Data Analytics

AI has taken a strategic place in the marketing domain. AI is being used to leverage the data and machine learning in campaign management, this strategy helps in achieving campaign goals effectively and efficiently. Most marketers use the AI in market research, data science, and real-time campaign analysis In the contemporary world of Big data, AI, Machine learning almost every campaign management platform uses AI to some extent. For example, the platform uses AI for customer behavior to display relevant messages at the right time for retargeting purposes. Some practical examples of AI 1.Google’s AI platform Rankbrain interprets the user intent when a search query is entered into the browser. 2. Google maps using machine learning to examine data, every time a user logs in to Google maps it uses machine learning to give suggestion to the user such as traffic-congested area’s etc.      3. Gmail uses machine learning to defend from spam. 4.Cre...

Consumer data for marketers With the advent of the internet, new technologies such as big data and cloud Database marketing has become omnipresent in every industry. Customer data gives an insight to the marketers about the customer preference, expectations and pain points. Hence, marketers can tailor campaigns around these factors to address them. The challenges involved in collecting customer data are privacy concerns, data restrictions, security demands and most importantly security breaches. Even though the data plays a vital role in getting consumer insights the challenges need to be addressed so as to realize and harvest the real benefit of the data. The advantages of collecting customer data 1. Customer segmentation: Customer analytics research depicts 70% of the companies’ collect cross channel data of the customers and segments it by different criteria. It basically empowers the company’s marketing team to better engage with the customers and also allows th...

In the 21 st century where data is considered to be the new oil not knowing your consumers want is a blunder. However, the advent of the internet has empowered business intelligence with various tools and techniques to identify the consumer’s needs and necessities so as to facilitate their business strategy around the customers and not the product. During 1960s marketing had more of mass targeting and attracting approach in which broadcasting channel dominated with more reach capabilities. The digital technology has empowered marketing with various channels and techniques to reach the target audience in a more precise and concise way so as to spend less and garner more revenue from the spend. The 21st-century consumers are more mobile, volatile in terms of their behaviour and preferences. However, at the same time, they are more attached to their digital gadgets than ever before in the past. In a contemporary world, every gadget is designed to read, record consumer in...

Now a day’s Big data and cloud services are the buzzing words in the IT industry. Even though Big data and cloud services are a bit similar, but technically they are miles apart in terms of functioning. In this contemporary, rapidly growing world every sector such as medical, social, banking, manufacturing produces a huge volume of data known as Big data and the internet facilitates the process of data generation. As we know Big data is nothing but the large volume of data it becomes difficult to process the data traditional methods and means using an on-hand database management tool. IBM, HP, Oracle are some of the leaders in the Big data management service provider industry. Cloud computing basically binds all resources of the computing under one roof, hence it is a perfect tool to process, manage the Big data and discover useful information out of it. Example the data processing framework called Hadoop uses Google cloud services. Gartner analyst Doug Laney  intro...

So Big data involves a lot of data, but how much data is considered as big data? Or in other words what exactly is Big data? Big Data Well, there is no one clear cut, unique definition to define Big data. A different person can define Big data differently speaking from a volumetric perspective. For example, some people define it as any data that is associated with, distributed across multiple systems. In general, distributed systems often produce much more data than centralized systems as every distributed system would have its own point of data registry. As the number of machines, services, applications involved increases so does the volume of data too. In some exceptional cases the distributed systems do not produce a lot of data. For example, if I connect a single hard-disk of say 1TB of capacity and share it with other computer networks in the office or house, technically I am creating only a distributed data environment only, hence many people opine it as not...