When customers go to a dispensary, they typically learn about sativas vs. indicas. But these classifications are a false dichotomy that cannabis brands need to stop using. The terms only characterize cannabis strains by the species of plant that they come from, but this strategy does not directly determine the results that a strain will produce.
The idea that all sativas make you feel energized or that all indicas relax you is untrue. There are many variables — such as the growing conditions, the presence of pests, and whether the product was cured — that influence a strain’s effects far more significantly than the classification of the plant it comes from.
However, it’s easy to understand how cannabis brands overlooked this fact. Sativa and Indica are different species of the cannabis plant with contrasting physical characteristics; they have dissimilar heights, leaves, and even odors. So, the assumption that the strains from these plants would produce different and distinct effects is sensible. But ultimately, the reasoning is false, and industry leaders are starting to talk about it.
What Do Industry Leaders Say?
Industry experts agree that the sativa, indica, and hybrid classifications are disingenuous. There’s no consistent pattern in an indica or sativa’s chemical compounds that explain why one would inherently provide a sedative effect while the other produces an uplifting one. Given that it’s the chemical components — such as the cannabinoids and terpenes — that create a cannabis strain’s effects, it’s illogical to suggest that all strains from a particular species will consistently produce specific results.
Ethan Russo, a world-renowned neurologist who’s known for his research in cannabis psychopharmacology, believes “the way that the sativa and indica labels are utilized in commerce is nonsense.” The clinical impact of cannabis has nothing to do with the height of the plant or the shape of its leaves, and Jeffrey Raber, Ph.D., agrees with this argument.
Known as the chemist who founded The Werc Shop, the first independent testing lab to investigate cannabis terpenes in a commercial capacity, Raber doesn’t believe that the sativa, indica, and hybrid classifications are sound systems. He claims “there is no factual or scientific basis to making these broad sweeping recommendations,” and he encourages cannabis brands to start understanding which chemical compositions lead to certain results when administered in certain fashions at certain dosages to various types of consumers.
How to Accurately Classify Cannabis Strains
Because it’s impossible to guarantee that every indica, sativa, and hybrid will produce specific results, it’s important for cannabis brands to learn about the effects of each of their strains and find a more intuitive way of classifying them to accurately inform their customers.
This classification should be done by taking a data-driven approach that’s founded on detailed and relevant information, not generalizations and misconceptions. At Strain Genie, for example, we developed an approach that used machine learning to create a master dataset of roughly 3,000 different strains.
Each strain had its own ‘feature set’ encompassing its various cannabinoids, terpenes, the ratio in which they were found, and the sentiment that members of the public applied to each particular strain (i.e., the kinds of words they used to describe it). The purpose was to develop categories that explained the effects of each cannabis strain that fell within them and to do so by prioritizing the chemical properties of each strain as well as the public sentiment towards them.
Once we did that, we used an algorithm to create three groups from our data. Interestingly, when 3,000 strains were sorted into just three groups, most of them fell into the categories of sativa, indica, and hybrid. However, because the goal was to produce groupings that gave precise indications regarding the likely effects of each strain, a greater number of groups was required.
We programmed the algorithm to provide the best way of assorting the 3,000 strains without restricting the number of groups. This time it produced five distinct categories, but we then created a CBD-only classification to produce a sixth.
The resultant six categories of strains suggested specific effects due to each strain’s unique chemical composition. Our groupings — known as energized, elevate, create, chill, sleep, and CBD-only — provide far more insight into a strain than its plant species could ever do. By moving beyond false dichotomies centered around plant species, our company can take a more nuanced look at cannabis strains, understand a strain’s effects, better inform customers, and stop providing misinformation that ultimately harms the industry.
How Cannabis Brands Can Improve Their Classification of Strains
This data-driven approach is more accessible than it may seem. If cannabis brands want to better understand their strains, they can take three simple steps to better classify their products according to their effects.
1. Construct Thorough Feature Sets
As described above, cannabis brands must observe the cannabinoids and terpenes found in each strain, as well as the ratios in which they’re found and the sentiment applied to that strain by the public. This type of semantic analysis uses relevant information to predict the effect that each cannabis strain will produce and should underpin all of their classifications.
2. Analyze the Data Using Machine Learning
Using an algorithm to convert the data from the feature sets into precise categories is a crucial step. Cannabis brands must create a matrix with columns for the relevant cannabinoids and terpenes and separate rows for each strain, and then populate them using the values they obtained for the feature sets. The matrix should also include the sentiment around each strain, whereby certain words are related to specific outcomes.
Afterward, the algorithm will use this information to produce classifications and assign the strains accordingly.
3. Brainstorm Creative Category Names
This strategy requires a human touch to the categories that the algorithm produces. Cannabis brands should note the details associated with the strains in each category and attempt to figure out why the algorithm grouped them the way it did. With additional research and analysis into each classification, cannabis brands can formulate terms that adequately capture the characteristics of each category.
Creating a Data-Driven Industry
It’s vital for the sake of the industry that cannabis brands stop perpetuating misconceptions about sativas, indicas, and hybrids. The first step to change is appreciating the chemical compounds that impact the effects of each cannabis strain. The second is packaging and translating this information in a way that’s accessible to consumers. By doing the work to better classify cannabis strains, brands can improve the consumer experience, increase faith in the industry, and help it continue to grow.