Web Ink Now: Big data. Rich data.

Web Ink Now: Big data. Rich data.

The term big data has gotten a lot of press.

My sense from speaking with people is big data is a buzz phrase that most marketing and sales people vaguely know a little about but most don’t really understand the potential for in their business. And very few organizations are actually taking advantage today.

I actually like the term “rich data” better. Rich data as a description is advocated by Nate Silver, a statistician and writer who analyzes in-game baseball activity and elections. Silver became well known for having successfully predicted the outcome of 49 out of 50 states in the 2008 U.S. Presidential election. Today he is the editor-in-chief of ESPN’s FiveThirtyEight blog and a Special Correspondent for ABC New and his book The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t dives into rich data in a big way.

No matter if you call it rich data or big data the concept involves using very large data sets and powerful analytics to generate real-time information that is valuable for making decisions.

For example, the American government monitors massive amounts of telephone and internet traffic against words and phrases that indicate nefarious activities and routes suspect traffic to analysts for further scrutiny. Rich data is used in sales and marketing departments to analyze website traffic and click activity, search engine word and phrase patterns, and social media streams all in real time. Making sense of this massive amount of data can be used develop strategies to grow revenue.

Data, data everywhere

At the micro level, a company could use a person’s social profiles on Twitter and other social networks combined with their customer data, and market demographics to draw a more complete picture of who they are as a person so they could be targeted with the right offers.

To some, this understanding sounds a bit creepy, but I welcome such informed approaches from the companies I do business with. It sure beats the untargeted one-size-fits-all pitches that most companies send. I would be happy to have the airline I frequent figure out that I take an international vacation every December with my wife and in the summer suggest places we might like to go based on the places we’ve been in Decembers past.

Rich data is also used in the macro to calculate sentiment analysis, the aggregate positive or negative attitudes gathered based on what people are saying about companies, brands, and markets on social networks.

When sentiment analysis is calculated in real-time, executives and marketers can learn that a blog post negative to the company is drawing attention and if appropriate they can respond right away. Or, in real-time, salespeople could be armed with the knowledge that a competitor has just launched a new product and know instantly what customers were saying about it.

Turning data into revenue

Companies are using rich data to generate revenue, either by being more efficient at what they already do or by implementing new sales and marketing strategies that would have been otherwise impossible without the opportunity for analysis of massive amounts of data in real-time.

For a macro example of rich data at work in sales and marketing, consider big box home improvement stores like Home Depot and Lowe’s or general merchandise stores like Walmart and how they might create specific sales strategies around the weather.

If early predictions forecast snow for a region, several days prior to the storm the store could move stock to the stores in the affected region. Things they know people purchase prior to and after snowstorms like snowblowers, shovels, generators and the like would be trucked from one part of the country to another based on weather.

Then they could create a dynamically updating website showcasing these weather-related products on the homepage, but only in the areas of the country affected by the storm. In other locations, other products would be shown.

As actual products are purchased and stock is depleted, the point-of-sale systems at each store could drive a real-time display showing how many units of the popular items are available at each store. This way, customers can expect a unit to be available (or not) and plan their visit to the appropriate store to make a purchase.

Marketers could monitor social media as well as mainstream media for keywords like “snowblowers” and comment as appropriate.

The home improvement store could then figure out (from loyalty card purchase records) which customers had not acquired shovels or snowblowers recently and target them with a content-rich email that focuses on tips to survive the storm and a video on what you need to know to buy a snowblower that is appropriate to your needs. As well, the email could include offers for appropriate products.

Real-time Google AdWords campaigns could be run, targeted to the precise time and location of the impending storm. I’d imagine AdWords headline something like “Snowblowers in Stock” and text “Popular models available right now at Home Depot in Massachusetts.” The ad would point to the dynamically updating real-time product availability page.

There’s a lot going on here. Rich data sales and marketing strategies rely on crunching huge data sets that no human with a spreadsheet can manage. It’s the future of real-time marketing that’s happening today in some forward-looking companies.

If you’re making the promise of rich data a reality, please comment here and let us know what you’re doing.

photo credit: Buzz Hoffman via photopin cc

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Web Ink Now: Big data. Rich data.

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