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Data Analytics is the process of examining datasets and finding out the conclusions from the information present within the dataset. This technique helps in the extraction of all the significant details from stacks of data available. In this time period, the marketing has been reformed so much that it has allowed companies to deliver more targeted audiences as well as to measure the ROI(Return On Investment)This plays a huge role in the marketing business and can boost up your business at that success peak which you have not even imagined.
Do you know how crucial the role Data Analytics plays in your business? Well, here is the answer to that question. This technique helps the companies in understanding your target audience, creating content strategies, evaluating ad campaigns, developing products.
Levels of Analytics:
There are majorly four types of analytics required in the Companies.
Descriptive analytics:
This is the first step of the complex process and is important for making decisions. In this method you can easily gain the powerful insights via simple arithmetic operations like mean, median, mode or minimum and maximum values.
Diagnostic Analytics:
This method allows you to dive deeper into the data and make comparisons with the previous historical data. The common techniques used here are drill down, correlations, probabilities, and identifying patterns. It offers just limited actionable insights
Predictive Analytics:
It helps us to see the importance of the quality of previous two analytics. The useful tools for predictive analytics are Python, MATLAB, and RapidMiner.
Prescriptive Analytics:
It helps in selection of the best options that can be advantageous to you in the future and also helps in focussing on answering a particular question of the specific field.
11 Best Ways To Use Data Analytics in Marketing:
Management reporting:
This management reports are a form of business intelligence. This helps in making decision and providing advice to the senior executive. The report mainly contains proprietary information. The trader can leverage big sets of data from campaigns and other activities in order to generate informative and comprehensive reports for leadership.
Search engine optimization:
Data analytics helps in filtering out search engine strategies for ensuring relevance for the target audience. This helps in collecting required data for acquiring organic traffic. With the use of this you can easily find what is going on or why it is happening, find your gaps and opportunities, and recommend to us how to act on insight as well as it estimates the expected outcome of the recommended action.
Customer Retention:
Big data analytics can go beyond the implementation of campaigns for gaining the loyalty of customers. If the data-driven customer retention strategy is taken rightly you will be in profit.
According to McKinsey report states that “executive teams that make extensive use of customer data analytics across all business decisions see a 126% profit improvement over companies that don’t.”
There are various advantages to improve your customer retention with analytics like it reduces the cost of acquiring customers, also provides upsell or cross-sell opportunities, and facilitates sustainable growth.
Optimization of content:
With the help of surveys and feedback, you can collect big data from your target audience. The data which are collected from customer response and also the actions taken by them in regard to specific fields. You can easily synchronize your advertising efforts with the feedback gained from the audience.
Dynamic pricing:
The data analysis helps in generating the right price or services for specific customers and also depending on the current market status, as it is one of the vital requirements of the marketing strategy.
Alex Shartis“Dynamic pricing uses data to understand and act upon any number of changing market conditions, maximizing the opportunity for revenue.”
The collected data helps in the analysis of prices, products, and the status of customer interest. This all helps the marketers for setting up the prices of the products and also providing them discounts based upon the customer behavior.
Measuring ROI:
Measuring the ROI of the advertisement is one of the major challenging task for traders. It is often challenging for businesses that invest in big data products of various kinds. There are multiple projects going on in the companies and the projects are at varying stage of maturity dealing with completely different stacks of issues.With the use of advanced analytics, the measures that are taken accounted for, are like the level of investment, actions and customer response as well as the channel in which it is transferred.
Forecasting:
This is a unique technique of collecting data and predicting future events based on the analysis of the present and past analysis of data. It helps in forecasting the behavior of the audience and adjusts the marketing efforts based upon the data.
Personalization:
Personalization helps in opening up new frontiers in marketing as well as allows the bigger organization to capitalize on first mover advantages.It is often considered personalization of customer experience is very crucial for marketing business. The main agenda of personalization is to interact with the audience in such a way that it optimizes for a desired return like more cash input, reduced friction and continued loyalty. The big data plays a huge role in this because it provides you the user generated data required to understand customer choices and preferences.
Data Scientist:
Data scientists should not be taken granted for the tasks they do. Data is the stack of numbers which should be analyzed perfectly and after that it should be converted into insights so that it could be used by the companies. Without the conversion of data there is no use, so it is very important to have a data scientist.
Channel visibility:
By analyzing the data the target audience can be tracked down from the initial to final purchase pathway. Marketers are now accustomed to the methods that are working with the availability of the insights driven by website cookies and click through rate (CTR).Al these things help them to spend the funds in the right channel.
Cross channel Visibility:
In this complex world full of people using social media via different accounts often becomes a challenging task as well as with this the interaction with the brands has also increased rapidly. But if the companies achieve this then they will be able to provide high quality and uniform experience irrespective of the channel. It is a difficult task to keep track of emails interaction, website visits and also social media engagement but with the use of data analytics you can convert the broad spectrum into narrow and cinched audience profiles.
So, all these are the lists of the methods that the companies are currently used for the enhancement of performance and profitability. But the adoption of this technique is quite low since it is not known to many of them. But once they will find out the benefits of predictive analytics they will not even give a second thought whether to or not. Will you choose this amazing and extra-productive technology? Let me know your views in the comment section present below.
