Wednesday Jun 24, 2015

Perspective from NRF Protect 2015: BJ's Wholesale + Analytics + XBRI = Big Data Value

As the booth opens at #NRFProtect 2015 this morning, here is a continued look at what some of our customers are doing to reduce and respond to fraud in stores. 

As we look at retailers who are using exception-based reporting to identify employee theft – which accounts for more losses than the higher profile categories of shoplifting and organized retail crime.  To curb the problem and improve employee oversight, retailers are using advanced analytics combined and proactive exception-based reporting to better identify cases for investigation; identify training issues or processes that need to be modified; and reduce shrink across the organization.

From Big Data to Actionable Insight

Last month we were fortunate to have Brendan Fitzgerald, Assistant Vice President of Asset Protection Operations, BJ's Wholesale share his perspective and lessons learned with Oracle Retail XBRi Loss Prevention.  Warehouse club retailer BJ’s with more than 200 locations in the Eastern U.S. saw an opportunity to identify theft with an analytical view of operations and cashier activity to identify shrink, fraud, and theft. The retailer implemented Point-of-Sale Analytics, using Oracle Retail XBRi Loss Prevention, to empower their asset protection organization with actionable insight. The multi-dimensional reporting and persona-driven dashboards allowed the company to transition from block-and-tackle data mining to an exception-based approach, to drive action at the regional, store, departmental or individual level. 

In the webinar, Brendan talked about the catalyst for Point-of-Sale Analytics, the requirements to get started, the organizational design to support the process change and the implementation approach. The uses cases for BJ's Wholesale sparked an interactive Q&A session throughout the conversation. Communication is vital to the success of an implementation to get the people, process and technology working in harmony. BJ's Wholesale did an outstanding job of truly partnering with the Oracle (formerly MICROS) team to manage through change management and service turnover. Listen to the webcast

The Oracle Retail XBRi Loss Prevention is now available as a cloud service and can automatically identify, track, and respond to unusual POS activity – including intentional fraud or innocent non-compliance – at the regional, store, departmental, or individual level. As it gathers real-world transactional trends, the Oracle solution refines its algorithms using learnings from every transaction. The result helps loss prevention teams consistently address and prevent fraud across regions. With hundreds of prebuilt reports and mobile applications, loss prevention specialists can take quick action and regional leadership can flag areas of concern anytime, anywhere. 

BJ’s improved its “case closers” more than 240% in the first year following its implementation of Oracle Retail’s XBRi exception-based reporting solutions and recently upgraded to a newer cloud-based version of the Oracle solution. NRF STORES Magazine highlighted BJ’s Wholesale Club in the June 1st Need to Know feature on Loss Prevention on June 1st. Learn how BJ's Wholesale Drives Shrinkage Down and Profitability Up

To learn more, be sure to visit us at the Oracle Retail Booth #1227 at #NRFProtect. If your schedule did not allow for travel to California, I would encourage you to reach out to learn more. I would be happy to get you in touch with our team of experts. Email: 

Monday Dec 17, 2012

Big Data Appliance

Today Oracle announced the next release of it's Big Data Appliance, an engineered system composed of hardware and software targeting the efficient processing of big data.  The solution leverages 288 Intel cores running Cloudera's distribution of Apache Hadoop in 1.1 TB of main memory.  This monster helps companies acquire, organize, and analyze large volumes of structured and un-structured data. Additionally a new versions of the Oracle Big Data Connectors and Oracle NoSQL Database were released.

Why is this important to retailers?  As the infographic below conveys, mobile and social have added even more data to the already huge collections of POS transactions and e-commerce weblogs.  Retailers know that mining that data will help them make better decisions that lead to increased sales, better customer service, and ultimately a successful retail business.

The Retailer’s Guide to Big Data


Monday Aug 15, 2011

Emerging Technology and Retail

Gartner recently released their 2011 Hype Cycle for Emerging Technology, and there are several items that impact the retail industry, many of which we've been studying carefully. The hype-cycle diagram is below, and for those that haven't seen one of these, technologies start on the left and move toward the right as they mature.

Source:  Gartner, “Hype Cycle for Emerging Technologies, 2011,” July 28, 2011

I have a few comments to make, starting with the mainstream and moving back towards the triggers.

Location Aware Apps- Foursquare and the like are pretty commonplace now days, and I believe there must be some consolidation coming.  We can't expect consumers to use multiple check-in apps, so I'm betting that Facebook will emerge as the winner with ShopKick sticking around as well.  Apps that find products in nearby stores will also flourish.

Predictive Analytics- Predicting demand is key to running a profitable business, so this is pretty mainstream with tier-1 retailers now.  Adding data sources like social networks to better predict trends should be coming soon.

Biometric Authentication- We added biometric authentication to our POS, but we haven't seen much interest from retailers.  I would guess not having to worry about passwords would be a big cost savings over the long run.

QR Codes- We added the printing of QR Codes on shelf-labels so consumers armed with smartphones can access detailed information.  This seems to be catching on with consumers, and I'm seeing QR Codes everywhere.

Consumerization- As I understand it, this trend means technology gets adopted at home first then makes its way into the business environment.  That's certainly what happened with the iPhone and iPad, Facebook and Twitter, and many Web 2.0 technologies.  These types of technologies will continue to follow young employees into retail stores.

