According to McKinsey Global Institute (MGI) forecasts, companies that want to continue to be competitive, develop and scale their business should invest in Data Science & Machine Learning projects. For this reason, presidents and directors must be ready for a new reality that brings with it the need to manipulate data and solve business problems based on conclusions, not guesses. Below we will present 6 Data Science examples of real life applications.
Data processing processes in order to obtain useful information and conclusions based on them help in strategic planning and prevent the incurring of previously unnoticeable costs in the organization. At this moment, you probably should ask: where can we use Data Science or Machine Learning? The detection of patterns in the data is really in every field and industry. Sooner or later, any company wishing to count on the market will face the need to digitize the organization. The greatest benefit for entrepreneurs will be speed combined with information about strategic importance, which together give an incredible opportunity to build a competitive advantage and be one step ahead of everyone.
6 Data Science Examples
1. Demand prediction for manufacturing industry
Companies are able to analyze the needs and behavior of customers using data analysis. The results of this analysis are crucial in understanding what products enjoy the highest demand in the market – which in turn translates into an increase in the quantity of goods sold. The sales forecasting and optimization of the supply chain also bring high value. Companies with the help of forecasts and appropriately learned conclusions are able to optimally allocate resources and have better control over expenditure.
2. Supply chain optimization in logistics industry
In logisticts industry it is very important to manage supply chains. With the aid of company optimization algorithms, they are able to shorten the delivery time and select the optimal route – thus reducing operating costs. Transport service providers are also able to predict the demand for services with high accuracy by combining historical data with information on consumer profiles and macroeconomic indicators.
3. Customer analytics in retail industry
In retailers business the most valuable information is included in receipts. Millions of transactions flowing from all stores combined together give a broad look at customer profiles. Quick processing of millions of receipts and conclusions drawn on this basis help to arrange products in the shop correctly and to compose appropriate baskets of goods – which, according to the Forrester research company, in 80% of cases results in increased sales and higher quality of customer service.
4. Recommendation systems in marketing & advertising
For marketers it is very valuable to analyze user behavior on websites, e.g. online stores. Based on this information, we can determine what are the tastes and preferences of customers. Customer Journey analysis allows you to create more and more perfect recommendation systems which on the basis of this information indicate in a specific time specific products that customers are willing to buy. Implementation of such systems helps stores to be closer to the customer and thus drive their business.
5. Credit scorign for financial institutions (One of the most popular Data Science examples Application)
In finance industry the key is the analysis of banking transactions and based on the insights supporting credit decisions . Fraud detection is also very important, which allows financial institutions to identify the most suspicious operations and pass them to a deeper analysis. The developed algorithms detect illegal transactions that would be very difficult to detect for employees manually.
7. Sales analytics
In fact, in almost every business, the analysis of sales data is extremely important – which as a example allows you to anticipate trends or profile your clients. The conclusions obtained from this type of analysis allow making decisions that have a huge impact on the company strategic planning. At the same time, the analyzes help keep pace with the dynamically changing market by thoroughly understanding the needs of their clients and monitoring their interests.
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Check our Case Studies and read our other articles to see additional solution coudl be implemented using data. If you see great potential for using Data Science & Machine Learning solutions in your business, see the article that will help you effectively implement Data Science and AI solution.