Month: October 2014

Creating the Social Company

/ Hari / Leave a comment

“Social Media success doesn’t happen by accident. It is Engineered!”

Any company or brand which has been successful in it’s social media campaigns or other plans, should have put in lot of efforts behind the scenes. Social Media enables people to connect with rest of the word easily, conveniently, meaningfully and in their own terms. Back in olden days, businesses treated customers as friends. The more a customer is welcomed and pampered, the more customer continues to make business and also recommended it to others. Customers were greeted by name on  Over the days, this started fading. And now, again, social media is enabling businesses to get back to that style. It helps brands to get into one-to-one conversation with a large scale of customers.

Social Communication: Social Media is an infrastructure which enables social communication. People share videos, photos, talk, like, express opinions, post thoughts etc on social media. This what people do with social media. And, this activity and interaction is called social communication.

Social Media Program: A social media program planned by any organization cannot be just a marketing plan or blogging. The purpose cannot just be conversation or engagement. It is a force-multiplier. It helps in amplifying any PR or advertising plan implemented by the organization. It can help in providing instant feedback from customers. It also helps in collaboration with customers. Be it Lead generation , Customer retention, Crisis management or Trend identification , a social media program can fit into any need of the organization.

Engagement: Initially, communication between brands and audience was one way. The brand would send a message to audience. The audience would either accept or ignore the message. If at all company wanted to hear from audience, it was through comment cards or customer service.

One Way Communication

One Way Communication

Then, during early Web 2.0, the online experience became more collaborative and user-centric. Blogs, Forums, Comments in websites enabled customers to talk louder and mostly direct to the organization/brand. Customers started feeling the strength of their voice. So did the Organizations.

Two Way Communication

Two Way Communication

Finally, now, social media platforms enable not only communication between organization and customers, but also between customers! This people to people communication has opened the doors to lots of opportunities and challenges for the organizations. On communicating within themselves, people discuss about a brand/product and share review about the same. This influences others in their decision making towards this brand/product. This is called the word-of-mouth. This channel also knows no geographic barrier. It cuts across the globe!

people to people communication

people to people communication

This word-of-mouth has to be positive and should be earned by an organization. And, this is why every brand should integrate Social Media into it’s business model and engineer its success in the same.

Classification & Regression

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Classification : Machine Learning is used to label input data based on the training data provided. This labeling of data is called classification. Here, the record is classified into one of the possible groups by the algorithm. The output here is the class labels.

Consider the familiar email Spam Classification example. Here, initially, a set of spam emails are used to train the model and then, any new email that hits your inbox is classified as either spam or not-spam. This is a Classifier Model in Machine Learning.

There various classifier models in practice. The right classifier for a solution depends various factors. Following are few common classifier model and reasons to choose them:

  • Boosting – often effective when a large amount of training data is available.
  • Random trees – often very effective and can also perform regression.
  • K-nearest neighbors – simplest thing you can do, often effective but slow and requires lots of memory.
  • Neural networks – Slow to train but very fast to run, still optimal performer for letter recognition.
  • SVM – Among the best with limited data, but losing against boosting or random trees only when large data sets are available.

Ref: An answer in Stackoverflow pointing to “OpenCV” book.

Prediction/Regression : Unlike Classification, regression is type of problem where algorithm finds a continuous number/value from the given input. A simple example would be – predict price of an house, given no.of rooms, area and location. Here, a training set of houses with known price are fed into the model. The algorithm comes up with an equation to apply on new inputs further. Another example is predicting the price of a stock, given various input features.

The output here is a continuous value of the target variable.

Training & Test Data

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Training Data : This is the data set which has the feature variables and target variable. This data is used to train the algorithm to derive the classification/regression equation. Training data is the data which used by the algorithm to learn from.

Test Data : Test data is the data set which is used to validate the trained algorithm. This data set will also have feature and target variables. The trained algorithm will be executed on the records in test data. Now, the actual value/label in target variable and the output value/label from the algorithm can be compared to measure the accuracy of the trained algorithm. Less the difference, more the accuracy!

Notes : Test Data can be a part of training data itself, but will be hidden from the algorithm during training and used fresh to test later. The test records can be randomly selected from the training data or certain set of records can be selected to be the training data. generally, a % (eg : 20%) of randomly selected records from available data is made as test data.