Summary:

Big data and the marketing world go together like peanut butter and jelly. Marketers want to present their brands in the most-effective manner possible and always put the right ad in front of the right person. Big data makes that possible at a whole new level.

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Big data and the marketing world go together like peanut butter and jelly. Marketers want to present their brands in the most-effective manner possible and always put the right ad in front of the right person. In theory, big data makes that possible at a whole new level.

Today’s analytic techniques and technologies can tell marketers not only what campaigns are working, but also where to spend next and — in some cases — the very language to use on their web sites. Here are five companies you’ll likely be hearing a lot more about if you’re not already a user.

1. Acxiom

Most people in the directed marketing world have heard about Acxiom — it has been around for more than forty years and has lots of identity data about people — but they’re about to think about the company in a whole new light if new CEO Scott Howe has his way.  “[Acxiom is] like the ’62 Chevy I first drove,” Howe told me recently. It’s comfortable and predictable, “but not super-sexy.”

Phil Mui

Howe’s plan involves, among other things, transitioning the company into a software-as-a-service model where customers can access and analyze their own data as well as Acxiom’s voluminous data sets. To make sure Acxiom does it right, the company hired former Google Analytics head  group product manager Phil Mui as its chief products and engineering officer.

Acxiom has the right data –and knows what other types it needs to get –but Mui wants to incorporate Google’s style of user-friendly services and high-end analytics to help deliver that data in the most-effective way possible. Thanks to social media, web television, mobile devices — pick a medium for capturing data and reaching consumers — “it will [very quickly] change from marketers having not enough data to having too much,” Mui told me. [But] the ability to serve up insights will be perhaps more valuable [than the data itself].”

Oh, but the data does matter. “Google will never be a company that is going to do marketing too well,” Mui said, in part because it doesn’t want to host personally identifiable data. However, marketers are hitting a wall because “because there’s only so much you can do with anonymous data.” Acxiom has valuable identity data and, utilized correctly, it’s a potential gold mine.

2. Applied Predictive Technologies

Applied Predictive Technologies is another older company — it has been around for about 12 years — but one that has its hooks into some of the world’s largest companies. Seriously, its customer roster reads like a who’s who of retail (Walmart), restaurants (McDonald’s), hospitality (Holiday Inn) and banking (Wells Fargo). Not that they’re complaining.

According to APT Founder and CEO Anthony Bruce, its customers love its cloud-based platform because it lets them ask entirely new types of questions of their data in order to better understand how to spend their marketing dollars. APT does this by collecting lots of client data — pretty much everything related to sales transactions, as well as demographic, geographic, competitive and other info — and enabling customers to figure out the business impact of any given decision. Those decisions, Bruce said, can be anything from where to target a specific promotion to advertise online or in print or to repaint the inside or the outside of a restaurant.

And APT users can ask question at any point in the process. For example, they can try to predict outcomes by analyzing similar decisions in branches with similar attributes, or they can analyze the outcome of a particular campaign and find out how, and why, it worked out or didn’t work out. They can even ask counterfactual questions, Bruce said.

Although customers’ own transactional data will always be the most-important aspect of APT’s platform, Bruce said the company has its eye on the deluge of data coming from new sources. There’s definitely value to be added by analyzing customer data against sensor, RFID, social media and other data sources.

3. BloomReach

I’ve covered BloomReach a couple times now, and the company just keeps getting hotter. After building up a roster of big-name clients while still in stealth mode, the software-as-a-service startup finally launched in February and has since announced even more household-name users, including Pottery Barn. As I explained when covering the company’s launch, BloomReach works its magic by automatically creating content on web pages, based on what someone is searching for, that will make visitors more likely to find content, click on links and buy merchandise. In order to do this, it employs a plethora of big data techniques and technologies and analyzes billions of web pages and customer interactions daily.

4. InsightsOne

The brainchild of the team that built Yahoo’s consumer analytics engine, InsightsOne launched in March with $4.3 million in venture funding. The company promises big things because of its big data roots, helping users more accurately place their ads across email, mobile devices and the web, and increasing profits by at least 10 percent — sometimes much more. InsightsOne hasn’t talked publicly about customers yet, but it does share a lot of information about its technology, which achieves micro-segmentation of consumers by applying techniques such as machine learning and graph processing atop a Hadoop platform to make sense of endless streams of data in real time.

5. MarketShare

MarketShare puts a lot of emphasis on big data, and the strategy appears to be paying off as interest in big data picks up. According to Co-Founder and CEO Wes Nichols, the foursix-year-old company, which targets chief marketing officers with cloud-based service that helps them better predict how to spend their budgets, has booked more revenue in the last month than it did during its first two years of businesses. Among those customers are Ticketmaster, which is using MarketShare to inform its dynamic pricing engine, and EA, which uses MarketShare to help it understand to price social market video games.

Essentially, Nichols explained, MarketShare cares about three types of data: where a client is investing its market dollars, what the business outcomes are, and literally hundreds of other variables (e.g., time, weather and price) that could affect those outcomes. And it goes deep in order to determine outcomes. If all you do is track clickthroughs, he said, you might miss that an ad campaign actually resulted in someone opening a piece of mail four months later.

Wes Nichols

MarketShare typically stores terabytes of this data for each customer, Nichols said, and some customer data sets have grown 100-fold in the past year. That’s why it’s a heavy Hadoop user and keep strategic relationships with Amazon and IBM around cloud resources. It’s also why MarketShare hires the best and brightest engineers and data scientists it can find in order to ensure a high-performance, highly scalable and accurate platform. “I think every rocket scientist that used to work in the space program in Los Angeles now works at MarketShare,” Nichols joked.

In the future, though, Nichols says marketing tools need to take the user experience to the next level, beyond the omnipresent dashboards that require users deciphering them in order to gain insight. “All our customers are swimming in dashboards,” he said, which is why MarketShare is working with Adobe on a product that actually “does quite a bit of thinking for the user” in terms of determining areas for improvement. Nichols compares it to software that takes the onus off of airline pilots by automatically reacting to certain conditions, but generating alerts when human action is necessary.

Feature image courtesy of Douglas Cumming.

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