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AI and CBD: How Machine Learning Can Help MMJ Card Services

Last updated on February 13th, 2024 at 02:07 pm

Artificial intelligence is growing increasingly sophisticated. From its origins in science fiction, AI can now be found everywhere today, from self-driving cars to smartphones and apps. The technology has also trickled down to SMEs, with different industries making use of machine learning in sales, marketing and business process optimization.

The medical marijuana card industry also stands to benefit from utilizing AI. Here’s how.

AI is a good way to start off customer service inquiries

When executed well, AI is capable of fielding customer services inquiries. Previously, having a large customer base meant that you needed more staff to process the volume of customer concerns and inquiries. Now, an AI chatbot can be used not only to filter and categorize concerns, but to actually solve lighter issues.

Of course, there will be times when they will not only want to talk to a real human, but also need to talk to one. However, having a chatbot reduces the labor involved in directing customers. No more having to have your customer support doubling as a switchboard operator and customer support all at once.

Another advantage of using an AI chatbot is that, if you do not have customer service at all hours, a chatbot can be functioning at all times. Thus, for things that do not require an immediate response, a chatbot may be able to help them – at least in the short term, anyway. At the very least, it may be able to keep a customer satisfied until they can speak to a person.

AI-driven Data Collection

As an MMJ telehealth service, there are a variety of data sets you can look for when it comes to your customers. These data sets include their state, qualifying conditions, physical attributes, etc. However, all of this is something you can discern manually. You can also acquire certain pieces of relevant data on the Internet, and use AI machine learning to try to get predictions that can help your company and customers in the future. Customer research is just as important in the marijuana industry as any other and will be critical to your success.

Recognizing your customers’ emotions using sentiment analysis

While reviews are a fairly simple way to gauge how people feel about your business, that’s not enough. Sentiment analysis helps by automating the process and removing human bias from the analysis. Sentiment analysis algorithms, at least good ones, tend to be able to check for certain words. Additionally, they can also use the context of a post or conversation to gauge positive and negative comments, as well as flag ones to examine. It’s always good to see where you are doing well and where you are not.

Once you’ve thoroughly collected and indexed comments according to their emotion and sentiment, you can then begin working to address them.

AI improves cybersecurity

Telehealth providers may be subject to HIPAA and data privacy protection regulations. To have the most robust security for your CBD telehealth business, you want to incorporate many different security measures.

AI can help mitigate data theft and unauthorized network intrusions through machine learning. It learns the latest in malware and spyware threats, recognizes ransomware, and flags suspicious traffic in the system. Data privacy is a very serious concern for telehealth providers, especially MMJ providers whose customers expect discreet protection of their personal information.

The benefits AI has conferred to the CBD industry are much the same as the benefits in other industries. Whether it be through  marketing, research, production, or distribution, AI has been an invaluable tool for the success of many businesses. It’s certainly aided by the fact that laws and regulations have been changing regarding medical cannabis and CBD products.

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Jennifer Evans
Jennifer Evanshttp://www.b2bnn.com
principal, @patternpulseai. author, THE CEO GUIDE TO INDUSTRY AI. former chair @technationCA, founder @b2bnewsnetwork #basicincome activist. Machine learning since 2009.