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7 things you can learn from your competitors about digital marketing

“Keep your friends close, keep your enemies closer.”
In the world of digital marketing, it’s too true. In all the businesses, it’s the competition that makes them push their limits and perform the best and deliver to achieve maximum satisfaction. You may have the best of everything. But ad it happens so, you might lack somewhere. So in order to set up milestones for others and achieve great success, learning from your competitors can help you. Here are 7 things that you can learn from your competitors.

1. It’s all about search.
We all are most certainly aware of Google’s search and its ranking. When you Google your business, what do you see? Where does your website show- in the first page or the following ones? Also, does it come above the organic searches or not. Questions like this will certainly help. While you are at it, check the same for your competitors’ website as well.

2. Locating target audience and then curating apt content.
What is the point of saying the details of a restaurant to the person who is looking for custom made jewellery? With this example, I can say for sure that that content intended for the target audience is an absolute. This relevant content can be posted in social media platforms along with embedding share buttons. Tip: keep a track of those shares and reaches.

3. Effective content, not everything is needed!
Continuing with the example cited above, for content, presence of the history of the custom made jewellery doesn’t need to be in the same place its description. In fact it doesn’t need to be there at all. Curation of content is considered to be important about 74% of the marketers. If you content is catchy, it is likely to get viral. Not just articles and blog, think out of the box and find other ways to portray the same.

4. Get your hands on “trending now”
You must have seen this in Facebook and Twitter. They identify the trending topics and show it. With the knowledge of these topics, you can definitely create catchy content. This newsjacking is definitely going to get attention of readers and grow your audience (at times, even exponentially).

5. Pictures! Not just Instagram, audience loves them too.
A picture is worth thousand words. In case of digital marketing, it’s highly effective. Along with well curated content, alluring animations, photographs, videos and infographic posts can help you spread your words better. And a picture tends to retain longer in memory than text. So when you put a quote or small message in the picture tends to be in minds of your audience for long and they will certainly refer to you!

6. Polls and surveys.
With all the things mentioned above, one more can keep you ahead of your competitors. Polls and surveys. Conduct effective market research using forms and also take various polls regularly. This will help you know what your audience likes and what they are looking for from you. This knowledge can be used to create well versed content. Along with right images and videos, you can create your own news and communicate with the audience.

7. Last but never the least, quality!
Quality. This terms literally translates and bets everything you are giving in for your digital marketing. It’s not the quantity that always matters. But with high quality information, you can keep your audience attracted to you and they will definitely look for more. The posts should be informative, innovative, and creative and yes, grammatically correct!

These are a few things that you can learn from your competitors. While you are working with let’s say content or image, think the objective behind it. What purpose does it? Whom will the content be effective? Will it inspire, educate or solve a problem? Keeping these questions in your mind, your content/visual should be an answer to it.


9 must know things about data before knowing data analytics

Living in the technical era where the technical advancements are heightened by the day, data analytics are something that can’t and shouldn’t be ignored. The key to survive this race is the right interpretation of the data that is available to the businesses and organizations.

Keeping an eye on the overwhelming amount of data can be a handful. There are five fundamental points that are essential when it comes to data analytics.

  1. It means the arithmetic mean. Or, generally known as the average. Advantage of mean is that it is easy to calculate and is quick.
  2. Standard deviation.
    Another statistical term, that means the measure of data spread around the mean. A high value of standard deviations means that data spreads largely from the mean and vice a versa. It is useful for determination of data points’ dispersion.
  3. Regression is known to model various relationships between explanatory variables and dependent variables. It is used to determine the current trends.
  4. Sample size determination.
    A sample helps to determine the right amount of data needed for analysis. With the help of standard deviations and proportions, you can define the size accurately.
  5. Hypothesis testing.
    Hypothesis testing is also known as t testing. It is done to check the credibility of population or data set.

After knowing the fundamentals that are essential for data analysis, let’s start from scratch. Lets know 9 interesting things about data before learning about data analytics in depth.


  1. We all know that every second a lot of data is created. By the year 2020, it is believed that about 1.7 megabytes of information, brand new, created every second. Talk about data getting bigger! It is also estimated that by that time, to accumulated data of the information universe will be about 44 trillion gigabytes.
  2. Did you know we create new data in every single second that passes? Right now, for the research of this article is have contributed some data. Statistics say that we perform about 40000 queries on Google alone! That makes about 3.5 searches a day and about 1.2 trillion searches a year.
  3. It has been seen that Facebook generates quite a lot of data. On an average, 31.25 millions messages are sent and about 2.77 millions of videos are viewed in a minute. In just a minute!
  4. It is also expected that by the year 2020, one third of the existing data, at least, will be passed through cloud. Cloud is a network of servers those are connected by internet. Cloud is a vast concept and is not certainly a myth. Google, on a daily basis, uses cloud computing to answer a query. It uses about 1000 computers just to generate search results in 0.2 seconds.
  5. The data wizards have said that if healthcare integrates big data, then it will save about $1000 per every child, woman, and man.
  6. It is known that The White House has invested about $200 million in projects dealing with big data. When it comes to a typical Fortune 1000 company, if they increase their data accessibility by 10%, the net income can increase at least $65 million excluding the original income.
  7. Not just the big fish, but retailers who invest it all in big data, can increase their operating margins and profits by 60%. It is clearly evident that big data is a boon. Not just for one organization, but, for all of them.
  8. As mentioned above, photos and videos generate a lot of data. Did you know that in YouTube about 300 hours of videos are uploaded by the minute? Not just that, about 1 trillion photos are taken now and then and about billions of them are shared online.
  9. An open source computing software, Hadoop is expanding its market. It is calculated that by the end of 2020, the compounded annual growth rate of 58% is forecasted and it will surpass $1 billion.

