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Data Science for the Internet Generation

Remember the old fairy tale about the little girl and boy who left breadcrumbs all along the trail on their way into the forest? Every time we visit the internet, whether it is to change our online status on Facebook, or to write a review of the restaurant we ate in last week, or to play a game with friends, or perhaps to purchase a rail ticket- we do just that, and this trail of ‘crumbs’ has become one of the most useful arsenal in the hands of those companies who know how to reconstitute it.

As individuals spend more and more time on the internet, it is important for companies to understand where they can make their presence felt on the World Wide Web, what is the online positive or negative buzz about their products and what does the level of engagement of customers with their online properties says about their overall brand.



The fields of web analytics, one of the world’s fastest growing industries, help companies study and understand their engagement with their customers. The two most frequently used applications of web analytics in the current world scenario are as follows:

Web Analytics for an E-Commerce Site

Social Media Marketing based buzz analytics

The challenges faced by an e-commerce website are not too different from those faced by a traditional retailer- how to attract a customer, how to engage a customer, how to make a customer purchase one or even more products, and how to get the customer to come back. Web Analytics can help in every stage of the sales funnel.

Common web analytic techniques for this stage include:

SEO: Identifying what words or triggers are most likely to attract target customers and search engine optimizing the website content for those words and phrases

Analytical Advertising: analyzing which websites and domains customers are more likely to visit so that internet advertisements can be placed in these locations

MIS and Dashboards: maintaining an hourly, by location dashboard of website views, time spent on the website, unique page views, and other relevant KPI to derive insights on customer behavior for online marketing spends.


It is essential that an e-commerce website, at the end of the day, incites purchase. A combination of click stream analysis, purchase driver analysis to understand customer drivers and user experienced based design can do that. A fast growing field within this realm is something very similar to the retail Market Basket Analytics- analytical algorithms to determine what else the customer is most likely to purchase provided he has purchased product B.

A prime example of this is the Netflix Prize. The world’s leading online rental delivery, Netflix announced a prize of $1 Million for the team which most improved upon their algorithm to recommend movies to a customer based on his previous viewing preferences. The winning algorithm improved on Netflix’s own algorithm by a little over 10%, which was considered significant enough for a $1 million prize in terms of its impact on customer loyalty and basket size.


Akin to consumer banks and large retailers, web analysts also use customer segmentation and clustering based on Recency (when did the customer last visit the website), Frequency ( how often does the customer visit the website) and Monetization (what is the size of transaction/length of visit) to reward loyalty, attract profitable but infrequent customers and retain those who were once profitable but now seem to be leaving.


The first step towards enabling customer engagement is placing analytically developed content on the website, most likely to appeal to the target segment. Some other analytical techniques used to help companies at this stage include:

Clickstream Analysis- To understand user traffic, landing pages, most frequently visited pages and most traversed path, ensuring that all the content in these pages is geared towards creating a purchase

User Experience Design- Using the insights developed through click stream and traffic analysis to create a seamless user experience in the website, reducing the number of clicks to desired outcomes (hence dissonance) and ultimately enabling purchase.


What is the customer saying about your traditional brick or mortar product online? Customers talk: in their online reviews and Facebook statuses and their Twitter feeds, and they listen- to industry experts, their blogs, web searches and discussion forums. It is essential for companies to be able to understand what the overall buzz about their product/brand is and to leverage it to create stronger brand resonance. Text and sentiment mining, conducted in an analytical framework can help us greatly really understand what a customer feels about the brand.




Web analytics from IBM - part of the IBM Coremetrics Digital Marketing Optimization Suite - sets itself apart by providing marketers with—not just data—but insights for increasing ROI.


The Web Analyst will provide thought-leadership in measurement and analysis for digital marketing. He/She will also identify new ideas and opportunities for analytic projects.

Responsibilities will include:

Perform marketing campaign and investment analysis for all Online advertising, and website related activities

Analyze data from online marketing campaigns: user response, web site conversions, feature usage, attrition rates

Conduct in-depth analysis of larger campaigns

Deliver online customer analyses, such as: Customer Segmentation, Retention Analysis, Lifetime Value Analysis, 

Advertising Research, Brand Research, Site Utilization

Define and implement routine reports (weekly / monthly) for marketing campaigns

Provide detailed monthly and quarterly results overviews to management

Participate and support in delivering results and presentations to clients

Provide recommendations on improvement of marketing efficiencies and effectiveness

Stay abreast of current trends and best practices in marketing analysis and tools/techniques