How and when should up-sell and cross-sell offers appear?

Monetate Marketing Infographics

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Monetate Marketing Infographics

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We wanted to use this opportunity to ask a wide range of questions and get pointers that would help the audience get a feel of the gamut of possibilities within the realm of Data Analytics. Gregory has been generous enough to quote pertinent references and also elucidate various aspects using the work covered by KDNuggets.

[LatentView]

According to you, what makes a Big Data Solutions/Services provider? Is there a minimal set of skills/capability that covers major requirements for most of the big data projects?

[Gregory/KDNuggets]

First, I want to mention that most businesses do not have Big Data problems. Quoting a recent article by Christopher Mims for Quartz Most data isn’t “big,” and businesses are wasting money pretending it is .

This is also supported by a recent KDnuggets Poll, where a median answer to the question Largest Dataset Analyzed/Data Mined  was in 40-60GB range, the data size which comfortably fits on a single laptop.

However, Big Data is where some of the most interesting and exciting problems are today, and it is the leading edge of technology.

Current Big Data environments require dealing not only with 3 V:

1. Big Data Volume, requiring Hadoop clusters or similar technology.
2. Big Velocity: for some problems, like Twitter analysis, military, or high-speed trading there are big and fast real-time data streams.
3. Big Variety: are there texts, images, video, links, or other inputs in addition to structured data?

But also Privacy issues, Data security, Data ownership, Data Quality, and Data governance.

However, the focus should not be on Data but on the value ‘hidden’ in Data. Extracting this value requires analytics and statistical skills.  Also, equally important is the ability to focus on the right questions, which requires business skills.  Finally, successful deployment cannot be done without organizational skills.

[LatentView]

Top three industries/markets that are prime for Big Data as it stands now (not based on the buzz around every data big out there but based on your observational history)?

[Gregory/KDNuggets]

CRM/consumer analytics and financial services/investment/banking are probably still the most popular, but we also see a lot of growth in Big Data applications in health care analytics, energy, and education. Here is an article we did last year that gives a glimpse of how Analytics was applied – Where did you apply Analytics/Data Mining in 2012?

[LatentView]

What are your views on how to make it big in Marketing Analytics?

[Gregory/KDNuggets]

By a combination of having good analytics tools, talented people, and developing expertise in several verticals.

[LatentView]

As an Analytics Service Provider, how does having Intellectual Property(Products, Solutions, Platform) asset(s) weigh against pure in-house professional services  expertise(math/business/modeling)? How does the needle tilt?

[Gregory/KDNuggets] 

Intellectual Property and proprietary platform is especially valuable for company valuation, for VC investments and possible acquisition.  On the other hand, open source platform like R or Python are more popular now and can be more effective and productive for analyst work.

Ideally Analytics Service Provider should have a combination of both some specific vertical knowledge and IP and skills in using existing platform.

Also, vertical, industry-specific business/domain knowledge is an important component.  You cannot take an expert from insurance and have her immediately be an expert in health-care, and vice versa.

[LatentView]

Your take on which trend (Omni-Channel Shopping for Connected Consumer Vs Mobile Wallet) is the top trend to watch out for in Retail this year?

[Gregory/KDNuggets] 

I don’t have any expertise on these trends, but Google Trends shows that there is a lot more interest in [Mobile Wallet] than in [Omni-Channel Shopping] – see Google Trends – Mobile Wallet Vs Omni- Channel Shopping

We would like to thank KDNuggets for this interview and Gregory for his detailed responses and references. We would surely like an opportunity to talk more with him in the future.

Folks – what do you think? Do you Agree or Disagree with the views expressed in the above answers? Do you have any supporting or augmenting thoughts? Or did you find this useful? Please leave your comments below.

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Being an analytics professional, I like doing interesting analyses on various hypotheses I have regarding what is going on in the world. Most recently, I’ve been thinking about how there is a mismatch between what the businesses portray and what the consumers actually feel about brands.

To test this hypothesis, I analyzed 100,000 tweets of four brands: Sears, Wal-mart, Kroger and Macy’s. And the findings are in line with the hypothesis. Consumers have a very specific impression of each brand which is the sum total of all the marketing efforts and in-store experiences e.g. we can see below in the first chart that Sears has a very distinct impression compared to other retailers. One surprising thing that I found in the analysis is that there is very little buzz around celebrity associations despite the massive marketing dollars which are poured into it.

