If you’re running a business today, then you probably know how unwise it is to underestimate the importance of data. In the digital age, we use big data and analytics to study target audiences, reach or contact those audiences, and manage our own operations. It’s no longer merely the way of the future — it’s required to compete in the modern market.
One way this data is being obtained is through applications utilizing machine learning. This data can be gathered from marketing campaigns, from customer responses to email blasts to big data acquired from video marketing campaigns. This data can help businesses glean helpful insights about their customers.
But what is machine learning? There are many different aspects to consider gleaning for a full understanding of the essence of machine learning, but it comes down to bots using and observing statistics in order to predict future results and adapting to reach a desired goal. Here are some sectors of business in which big data and machine learning are being most effectively used in 2019.
Predictive analytics have found a home in marketing. Data analysts and data managers are key to this process. They are able to give marketers the 411 on what is working and what is not within specific campaigns. Who is buying what you’re selling? What ads get watched more? Who is watching them? Analytics can provide answers to these questions.
Integrating analytics into marketing enables you to reach your audience in the most effective ways. But machine learning is a relatively new way to obtain these analytics and plan your next step in a marketing strategy. Bain & Company’s Elizabeth Spaulding described the abilities of machine learning in marketing as such:
Importance is being placed on precision in marketing—reaching the right user in the right moment with the right message. But this level of precision can’t be accomplished by humans alone. Machine learning technology is enabling smarter marketing by allowing marketers to drive customer intimacy at scale. Now, marketers can learn what customers want and react to their changing preferences in real time.
Thus, it is with a new level of efficiency that data can be applied to marketing, thanks to AI machine learning.
AI has recently been making an appearance in the practice of inventory control. Some of the most fascinating examples of this are via camera technology. For instance, Japanese researchers have been experimenting with store security cameras that can identify thieves via AI. If the camera notices something suspicious, it is programmed to notify a store employee. The camera can also use facial recognition software to identify potential criminals. Talk about inventory control!
This intersection of camera recording technology and AI doesn’t stop at product security. Warehouse Anywhere theorizes that the next big step in inventory management as a whole will be optical AI systems. They have some specific ideas of how this will play out:
The next level of warehouse inventory systems will be optical systems mounted on autonomous ground-based or aerial platforms. These systems use machine learning to read existing labels without barcodes or RFID (radio-frequency identification) and maintain an up-to-the-minute inventory.
This is an unbelievable use of machine learning and data analytics — and it’s right around the corner. The abilities of this technology will increase efficiency in inventory management and potentially revolutionize it altogether. While we don’t actually know how long it will take for this technology to see widespread use, it will without a doubt change the way inventory is accounted for when it does.
Last but certainly not least, machine learning is leading the way toward conversions. But another way in which this is happening is coming down the phone line.
CallRail described the use of AI in call analytics, typically done for marketing and sales conversions, in terms of lead scoring:
Lead scoring allows you to quickly view which leads are qualified so you can focus on what is driving conversions to your business. Automated lead scoring, based on machine learning technology, allows you quickly identify which can show you which leads to focus on.
The idea that machine learning can record system-scored leads from phone calls is impressive. Though it may not be too surprising, considering the ways the same kind of technology is being used via camera, it is efficient to think of a machine listening to a voice recording and recognizing response trends and how they relate to telemarketing sales. The difference between a machine and a human doing this is that the former can analyze such data with every other specific sales record all at once.
Software like this, has aided in other forms of data science as well. One tool being used in manufacturing and supply chain management is price optimization, one of the top uses of data science in manufacturing. This involves using analytics to find the best price for making ends meet, maximizing sales, and matching what is realistic for customers to spend money on.
With AI’s potential for analytics and how that may affect the supply chain as a whole, it’ll be interesting to see how business practices change in the next few years. Whether it comes by way of more niched target marketing, careful inventory management, or price optimization and conversions via voice recording analysis, it’s clear that machine learning is the future. Only time will tell how this will ultimately change industries and markets across the board. As rapidly as technology is changing, we could be talking about something far advanced beyond these methods within the next decade!
Do you use machine learning for data analytics in your business? In what ways? Let us know your answers in the comments below!