It can be seen that data was, is and will be increasing along with growth in businesses. Every business, in one or another form will be using data analytics. So are you ready for the big step?

Data Science

Data Science 101

Data science happens to be quite a broad topic. There are various answers to the question- “What is Data science?” Data science is known to be such a practice which involves a lot of points. Some of the key points that are focused upon are-

  1. It involves formulation of various hypothesizes and answering to known and unknown problems that generates a business drive.
  2. Working on the provided or existing set of data and also looking up for various analyses to collect data that is relevant.
  3. After the extraction of relevant data, it needs to be summarized in such a way that any common man can understand and put it into use. This helps in generating an effective business drive.

Data science is known to be a perfect blend of various disciplines such as algorithm development, data inference and technologies. These are essential to solve critical problems analytically and generate values.

We know, in the core of any warehouse, data is the uncrowned king. Raw data is streamed and stored at the organization’s warehouses from where the relevant information is extrapolated and important points are learnt. There are various steps with the help of which information is uncovered from the raw streamed data.

  1. First the information arrives at the data warehouse.
  2. The next process is about discovering the insight of data. In this step, it involves the extraction of information from the raw data. Data scientists bury themselves in the grain-level to mine and uncover information that will help organizations make witty business decisions. This involves solving complex problems, understanding the trends and complex behaviors. Thus, they surface the hidden inferences that are essential.
    Basically data scientists act like detectives while mining out the insights from raw form of data. They investigate and find leads. They follow them up which helps them to understand the complex problem statements or patterns. Hence, they breakthrough through the maze of data and come up with relevant information.
  3. Next step is development of “Data product”. What is data product? Data product is the asset to technology that uses data as an input. It returns results that are algorithmically generated. This is different than data insight. In case of data insight, the outcome generated helps an executive or rather advices an executive to make a smart business move. When it comes to data product, it a technical function that actually sums up the algorithm and can be directly integrated into the core applications of the organization.

From data insight to data product, data scientist plays a vital role in all of this. Fabricating algorithms, testing, refining the products, deployment of the same into core process, data scientists play the central role and can be termed as the head of the system.

What does it take to be a data scientist?

No, one doesn’t have to become Sherlock Holmes. Data science is known to be a perfect blend of three skills.

  1. Mathematical expertise: this skill for a data scientist is like preaching the choir. As a data scientist, a person should have the ability to see through the problems, patterns, dimensions and correlations. A myth that follows is that data science is all about statistics. But it isn’t so. Statistics are an important part, not the ONLY part.
  2. Technology and hacking: here hacking refers to breaking into the problem with high level of creativity using adequate technical skills. They have to build smart things and come up with clever solutions.
  3. Strong business acumen: as a data scientist, one has to think out of the box. This skill helps them to distinguish and manage business goals and projects effectively.

Another question that comes up – “Is an analyst and data scientist synonymous”?

Well, no. They are certainly mutual but not synonymous. An analyst is a person who just looks at the provided data and gain insights. They generally work on the surface i.e. the database level and also the report level. But a data scientist is a person who works with the raw data who can derive o rather breakthrough the insights and generate data product.

For any company, the secret recipe to success is data science.

Importance of Middleware in Node.js Express Framework 

Middlewares are set of filters which every request to the web server has to go through. Node.js enables the creation of web server on the fly, all the requests which are received by the web server are first filtered by stack of filters, this is a very helpful feature, each filter performs a specific task like session handling, error handling, form field handling, logging etc

Node.Js Express framework’s Middleware are one of the useful feature which has simplified the development process.

How we stack our middlewares is important as certain tasks needs to be executed before others are done. 

A very useful filter here is the cookie parser filter, this needs to be run before session handler middleware is invoked.

 Error handling middleware should be invoked in the last which means if everything fails then do this gracefully.

Most of the common middleware we use are static middleware to handle the images other digital assets.

Another important area of middleware are the Connect middlewares. These are commonly used to address the regular web application pain points

bodyParser: Helpful in packing form fields submitted from HTML form.

morgan: Very popular for debugging and logging.

cookieParser: For sending and receiving cookies

Similar to the above there are many middlewares which simplify the development process.

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Node.js – How it started

Introduction to Node.js

There is lot of confusion about Node.js, whether it is a new language or a new framework, but its neither. Node is a javascript runtime environment built using V8 engine. Ryan Dahl is the man behind Node, he developed 

this in 2009, it all started with a little progress bar written by him for the flickr photo sharing web service. This little code impressed the people at the JSConf.

Looking at the standing ovation he received for his presentation Ryan went on further developing this code base which eventually came to be known as Node.js

Node.js is single threaded where as PHP, Ruby, apply multi-threaded approach. Though its single threaded, Node uses the asynchronous approach to handle multiple connections, using this approach millions of concurrent requests can be handled. For addressing scalability issues Node.js is the perfect answer.

Technically Node’s approach is non-blocking asynchronous execution

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Node.js & PHP

There are lots of similarities between PHP and Node.js, while PHP is all about blocking API, Node.js implements non-blocking approach. If we put aside this main difference, Node.js is very similar to PHP in terms of language constructs etc.

Looking at the history of PHP, it was launched in year 1995 and slowly it gained prominence, now even after the launch of so many languages PHP is still the preferred and the most widely deployed web application environment in the world. 

Coming to Node.js, it has a recent history, launched in the year 2009, Node gained prominence much quickly, currently Node is preferred for enterprise web applications where latency is matter of concern, using Node.js non-blocking approach we can design applications which can handle thousands of concurrent connections. 

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