At Hansa Cequity, we believe Analytical Marketing  will be the biggest competitive advantage enterprises will have in the next decade or two. Successful enterprises of tomorrow will be the ones who can organize and leverage customer information at speed ,to optimize their marketing performance, increase accountability, improve profit and deliver growth. Hansa Cequity insights will bring to you trends and insights in this area and it's our way of sharing best practices so as to help you accelerate this culture and thinking in your organization. We call this kind of an approach Analytical Marketing and we will constantly bring in "best practices" for improving your capabilities in Analytical Marketing.

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Making Analytics easier to digest!!

Posted by Ajay Kelkar on Sun, Mar 21, 2010
  
  
  
  

Chandresh (a senior Analytics professional from Vodafone) made an interesting comment on my earlier post and pointed out the criticality of using expertise that can make sense of the results. Here is what he said: "However I tend to not agree with the idea of KISS for going analytics way. Simply putting, doing analysis in Excel or any program isn't really a challenge, the real challenge is to hire a skill set that know what to interpret from the output which doesn't come from 'common sense' always. "I completely agree with him. There is no doubt that expert skills will only improve the analytics. However, the thought process around KISS is to ensure that you take analytical marketing to the organization in a way that it does not get abandoned right at the start as a technique, which is "too high end for our practical world". That was the limited point I was making. What this does is it allows us to execute the analytical output through a variety of marketing channels in a better way because it has a better chance of gaining confidence of people at the front end! Chandresh's comment reminded me of some interesting thoughts put out by Amresh Tripathy in his blog and here is an extract: "The popular statistical techniques frequently used in business analytics like linear regression and logistic regression are more than half-a-century old. System dynamics was developed in 1950s. Even neural networks have been around for more than 40 years. SAS was founded in 1976 and the open source statistical tool R was developed in 1993. The point is that popular analytical techniques and tools have been around for some time and their benefits and limitations are fairly well understood. An unambiguous definition of the business problem that will impact a decision, a clear analysis path leading to output, thorough understanding of various internal and 3rd-party datasets are all more important aspects of a predictive analytics solution than the choice of the tool. "I couldn't agree more with Amresh who has written this lovely series of posts -worth a read! http://amareshtripathy.com/2010/03/08/predictive-analytics-8-things-to-keep-in-mind-part-4/

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Thanks for again giving me an opportunity to present my point of view. You have raised a really valid point on understanding of “too high end for our practical world” kind of reservations to management. But I think in the first place if the skill building has been done with proper understanding of analytics, the management shouldn’t be really thinking in the above quoted lines. The challenge for any organization is to differentiate between an analytics professional and an analytics researcher. In other words “too high end for our practical world" should not really be the talk of the town for the management who’ve understood this difference, as ensuring that the analytical solution is sophisticated enough to withstand the market, business and data turbulences and at the same time is practical enough to be executed in a given organizational framework is the key differentiator between the professional and researcher. While organizations who hire the skills purely from the researchers perspective fail to utilize the true power of analytics and this remains the “too high end stuff” perception for them. I sense that the situation of building the analytical research capability to a business is shoddier than having the ‘excel’ capability or professional analytics capability, as in the former case you spend a lot of resources in building the capability but fail with “too high end for our practical world” perceptions.
Posted @ Sunday, March 21, 2010 6:37 AM by Chandresh Sanwal
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