Will Kelly tells us about some unconventional uses of Big Data and predictive analytics that are happening across multiple industries.
Here on the Big Data Analytics blog, we’ve touched upon a lot of the conventions, challenges, current thinking, and business models around Big Data and predictive analytics. However, amongst the FUD (fear, uncertainty, and doubt) and hype around Big Data there are companies who are putting the technologies to use in some unconventional ways.
Track open pit data mining
Take a look at how Hitachi Data Systems (HDS) is putting Big Data and predictive analytics to work in support of some serious industrial applications in the heavy construction, mining and transportation industries. When I spoke with Michael Hay, vice president of product planning, Sara Gardner, senior director, software product marketing, and Asim Zaheer, senior vice president of worldwide marketing, they gave me an overview of how Big Data and predictive analytics work on board their heavy mining equipment.
This post entitled Hitachi Machine Data in Action: Open Pit Data Mining by Sara Gardner highlights how Hitachi is putting Big Data to work in support of open pit data mining machinery. I’m not talking about mining for data but underground mining for minerals. Gardner’s post illustrates some of the extremes to which machine data pushes Big Data and predictive analytics in which to accomplish what to many of us are unconventional business tasks.
Some Big Data industry insiders see the application of Big Data and predictive analytics as part of heavy industrial equipment and transportation systems as a major contributor to overall Big Data growth in the future.
Improve ecommerce customer experience
While competition is tough inside retail stores, some of the same issues extend to eCommerce. A startup named bloomreach, is aiming to use Big Data to enhance the ecommerce customer experience. This is about delivering up custom pages based on a customer search, not just the site’s user experience. The bloomreach technology focuses on content discovery through interpreting product demand.
Raj DeDatta, CEO of bloomreach gave me an overview of how the company applies Big Data technology as part of enhancing ecommerce customer experience. Their technology sits on the backend of large ecommerce sites and enables them to assemble new product landing pages on the fly that best match customer search criteria while maintaining a strong customer experience.
While the application of Big Data as part of ecommerce and customer experience may seem mundane to some, it points to three paths. The first path is Big Data challenging content strategists, information architects, and designers in the ecommerce world. The second path is Big Data fluency becoming a requirement for future generations of ecommerce professionals. The third, perhaps most important path, Big Data technologies on the backend of ecommerce websites becoming necessary to grab customer eyeballs during searches or risk losing the online sale to a competitor.
Apply analytics behind the register and in the call center
Big Data is perhaps best known for tracking customer behavior. However, with the Hitachi Business Microscope HBS applies Big Data and predictive analytics to the other side of the customer/seller equation by applying the technologies to the business side of large customer service centers and the cash register side of retail stores.
The business microscope captures “the emotional times,” using sensors like a customer vocalizing frustration, or foot traffic via the swipe badges to determine who in the call center is interacting with whom via the proximity of their badges.
In a retail environment, the business microscope can study foot traffic and then return data to help optimize the floor layout.
Big Data can track behavior across customer interactions and provide businesses another channel of actionable information to best serve their customers and compete in business.
Price NFL tickets dynamically
Living in Washington Redskins country as I do, ticket-pricing complaints are common during football season. Friends of mine in other metropolitan areas have a love/hate affair with the ticket pricing of their NFL teams. The NFL is using Big Data and predictive analytics from FICO to determine dynamic pricing for tickets.
Big Data and predictive analytics powering pricing probably happen more than we know as consumers. It’s just that FICO and the NFL are being open about their project at the case study/use case level. However, such Big Data and predictive analytics projects are at least certain to be at the requirements-gathering stage across other consumer-demand-driven industries.
Improve subscriber retention rates
The premium subscription market faces added challenges in today’s market, as subscriptions are amongst the first line items ripe for budget cuts. Scout Analytics, a startup is applying Big Data and predictive analytics to help companies including Software as a Service (SaaS), information services, and digital media improve their subscriber retention rates.
Scout Analytics touts a 10-15 percent increase in revenue from subscription renewals. It acts as a data hub and ties into sales quotas helping sales teams capture more recurring revenues.
(Un) conventional Big Data and you
The unconventional applications of Big Data and predictive analytics in this post show how Big Data and predictive analytics are becoming a foundational technology across multiple industries. Despite the frequent challenges of adopting Big Data and predictive analytics, these unconventional and niche applications of the technologies show even further that our personal, business, and online lives are only becoming larger parts of business platforms today and in the future.
This article was originally published by CNET TechRepublic on May 5, 2013