Data Science for Commerce And Management
Data science must be incorporated into the marketing strategy since it may help understand customers and how they engage with brands as the marketing landscape becomes more complex. Making more informed choices will enable you to reach your target market and accomplish your marketing and business objectives.
Businesses that use data-driven (personalized) marketing strategies have a five-fold higher ROI. That's why companies are shifting towards data science. As a student of BBA/BCOM it becomes necessary to learn this skill for a better future. Let's see how, in the field of Commerce/Marketing data science is used:
Defining marketing analysis
Incorporating analytics means using data and business intelligence to inform marketing decisions. This may include measuring marketing activity and performance as well as gathering insights into customer behaviour. The goal is to better understand how customers interact with the brand.
How incorporating data science into your marketing plan works:
There are various ways we can use data science to optimize a marketing plan. We can start with the following tips.
a) Personalize the customer experience.
Customers generate data through their habits. We can use this data—such as their browsing behavior, previously purchased items, or abandoned carts—to personalize their touchpoints. Use the data to create and send customized messages to these customers. Data science and business intelligence will help to customize marketing messages so that they are appealing to target consumers.
For example, let's say your marketing plan is to use retargeting ads on Facebook or the Google ad network. You can identify which clients are most likely to respond to these ads using big data and analytics (a subset of data science), and then you can give them targeted communications to persuade them to convert.
b) Inform about the SEO strategy.
Data Analytics can help you understand which keywords generate the most traffic. We may use these keywords in website content and blog posts to rank higher on search engine results pages.
Analytics can also help track a website's click-through rate. This metric will give you an idea of how effective the website is in terms of SEO. If the click-through rate is low, it means that we need to optimize the website better for search engines.
In addition, the data can help us determine which websites are sending the most referral traffic. We can then focus on building relationships with these sources to increase exposure for your business.
c) Build buyer personas.
One of the best ways to use the science of data is to create buyer personas based on the behavior of your target audience. Our data should give you insight into their needs and wants and help create buyer personas that guide our marketing efforts.
Using business intelligence and data science to create buyer personas can greatly improve the success of marketing efforts. Personas can boost email open rates by two to five times and CTR rates by 14%, according to research.
The goals and desires of your target audience can be met by using accurate buyer personas to guide your marketing strategies.
Data science in risk management
a) Credit risk modeling
In order to anticipate categorical, continuous, or binary output variables (default/non default), banks typically utilize traditional credit risk models. Data science can be applied to optimize parameters and enhance the variable selection process in existing regulatory models.
Despite the fact that they have non-linear properties, data science methodologies can result in straightforward and logical decision criteria. While classification approaches like support vector machines can forecast important credit risk features like PD or LGD for loans, unsupervised learning techniques can be utilized to explore data for conventional credit risk modeling.
Financial services firms are also increasingly hiring outside consultants who use data science methods to develop their revenue forecasting models under stress scenarios.
b) Fraud detection
Since credit card transactions offer banks a rich amount of data to process and train unsupervised learning algorithms, banks have been utilizing machine learning approaches for credit card portfolios for years. Due to the availability of models to create, train, and evaluate huge volumes of data, these algorithms have historically been quite accurate at predicting credit card fraud.
Credit card payment systems are equipped with operational tools that monitor card transactions to assess the likelihood of fraud. The rich transaction history available for credit card portfolios provides banks with the ability to distinguish between specific features of fraudulent and non-fraudulent transactions.
c) Conduct of the merchant
Technologies such as natural language processing and text mining are increasingly being used to track the activity of traders for fraudulent trading, insider trading, and market manipulation. By analyzing email traffic and calendar-related data, login/logout times, and call times combined with trading portfolio data, the systems are able to predict the likelihood of trader error, saving financial institutions millions in reputational and market risk.
Churn Modelling
Customer churn refers to a customer's propensity to stop using a service they had been utilizing and terminate their membership. The percentage of visitors who abandoned a website in a brief period of time is known as the bounce rate. The customer growth rate, which tracks new clients, is the inverse of this. Customer retention is a key part of the business strategy for all subscription-based services. In order to predict customer churn rates and take appropriate preventive measures, it is necessary to collect and analyze information about customer behavior (purchase intervals, total period you are a client, cancellations, follow-up calls and messages, online activity) and find out which attributes and their combinations are characteristic for clients at risk of leaving. Knowing in advance which customers are likely to leave early, especially high-income or long-term customers, can help a company target them precisely and develop an effective strategy to convince them to stay.
Data science can help predict the customer's chance to leave the bank, an insurance policy, etc. Using machine learning, we can predict the customer's banking behavior based on their previous data, like their credit score, estimated salary, and customer details. Interested in online shopping, etc.
At last, we were able to analyze how there are so many important tasks in insurance, banking, trade, and finance that can be automated by Data Science. So to reach a higher point in the industry or to get your dream job, it is very important to learn this skill.
Theta Academy is the way to learn Data Science for Commerce and Management Sector.
Why to choose Theta Academy for online learning?
- Provide training of Data science/AI with different syllabus and structure for every field or branch of study
- Guest lectures of Experts from Mercedes,Biocon,Muthoot Finance,Microsoft etc.
- Technical assesment,Interview and competition Platform will he provided (worth Rs7000/-)
- Free Learning from World's Top Publisher:Oreily
- Free Pro Subscriptions for Analytics tool such as Power BI
- Weekend session for Doubt clarity
- Doubt clarity in 15 minutes (24/7) through telegram account
- Government approved certifications
- Online classes with smart panel Screen.
- Placement Assistance available.
- No Prior knowledge of Mathematics and Coding needed
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