Saving vs. optimization: why retailers should avoid this common pitfall

by Anh H. Nguyen


The idea of saving - scrimping, pinching pennies - is simple: you tighten your belt and cut corners whenever you can. The reasons for saving may vary but its results all boil down to one thing: you do not earn any money by saving, you merely budget what you already have.


The concept of optimizing is more complex. It involves strategic thinking and runs the gamut of adjusting, rearranging, improving, investing, pivoting, repurposing, and modifying. While many people confuse saving with optimizing, saving actually makes up only a small component of optimizing. In many cases, saving is actually counterproductive to optimizing.


Saving is straightforward: you can plan ahead exactly how much you save at specific intervals and how much you end up with. Optimizing is less so: the crux of it is that you need to account for every action you take, since each penny you choose to spend or not spend needs to generate n-fold (n>1). Saving requires the mental capacity of a first grader (willpower notwithstanding), optimizing may escape even business veterans with multiple prestigious degrees. Saving is low-risk and predictable, optimizing is high-stakes and uncertain. Saving implies short-term coping, optimizing entails long-term planning. The ability to optimize (or lack thereof) is what separates the followers from the leaders.


At the individual level, there are many situations that call for choosing between saving and optimizing. One classic example is the footwear choice dilemma: should you invest in a pair of sturdy boots that cost 100 bucks, or buy a pair of flimsy knockoff for 25 dollars? Choices vary depending on the choosers' preferences, habits, and incomes. But the pricier boots last longer, fit better, feel more comfortable, and are often more timeless in terms of style. In the end, they may very well endure your coffee runs and morning walks 4 times longer than the cheap boots, which actually saves you money in the process.


Cost cutting may be effective in the short run but proves harmful eventually.


While many have personally learnt that lesson the hard way, for business owners, prioritizing saving over optimization may lead to catastrophic ends. Cutting costs almost always comes at the expense of either employees, customers, or both. A struggling hotel chain, for example, may opt to: 1, cut employees' benefits, which breeds resentment and lower their performance, or 2, reward employees for cutting costs, which may keep them contented for the moment but ruin guests' satisfaction. Both choices ultimately reflect negatively on their brand and largely contribute to longtime guests switching their loyalty. Similarly, a popular restaurant may cut down staffing, hire less talented chefs, and swap premium ingredients for subpar ones in the name of "financial shrewdness". Customers wise up fast and that restaurant may never regain its footing.


For retailers as well as everyone else, the lesson here is "You cannot save your way to prosperity." (Ian Altman) In dire circumstances, retailers may panic watching revenues fall and customers disappear. Jumping to saving as a knee-jerk reaction is understandable, but still an impulsive mistake nonetheless. Instead, it is beneficial for retailers to take a step back and weigh options in order to formulate a concrete optimization plan. But in order to see those options clearly, retailers need data. Here are a few cases where AI technology could assist retailers in acquiring the aforementioned data:


Case study #1: A shoe chain is having a hard time due to Coronavirus. Traffic dwindles and many store assistants are languishing in empty stores. One solution would be to lay off 50% of staff, which would save money. But this drastic measure may evoke a "sinking ship" feeling among the remaining staff and customers alike. A more reasonable course of action would be to look at the camera footage to determine which time slots draw the highest amount of traffic and distribute the most productive staff to those. The rest could work on a part time basis or do more back-office tasks. This middle ground works on both levels: customers get better service when they come in and staff is (relatively) happier.


Traffic monitoring chart comes in handy to determine staffing.


Case study #2: A fashion retailer deem their marketing budget inflated and hope to cut it down by 50%. Palexy's In-Store Analytics points out that there are a few overlooked customer demographics in their stores. Palexy's suggestion: let's compromise by reducing the marketing expenses by only 25% but focusing solely on these newly discovered demographics instead of distributing them evenly across all demographics. The optimization process could go further when combined with other parameters such as store locations and traffic slots for maximum effects. The retailer ends up making a tidy profit.


Case study #3: A supermarket chain looks at their worst-performing store and decides to close it to save on rental cost and employees' salaries. They consider the loss of deposit and opportunity cost acceptable, but still find it puzzling that a store in such a prime location could not attract more visitors. Once they look at their camera feeds and see an overwhelming percentage of their customers are middle-aged female Koreans, they decide to introduce more specialty foods and beverages pertaining to Korean tastes, employ employees who could speak basic Korean, and prop up more signs and promotions relevant to Korean culture. The number of customers soars. In the course of a few months, this store has risen to the top of their chain in terms of both customer satisfaction and revenue.


Location with a strong ethnic leaning favors a more diverse selection.


It is clear from these examples that while saving may masquerade as optimizing, it almost never yields noteworthy victories. Desperate business owners often treat saving as the first resort and give it little forethought, but with a little creativity and help from fact-based data, they may as well turn it into optimizing for, well, optimal results. The best time for optimization is when an organization is in a tight corner, since it provides the necessary catalyst for change. Moreover, while optimization is a nice bonus when the business is going well, it is a matter of critical importance when the business is not. In fact, a doomed retailer with a saving mindset could go out of business in a matter of months, but one that focuses on delivering exceptional value could bounce back and claim the market in others' absence. A positive state of mind, an ability to see the big picture, and the usage of a well-rounded AI tool are all it takes for optimization to take place.



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