digital marketing and data science
Why We Need Data Science ?
Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.
This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how Data Science is being used in various domains.
How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?
Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms.
Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.
What is Data Science ?
Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.
First, let’s see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?
The answer lies in the difference between explaining and predicting
Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.
Now we know about what is data science so now we will disuses about how data science is useful for digital marketing.
Today, brands need to create a powerful presence in the online world in order to be successful; otherwise, they will be left out and become a redundant brand. Besides impressive growth, the audience reach in the digital world is almost limitless. This is because a brand can reach millions of customers on the net, not just within their country but also around the globe. The internet is, therefore, a great medium through which brands can ensure that they remain important contributors to the lives of their clients, customers and stakeholders.
Being present in the digital medium is therefore of vital importance for brands and digital marketing companies around the world. As people consume more and more digital content, almost every second of their life especially through mediums like smart phones, laptops and desktops, companies need to invest in smart solutions that can help them make use of the immense potential of the internet and its related fields. Today, almost six billion people around the globe use smart phones to access information/services/goods of different kinds and that is why ignoring digital communication is one of the biggest mistakes that any brand can make in the current time.
One of the main areas in which brands have failed to implement their digital analytics solutions. According to a recent study, there is a huge talent and hiring gap in the field of digital analytics arena where many digital companies said that they were in dire need of professionals who had expertise in the field of digital analytics. This means that if you are in the field of online marketing content, then professionals need to gain a lot of knowledge and digital analytics skills in the field of data analytics as this is what will help them take their campaigns and policies to the next stage. Data professionals, therefore, need to upgrade their digital analytics skills and strategically plan their policies so that it can improve their overall content strategy and policy.
The Harvard Business Review declared the job of a data scientist as the sexiest job of the century. In the next two years, according to Gartner, there will be more than four million big data opportunities and only a third will be successfully filled, which is a great challenge for the industry. All the digital analytics firms in the world are rapidly moving towards big data and that is why there are new updates in the field of mobile data, performance data, campaign data, product data and even the manner in which data is being tracked by the data scientists. All these changes means that there are two major implications for this change.
The first is that brands and digital analytics companies across sectors can strengthen their digital plans by investing in good content marketers and SEO professionals. Data skills are therefore a very important aspect of the future of digital analytics. The second aspect of big data growth is that as campaigns grow bigger and more complex, data analytics and technological capabilities will also have to grow in a rapid manner to adjust to this demand.
Data capabilities will need to become sophisticated and capable, so that larger campaigns can be implemented in a successful fashion. That is why companies and organizations need to evaluate the digital marketing plans of the company in a strategic manner. In short creating and implementing an digital analytics program needs four steps which includes defining the digital analytics metrics and developing a plan, collection of data, development of reporting features and capabilities and lastly ongoing analysis and implementation.
Digital analytics is therefore one of the prime focus areas for all brands and companies, especially in the coming years. The term digital marketing first appeared on the scene in the year 2011 but it rekey gained a lot of prominence in the year 2013. According to the latest digital analytics trends, Google anticipates that digital analytics will gain even more traction and popularity in the coming years as well.
Things needed to create a successful digital analytics plan
One of the first things needed by any brand that wishes to employ data analytics tool is the employment of people who are really passionate about analytics and gaining insights from data. Many brands and companies, especially at the mid-level are still unaware of the huge potential and opportunities that digital analytics big data can provide them, especially in terms of digital growth and expansion. Sometimes, even big organizations fail to understand the growing need to employ data analytics solutions and that is why they continue to invest in old techniques and methods.
Another big step needed for implementation of data analytics is a strong and detailed budget. Many companies are guilty of investing less money in this field as they feel other departments require investment and not digital analytics. While this might be true in the short run, data analytics has huge returns especially in the long run. While data analytics requires heavy investment in the beginning, this investment is extremely important as it helps brands to get ahead of their competition and also make use of the immense opportunities available on the digital analytics platforms.
The next step in data analytics is the creation of key performance indicators or KPIs which will help you create strategies that are strong across issues including customer engagement, reach and conversion. This is why brands need to invest in individuals who have intense and comprehensive knowledge about the various aspects of digital analytics marketing . At the same time it is important to create a balance between all the aspects of the metrics. If there is too much focus on one part of the metrics, it can lead to the completion of only a few goals. Brands and companies need to keep the big picture in mind while creating and deciding the key metrics so that add even greater value and influence in the market.
While deciding digital analytics, it is important that brands overview their digital analytics strategy at regular intervals so they can find out how effective their campaigns and solutions are. It is extremely important that brands evaluate their success on different social platforms as well because different social media platforms can have different levels of success. An analysts need to know which campaign can work in which platform and implement the said campaign on that platform which can create the maximum effect and reach.
One of the most important and essential part of data analytics is data visualization. Visualizing data in an effective manner is extremely important as it is only then data analysts can make sense of the data. Unless companies can make sense of data in a successful manner, there are no tangible benefits of big data. In short, data visualization is the most critical and important part of collection of data as raw data is of no use to anyone. Visualizing data in the form of information, you can valuable insights and data analysts are the map that can help brands to make sense of the vast amounts of data present in the company. Data visualization allows companies to interpret data and through this method, data analysts can identify hurdles in a process successfully and fix them before they result in a full blown crisis that can damage the growth of the company.