promote high-growth entrepreneurship as a catalyst for job growth in America. But, small business owners can take their fate into their own hands, if they embrace the high-growth entrepreneurship that data analytics has been proven to make possible. Startups that begin their business with a commitment to data collection programs have the opportunity to reclaim the narrative, and prove to their customers and investors that innovators are the ones bringing the jobs. Established small businesses can choose to rebrand themselves using insights gathered from data analytics and take the same approach, or utilize some of the advantages inherent to companies with a human presence in a community. Prioritizing data analytics in the business model does not detract from these advantages, and should actually enhance them.
For example, a smaller restaurant can less riskily implement a modest loyalty rewards programs like Tesco's for the purpose of profiling and retaining customers, while chain food distributors who race to the bottom cannot always afford the marginal cost of cutting the price even further than they already do. In 2016, after Chipotle's P.R. nightmare with E. Coli, the company rolled out a perks program to help get people back in stores, coupled with an ad campaign that emphasized health and food purity. However, already taking a hit on production costs with their public commitment to selling fresh ingredients, the chain knew that this rewards model would not be sustainable as a permanent fixture. A competing Mom & Pop burrito joint could demonstrate a commitment to loyal customers to distinguish themselves and even steal customers from Chipotle by implementing a rewards model and continuing it indefinitely. The data collected from this program could be analyzed and modeled to better serve their customers, revise their menu, or even tweak the rewards program itself so that more people use it and more information can be extracted. Making use of the power of predictive modeling, Mom & Pop would be able to gain more objective insight into the strengths and weaknesses of their business, and could maybe even survive their own bout with E. Coli burritos.
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