All posts by Shalini Raghavan

Analytics & Optimization From Data Stockpile to Decision-Ready Organization in No Time Flat

Jun172015

There’s little need to belabor the point that many businesses today generate more data in a single day than they used to in whole year. A few years ago storing all of this data at a reasonable cost was the biggest challenge. Newer technologies now solves for that. However, data by itself is neither useful nor meaningful. Simply sitting on a stockpile of data provides minimal value. The real value is to make your data “decision-ready.” Simply put to take data process, augment and analyze it to improve its immediate decision value. Whilst seemingly difficult, there is a tangible way to continuously get your data to state of decision-readiness. I’m not talking about a one-off project that takes a herculean kind of effort similar to the Manhattan Project. Rather I’m talking about systemic and automatable ways to achieve this goal. Questions to Ask Start out by asking yourself what problems... [Read More]

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Analytics & Optimization Big Data Analytics’ Experimental Phase

Apr242013

By Shalini Raghavan Great analytics – like most teenagers, budding artists and even entrepreneurs – needs to experiment. Find answers to questions, test theories, plot unchartered territories and discover more. Variances between simulated and actual results may be opportunities not only for correction, but for experimentation. Using analytic learning loops in conjunction with systematic experimental design, companies may discover opportunities that are not evident to competitors and gain forward-looking insights into how customer behavior is evolving. The quantity and variety of data used increases the range of experiments companies can conduct in a short amount of time while still producing statistically significant results. In addition to testing variations on well-performing decisions strategies, companies should test strategies that are beyond the edges of business as usual and organizational comfort zones. The edge-probing deliberately introduces controlled variation into the production data, thereby expanding what can be learned from it. Moreover,...

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Customer Engagement FICO World Preview: Big Data and Analytics – Keys for a Stronger Customer Experience

Apr162013

Consider the following: Over 12 years ago, Gartner coins the expression “Big Data.” Over 2 months, ago a notable venture blog declares that “Big data is dead.” – Venture Beat Also 2 months ago, the NY Times tries to uncover the etymology of “Big Data.” Some good sleuthing by the Times reveals the author of this expression to be John Mashey, Chief Scientist at SGI in the 1990s. In Mashey’s words, “I was using one label for a range of issues, and I wanted the simplest, shortest phrase to convey that the boundaries of computing keep advancing.” So why has Big Data turned into big disappointment for so many people? Looking back, we see some systematic reasons that make it obvious that this disappointment is self-inflicted! All efforts on Big Data focused on data and infrastructure. Merely storing your data was not going to advance the boundaries. In fact, John Mashey warned about this. In 1998, he delivered a presentation titled “Big Data and the Next Wave of InfraStress Problems, Solutions and Opportunities.” He cautioned that,...

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Customer Engagement Learning from the Here and Now

Mar292013

I still get offers for diapers. My child is a teenager. Five years ago I became a loyal  buyer of a particular brand of yogurt. Yet for the past three years, I consistently receive offers on  brands of yogurt that I don’t buy anymore. Some marketer somewhere missed the boat, and is working with old data, very old data. Big Marketing decisioning needs to be based not only on historical data, but also on data being collected frequently – In some cases, via constant streams from any sources – It’s responsive to change. Learning from and adapting to changing behaviors and market conditions happen at both the back end and front end of decision processes. At the back end, the broad range of data being analyzed and the emphasis on transactions means that predictive data elements are frequently refreshed. Many of our customers are generally collecting daily customer-level response data from transactions and generating new propensity scores for customers on a frequent basis. In addition, automation is used to update predictive models every 90 days to...

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Customer Engagement Customer Relationships: From Insight to Multi-Channel

Mar222013

By Shalini Raghavan Multi-channel, omnichannel … stereophonic.  Simply put, marketers are looking to reach customers in whatever way is most appropriate.  This could mean a store, a website, a catalogue, email, text, Twitter, Facebook, LinkedIn... But a major challenge is the disconnect between insight and action. In January we released the results of a survey that showed that for most companies this disconnect is about three months … 10 to 12 weeks too long! In building customer relationships that span multiple channels, time is of the essence. Have you ever come back from a vacation, and find that you are still getting offers for the Holiday you just took? Where were these folks two months ago when you were planning that trip? Once you determine how to answer your million dollar marketing questions, it must be output in an immediately executable form. Generally this is in the form of a “decision tree” of business rules. But unlike rules that have been authored by marketers based on empirical evidence and judgment, these rules...

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Customer Engagement Three Techniques for Scientifically Balancing Complex Marketing Decisions

Feb192013

By Shalini Raghavan Many companies are getting a head start with Big Marketing using predictive models with tried-and-true business rules libraries for common marketing decisions, such as up-selling and cross-selling strategies. Yet, with bigger and bigger data sets and an expanding range of analytic predictions to take into account, the decision process can grow quite complex. Companies that rely on simple business rules alone may soon find it difficult to manage or even fully comprehend how they are making decisions. Use of data-driven techniques for modeling and optimizing decisions processes is essential for effectively bringing Big Marketing intelligence into operations.  Some key techniques that we are using with our clients today include: Action-effect models predict how likely the customer is to respond to a particular action within a particular time period (e.g., is this customer likely to purchase if offered a 20 percent discount on lawn mowers valid for 30 days?) and what the effect would be on KPIs (e.g., response rate, sales, interest...

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Customer Engagement Location-Based Marketing: Three Barriers to Success

Nov202012

As I wrote in my previous blog, Location-Based Marketing (LBM) is an area of innovation enabling marketers to tap the tremendous mobile opportunity.  However, implementing LBM is not without its challenges and considerations.  Here are my top three: Privacy: It's by far the most critical of considerations when deploying an LBM service. Location-based services gather a wide variety of data. The data collected could contain sensitive personal information, such as places widely visited, place of work and place of residence – things that consumers may not want to be disseminated widely by a marketer without their permission.  Data De-identification: To address privacy, many marketers will deploy de-identification technology, cleansing data of personally identifiable information. It certainly helps to address privacy concerns, but it is not without its limitations. From a data scientist perspective, de-identification approaches can significantly reduce the utility of data for downstream analytic purposes. This means that while data can...

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Customer Engagement Location-Based Marketing Comes of Age

Nov132012

By Shalini Raghavan In the 1960s fictional universe of Star Trek, Capt James T. Kirk could flip out his communicator from an alien planet and speak to a crew member aboard his orbiting spacecraft, the Enterprise. The communicator looked much like a modern day mobile phone and allowed communications across astronomically large distances.  That was science fiction. Fast-forward to 1973, inspired by the communicator, Martin Cooper, an engineer with a small company called Motorola, invented the first mobile phone. The rest of the story was history … this device led the way for mobile communications across the globe. Today virtually everyone has a mobile phone, and it is estimated that by 2016, there will be more than one billion smartphone users worldwide.  The wave of mobile innovation has acted as a springboard for a variety of location-based services. Today location-aware services are even available on physical objects with embedded sensors—from... [Read More]

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