7 data science use cases for business
With these 7 data science use cases, you’ll be able to see how data analysis can help you make your business more profitable and competitive.
Data science is a powerful tool that can be used in many different ways. The data it generates can help you make better decisions on everything from marketing to product development. You can use it for forecasting, predicting outcomes, and optimizing outputs. It can also be used as a competitive edge over your competition.
To avoid being left behind, it’s time to take your business into the future with data science. With these 7 data science use cases, you’ll be able to see how data analysis can help you make your business more profitable and competitive.
Pinpoint customer loyalty and trends.
One of the simplest methods for businesses to enhance sales and profitability is to maintain and increase sales to current customers rather than acquire new ones. According to statistics, acquiring a single new client might cost five times as much as aggressively retaining an existing, valuable customer. This is a significant difference.
This significant disparity is one of the key reasons why businesses in every industry are trying so hard to retain their most valuable clients and boost overall sales to loyal customers. This transition is being driven mostly by internet retailers.
Working with data science is a specialized skill, however. You can’t just start using data science tomorrow without any familiarity with analytical platforms, or how to read and interpret the data. This is why a lot of successful businesses use consultants that offer data science solutions, like RTS Labs.
Segment your customers by habits.
To effectively segment your customer base, you need to know what each segment actually means. How do people think about your product or service and, more importantly, what questions do they typically ask when they look to buy or sign up for your product?
A data science outsourcing firm can help you generate data, understand what people are searching for, and understand what problems each segment is trying to solve.
The goal of using this method, in this case, is to discover trends in consumers who buy certain items. As a result, you’ll be able to build a marketing campaign just for these customers.
Optimize your workflows and process
SMBs are increasingly relying on data and analytics to discover and correct inefficiencies. A global agricultural equipment company, for example, was having issues with its training division, with hired rooms for dealership training sessions frequently lying vacant.
These inefficiencies were almost always found at the end of the year, long after it was too late to do anything about them. But the company’s managers were able to learn more about their training issue by analyzing data that showed inefficiencies in how trainees were being assessed.
You’re able to automate data gathering across several platforms and give insight with the help of the customer. The entire collection process is taken care of for you.
Internal process management
Managing complex and dynamic processes within a company with outdated technology and procedures is getting increasingly difficult. Data and analytics may aid in the automation of various operations and provide data-driven insights.
This is an example of a mid-sized telecommunications firm that offered network solutions to its clients. Typically, this involved acquiring a significant number of lines from various suppliers and linking them in a controlled network. They had tens of thousands of lines that required monthly payments.
When customers canceled individual lines, the third-party supplier did not always cancel them as well. As a result, payments were made on a monthly basis for lines that produced no revenue.
Industry wide insight
Analysis of a variety of market situations for insights that can be readily accessible by teams across the firm is a common method for determining business value. A worldwide pharmaceutical business, for example, must swiftly assess a range of industry-wide concerns in order to make product price decisions in 90 different locations.
Their solution must allow pricing teams to easily compare and iterate on circumstances. The company was able to use a variety of data assets that they already have within the organization, such as clinical trials, market research, industry benchmarking, financial predictions, and more, by using a scalable modeling engine and sensitivity analysis.
Logistics and supply chain management are two of the most pressing issues confronting the industrial sector. AI has the potential to transform manufacturing by enabling for better resource use and value chain management. AI may contribute to transformation in the industrial sector in a variety of ways, including:
- It is possible to keep track of supplies using various applications in order to ensure seamless functioning.
- Demand forecasting for a given product in order to enhance logistical management.
Dark data isn’t frightening or evil in any way – just the contrary, in fact. Dark data is defined as data assets that businesses gather, process, or store but never use.
It’s the information that matters, yet it gets lost in the shuffle. Examples include unused client data, opened but not removed email attachments, and out-of-date customer service requests. Dark data is expected to account for 93 percent of all data by 2020, and an increasing number of firms are prepared to use it.
They accomplish this in part by analyzing data from customer service logs to determine which media a client used to start contact and how long the encounter lasted. This dark data enables a company to discover a client’s preferred mode of contact in order to provide better customer care in the future.
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