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How to avoid missed sales opportunities with Schedule to Demand

by Anh H. Nguyen

A self-fulfilling prophecy is the phenomenon of a prediction becoming true simply because at least one person takes stock of it to some extent. Whether the involved party wants to facilitate it or avert it, his belief in the prediction influences the chain of thoughts and actions that eventually lead to its fulfillment. As plot devices, self-fulfilling prophecies are rampant in literature and arts. Take Cronus, the Greek Titan, for example. As the son of Gaia, the Earth goddess, and Uranus, the Sky god, Cronus committed the infamous deeds of castrating and usurping his father as the ruler of the cosmos. Gaia then warned Cronus of his destiny: he would be similarly overthrown by his own sons. To prevent this, Cronus devoured every single child born to him. As the story goes, his horrific act resulted in his wife turning against him and raising the sixth son, Zeus, in secret. Once he was strong enough, Zeus claimed vengeance upon his father just as Gaia had foretold.

A simple illustration of the mechanism of self-fulfilling prophecies.

The curse of wrong expectations in retail

One may dismiss self-fulfilling prophecies as fantastical tales of coincidences and karma, but it is surprisingly common in real life. The stock market demonstrates this the best: the mere optimism that a company would do well is enough to raise the price of its shares; conversely, fear causes stock prices to quickly tank. For retailers, especially ones with large chains, incorrect assessment of stores' potential could result in a vicious cycle of missed opportunities. When the management deems a store to be low-performing, they may be less inclined to invest more resources in it, resulting in it becoming even lower-performing. This goes on long enough and the store may be closed down for good. The executive may think they were making good business decisions while it could not be further from reality. Woulda, coulda, shoulda: every action concerning this supposed "runt of the litter" store stemmed from a faulty judgment in the beginning. However, overestimating comes with its own caveats such as wasting resources, growing too big too fast, taking unnecessary risks. Without making consistently correct appraisals, a business is doomed.

So how should retailers gauge accurately the ability of their stores? Albert Einstein once wrote, “Everybody is a genius. But if you judge a fish by its ability to climb a tree, it will live its whole life believing that it is stupid.” Ain't that the truth, and it applies to far more than education. Most of the time, retailers look at the number of buyers to determine a store's prospects while it should rank last on the evaluation scale. In the book Behavior Analytics in Retail, author Ronny Max gives the hypothetical example of Hello and Goodbye, two identical stores that belong to the same chain. Based on POS data, Hello has double the daily sales of Goodbye and more transactions as well. Its comparatively better status leads to prioritized scheduling, inventory, and other functions. The corporate office allocates more desired items and more staff hours to Hello, which leads to even higher conversion rate and further cements its superior position. But interestingly, the number of visitors to both stores is roughly the same, which means they attract the same volume of demand. The corporation's short-sightedness fails to take into account actual demand and robs Goodbye of its earning promise. Their further actions only serve to perpetuate the loop.

Hello and Goodbye store comparisons.

The core metrics to watch out for

A store's untapped power should be judged by the sales opportunities it represents, not how many of which come to fruition. And sales opportunities are represented by store visitors. But counting store visitors alone does not suffice - in order to form a concrete business plan, retailers need more than that. Below are the seven KPIs that retailers need to acquaint themselves with, and their specific prerequisites differ from industry to industry. There is one thing they have in common though: they are all incredibly easy to overlook. The truth is without the help of an AI analytics software, it is extremely difficult or even impossible to keep track of them. What Palexy could provide retailers is a thorough, advanced diagnosis of these seven KPIs to help increase sales. A brief summary of these KPIs is as follows:

1. Sales opportunities: In general, this means traffic: the number of people entering, exiting, and occupying the stores. Depending on the store's characteristics, demand may differ greatly - a high-end fashion boutique may have far fewer visitors than a gift shop in a museum, for a example.

Drawing more store traffic is often a retailer's priority.

2. Sales conversion: This measures the percentage of browsers-turned-buyers per period of time. As traffic goes up, many stores see conversion go down because of lack of services. An efficient retailer seeks to better equip their stores to deal with those fluctuations.

Increasing conversion rate could create a compound effect upon sales.

3. Service intensity: This encompasses many facets, such as Average Service Time, the ratio of Visitors to Sales Associates, and the quality of Sales Associates in the store. Retailers could learn to optimize the value of their Sale Staff with these metrics.

Using daytime traffic map in tandem with service intensity is ideal for staffing optimization.

4. Service productivity: Most commonly indicated by the Sales per Hour of each employee, this metric predicts their respective future performances. It operates based on the assumption that if everything stays the same, a sales associate is likely to repeat their past achievements. When calculating staffing, retailers need to combine both service intensity of the store and service productivity of the employees to maximize profitability.

5. Browsing behaviors: While retailers could see clearly customers' buying behaviors thanks to POS data, their browsing behaviors mostly remain in the dark. So much precious browsing data such as how long customers stay in specific areas and how they interact with the staff often go to waste, simply there used to be no reliable way to record them. In-store activities need around the clock monitoring in order to dispel erroneous assumptions.

A browsing behavior graph based on locations within a store.

6. Queue management: While queuing is often the last stage of the shopping journey, it still remains an important part of the customer experience. A broken queuing system will leave a bitter taste and affect the brand negatively. Queue management should measure queuing behaviors in real-time to optimize both customer satisfaction and payroll cost.

A graph detailing the percentage of different queuing times per total number of visits- the more shorter queuing times prevail, the better.

7. Predictive Scheduling: This measures how flexibly and efficiently an organization could adapt to the changing demand while keeping minimum idle labor. A retailer that scores high on Predictive Scheduling could seamlessly dispense labor resources when needed without sacrificing customer services.

Increasing sales is forever a work in progress, but starting with a detailed analysis of these seven KPIs is a surefire way to head towards the right direction. With Palexy's comprehensive diagnosis as the first step, retailers could follow up with a tailored "course of treatment" to ensure maximum success down the road. For more information, please go to



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