We live in a world where data is everywhere. This means that we are no longer locked into the tech sector, but have also seen data emerging in more traditional industries that have adapted to the ‘sharing economy’, which is an example of taxis moving to the uber-type model. Along, and hotels that go to Airbnb. The potential for disruption through data is quite deep, as more and more of our jobs face data and circumstances in times of epidemics underline how much we rely on data and digital agendas for our daily relationships. With this increase in digitized activity, it is now very difficult to have a business activity that does not leave a digital footprint. This accumulation of data-driven activity is gaining momentum as we move towards digital technology, creating an opportunity for real innovation. So, in this article we are going to cover the best importance of Data Science.
A potential buyer always compares the products offered by different companies before making a final decision, and the algorithm can help the user make choices. By getting to know their customers better and collecting data about their preferences and behaviour, they can understand what their customers really need and thus, reduce customer churn. It is also possible to increase cross-sales or up-sell by providing buyers with ML-generated recommendations. Therefore, you can serve more customers and sell more products or services in less words, and increasing your sales conversion rate. Let’s look at some examples of companies that use the benefits of data science technologies. Netflix analyses the behaviour of its users and provides a selection of content based on their previous preferences. YouTube makes personalized recommendations for users based on views, likes, dislikes and many additional criteria. Google shows targeted advertising where users go and what they buy.
Evidence-Based Decision Making
A business can substantially eliminate its risk through data science. Data can be gathered from multiple channels and analysed to create models that simulate alternative actions. This practice helps to ascertain the best possible outcomes and enables the business to make decisions supported by evidence. When operating in uncertain environments, data-linked suggestions become a cushion for further powering.
Considering relationships, patterns, and differences in the same data, experts can build models preventing businesses from losing many people to fraud. ML methods can be used to avoid scams committed by one person and large-scale crimes by a group of people. Big data analytics and machine learning help identify cases of money laundering and trace criminal chains. For example, when AI-powered systems notice suspicious relationships between a group of people and their unusual behaviour, it is easy for insurance companies to unmask fraudsters who misinform the actual damage from an accident.
Know target audience
Consumer data can be collected from various sources. Organizations typically use Google analytics and customer surveys to gather data points on their target customers. But all these data points may not be useful on their own. They can be combined with other data points to find meaningful information. Data science facilitates this practice and helps business teams learn more about their target audience. Performing a comprehensive analysis supports accurate target group identification and successful advertising and promotional campaigns.