Nowadays, there is a tendency to hire data scientists or even to form data science groups in companies. And this is true not only for specific activity sectors or for large organizations. Small and midsize business more and more involves data scientists work to get actionable insights from collected information. So, how data helps to run and grow everyday business?
There are several areas where collected data and the insights draw from that data can have a significant impact on business.
Improved Decision Making.
Analysis of the customer’s behavior and personal data, its feedback give opportunities to modify products and services that better suits the current marketplace.
The most widespread and famous example encountered by anyone who uses the Internet is website recommendations and advertisements. Recommendation systems use previous search results for a user and propose the most relevant goods or services for the client. Amazon, Google Play, Netflix, Twitter and many more use such systems for digital advertisements.
Client’s behavior can be used for a better management. For example, you can define how many people do you need to put on staff at any time period to improve customer service.
Refining Marketing Strategy.
Customer data is important. It helps companies understand how consumers are engaging with and responding to their marketing campaigns and find new target market that they can capitalize on. As follows, a company can define how to sell a product to those who need it at a reasonable cost, at the right time and using the right channel.
The logistics industry is enormously big. It also means having to deal with big data from a wide variety of sources: vehicle tracking data, geolocation data, sensor data, sales, customer service, etc. Delivery path optimization, warehouse optimization are case studies of the data science in logistics. Predictive analytics can provide more precise market forecasting, what helps to decrease losses due to an oversupply or undersupply of inventory. Data science is used to improve the operational efficiency in logistic companies like DHL, FedEx, UPS, etc.
Predictive analysis can improve HR management. Internet technologies and increasing mobility of people make the hiring process very competitive. The company spends 23.7 days on average to hire a new employee, while the best candidates stay on the market for only ten days. The predictive analytics technologies help HR teams to filter through thousands of resumes and create a base of the most promising candidates on a market. The advantage of using data science in hiring is in decreasing of hiring time and increasing quality of candidates for the company. Predictive hiring in the recruitment process is successfully used by JetBlue, Wells Fargo, and others. Using of the Big data analysis, companies can also detect potential mistakes and flaws in work, help in employee retention, increase employee engagement and productivity.
The data science usage provides more opportunities for a company in its daily work routine and in the choice and correction of the development strategy. It allows the business to be competitive on the market.