In our previous blog we have tried to cover what is data analytics. I hope that gave you some good understanding and starting point. But in that blog we have looked more from IT perspective. We did not touch upon different functional areas of data analytics. In today’s blog we try to cover some of the major practice areas of data analytics.
Customer Data Analytics:
Wikipedia defines customer Analytics as “Customer analytics ” is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management.” As an example, predicting customer behavior is key for today’s businesses and customer data analytics is one way to address it. We can also say that “Customer Analytics is the art and science of capturing data from business transactions then transforming it into a valuable insights which can be used for guiding and driving your key business decisions”
There are various areas where customer data analytics can be applied. Few key ones are
- CSAT Analytics (Customer Satisfaction)
- Customer Segmentation
- Customer retention
- Cross selling and up selling etc.
In today’s world, Internet (or Web) is an important aspect of life. Be it individual user or a business. Hence lots of business transaction happens over internet. And hence it is very important for businesses to monitor their engagement with customers on internet. Another important aspect is advertising over internet and there are lot of companies which offer this kind of service. Google is one of them. Google ads are market leaders on this. Business houses are very interested to know what is happening to their promotions on internet and how customers are responding to their promotions. There are various tools available. Google Analytics is a very good tool which is integrated very well with google advertising. The aim is to initiate your thinking in this area. Hence if your interest lies in this I recommend that you start checking this on internet. I will also cover more of this in my future blogs.
Social Media and Text Analytics
This is something very new and a very important area for business houses today. With Facebook , twitter and other social media wesbites, lot on interactions happen over these websites. These websites have a huge user base and hence it offers lots of opportunity for businesses to advertise their products/services on these websites and also engage users. Get their feedback. These website give a very convenient way for businesses to talk to users. And feedbacks can be positive or negative. And hence it is very important for these businesses to analyze these posts and take a informed decision. Since all these are mostly unstructured data (text), it is a different challenge.
This still a evolving field and offers lots of opportunities.
Pricing and Sales Analytics:
Pricing and Sales analytics is an important field. It includes areas like Cross Product forecasting, elasticity models, price optimization scenarios, profitability analysis etc. All these functions are very important. Objective includes identifying problem before they get out of hand for example Inaccurate Sales forecast, low profit margins, low business, identifying sales opportunities etc. You might be thinking how IT and Data Analytics fits in here. Well understanding data is a functional domain and data analytics do fit in here, but also need to make this data available with the desired quality and accuracy is the function of IT. There are specific software available for this kind of analysis.
Fraud and Risk Analytics:
Very important area in finance domain. Specifically for companies in banking and insurance sector. It’s about forecasting uncertainty. A simple example, you take a loan from Bank, in background the bank does lot of verification on you. They check your credit history and other parameters. Why do they do that ? They want to understand how risky are you as an individual when providing a loan. And there is a lot of calculation which goes behind it. Some statistical models are run etc to understand the risk and there are specific softwares available to do this. But not only that, the risk nature depends on type of business, customers etc and hence companies need customized models. Hence domain experts and data analytics professionals are in high demand here.
HR is an important function of any company. One of the biggest challenge for companies is to hire and retain good talent. Retaining a good talent is the key. Human Resource analytics helps provide and organization with insights for effectively managing employees and employees are key to achieving business goals. They are the most important asset for any company. It is important to identify what data to capture and how to create model around it to project and predict HR capabilities. Almost all big com
Retail is one area where a huge amount of customer interaction data is generated. Ex POS (point of sales) terminal itself generate a large no. of data every day. This offers a good opportunity for retailers to employ analytical methods as decision support tools for operation and management tasks.
We covered about this briefly in our previous blog on introduction to data analytics. Let’s look into more details on why do we need data visualization tool.
In today’s competitive business environment, quick decisions are key. There was tome when simple reports were sufficient, today executives need to present data in such a way that it tells a story which is easy to comprehend. The dashboards today needs to be more attractive as well as interactive. And hence graphics elements of a dashboard has become very important.
There are many tools in market now to achieve this. For example QlikView and Tableau.
There are many more areas , like Sports Analytics, Marketing Analytics, Health Care, Big Data Analytics etc … which are various streams in analytics. My aim for this blog is to give you a feel of the vastness of this area. It’s not just about gathering data and creating reports out of it.
If you still have any questions around analytics you can write to email@example.com