A New Dimension in Cannabis Retail
By Cody Funderburk for Slingr Labs.
In the rapidly evolving cannabis industry, marked by a projected compound annual growth rate (CAGR) of 34.03% from 2023 to 2030, technological innovation stands as the vanguard of retail transformation. This exponential growth, underscored by the increasing legalization and acceptance of cannabis, has culminated in a new era of unprecedented competition for retailers. As the legal cannabis market matures, retailers are increasingly interested in identifying data or strategies that enable them to categorize their customers at scale effectively.
The evolution of data analytics and the digitization of customer data has catalyzed the transition from a priori to post hoc segmentation strategies. This paradigm shift, favoring data-driven segment creation, enables retailers to craft more precise and reflective customer clusters, thus facilitating targeted marketing solutions and optimizing resource allocation. In retail, elucidating consumer segments is crucial for leveraging market potential and securing a dominant share. Such an understanding necessitates a granular approach to customer retention, emphasizing the criticality of deep analytical insights into consumer behavior and preferences.
The integration of unsupervised machine learning (ML) techniques, such as clustering and dimensionality reduction, into the segmentation process exemplifies this transition, allowing for a more agile and comprehensive understanding of consumer segments. Coupled with the proliferation of loyalty programs, these tools give retailers a granular view of consumer behavior, empowering them to tailor their strategies with unprecedented precision and effectiveness. Low-code solutions increasingly enable managers to implement and derive insights from these technologies without advanced coding knowledge.
Traditional methods of segmentation analysis, such as demographic or RFM (recency, frequency, monetary) analysis, fall short of providing such granularity into the actual purchasing patterns of customers. Deep learning algorithms can identify complex patterns and behaviors that are not apparent through traditional analysis. For cannabis retailers, this means the ability to move beyond primary demographic or behavioral segments to create highly nuanced profiles based on consumption habits, product preferences, and even psychological patterns. By utilizing cannabis-specific data, such as patterns of flower and concentrate usage, and incorporating ML techniques like K-Means and Agglomerative Hierarchical Clustering, retailers can employ cutting-edge strategies to gain a deeper understanding of their consumers.
ML-driven predictive analytics help retailers forecast trends, manage inventory more efficiently, and optimize pricing strategies. Deep Learning can process historical sales data, seasonal trends, market dynamics, and even social media sentiment to forecast demand for different cannabis products. Retailers can use these insights to optimize inventory levels, reduce stockouts or overstock situations, and plan product launches. Predictive analytics can also help identify emerging consumption trends, allowing retailers to stay ahead of the market by stocking products likely to become popular soon.
As AI, low-code solutions, and other technologies democratize capabilities across inventory management, customer relationship management (CRM), and payment processing, the question arises: how can dispensary owners carve out a unique space in this burgeoning market?
The answer lies not just in adopting the latest technology but in leveraging it to craft a compelling retail experience. In this context, strategic retail design emerges as a critical differentiator, offering a tangible avenue for businesses to stand out in a technology-saturated market. The future of cannabis retail, therefore, will not solely be determined by who can implement the most advanced technology but by who can integrate these tools to create a memorable, immersive, and interactive shopping experience that resonates deeply with the community and customers.
Emerging Technologies in Cannabis Retail
Jointly's new AI bot, Spark, assists dispensary customers in shopping and employs unique strategies to increase dollar-per-customer by suggesting personalized baskets tailored to each customer's preferences. In fact, AI shopping assistants can create personalized bundles aimed at distinct customer segments following the MECE principle (Mutually Exclusive, Comprehensively Exhaustive), thereby helping retailers enhance inventory and revenue KPIs while meeting the unique needs of consumers. This offers the opportunity for personalization at scale, allowing cannabis businesses to grow into new markets while offering a customized experience.
Jointly’s capabilities extend beyond revenue maximization, helping patients make educated decisions about products best suited to their needs. For now, Spark’s shopping features are being introduced in select U.S. cities, including Los Angeles and greater Oakland in California, and Santa Fe, New Mexico. The Cannabis Company Dispensary in Mississippi partnered with Jointly last year to focus on educating patients about the benefits of cannabis. Though, if you’re worried about AI taking jobs from budtenders, Jointly’s website reassures readers “Jointly Spark Pro AI Budtender Assistant is designed to empower your budtenders, not replace them… With Spark Pro, budtenders are armed with data-driven insights from real cannabis experiences and ratings, giving them the confidence to excel at the sales counter, upsell with ease, and send every customer home happy with the best-performing products for their desired experiences.”
Further, AI provides cannabis retailers with the ability to price items dynamically to capitalize on fluctuations in demand throughout the typical day or week. Augmented Reality (AR) and Virtual Reality (VR) further empower retailers with a world of possibilities to revolutionize the customer experience. Contemporary technological innovations will impact not only retailers but the entire supply chain. Cannabis producers can utilize AI to maintain ideal environmental conditions, leading to heightened consistency and standardization of cannabis products. Beyond production, AI can significantly enhance logistics and distribution, enabling predictive analytics for demand forecasting, optimizing routes for delivery, and automating inventory management.
Blockchain, a technology best known for its role in cryptocurrency, facilitates secure and transparent record-keeping. This feature has already shown benefits for cannabis retailers by enabling cashless transactions in an industry often restricted from accessing traditional banking and payment processing services. Beyond its utility in financial transactions, blockchain offers numerous other potential applications within the cannabis retail environment, such as ensuring seed-to-sale regulatory compliance through immutable record-keeping and innovating customer loyalty programs with blockchain-based rewards. Qredible, a regulatory technology platform, utilizes blockchain technology to ensure seed-to-sale traceability. “The COA reports are ordered through the Qredible platform so they can be vaulted using AI and Blockchain technology in the Qredible system to avoid tampering.”
