Analytics In Convenience Stores – Still A Distant Dream

Analytics In Convenience Stores – Still A Distant Dream

To make sense of customers’ aspirations in today’s byzantine business environment, retailers of all hues are scrambling to get a bigger share of the analytics pie. But, when it comes to convenience stores, the power of analytics has been utilized at a suboptimal level. Ever since the introduction of convenience store model in 1927, it has been one of the principal channels through which consumer packaged goods (CPG) have been delivered to the end users. As the C-stores can serve the modern time-constrained customers with ease, currently they are on a high growth trajectory. Whether operating as a non-oil segment store or oil company managed outlet, growth is glaringly visible in both formats. It is forecaster that revenues generated from C-stores (excluding the stores attached to gas stations) in the United States will touch $63.9 billion by 2015.

Despite all the promising statistics, convenience stores have constantly been given a run for their money by big box retailers. Furthermore, issues like fluidity of gas prices, regulatory requirements, consumers’ rising aversion to smoking, staffing bottlenecks, or shrinkage are posing seemingly taxing challenges to overcome. However, just being data-driven will not help the C-store owners to reach a state of nirvana. While enforcing an analytics regime convenience stores face an array of hurdles:

  • The word “convenience store” is generic in nature. Contrary to common perception convenience stores are not homogenous. In practice, there are six types of C-stores namely, traditional convenience store, hyper convenience store, kiosk, limited selection convenience store, expanded convenience store, and mini convenience store. Moreover, the customers and their needs vary drastically based on the location of the store. So, managerial decisions about packaging, distribution, and promotion cannot be identical across the variations. Implementation of analytics should not be etched in stone; it has to be pliant enough to cater to individual store’s needs. Otherwise, it will be a costly self-defeating exercise.
  • Convenience stores have unusually extended shopping hours; in some cases it is round-the-clock. Again, presence of convenience stores at high traffic zones makes them vulnerable to shoplifting and violent robberies. These peculiarities have a telling impact on the staff members and manifest in steep attrition rate. Thus, occasional dearth of trained manpower is a stumbling block to all around productive analytics culture.
  • Traditionally, convenience stores have been late in lapping-up the benefits of sophisticated analytics tools and stayed behind the curve for long. Now, as the wheels have been set in motion, corner stores should sling-out all the queasiness about analytics.  Mindset issues should never put a brake on successful adoption of business intelligence modules.
  • Price fluctuations in international fuel market have the potential to throw a monkey wrench in the works. C-stores attached to gas stations and operated by oil enterprises are susceptible to swings in global oil prices. Revenues of these stores hinge on the flow of traffic at their respective gas stations. When international conditions get gloomier and revenues take a nosedive, convenience stores may be wary of investing in analytics.

Over-dependence on intuition or gut feeling is not the appropriate strategy to save the day. Leveraging robust predictive approaches fortified with right analytics tools is the smart solution to get actionable insights.

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