It’s by no means an exaggeration to say that data science has penetrated every major market in the United States. Whether you’re in the real estate industry or are a supplement manufacturer, the chances are that your company has invested a great deal in business intelligence and data science tools they can use to enhance business processes, marketing campaigns, and most importantly, profits.
The use of data analytics to gain valuable insights into business processes and customer demand has grown so much that it’s spawned the phenomenon known as big data. Qualified data scientists can use stats to tell you everything from what kind of supplements people in your region are the most interested into different business problems that have nothing to do with your customers. Continue reading to learn how data analytics is impacting the supplement industry.
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Supplement makers use data science to find out what’s in demand.
As a supplement manufacturer, you’re probably well aware that people have become more health-conscious than ever, and the health industry is cashing in on this trend. Of course, whenever you mention health and supplements, weight loss is one of the main things that come to mind.
However, by using data analytics, data scientists have helped companies to manufacture weight loss pills for underserved demographics. By using a data science model to determine customer interest, they can tailor their products to them.
Menopausal women are at a crossroads in their life during which their hormones are changing rapidly and so is their entire biochemistry. That’s why MenoLabs uses data science to keep up with what solutions women going through menopause are looking for the most. Hence, MenoLabs has launched a menopause weight-loss probiotic that helps promote gut health, healthy weight, and overall health.
By using machine learning algorithms that scour internet search data, data scientists found out that weight loss and immune system health were among the top concerns for menopausal women. Even though you might not hear women speak much about menopausal weight gain, the data science doesn’t lie, and that’s why MenoLabs responded with the solution their customers were looking for in the form of a probiotic supplement to support a healthy gut and better digestion.
Supplement companies use predictive analytics to get insights into future events.
How beneficial would it be to your company if you could see changes in your market coming well before they arrive? You’d be able to stay on the cutting edge of an ever-evolving industry and live in the cause rather than the effect of demand changes.
With predictive analytics, companies can find trends in stats that point to future events and plan accordingly. One of the hardest things for manufacturers to recover from is when they make too much of a product for a sudden change in demand and they’re stuck holding the bag. Machine learning algorithms can find trends in data and create predictive models that companies can use as a crystal ball to see what trends are on the horizon.
Manufacturers use data science to keep their equipment running.
There are few things more detrimental to a manufacturing company than equipment failures. When a machine goes down, it can take your production with it and leave you and your customers high and dry.
By applying predictive data analysis, your production equipment can actually tell you when it’s most likely to break down well in advance so you have time to schedule maintenance and downtime without production taking a huge hit. You can schedule your maintenance before you ever begin having problems with your equipment.
Manufacturers also use prescriptive analytics.
You read in the previous section about predictive data science processes, but did you know that data science can be prescriptive as well as predictive? It’s one thing to have a data science platform to warn you about future events, but you’re talking about a different level of data usage when you talk about using data models to prescribe solutions.
Big data is revolutionizing the way supplement manufacturers maintain their operations, develop products, and handle demand changes. As you can see, big data is kind of a big deal!