The journey from the initial planting of a cannabis crop to its final sale traverses a challenging terrain marked by strict regulatory requirements, detailed supply networks, and an increasing call for transparency. Within this context, blockchain technology offers a revolutionary approach to tracking cannabis from seed to sale. This innovation holds the potential to transform the industry's operations fundamentally, signaling a paradigm shift in its operational dynamics.
Solink is another new technology company that empowers cannabis retailers with a variety of solutions, including loss prevention and security. Cookies, one of the first global cannabis retail brands, utilizes Solink technology to prevent theft and discount abuse. This technology safeguards against another regulatory risk: over-discounting. In certain states, like Washington, retailers cannot sell items for less than the wholesale price. Perhaps one of the most innovative qualities of Solinik’s software is a heatmap solution that helps cannabis retailers make data-driven decisions to optimize store layout.
Cannabis Retail in 2030 and Beyond
Global cannabis sales are expected to increase from $13.4 billion in 2020 to $148.9 billion by 2031. The future of cannabis retail not only involves technological saturation, in which businesses are equipped with increasingly sophisticated and user-friendly software to understand their consumers and optimize revenue, but is also marked by regulatory universalization and product standardization. The march towards regulatory uniformity and product homogenization will require retailers to carve out distinctive brand identities. As the landscape tilts towards market consolidation, fostering an environment ripe for the emergence of powerful conglomerates, the dynamics of competition will shift. Differentiation will hinge less on product variety and more on creating resonant, engaging customer experiences that leverage cutting-edge technology, including blockchain for transparency and AI for personalized interaction.
The leaders in this space will be those who understand that the ultimate competitive advantage lies in crafting an experience that resonates on a personal level with their customers, harnessing technology to identify opportunities that will deepen connections and enrich the consumer's interaction with the brand. As we look towards 2030 and beyond, the defining factor in cannabis retail will be the ability to integrate technology with a customer-centric focus, setting a new standard for excellence in the industry.
Glossary of Terms
Compound Annual Growth Rate (CAGR): A measure used to calculate the mean annual growth rate of an investment over several years. It represents one of the most accurate ways to calculate and determine returns for anything that can rise or fall in value over time.
A priori: In the context of data analysis, a priori segmentation strategies refer to dividing customers into groups based on predefined criteria before analyzing the actual data.
Post hoc: This term means "after the fact" and in data analysis, it refers to creating segments after the data has been reviewed, typically using statistical methods to identify patterns.
Machine Learning (ML): A field of computer science that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed.
Clustering: A machine learning technique that involves grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.
Dimensionality Reduction: A process in machine learning and statistics where the number of random variables, or data features, is reduced to a smaller set of key variables. This helps simplify the data, making it easier to analyze and visualize while still preserving the most important aspects of the original dataset.
Deep Learning: a type of artificial intelligence that mimics the way humans think and learn by using layers of algorithms called neural networks. These networks can learn and make intelligent decisions on their own by processing large amounts of data, allowing them to recognize patterns and solve complex problems like driving cars or recognizing faces.
RFM Analysis: A marketing analysis tool used to examine how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary).
Predictive Analytics: The practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.
Augmented Reality (AR) is an interactive experience of a real-world environment in which objects are enhanced by computer-generated perceptual information.
Virtual Reality (VR): A simulated experience that can be similar to or completely different from the real world. It uses computer technology to create a simulated environment.
Blockchain: A system in which a record of transactions made in Bitcoin or another cryptocurrency is maintained across several computers linked in a peer-to-peer network; blockchain is particularly known for its enhanced security and data integrity features.
Mutually Exclusive, Comprehensively Exhaustive (MECE): A principle often used by consultants to segregate information into mutually exclusive and collectively exhaustive elements. In retail, the MECE principle is commonly applied to product bundling.
K-means clustering is a method for grouping data into a specified number of clusters. It works by placing random points (called centroids) in the data space and then grouping data points closest to each centroid together. The centroids adjust their positions based on the average location of the points in their cluster, repeating this process until the groupings don't change much, effectively finding clusters of similar data points.
Agglomerative Hierarchical Clustering: Agglomerative Hierarchical Clustering is a method where each data point starts as its own group, and pairs of groups are continually merged based on their similarity. This process repeats until all data points are unified into a single group, forming a hierarchy from the individual elements up to the complete dataset.
Cody Funderburk, a forward-thinking Cannabis Retail Strategist and Medical Cannabis Consultant based in Seattle, brings a rich background in business strategy and cannabis science to the rapidly evolving cannabis industry. They hold dual undergraduate degrees in Business Administration, are pursuing a Master of Science in Medical Cannabis Science and Therapeutics at the University of Maryland, and serve as the President of the Medical Cannabis Student Association. Cody's professional journey includes impactful roles in retail management and advisory capacities, emphasizing social equity within the cannabis sector. With expertise in data analysis, brand development, and business strategy, they advocate for a contemporary and data-driven approach to cannabis retail. Cody's commitment to community, education, and innovation underscores their role as a key influencer in shaping the future of cannabis retail, leveraging technology and policy to foster growth and inclusivity in the industry.
