Archive for the Analytics Category

Jun 20 2014

How can you maximize your ROI through Targeted Marketing Campaigns

Every consumer company that exist in today’s world have vast amount of transactions data, containing information about every product that is sold and about every individual who has purchased it. Propensity and Uplift modeling would help in answering questions like:

  • Which marketing campaign will allow you to reach more customers?
  • Which is the customer segment on which the marketing campaign should be targeted?


aaum logoPropensity’ which literally stands for ‘change in behavior’ helps in finding which of the marketing campaign has the most positive impact on your ROI. This model works on the basis of score matching technique and then comparing the customer group who were exposed to the campaign (treatment group) and the customer group who were not exposed to the marketing campaign (control group). Comparison is made with respect to the underlying characters of every transaction between the treatment and the control group.


Uplift modeling helps you to identify and target the customers who want to hear – isolating the ones who don’t. Many marketers overlook the pricey consequences that can result from sending mass, untargeted emails or marketing communication messages to their entire audience.

Uplift modeling has helpe dUS Bank better target its customers, and as a result, it has reduced its mailing volumes by up to 40% and has achieved a five-fold increase in campaign return on investment compared with existing programs”

There is a fundamental segmentation that separates customers into the following groups:

The Persuadables : customers who only respond to the marketing action because they were targeted

The Sure Things  : customers who would have responded whether they were targeted or not

The Lost Causes  : customers who will not respond irrespective of whether or not they are targeted

The Do Not Disturbs or Sleeping Dogs : customers who are less likely to respond because they were targeted

LoyalSIGHTS helps you to identify the persuadables, the only segment that provides true incremental response.

Jun 10 2014

Crouching “NoSQL” Cautious “SQL”

“According to analysis by Wikibon’s David Floyer (and highlighted in the Wall Street Journal), the NoSQL database market is expected to grow at a compound annual growth rate of nearly 60% between 2011 and 2017. The SQL slice of the Big Data market, in contrast, will grow at just a 26% CAGR during that same time period.” NoSQL adoption will go big bang but will it impact all the organizations? Will SQL lose this battle? Does everybody need to care about NoSQL? First of all, there is no battle to begin with. NoSQL doesnt mean NO SQL. It simply means “Not only SQL”. Wikipedia defines NoSQL database as

                  “A NoSQL or Not Only SQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. The data structure (e.g., key-value, graph, or document) differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS. There are differences though and the particular suitability of a given NoSQL DB depends on the problem to be solved.”


Let us understand SQL and NoSQL a bit more better!



Clearly each one has its own merits and shortcomings. There is no standard solution that fits to all business requirements. Sometimes it is more shocking to see how people react/make decisions on this front. See for yourself one such fight.



Platforms, technologies should be chosen to fit business and not vice-versa.  While there are problems SQL isn’t suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Therefore SQL atleast for now should be good enough for most of the organizations. Right now, it’s fair to say NoSQL is only relevant to a very minor proportion of businesses. But that proportion is very very significant! The changing data arena would sooner see a technical innovation driving business innovation very similar to what we have seen with smart phones?

There wasn’t a need for smart phones but the technical innovation provided business opportunities. Likewise, big data promises a huge opportunity for NoSQL.