Fortunes have been made from best-selling teas. But how are today's successful blends created?
Developing a great tea is partly an art: picking the right ingredients and combining them in the right proportions to please the palate.
But it’s also science. That means data.
Master tea blenders rely heavily on a wide variety of market research to select flavors and judge how to blend them. Numbers are needed to help guide sensory intuition because today’s blends go well beyond Camellia sinensis to feature herbs, spices, essences and extracts, sweeteners, juices, dried fruit, and more.
The data can show which ingredients are trending and where each one stands in its sales life cycle. Finding popular ingredients requires looking at data from sales of not just competing teas, but also other beverages and foods.
Data can show whether or not a blend is suited to a particular sales channel or venue. Demographic data is especially important. So are trends related to geographic location. With so many moving parts in the equation, it takes discipline to select and apply data in the right way to generate successful results.
A master’s voice
These are lessons gleaned from a recent presentation by one of the industry’s top figures: Christopher MacNitt, tea development lead at Starbucks, purveyor of the Teavana brand. Speaking at the North American Tea Conference in September, MacNitt said that a best seller can emerge after he sits quietly and identifies “things that really excite me.”
“A lot of the inspiration comes from inside; it’s opening yourself up to the opportunity for things to inspire you,” MacNitt said.
But that’s just the start. “I must take my excitement, that’s all bottled up, and go find out if that matches up with what excites people in the marketplace,” MacNitt explained. “Sometimes it does, sometimes it doesn’t.”
That’s when numbers are needed. Useful data sets are available from marketing firms that track menu trends, packaged goods sales, trending ingredients, and newly launched products.
A blender can spot demographic preferences from consumer surveys and focus groups as well as point-of-sale data describing annual sales volume. Companies that offer this kind of information include Nielsen, Spins, IRI, and many others.
Product lifecycles
Popularity changes in time, so you need to analyze raw data like sales volume in order to see the position of a single ingredient in the traditional four-phase product life cycle, MacNitt said.
Inspiration for a tea blend might come from a sales trend in another category of beverage or even food. Look for fresh, new flavors emerging in products like sodas, spirits, desserts, or candy. “The reason why is that early growth gives you an opportunity to be exciting and unique,” he said.
“When looking at trends, particularly in product life cycles, we’re not necessarily looking at revenue per SKU. You could break the data out like that, but generally, we’re looking at macro trends. So, we’re aggregating. If we were examining the product lifecycle of, let’s say ‘peach,’ we’d aggregate all the peach flavors within the category, including those on the periphery, peach cobblers, for example,” he said.
It’s good to be fresh and new — but not too fresh and new. “If you want to bring an innovative tea flavor forward, for instance, attach it to a more mature flavor profile to make it accessible,” he advised. “Bring people along a journey so they don’t feel like they’re going too far out on a limb.
“We often see that tea flavor profiles push the boundaries of current expectations. Young tea drinkers want to explore, but if everything becomes ‘Sichuan pepper and yuzu,’ you’re going so far into the unknown that people have no anchor point. You need to anchor the profile to something familiar,” he said.
He explained that the bergamot flavoring in Earl Grey tea hovers in the “mature” phase of the lifecycle seemingly forever, while black currant teas tend to decline for a while before coming back into vogue. Ingredient trends are not universal. They can vary according to product category and venue.
“It’s essential to recognize that product life cycles are channel specific. So, your food service trends, your mixology trends, your CPG [consumer packaged goods], looseleaf, all these different formats, different flavors could be assigned different life cycle spaces, depending on which channel you’re in,” he cautioned. Life cycles can also track products during different seasons, months, and even times of the day.
“Consider ‘dayparts.’ Is this a drink they order to take home at the end of the day? Avoid tropical fruits if you’re trying to sell them something to drink in the evening. It’s just not what people are looking for. They’re looking for things like cream,” MacNitt said.
“In particular, vanilla tends to be an evening-time flavor profile, maybe some soft florals, but nothing that’s technically very overpowering, and certainly nothing that’s like, ‘jazzy,’” he said.
The question is not only what taste people want, but also who wants it. Look closely at demographics. “Consider chai tea latte. Women like them slightly more than men. Gen Z and millennials love tea lattes. Gen X and boomers and Asians don’t. Lattes are popular in the West and Northeast but not in the American South. Families with kids frequently buy chai lattes. Those without kids don’t,” MacNitt said.
“The peaks define your target customers. In this case, chai latte, my target is not a boomer. Boomers have no interest in a chai latte; instead, we target millennials and Gen Z. So, my packaging needs to be super on-point, with a really, really good story.”
Gen Z prefers tea to coffee, whereas millennials split their choice of beverage 50/50 between the two, MacNitt said.
He sees “a huge demographic shift” whereby millennials specifically choose sweet fruit, fruit-flavored, fruit-forward blends. “Our Gen Z/zoomer demographic wants sweet herbal. They are very much more in the herbal space, preferring herbaceous blends. So, it’s a different flavor profile and blend composition,” he said.
“What we need to account for in the data is that when we’re talking about tea, we’re talking not just about Camellia sinensis. We are primarily talking about herbs,” he said.
Location counts too. “Regionalization can be very important, particularly if the product you are considering is saturated in a specific area of the country or the world,” he said.
Framing the data
Perspective matters. “You need to validate that data. One of the big things that we fail to do well with data is to ask the right questions. Make sure that your questions are top-notch. In many cases, you see is that marketers ask questions that make no sense, at least for us who are in the tea space and understand the product,” he said.
“Data can be very tricky. There’s a bit of a gap between those who are executing the data science and the people who are producing and developing the product. So, in many cases, I have to do six or seven iterations of follow-up questions just to ensure that we’re dialing in exactly what we’re trying to find out,” he said
“The truth is, basing decisions on bad data is like throwing a dart at a dartboard and just going with whatever product it lands on,” MacNitt cautioned.
