As I’m guessing everyone is aware of by now, MLC’s B2C team is currently knee-deep in our 2012 research project. This year, we’re looking into analytics and “Big Data” – a space where there seems to be a lot of potential (and a lot of hype) but not too much in the way of best practices or frameworks for moving forward.

So we’re currently trying to tease out, exactly, what people are using analytics for, and what ultimate goals those actions feed into. When we’re on the phone with members, overwhelmingly we’re hearing that data brings enterprises closer to the consumer, leading to all sorts of better outcomes: more resonant marcomms, higher margins through more effective price discrimination, and, for some companies, better products that arise through access to protected, proprietary data assets (like Nike+).

I could imagine two ways that data might feed into customer centricity (whether it’s helping or hurting). Story number one more or less goes: we as a company had very little idea who our customers were, what they liked, how they socialized and what kind of products they bought from others that they could be buying from us. When we integrated advanced marketing analytics and unstructured data, the numbers told us more about our customers than we already knew, and we became more customer-centric.

The other story goes: we as a company had very little idea who our customers were, and therefore we integrated big data and advanced analytics. But we couldn’t choose which data to use, and our analysts and marketers got caught up in a never-ending cycle of analysis paralysis. Moreover, thinking about the consumer as an abstract concept in data led to people forgetting the importance of experience and observation. In the process, we lost sight of the softer, qualitative ways that we learned about customers, and ended up becoming less customer-centric.

Which of these is more plausible? I’m not sure, but my gut says it’s the second story. I can count the number of companies with great, consumer-apparent uses of data on my fingers and toes, and analytics vendors have bigger appetites than that; there are surely hundreds of companies out there with data on their hands of varying effectiveness.

So, we thought we’d bring the question to you. Answer the poll below to let us know how you feel about data and analytics’ role in customer-centricity. Want to add some details? Let us know in the comments section.

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Yesterday afternoon, I watched eMarketer’s recent webinar on measuring social media success.  What particularly caught my eye were the top challenges that marketers face when managing their social media marketing efforts: measuring the ROI, making the case for investment, integration/measurement with other marketing channels, getting the right talent, and deciding who does what.

This list was eerily reminiscent of the results from MLC’s Marketer Quick Poll from a couple of months ago.  Only in our case, we had asked marketers about their top challenges on the data management frontier.  If these challenges are so similar between such different subjects, then perhaps it’s time to reposition and take a step back to look at the broader marketing environment.

The easiest big-picture framework that came to me was the traditional supply-and-demand curves.  For simplicity’s sake, we can consider the consumer market for baby food.

Assume we hold the supply curve constant.  To increase the amount of consumer surplus under the demand curve, we can do one of two things:

1. Try to make our captured demand hug the full consumer demand closer.  (Gerber battles Baby’s Best!)
2. Attempt to shift both demand curves further out along the supply curve.  (Expand the economic pie – for instance: Gerber using analytics to discover that older adults without teeth were an underserved market)

Most marketers would agree that achieving both would be ideal, and if they had to pick, they’d aim for the latter.  But if we look at actual practices, most marketing departments are focusing their social media and analytics efforts in the first one.

Their thought process might go something like this:

Sure I’d like to just burst through the innovation bubble and find a whole new untouched consumer population…

But we don’t have the innovative power, and it certainly won’t be easy justifying riskier, creative ventures to the rest of the organization.

Besides, the consumer landscape is changing so fast, I’m having a hard-enough time just keeping up with my competitors!

So let’s just work on speeding up current activities and getting as much consumer information as possible.  Who knows, maybe we’ll get lucky and come across something that will push innovation forward!

However, while aiming for “real-time” relevance has its merits, it may not be the smartest way to secure customer value and loyalty.  Consider the following: are marginal increases in market share sustainable?  Are consumer preferences really changing so quickly, or does it just seem that way with recent technological/analytical advances?

We’ve recently been thinking that focusing on speed may lead to smaller marketing improvements with fleeting market advantage.   Keep an eye out for our survey in February, when we’ll be gauging Marketing Agility (speed, flexibility, and all the factors that represent entrepreneurial readiness).  Participating companies will get a benchmarking report.  Email me if you’re interested in taking the survey or learning more: yzhang@executiveboard.com

For the last few months, we’ve been working on our major research project of 2012. As you’ve probably read, it’s all about data and analytics – how companies should use the Big Data consumers generate to create more compelling products, experiences, and messages.

But bridging the gap from data to action almost always requires an intermediate step – testing. And with big, real-time data, the B2C space just might be entering a golden age of testing and learning, as the test-to-results cycle speeds up and decisions can be made faster.

It’s important, though, to keep the possibilities here realistic – tweaking products, experiences, or messages will almost certainly produce marginal results, ones that might be eaten up by the macro factors at play in any business success – technological trends, the economy, that sort of thing. Case in point is Sears/KMart – a company that’s come on some hard times in the last few years, and one that recently announced that it would close between 100 and 120 stores in the coming year. Read More »

(from guest blogger Jonathan Rudick, VP of Customer Experience at HSBC)

Good use of analytics is the only way to know what transactions are happening on a large scale.  Especially with financial services, data measures both transactions and behaviors, and leveraging this data can aggregate these trends and analyze them, providing a better understanding of the customer.  This deep customer understanding can help improve service across time, allowing the customer to develop a deeper and more loyal relationship with the brand.