Mesh Networks- RFID buzz died off for a while but seems to be making a comeback.  We've had several retailers express renewed interest in tightening their supply chain using tags that are getting cheaper.

In-Memory Database- Over the past three years we've seen the rise of engineered systems where the software and hardware are engineered to work together yielding better performance.  Memory continues to be cheap, so moving as much data into faster memory makes perfect sense.  In addition to Exadata, look for more on this topic from Oracle in the near future.  Open World is just around the corner.

Cloud Computing- I'm a big fan of utility computing regardless of where the resources are located (private, public, hybrid), which is why I really like Amazon's EC2.  But I'm just not sure retailers are ready to give up so much control.  After all, it only takes one hiccup on Black Friday to ruin a year.

Augmented Reality- The software libraries to support augmented reality apps are finally maturing, so we've started exploring uses in retail.  Get ready to think of advertising and reporting in new ways.

NFC Payment- The technology has been ready for years, but until recently there haven't been big sponsors.  Now we have Google and Isis with competing approaches.  This will take-off, but it will take a while for all stores to be retrofitted with readers.

Social Analytics-  I keep hearing that social for retail is overblown, but I truly believe it should be part of any retailers marketing activities.  Yes the ROI isn't very clear, but since when has marketing had a clear ROI?

Gamification- People are competitive so using games to drive behavior is a natural fit, especially for the younger generation that is never far from an XBox, Wii, PS3, or DS.  Look for more retail tie-ins with games from Zynga.

Big Data- I'm surprised this wasn't placed further along on the chart as I believe the technology is pretty mature, with several competing platforms.  The key is to make Big Data and traditional database systems work together.  This is how retailers will move from segments to individuals and achieve one-to-one marketing.

Video Analytics- As bandwidth and storage continue to get cheaper, video becomes more accessible to retailers.  There's so much information we can gleam from customers by watching how they shop.  Look for interesting combinations of video and location-based applications.

Overall, I believe the major emerging technologies are well represented on the chart.  I can't think of anything that's missing, can you?

Tuesday Jul 05, 2011

Psychographics and Demographics

At Crosstalk when Gary Vaynerchuk spoke he told the story of researching a first-time wine customer and finding out the customer was an avid Chicago Bears fan (American football).  To thank him for his first purchase (which was fairly large), Gary's team sent the man an autographed jersey they bought on eBay.  The customer was so impressed, he said he'd only buy from Gary's wine store.

Progressive auto insurance offers customers the option of installing a device in their cars called the Snapshot.  It records driving habits such as hard braking, quick acceleration, and speed driven for a set period.  Based on the data collected, Progressive can offer good drivers up to a 30% discount on their premiums.

What do these two seemly unrelated stories have in common? In both cases we've moved beyond demographics and looked more closely at individual traits, like loving a sports team or a having a particular driving style., such as white, male, age 35-45, married, employed can help with targeting at a gross level, but to continue moving the needle we must incorporate psychographics, such as college football fan, mountain biker, boy scout leader, and fiscal conservative.  Today's population shares personal information via social networks, and tomorrow's population will continue to be less concerned about privacy.  This presents an opportunity for marketers to collect activities, interest, and opinions that help hone marketing, which benefits both retailers and consumers.

Today, technology can process so-called "big data" to create profiles that contain both demographic and pyschographic data about consumers.  In many cases, consumers will give up this data voluntarily in exchange for a better shopping experience.  Retailers need to start extending their CRM systems today to house such information so they are able to compete as shopping gets more personal. 

Wednesday Jun 29, 2011

Big Data for Retail

Right up there with mobile, social, and cloud is the term "big data," which seems to be popping up lots in the press these days.  Companies like Google, Yahoo, and Facebook have popularized a new class of data technologies meant to solve the problem of processing large amounts of data quickly.  I first mentioned this in a posting back in March 2009.  Put simply, big data implies datasets so large they can't normally be processed using a standard transactional database.  The term "noSQL" is often used in this context as well.

Actually, using parallel processing within the Oracle database combined with Exadata can achieve impressive results.  Look for more from Oracle at OpenWorld as hinted by Jean-Pierre Dijcks.

McKinsey recently released a report on big data in which retail was specifically mentioned as an industry that can benefit from the new technologies.  I won't rehash that report because my friend Rama already did such a good job in his posting, Impact of "Big Data" on Retail.

The presentation below does a pretty good job of framing the problem, although it doesn't really get into the available technologies (e.g. Exadata, Hadoop, Cassandra, etc.) and isn't retail specific.

So when a retailer asks me about big data, here's what I say:  Big data refers to a set of technologies for processing large volumes of structured and unstructured data.  Imagine collecting everything uttered by your customers on Facebook and Twitter and combining it with all the data you can find about the products you sell (e.g. reviews, images, demonstration videos), including competitive data.  Assuming you could process all that data, you could then personalize offers to specific customers based on their tastes, ensure prices are competitive, and implement better local assortments.  It's really not that far off.


News and ideas about the retail industry with a focus on customers, innovation, trends and emerging technologies.

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