Every cannabis business today is living in the world of big data. One of the greatest challenges this industry faces isn’t getting information on consumers, products, or sales. Rather, it’s pulling something useful from the data they’re already collecting. Two methods of digging out useful insights are data mining and predictive analytics.
Data mining and predictive analytics are sometimes confused with each other or rolled together, but they are two distinct specialties. As you examine the business data your cannabis company already collects, it’s important you understand the differences between data mining and predictive analytics, the unique benefits of each, and how using these methods together can help you provide the products and services your customers want.
What is data mining?
Much of what you do produces data. Did you use a loyalty card last time you went grocery shopping? You can bet the grocery store was eager to collect all the information it could about this specific trip and your buying habits. Your credit card company got in on the game, too. Then, after you put the groceries away and sat down to watch your new favorite sci-fi show on Netflix, the media giant was learning about you through data points. These are all examples in our daily life, but your company and your team is also generating a tremendous amount of valuable data in your seed-to-sale, sales, accounting, and other software & spreadsheets.
What happens to all of this data? How do your grocery store, your credit card company, and Netflix use it to give you more personalized service? How do they use it to encourage you to buy more? And most importantly, how can you use this data to increase profits and decrease stress?
Data mining plays a key role in this process.
Investopedia has an excellent definition of data mining: It’s “a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies.”
In other words, data alone is pretty useless, even if you have massive amounts of it. The only relevant data is actionable data. To make sense of your data, you need a simple data toolkit to organize your information in an intuitive format with immediately actionable insights. Data mining your cannabis business data is incredibly valuable and critically important, and the graphic below outlines the necessary steps for any successful data project. This is exactly how we built our modules & analytics.
Cannabis businesses need to organize then interpret their data
- What time they visited your site
- What device they used to access your site
- Which pages they visited
- Which items they put into their shopping cart
- Which items they purchased together
- Whether they compared items
- How often they come back to your site
This is only a fraction of what you can learn about a single person. Think about what you could learn from all the visitors who land on your site or visit your store each day. Once you’ve captured all that information, it’s time to process and use it.
Step One: Data Warehouse
Unsurprisingly, the first step in the data mining process is collecting all of that information and electronically storing it in a data warehouse. A warehouse can exist on a company’s private server or on the cloud.
Step Two: Organization
There’s no way you can glean useful insights from unprocessed data. Many companies choose to hire an analyst, data scientist, or company like Cannabis Big Data to create organizational rules for the data warehouse.
Step Three: Insights
With the right organization, you can use specialized software to begin identifying patterns and trends in your data. For example, you may discover that women aged 40 to 55 are more likely to buy topicals if they first purchase tinctures. It stands to reason that if someone in that demographic purchases tinctures, you should create an algorithm on your site or for your store staff that encourages them to buy topicals as well.
Cannabrands can use data mining to get to know your customer
The more you know about your customers, the better you can serve them. Effective data mining allows you to:
- Discover patterns in massive amounts of data that would be impossible for a human alone to comb through
- Make better purchasing and pricing decisions
- Maintain the right inventory on the shelves at the right time
- Market more effectively and more personally to customers
The results of data mining are easy to predict. You save on costs, increase your ROI, and impress your happy, loyal customers. Another often overlooked benefit is the increased efficiency and peace of mind in how you make decisions – since everyone’s on the same page look at the same reports, it’s much easier to align everyone around what needs to be done and why. Here’s one more big benefit of data mining: it is essential for effective predictive analytics.
What is predictive analytics?
Data mining gives you the insights, but what are you going to do with this information? In many ways, predictive analytics is the logical continuation of data mining. Predictive analytics is the means by which a data scientist uses information, which is usually garnered from data mining, to develop a predictive score for a customer or for a certain event to occur. Simply put, “predictive analytics” uses data to suggest what may happen and how likely it is to happen.
Sophisticated cannabis companies often use these predictive scores to:
- Assign a consumer a lifetime value based on how much they are predicted to spend with a company
- Determine the best next offer to a customer based on demographic information and past actions
- Maximize yields & efficiency in their cultivation and extraction facilities
- Develop models for spending and growth
- Forecast future sales numbers
- Inspire investors & buyers to understand the full value of your business
One good way to understand how predictive analytics works is through an event roughly 64% of Americans have faced: applying for a mortgage. Banks, understandably, don’t want to give mortgages to risky applicants who may default. Therefore, when potential homeowners come in to request a mortgage, they have to give the bank lots of information, including:
- Current income
- Employment status
- Savings-to-debt ratio
- Credit score
The bank uses this information to predict whether the applicant would be a low or high risk for a mortgage. It also uses the information to determine how much money and what interest rate it is willing to offer the applicant. Of course, banks will never be able to predict with perfect accuracy who will pay their mortgage and who will not. The 2007–2008 housing crisis demonstrated the fallibility of bad predictive models. However, strong predictive analytics can certainly improve decision-making and increase profits for your business.
Predictive analytics works off of good, clean data
How is Netflix so good at pinpointing the right show for you, and how does it decide which new shows to greenlight for its viewers? Good predictive modeling requires three important predictive analytics tools:
The first ingredient for predictive analytics is good data. According to Thomas H. Davenport in the Harvard Business Review, “Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.” We recently wrote about dirty data and what that means for your cannabis business. Suffice it to say that the data you’re already collecting is “good” and “clean” enough for you to start seeing immediate value.
Not just anyone can dive into mined data and figure out whether a grocery store should increase its order of Pop-Tarts by 25% for the third quarter. Many large companies in other industries hire data scientists to carefully comb the data and pull out correlations and predictions. Smaller companies in the cannabis industry may not have the resources to hire a dedicated analytics team, so they turn to technologies with integrated data toolkits.
Every predictive analysis is undergirded by certain assumptions, which must be monitored and updated over time as trends and opinions change. One of the reasons banks were so willing to approve mortgages so often in the early 2000s, even for applicants with low income and poor credit, was because they operated under the assumption that housing prices always go up. As soon as housing prices started to sink and overstretched customers went underwater, defaults skyrocketed. This outcome can largely be blamed on basing decisions off unsupported assumptions.
Your cannabis company can benefit today from data mining & predictive analytics
It’s invaluable to know what your customers are most likely to do, what they are most likely to want, and how much they’ll likely spend to get it. With the right information, predictive analytics can dramatically improve your marketing success by helping you to find the right audience at the right time at the right place with the right message.
Your recent Amazon purchase or Netflix binge is proof that predictive analytics works.
How should you use data mining and predictive analytics?
Both data mining and predictive analytics deal with discovering secrets within your company’s existing data, but don’t confuse these two different methodologies. The best way to understand how they differ is to remember that data mining uses software to search for patterns, while predictive analytics uses those patterns to make predictions and direct decisions.
In this way, data mining often functions as a stepping stone to effective predictive analytics. While data mining is passive and provides insights, predictive analytics is active and offers clear recommendations for action.
As a cannabis business owner or manager, you need to master your data or you’ll get squeezed out of the market. Yes, that avalanche of information can seem intimidating, but rather than running away, embrace it. Tools like data mining and predictive analytics can give you priceless insights into your grow, extraction, products, and customers, as well as into greater trends in your industry.
This article was adapted and reprinted with permission from the data mining & predictive analytics team at Salesforce.