Geeks Rule! Using Big Data analytics at Nordstrom and LinkedIn


Share on LinkedIn

At the Marketing Optimization Summit in San Francisco today, Akin Arikan of IBM opened up his talk by saying that years ago the web analytics geeks could only hope someday to get a seat at the table with business leaders.

Well folks, that day has arrived. Jim Sterne started the eMetrics Summit many years ago to focus on the nascent web analytics market. I remember at the time wondering if/how it would work out. Clearly he had the vision that web analytics would be part of a larger marketing optimization space, and the conference has evolved towards that vision over the years. Well done, Jim!

Under the current umbrella buzzword of Big Data there are a plethora of solutions available from mega vendors like IBM and SAS, to lots of startups and smaller firms seeking to optimize one thing or another. If you’ve got data, you can find a tool to make better decisions using it.

I only had a few hours at the Summit, which was co-hosted with Predictive Analytics World and a few other related conferences, all supported by Rising Media. A few highlights I’d like to share…


One of my favorite retailers, Nordstrom is an old company that is embracing new technologies. James Steck, part of the Advanced Analytics group there, discussed how Nordstrom used JMP to understand product and brand relationships. The idea is simple: figure how to promote the right products and brands to the right customers, maximizing revenue in the process.

That’s not a simple problem when you’ve got a busy website ( along with 225 stores doing about $10B in sales annually. Using JMP they are able to figure out, for example, which segments of customers are more likely to buy brand Y and first buying brand X. Armed with that info, marketers can make better promotion and merchandising decisions.


Another interesting example is LinkedIn, which accumulates a massive amount of data as its users interact. According to Scott Nicholson, Senior Data Scientist, LinkedIn can use that data to help user make better decisions — decisions that can be good for them (e.g. future job opportunities) or for LinkedIn (e.g. presentation of the right ads for monetization).

What complicates matters is that LinkedIn offers lots of choices — 40 different actions according to Nicholson. Using various analytic techniques too geeky to go into here, LinkedIn could serve up experiences that are more personalized to the user, or ads that are more likely to be clicked on. Side note: I wish they would apply more of that analytic wizardry to the user experience, which I still find clunky compared to, say, Facebook.

Integrated suites

Akin Arikan (IBM) gave a great presentation about how all the digital bits need to come together. I suppose some of this is self-serving, because IBM is one of the few companies that can do this, having acquired Unica, Coremetrics and other companies in recent years.

In the recent CMO study, IBM found top issues were an explosion of data, social media, growth of channel and devices. I don’t think there’s any question that companies will be seeking integrated marketing/analytics suites much the same way that CRM was the hot ticket 10 years ago to bring sales, marketing and customer service together. But an equally important trend, as Akin pointed out, is the integration of analytics into every fabric of the business, including offline.

ROI, we got it!

One contrast with some recent social business conferences I’ve attended, is that the analytics crowd has a compelling ROI story. Naturally, if you have the data and the tools to analyze the data, you can figure out how to optimize decisions and justify an investment. The “social” business case is a bit more nebulous, which is not to say it’s not as important. But, like it or not, most CxOs like to think of themselves as fact-based decision makers, and analytics lends itself better to such left-brained types.

Of course you can apply (text) analytics to social data; it’s one of the contributors to Big Data. One application is monitoring social media to find unhappy customers before their venting goes viral. But another interesting application is understanding social influence. Kxen, a predictive analytics vendor, said they can analyze cell phone relationships to understand how one user might influence another (e.g. friends and family). This is heading in the right direction, because a customer’s value is more than what he/she individually purchases.

Closing thoughts

There’s a lot of hype right now about Big Data. I guess the tech industry needed something to push after Web 2.0 / Social whatever played out. But analytics is just a tool, let’s hope that companies will use it wisely.

In the CRM heyday, there was too much focus on extracting customer value, and not enough on delivering it (that’s why we have CEM as a counter balance). With analytics it’s possible to do both: improve the customer/user experience while also maximizing revenue and profit.

Here’s hoping business leaders will remember there are real flesh and blood people behind all that data being collected and analyzed. Personally, I don’t want to do business with The Borg. Do you?


Please enter your comment!
Please enter your name here