This article was contributed by blogger and translator Frank Hamilton.
Businesses hire data scientists to perform a bulk of work related to artificial intelligence, improve their overall performance, and come up with new product ideas. In today's article, we'll go over some great data science use cases that many businesses are currently using. If you want to take benefit of those, you might take a data science course to gain expertise in this area.
Anomaly detection helps identify steep downswings and upswings in sales data, as they occur. Building an automated anomaly detection system can help a company quickly address problems and opportunities that affect its bottom line. This use case can also apply to other business functions like manufacturing and supply chain management.
Image classification involves creating algorithms that automatically classify images of products based on specific attributes, such as their color, product types, and so on. In the long run, this can be less time intensive and more accurate, as compared to manually classifying hundreds or thousands of images in a company's database.
The growing demand for image classification software necessitates the creation of more sophisticated image processing systems, which enable powerful ML and AI-based image processing. That's where embedded software development services may come in handy.
Recommending Products to Website Visitors
A company can capitalize on cross sale and upsale opportunities by recommending products based on the search and past buying behavior and attributes of website visitors.
A recommendation system would give customers and website visitors a clearer idea on what offerings can best fit their needs. Amazon is the most obvious example of a company that uses this approach.
Forecasting involves forecasting metrics, such as sales number of orders, profits, products sold, etc to enable a clear idea of where those metrics can land in the next several months or a couple of years in an organization.
Customer acquisition involves tracking the probabilities of prospective customers buying a company's products or services based on current and present customers with similar behaviors and attributes.
This process helps businesses calibrate and maximize the success of their customer acquisition strategies.
Customer Segmentation and Clustering
Lots of companies still rely on manual methods when it comes to segmenting their customers based on characteristics like price points, survey results, geographic areas, industries, etc.
Using clustering techniques can help companies segment their clientele based on factors that are more difficult to pinpoint, their behavior patterns, for example. This approach makes it possible to apply personalized marketing and sales strategies to each customer segment.
Identifying Drivers of Positive and Negative Outcomes
Manual techniques to find drivers of sales trends can be inconsistent and time-consuming. Using automated methods to identify factors that drive positive and negative outcomes can go a long way toward helping companies maximize their success. Examples of metrics this approach can apply to include sales, number of orders, profits, and so forth.
Customer service reps often spend too much answering frequently asked questions and managing customer requests. That's where a chatbot comes in and saves the day. A chatbot can answer many types of questions based on the data you feed to it. Additionally, they can create a list of products your customers might like based on their past buying behavior and preferences.
Thus, chatbots can be great tools for directing customers towards making optimal buying decisions. Chatbots can also integrate with websites, social media accounts, CRM software, smartphone, texting systems, etc. There are already multiple high quality chatbots on the market that companies can simply buy and customize.
AI-powered chatbots have the ability to learn over time. With each new task they accomplish, they get better at solving problems, thereby boosting the overall efficiency of your business. For the development of chatbots most customer software development companies use such technologies as machine learning, natural language processing, AI-powered tools, and many more.
But some companies have unusual integrations of software and unique company objectives, which may necessitate the need for data scientists or developers to create a chatbot from scratch. For more information, go to Sirinsoftware.com.
About the author
Frank Hamilton is a blogger and translator from Manchester. He is a professional writing expert in such topics as blogging, digital marketing and SEO. He also loves traveling and speaks Spanish, French, German and English.