How Data Analytics is driving Marketing?

 Data is the new buzz in the world and it has already gained a lot of traction. Data is said to be more valuable than oil in the coming years. It is the information that helps build a strategy.

Data can come in many forms including raw, processed and unprocessed data and data analyst typically use three types to collect data i;e facilitated downloads, API’s, scrapping the web

Let’s talk them in more detail.

Structured data is more designed and not mixed. It is a relatively neat and tidy data with proper rows and data sets. In unstructured data, everything is mixed and is usually in raw form.

With the help of data in marketing, marketers can get to know if the customer will buy the product in future and for how long it will keep on buying. This might sound like something unrealistic but it is possible.

There are three types of data Analysis:

1) Descriptive Analytics

2) Predictive Analytics

3) Prescriptive Analytics

Descriptive Analytics

It consists of looking at the past and try to fathom what happened, for how long and how often. It also includes alerts which states that something unusual is happening and also gives the actions needed to address the abnormality. Once we are done with descriptive, comes the predictive and prescriptive data.

Predictive Analytics

It involves considering what will happen in the future. Randomized testing is also a part of predictive analytics which looks at how look at changing the price or increasing advertising and what will happen through experiments and A/B testing.

Prescriptive Analytics

It includes finding out what’s best that can happen out of all the options out there. It basically tries to provide a recommendation (prescription) on the actions that need to be taken to achieve an objective or a goal.

A lot of data that is used by the companies from Cloud Servers, AWS, Azure and these systems allow marketers to understand more about the consumers and get deeper insights. 

According to Harvard Business Review 34% of the organizations on the top positions that were using analytics got 6% increase in profitability and were 5% more productive. For example, P and G is an analytics driven company and are one of the biggest advertisers in the world. The organization makes use of Simulation Analysis to guarantee the best product performance by contemplating various models of the product simultaneously. Similarly, it uses predictive analysis in the development of one of their dish washing liquid to predict if the user can smell the fragrance throughout the period the liquid is used in the process.

Text Analytics is another important thing that cannot be missed by the marketers. It consists of processing the text data and convert it into a number.

There are software like R. that can use the text data can convert it into something called a review sentiment For example Air BNB, a famous vacation rental portal in the sharing economy, make use of text analytics to find out the good words in the review. If the review sentiment scores close to 1, it’s a good review.

In text analytics, you would simply gather all the reviews and run it through a code in R. Thus, you will come up with a review sentiment score ad you can take that data and plug it into a predictive model

Data in marketing is also collected in form of gauging the sentiments of the customers by carrying out a 'Sentiment Analysis' which focuses on collecting web data and data from social media platforms. By looking for this data, marketers can get an idea about the products that are getting popular.

 Marketers can also get an idea whether the product is being mentioned negatively or positively because sentiment includes both.

Negative sentiments of the customers regarding the product can be a warning to find out what’s wrong. The sentiment analysis data includes elements of the POS model related to the social media channels.

Social Media Analytics is another pivotal part in the overall arena of using data analytics in marketing.

 With the swiftly increasing consumption and demand of social media platforms, monitoring of social media platforms where the relevant audience is available is vital for getting insights about the consumption habits of the audience.

Tools like Hoot Suite can provide you insights and lots of data about what customers are talking about on social media sites like Facebook, twitter and others. Furthermore, information about brand mentions, levels of SM engagement for a campaign can be available as well which can become handy.

The structuring of raw data is the most time consuming process because data is available in massive quantities and making some sense out of that data is the most important part. Statistical tools like regression which includes simple regression and multiple regression, software like R are imperative to be used and take out insights.

 Data is everywhere now whether it the Hr, finance, marketing or operations department of any organization. Data driven marketing is becoming popular day to day and therefore marketers need to keep an eye for all these to drive better results out their marketing activities