Analytics tool selection made easy!!

Almost every large organization today is jumping onto the analytics bandwagon.  Given the continued presence of economic pressures and cutthroat competition, all are keen to use analytics to maximize competitive advantage. Unfortunately, the field of analytics can be complicated and confusing, with an overabundance of terms to understand and myriad options to select from. Starting with text analytics and predictive analytics, the list goes on to social media analytics, data analytics, mobile analytics and possibly many more in the future.  

So, do you really need all of the analytics tools to get ahead of the pack, or will just one or two suffice? Let’s take a brief look at each of the options in order to decide.

Text or Social Media analytical tools identify the sentiments or tonality of a target group from social networking sites such as Facebook, Twitter, and also from micro and macro blogging websites such as personal blogs. Additional data could also be garnered from customer surveys or call center scripts.

Data analytical tools examine raw data with the objective of drawing some conclusion or insights that would otherwise remain unknown. This category of analytical tools encompasses data exploration with visualizations, dashboards and charts, and includes data mining features to uncover hidden patterns and relationships. It works over customer behavior, fraud detection, market basket analysis and similar areas.

Predictive analytical tools predict future trends, behavioral patterns or likeliness of the occurrence of specified events, based on historical data and using various statistical techniques. For example, establishing customer churn and retention parameters by linking to various social, economic or even political factors);  or, determining a credit score based on past credit behavior.

Business analytical tools apply statistical techniques to particular business areas such as supply chain analytics, marketing analytics and sales analytics, to help make better informed decisions.  

Mobile analytical tools are relatively new compared with the analytical tools described above. These are used to understand the behavior of mobile users in order for companies to target location-based groups using customer-specific campaigns. With mobile analytical tools one can track the usage of mobile devices for bill payments, travel bookings, and so on, for better marketing effectiveness.

The analytical tools selected would differ based on business verticals and business needs. Retail and consumer product organizations principally depend on large volumes of point-of-sale data, as they may not have any source of customer data other than loyalty cards. These organizations would like to understand what consumers think about their brands, their products, their customer service, and how they compare with competition. Hence, text or social media analytical tools would be the first place to start. Later, data analytical tools could be deployed to explore the massive volumes of transactions to find patterns in consumer behavior and market basket analysis.

In sectors such as banking, telecom, and insurance, a huge amount of customer data is available and innumerable transactions are generated daily.  So, using predictive analytical tools to examine customer churn, cross-sell and up-sell, customer profitability, underwriting price optimization, segmentation, and life-time value would help generate more revenues.

The manufacturing and services sectors face the problem of warranty and support costs. Hence deploying data analytical tools to establish patterns of emerging issues, which can be fixed before they occur, would help reduce warranty costs tremendously. Again, using social media analytical tools for product improvements would help such organizations remain competitive.

Very large organizations generate massive amounts of data and invariably use analytical tools with a higher level of sophistication and maturity. This is where industry leaders such as SAS, R, SPSS and others play an important role, and all of above analytics categories are applied effectively.

On the other hand, SMBs often face challenges of infrastructure, skilled resources and may lack the maturity required to use analytical tools. Such organizations need to study the problem at hand, carefully evaluate which tool fits the requirement, and then take an appropriate decision.

To derive any benefits at all from analytical tools, organizations must ensure that quality data is available for the analytics team to work with. An analytics initiative implemented as an enterprise wide system at one go cannot hope for significant success.


For any analytics initiative involving the use of one or more of the analytics tools described above, keep in mind the following points:

  1. Start with a focused, small proof of concept to tackle one business problem.
  2. Identify all the relevant data sources required for that business problem.
  3. Put suitable people in place, well experienced in the nitty-gritty of analytical tools.
  4. Measure the actionable part after you obtain insights from the analytical tools.
  5. After successful execution of the above, explore further with other analytical tools , one step at a time.

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