“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 for tracking six Key Performance Indicators (KPIs) helpful to tea retailers.
Routine bookkeeping data, including receivables and profit and loss reports, are essential, but not sufficient, according to Rowe, an educator, who formerly headed Google’s in-house tea program. His market research firm is in Washington, D.C.
Rowe suggests tracking KPIs that help retailers to 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 monitoring your business’s performance over time. KPIs are objective metrics that help a business owner understand their business. While many metrics will help tea businesses understand their performance, one metric is critical to track as your business begins to shift toward selling more iced tea: stock turn,” he said.
Stock turn is a measure of how quickly your shop’s product is sold, and a measure of inventory efficiency. Calculating it will tell you how long 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 in the Northern Hemisphere shifts from hot tea to iced tea. With warmer than average spring temperatures predicted, retail tea businesses need methods for tracking how this shift impacts their operations to continue running leanly and maximizing returns, Rowe explains.
“Let’s walk through an example. Looking through last year’s business, you know you sell around 75 lbs of Earl Grey every winter. But you rarely make iced Earl Grey, and in the spring, you start selling more and more iced tea.”
“At the beginning of winter, on December 1st, you have 10 lbs of Earl Grey in your inventory. And, at the end of winter, February 28th, you have only 5 lbs. Note that this does not mean you sold 5 lbs—you could have restocked during the winter.”
“To calculate stock turn, take your average inventory for the period: (5lbs + 10lbs) / 2 = 7.5 lbs. Then, divide your total yearly sales by this number: 75 lbs / 7/5 lbs = 10. This means that if you sold earl grey at the rate you did all winter, you’d have to purchase tea around 10 times that winter.”
“Now, look at your spring sales. You sell around 10 lbs of Earl Grey through the spring. You have 5 lbs on the shelf on February 28th, and at the end of spring, in late June, you’ve still got 5 lbs on the shelf. Your average inventory has plummeted (to 5 lbs), and your stock turn ratio has dropped to 1. Now, you’d only have to purchase tea just once that spring.”
“So, what has this example tell us? In winter, you were understocking a critical tea. You kept running out, making multiple orders a month. But then, if you’d kept up that purchasing rate, you would be wasting precious inventory space all spring with excess Earl Grey. All spring that tea sat on the shelf – you were overstocked and should have devoted more space to more popular spring teas,” writes Rowe.
“As the spring shift to iced tea continues, stock turn can provide insight into how to use your inventory space wisely and reduce unnecessary expenses,” according to Rowe.
Stock turn is one of many KPIs that can help your business improve.
Understanding changing customer behavior is more critical than ever. While KPIs like stock turn provide insight into managing inventory over transitions, it’s just as important to predict what your customers will be purchasing across seasons. Tea businesses are increasingly turning to technology like predictive algorithms to improve their sales.
Sinensis Research provides services like predictive modeling of tea preferences for customers; here’s a basic example:
“An online tea store tracks customer purchases. Several customers regularly purchase rooibos. And, 50% of those customers also regularly purchase Assam. However, 70% of those customers purchased Earl Grey once, and then didn’t ever purchase it again,” according to Rowe.
“You’ve got a new regular rooibos purchaser and are deciding what tea 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, a predictive algorithm looks at behavior over time. The rooibos customers are trying Earl Grey, but not continuing to buy it. Your performance will be better if you market the customer Assam, which they might then, in turn, start buying regularly.
“This is the simplest kind of prediction algorithm – looking at frequency and trends in purchasing behavior to decide how to market teas. But with more data, you can hone the right teas to the right audience and drive more sales in-store or through your site,” writes Rowe.
Source: Sinensis Research