Using Big Data to Win in Cannabis
Leveraging Big Data could unlock a lot of value
The cannabis industry is increasingly relying on Big Data (BD) or advanced analytics to generate insights and inform decision making in areas like improving cultivation practices, optimizing product pricing and enhancing consumer targeting. I have seen cannabis companies leverage advanced analytics to reduce production costs by 14%, increase product margins by 27% and improve marketing ROI by 124%.
BD is an integrated suite of statistical tools, data skills, and computing technologies that enable an organization to mine and synthesize relevant data, convert it into insights and then use those insights to inform decision making and support continuous improvement activities.
Given the sector’s razor-thin margins, building and leveraging a strong analytics capability could be the difference between winning and losing in the marketplace.
Notwithstanding its promise, many BD initiatives (regardless of industry) fail to deliver the goods. Three culprits often plague cannabis firms:
The BD initiatives are driven by IT, not the business function. For example, IT’s priority is on assembling technology and tools not on serving the functional groups who are accountable for key business metrics. Another common failure is the lack of available, quality data. To wit, producers looking to build brand loyalty will rely exclusively on quantitative data while ignoring important qualitative feedback from online sites like Reddit or Twitter. Finally, the rapid, ad hoc growth of many cannabis companies led to the establishment of heterogenous computing infrastructures, complicating a key requirement for efficient BD - having centralized, integrated data.
How do you get the most out of your BD strategy and investment?
1. Mobilize an internal BD team that includes IT and other key business functions like Finance and Operations;
2. Glean BD ‘best practices’ from other firms. Understand that technology is just a tool. You need motivated and skilled data experts to do the important work i.e. the analytical thinking;
3. Define some key questions or hypotheses that deliver on your priority metrics and business needs. Start with a pilot project in one functional area to gain experience;
4. Find the quantitative and qualitative data that feeds into your hypotheses. Confirm the data is accessible, current and accurate;
5. Prioritize generating actionable insights. Don’t just crunch data because you can. Ensure your decision makers understand Statistics 101. For example, correlation is not causation.
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