Many marketers understand that analytics are creating more possibilities to understand the customer than before, but a recent IBM study found that 71% of CMOs aren’t prepared to use this data to help their business.  There are four main keys to getting the most out of data: Read More »

Despite increasing pressure to provide Sales with a robust pipeline of qualified leads, most B2B marketers admit they don’t have formalized processes in place for things like lead generation, qualification, scoring, or nurturing.  Many are turning to marketing automation – the use of technology to systematize and automate many marketing tasks and processes – to add a little method to their madness.  It is a hot topic in the marketing trade press and a solution space crowded with vendors (all of whom promise extremely impressive returns).  We first saw marketing automation emerge at the top of marketers’ lists about a year ago when we fielded a short poll asking members where they were planning to make investments in the coming year.  In response, we decided to do a deep dive on the topic and help our members figure out the ins and outs of success.

Through a combination of quantitative and qualitative research we discovered a few key lessons that everyone considering, implementing, or optimizing marketing automation tools should know.  Our findings, ideas, tips, and best practices (including data from a benchmarking survey of 161 B2B marketers) are all collected on a dedicated Marketing Automation Key Findings page on our website.  Top takeaways include: Read More »

Business intelligence initiatives are complex, cross-functional processes that are only as strong as the weakest link in an organization.  What might that weakest link be?   Well, if numbers tell us anything, the weakest link may well be marketing intelligence.  A recent IBM study reveals that over 70% of CMOs feel unprepared to deal with the explosion of big data.  And our own research finds that – of all internal functions – people in Marketing have the least confidence in their own data (perhaps a problem with perception as much as with the data itself).  This is also the view put forward by Gert Laursen, head of Customer Intelligence at Maersk Line in his book, Business Analytics for Managers: Taking Business Intelligence Beyond Reporting .

Mr. Laursen’s latest book, Business Analytics for Sales and Marketing Managers, tackles marketing analytics head on.  He explains a pragmatic “top-down” approach for determining what data to use, which algorithm to select, and how to implement the results.

Intrigued by his insights, we took some time last week (a windy Friday afternoon in Denmark) to chat with Mr. Laursen about the role of data and analytics in marketing.  Here are a few interesting nuggets from the conversation: Read More »

By Chris Frank, a former marketer at Microsoft and the author of a new book on decision making.

We have become a world of data addicts. But with more data comes the feeling of, “How do I make sense of all this? Can’t we break this down into a handful of simple points?” Critical questions about market demand, customer buying behavior, subscriber acquisition, brand positioning and your product’s roadmap are great. However, the interesting discussion comes to a screeching halt when the data arrives. Instead of being a well-arranged piece of music, it is a mash-up of sounds. The volume drowns the substance.

Data Rehab

Information is essential to making intelligent decisions, but more often than not, it simply overwhelms us. The 24/7 data explosion around us is both troubling and addictive. Consider that this year, The Economist estimates we will create 1,200 exabytes of data or more than 22 million times the amount of information contained in all the books ever written. That’s eight times the amount in 2005 and the annual volume is increasing geometrically.  The question isn’t how to stop the deluge, but how to get real value from it. How do you find the truly essential nuggets of information and use them with confidence to effectively grow your business and distinguish yourself in your company?

The answer, ironically enough, is found in asking questions. The smartest person in the room is the one that knows the questions to ask to separate the wheat from the chaff. This leads to discovering relevant facts, developing insights and delivering them with impact. Adapted from a new book, “Drinking From the Fire Hose”, below are three questions to ask yourself whenever you are suffering from information overload: Read More »

Health and pharmaceutical marketers have a tough job: they work for some of the biggest companies in the world, but gaining and maintaining the trust of their customers is incumbent, in part, on providing individual service and cultivating a reputation as something other than a faceless corporate behemoth. But what if the industry could turn size into an asset?

Readers familiar with the outer reaches of the social media space may have heard of a concept called “the quantified self“, an ethos and accompanying suite of technologies designed to give average people data and insights into one of the most mysterious things in the world – the workings of their own bodies. Nike+, which we wrote about a few months ago, is a “quantified self” technology, as are more comprehensive solutions like the Jawbone Up, which – in addition to physical activity – can track daily calories burned and sleep quality.

Part of the value of quantified self technologies is ease of comparison – you can easily figure out if something that’s going on with your body is normal or cause for concern. But in order to get enough data for comparison, you have to have a critical mass of users – and quantified self stuff is probably not mainstream enough for statistically-significant sample sizes for any given question.

Well, what if there were organizations with hundreds of thousands of health consumers – enough to provide adequate sample sizes for just about any “is this normal” question, with a vested interest in those consumers taking preventative health measures? Oh, wait, there is! Read More »

We’re well into this year’s research into marketing’s use of Big Data, and we’re starting to learn a lot about what marketers expect from the technology and how consumers might react. It’s clear there’s a ton of promise here, but there’s also significant reason to watch out for the hype.

If your’e interested in Big Data’s potential for Marketing, please join us for a webinar in a few weeks, featuring experts from Intel, Hearst, and Maersk Line.

In the meantime, here are a few reasons we’ve spotted to question the potential, with the caveat that we’re purposefully not covering the positive here: Read More »

Thanksgiving is just around the corner. And if you’re the chef, you know not to overstuff your turkey unless you want to risk a bird explosion in the oven. Ask Rachael Ray or Martha Stewart if you don’t believe me.

I know, it’s tempting to put in just a little bit more deliciousness. If we have it, why not use it?  Be it the extra stuffing or the bunch of variables that haven’t made it into your very comprehensive analysis. Maybe it’s the regret at seeing the extras “go to waste”; maybe it’s the urge to supersize in response to complexity and diminished control.  Cooking up an insightful model from a mountain of data can be just as stressful as cooking a banquet. Read More »