No doubt, social media has become an integral part of marketing divisions but its real power– to determine consumer sentiment – is still grossly underutilised. In today’s highly competitive marketplace, businesses need to be extra attentive to what consumers are saying and what better a place to learn that other than social media, to which consumers pour their hearts out 24 hours a day in their highly connected lives through smartphones, tablets and desktops.

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LatentView is pleased to  invite you to attend the best analytics conference in San Francisco on April 15-16, 2013.

Join us and learn how can we help you leverage bigger data for prediction and drive bigger value!

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Kiran works as a Senior Analyst at LatentView and holds a Dual Degree from IIT Madras. He enjoys watching and playing football.

Like many others, one year ago I was more of a spectator to the media hype surrounding the new buzz word “data scientist”. Being a data spying professional myself, I wanted to do a deep dive search and find out more about the origins of this newly evolved role. With some ground work, I have learned that over the years many technology companies like Facebook, LinkedIn and Google had data centric teams completely focused on developing products or services to create more value for their customers. Immense success achieved by these teams has in-fact led to the origin of this new term “data scientist” and it has gone main stream since then. Some work aspects of a data scientist have lots of similarities with traditional roles such as Statistician, Predictive Analyst, Business Analyst and Business Intelligence Analyst.

According to D.J. Patil, one of the pioneers in the data science field, data scientist is a new kind of computer scientist — a gig that’s one part mathematician, one part product-development guru, and one part detective and dare I forget one part sexy! As Patil claims, by 2020 some 50 billion devices, from cars to appliances, will be talking to one another and companies will soon need teams of data scientists to sort through everything from internal inventory metrics to customer tweets. Anjul Bhambhri, vice president of big data products at IBM, describes the data scientist role as “part analyst, part artist”. She says, “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”

After chewing down much more information than I could swallow, I would have to say that a data scientist possesses strong domain knowledge, identifies right business problems and gathers data from multiple diverging sources, not just limiting to one single source like a CRM system. The data scientist then proceeds to write computer algorithms to scrape the data, parse and convert it into an analysis ready format. Once the data handling part is done, he/she would do what I like to call a “what if” analysis, question existing assumptions, mine for hidden patterns, perform statistical hypothesis tests and find out innovative ways of solving the pre-defined business problem. Better equipped with data and analytical insights, the data scientist would then communicate the findings and recommendations to business end users through a nicely crafted story.

In short, data scientists are not limited to one concrete definition or description, they do it all.

We are living in an age where information is the new oil and the demand for data scientists is rapidly increasing. I would say those who refuse to accept this would soon become part of history.

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The music industry has been hurting badly for years, but a turnaround may be coming with a little help from big-data.

We’ve all heard about the losses faced by the music industry in recent years with reports of total revenue falling from nearly $15 billion in 1999 to slightly more than $6 billion in 2009. And although Apple last month announced that we’ve now collectively downloaded more than 25 billion songs on iTunes alone, sales of digital downloads haven’t offset the industry losses. I believe big-data will play an enormous role in turning things around — sooner rather than later. Read More…

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Yesterday, Groupon CEO and founder, Andrew Mason, announced his ousting with a quirky letter. In it, he pointed to the following misstep in management:

If there’s one piece of wisdom that this simple pilgrim would like to impart upon you: have the courage to start with the customer. My biggest regrets are the moments that I let a lack of data override my intuition on what’s best for our customers. This leadership change gives you some breathing room to break bad habits and deliver sustainable customer happiness – don’t waste the opportunity!

In the end, understanding customers is quite simple—engage with them; listen to them; derive insights from data you have on them; show them you care; and manage your interactions with them proactively. Is there room for intuition in all this? Absolutely! Business decisions primarily rely on interpreting data well (even when you have limited data), and exercising sound judgment, honed through intuition, on what to focus on.

In an e-commerce business that tends to rely on building digital platforms with automated decisions, the importance of human judgment is sometimes underestimated, and lack of data blamed for decisions gone wrong. In a business context, machines can’t imagine the future well enough yet and may look for set patterns from the past. Humans can do this and should. Managers still need to own up for their judgment calls. Where machines can play a perfect role is in translating sound business judgment into algorithms that make millions of small decisions in an increasingly complex world of big data.

What then is the recipe for effective data-driven decisions? Identify enough perspectives and opinions for the problem at hand, seek dissent and competing arguments, develop fact-based tests of competing hypotheses, and then decide between well-elaborated choices. Pairing high-quality human intelligence with machine intelligence then becomes key for effective decisions.

Many times, a sound decision is asking the right questions of the data, rather than seeking the right answers from it.

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