If the pervasiveness of dollar menu mentality is any indication, price points are crucial to competitiveness in the fast food industry. So why is big data underutilized or outright ignored in the typical fast food supply chain? Still caught in a lingering procurement cycle plagued by shortages, safety scandals and other — admittedly important — distractions, the fast food industry is understandably hesitant to tackle yet another potential issue. As big data sweeps through industries like healthcare, leaving cost saving efficiency in its wake, it's becoming harder to ignore the benefits of data scrutiny. So what can big data really do for fast food, an industry typically too focused on the road ahead to pay attention to the road behind?
Big Data Spots Trends
It's no secret that market research factors into most "new product" decisions in fast food, but using data allows companies to see what consumers want in order to test it. If metrics are available to mix and match ingredient popularity — say, vine-ripened tomatoes or specialty bacon products — it becomes faster and easier to theoretically construct a success-oriented sandwich product. If flavored iced teas or a certain soft drink historically sells better in one season over another, that might signal a parent company to roll out a promotion or limited edition product that features it during that "sweet spot."
Consider the marriage of pork product and pitch-perfect trend analysis that is the McDonald's McRib: CNBC's Katie Little notes that there are even "fan sighting" blogs that track the appearance of this limited edition sandwich. While there has been comparatively modest advertising for the McRib, most of the item's perennial hype has come from using big data to determine "hot" markets for appearances.
Big Data Notices Issues
You may already be reviewing past performance within your fast food supply chain, which is a step in the right direction. Imagine, however, if a series of issues — poor product, late delivery, etc. — has emerged from a manufacturer and spurred the C-suite to replace them. Taking a broader view of the situation through supply chain metrics might reveal a problem with the region, instead, and shift the blame and corrective action target to a 3PL company rather than the manufacturer. Now the company has lost a blameless manufacturing partner and continues to use a faulty logistics system, continuing the chain of problems — hardly a positive outcome.
Big data not only highlights issues, it ensures that the solutions are being applied to the correct source. At the store level, big data can also be used to redesign or completely remodel functional areas such as drive-thrus. By analyzing the amount of time it takes each customer to receive their order and the commonalities between late orders, a QSR business can determine their pain points with increased accuracy.
Big Data Helps With Pricing
How much will customers comfortably pay for a food product before they're squeezed out to a rival? That's the core question determining profit in the QSR world, and answering incorrectly or taking leaps of faith too often can damage a brand. Big data provides a safe sandbox to coax out realistic price points, giving restaurants metrics to examine side-by-side. This data can show the "drop off" at a certain cost ceiling, as well as the volume tradeoff to be expected if an item drops in price. These numbers, in turn, determine how much procurement teams can leverage when sourcing raw products and ingredients, keeping unnecessary surplus and waste out of the supply chain. Using big data to set consumer-facing pricing worked for grocery giant Kroger, so it stands to reason that the method has a lot to offer the considerably lower SKU-count setup of a QSR.
The supply chain is the QSR industry's major artery for competitiveness; the decisions and pricing made within the chain determine the success at the store level. Ignoring data is like allowing employees to work for years without a review or never asking for customer feedback surveys. While the business may remain operational at a very basic level, it's not primed to grow, prosper or weather any potential industry storms.