Tea Performance Indicators Retailers Should Track

(Getty Images/Betsie Van Der Meer)

"The first step to taking a tea business to greater profitability using data is to understand what data to collect. There can be a temptation to document everything and see what trends emerge. But, a more measured approach can save significant time, and have a greater impact," writes Sinensis Research founder Abraham Rowe.

Rowe created free-to-use online tools fortracking six Key Performance Indicators (KPIs) helpful to tea retailers.

Routine bookkeeping data, including receivablesand profit and loss reports, are essential, but not sufficient, according toRowe, an educator, who formerly headed Google’s in-house tea program. Hismarket research firm is in Washington, D.C.

Rowe suggests tracking KPIs that help retailersto understand better how their customers are engaging with their business.Examples include customer retention and conversion.

“Key performance indicators are among the best tools for monitoringyour business's performance over time. KPIs are objective metrics that help a businessowner understand their business. While many metrics will help tea businesses understand their performance, one metric iscritical to track as your business begins to shift toward selling more icedtea: stock turn,” he said.

Stock turn is a measure of how quickly your shop’s product issold, and a measure of inventory efficiency. Calculating it will tell you howlong your tea is going to sit on the shelf, taking up space and becoming stale.

Hot to cold

Consider this common situation. Every spring, the tea industry inthe Northern Hemisphere shifts from hot tea to iced tea. With warmer thanaverage spring temperatures predicted, retail tea businesses need methods fortracking how this shift impacts their operations to continue running leanly andmaximizing returns, Rowe explains.

“Let’s walk through an example. Looking through last year’sbusiness, you know you sell around 75 lbs of Earl Grey every winter. But yourarely make iced Earl Grey, and in the spring, you start selling more and moreiced tea.”

“At the beginning of winter, on December 1st, you have 10 lbs ofEarl Grey in your inventory. And, at the end of winter, February 28th, you haveonly 5 lbs. Note that this does not mean you sold 5 lbs—you could haverestocked during the winter.”

“To calculate stock turn, take your average inventory for theperiod: (5lbs  + 10lbs) / 2 = 7.5 lbs. Then, divide your total yearlysales by this number: 75 lbs / 7/5 lbs = 10. This means that if you sold earlgrey at the rate you did all winter, you'd have to purchase tea around 10 timesthat winter.”

“Now, look at your spring sales. You sell around 10 lbs of EarlGrey through the spring. You have 5 lbs on the shelf on February 28th, and atthe end of spring, in late June, you’ve still got 5 lbs on the shelf. Youraverage inventory has plummeted (to 5 lbs), and your stock turn ratio hasdropped to 1. Now, you’d only have to purchase tea just once that spring.”

“So, what has this example tell us? In winter, you wereunderstocking a critical tea. You kept running out, making multiple orders amonth. But then, if you'd kept up that purchasing rate, you would be wastingprecious inventory space all spring with excess Earl Grey. All spring that teasat on the shelf - you were overstocked and should have devoted more space tomore popular spring teas,” writes Rowe.

“As the spring shift to iced tea continues, stock turn can provideinsight into how to use your inventory space wisely and reduce unnecessaryexpenses,” according to Rowe.

Stock turn is one of many KPIs that can help your businessimprove.

Understanding changing customer behavior is more critical thanever. While KPIs like stock turn provide insight into managing inventory overtransitions, it's just as important to predict what your customers will bepurchasing across seasons. Tea businesses are increasinglyturning to technology likepredictive algorithms to improve their sales.

Sinensis Research providesservices like predictive modeling of tea preferences for customers; here's abasic example:

“An online tea store tracks customer purchases. Several customersregularly purchase rooibos. And, 50% of those customers also regularly purchaseAssam. However, 70% of those customers purchased Earl Grey once, and thendidn’t ever purchase it again,” according to Rowe.

“You’ve got a new regular rooibos purchaser and are deciding whattea to send them. If you look at the raw stats, you might recommend Earl Grey -it is, after all, the most purchased tea by other rooibos drinkers. However, apredictive algorithm looks at behavior over time. The rooibos customers aretrying Earl Grey, but not continuing to buy it. Your performance will be betterif you market the customer Assam, which they might then, in turn, start buyingregularly.

“This is the simplest kind of prediction algorithm - looking atfrequency and trends in purchasing behavior to decide how to market teas. Butwith more data, you can hone the right teas to the right audience and drivemore sales in-store or through your site,” writes Rowe.

Source: Sinensis Research