Data holds the potential to unlock new avenues in various sectors of data economy. However, the real potential of data can be harnessed only when it is accompanied by the analytics component. The wonderful combination of data and analytics can pave the way for its application in various sunrise sectors. Data Analytics can help businesses at various stages like planning, execution, implementation and decision making. With the help of predictive and prescriptive analytics, data analytics can guide a business in the market which is riddled with fluctuations.
Before getting into the details of data analytics, let’s quickly examine the need of data analytics certifications in improving one’s career prospects.
Analytics and certification
Data analytics is emerging as a newfound economic capital market for different businesses that are passionate about investing in technological ventures. Needless to mention, data analytics can help a business scale new heights by expanding both horizontally and vertically. Analytics can help in the formation of a roadmap that guides a business in a fluctuating market by following a pragmatic approach. However, as the market of data analytics continues to expand, the need to hire data analysts becomes even more significant than ever before. Education system of the country is placed in a manner where analytics capabilities are underdeveloped. In order to harness analytics capabilities, the reskilling of professional workforce is the need of the hour. Different institutes and sector skill councils are providing data analytics certification programs to match the demand supply curve traced by business organisations operating in the analytics sector.
How can data analytics help your business?
Some of the benefits of business analytics are briefly discussed in the subsequent sections:
Decision making in a quantitative manner
Data Analytics helps in mining out critical information of a particular project that is difficult to collect through orthodox channels. This information gives a 360 degree view of the needs of the customers, product requirements as well as the suitable investment opportunities. When a business decides to expand in a particular region, it can do so with confidence by relying on a quantitative strategy. A quantitative strategy not only guides different business processes but also helps in informed decision making. This can be understood by the following analogy.
Suppose a technological business wants to explore the market potential of cryptocurrencies and is desirous of designing various products that can help customers to mine Bitcoin. However, the regulation prospects of the cryptocurrency are still in dilemma as the government has not given a positive nod to this particular mode of currency exchange. With the help of data analytics, businesses can explore the market potential of cryptocurrencies and arrive at the possibility of this mode of currency exchange being approved by the government. With the help of above facts and figures, a business can decide on its prospective investment opportunities related to cryptocurrencies.
Effective marketing strategies
The stage of development of marketing strategies is the most important stage for any business. Data analytics forms the backbone of marketing strategies as well as their deployment. Marketing strategies can be broadly divided into two main types.
The first is called general customer marketing and the second is called targeted customer marketing. The first strategy relies on tapping the greatest possible audience as it looks at the business prospects from a utilitarian perspective. This means that the profits of a business can be maximized if it attracts a very large customer base.
On the other hand, the second strategy of targeted customer marketing believes in generating large revenues from a specific customer base. The techniques employed in targeted marketing campaigning include customer recommendation, customer personalization and customer attention and retention. The idea is to engage with customers on a long-term basis. This can be done with the aid and advice of data analytics and tapping a customer base relevant to the products and services that a particular company deals with.
For instance, if a company provides cheap transportation facilities in a particular metropolitan city, it becomes redundant to target employees that are frequently working from home. This may even waste a lot of resources that the company spends on targeting this particular customer segment. On the other hand, a data informed company that wants to make a mark in the transportation sector may tie up with companies that host a large number of inhouse employees. The data bank of such employees can prove handy and they can be targeted as a prospective customer base to launch a new taxi service at an affordable price.
Customer grievance redressal mechanism
In the present times, companies usually support a very large customer base that needs to be intricately handled. A very large customer base can serve as a magnet to attract new customers, thereby, magnifying the business prospects. However, if the feedback of the customer base is negative, it may act as a hurdle to attract new customers.
This is where the question of customer grievance redressal mechanism comes into action. As the customer base of a company is very large, it becomes important to prioritise the service request so that effective customer service delivery is ensured. With the help of data analytics, the service requests can be clustered into the categories of urgent, important, less important and least important. Accordingly, data analytics can help a company analyse its services in an appropriate manner and address the grievances of its customers in a time bound way.
With the help of data analytics, it becomes easy to channelize the mode of service delivery to the clients. Streamlining operations is not only about following a particular algorithm or model for the purpose of service delivery. It is also about incorporating the feedback of the customers in the product itself so that the prospects of a business can be tuned in accordance with the needs of the client. This is where the role of analytics comes into play. If the business is fully automated, the feedback mechanism can be directly incorporated at a particular stage of service delivery. If the business is semi automated or manual in functioning, certain steps can be taken that may help in streamlining operation after necessary modifications at a particular stage.
What powers advanced data analytics?
The modern processes of data analytics are powered by machine learning, data management and predictive analytics. This combination of three power technologies has the potential to scale up a business by leaps and bounds. Machine learning aids data analytics by improving the power of prediction without the need of explicit programming. On the other hand, data management helps in authenticating the reliability and validity of different data sets, as well as, in the formation of a centralized management platform. Similarly, predictive analytics makes use of advanced algorithms to predict customer choices and boost the revenues of a company in the long run.
The entire journey of data analytics can prove to be a game changer for your business. It can not only help you overcome challenges in the process of data collection but can also help in devising future strategies to plan your business in accordance with the changing market. The art of data analytics perfectly overlaps with the strategy of effective decision making and this combination holds the promise to take your business to the highest pinnacle possible.