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 https://nrf.com/news/need-know

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: oneretailvoice_ww@oracle.com 


Tuesday Jun 23, 2015

Perspective from NRF Protect 2015: Adidas Uses Oracle Retail XBRi to Reduce Fraud at the Point of Service

Analytics and exception-based reporting, made available across all stores brings Big Data-style science to loss prevention

In advance of NRF Protect, here is a look at what some of our customers are doing to reduce and respond to fraud in stores. This is the first in a two-part series. To learn more, be sure to visit us at the Oracle Retail Booth #1227 at #NRFProtect this week in Long Beach, CA. 

Retail loss prevention professionals are well aware that employee theft and employee-related fraud account for the biggest single segment of shrink. According to the November 2014 Global Retail Theft Barometer, employee-generated shrink accounted for just over 40% of the previous year’s $128 billion total, even more than the one-third generated by shoplifting and organized retail crime.

Given these facts, retailers have a compelling interest in understanding and curtailing employee-generated shrink. The conundrum, however, is that no retailer can effectively investigate every single transaction in every single store. Fortunately, employees who commit fraud tend to follow specific patterns. By using tools that apply science to the problem, retailers can shift this challenge from a Big Data problem to an opportunity for insight.

One of the most important loss prevention tools is exception-based reporting, using advanced algorithms to constantly monitor point-of-service (POS) activity, identify potentially fraudulent transactions, and alert specialists automatically. Trends, outliers and “red flags” can be measured and tracked by region, store, or individual employee. By providing essential data to multiple levels of staff – from individual loss prevention specialists in the field to regional managers – an organization can effectively empower their team to root out fraud, and act quickly to resolve it. Doing the same thing manually is impossible when transactions multiply over dozens or thousands of locations. 

For adidas, the global designer and manufacturer of athletic shoes, clothing and accessories, it was nearly impossible to consistently identify the causes of shrink and fraud in its 2,470 stores worldwide. The company was unable to perform loss prevention exception reporting and faced operational challenges including lack of data protection, multi-system misalignment, difficulty adjusting to time zone and language variances, and system failures resulting in non-compliance issues.  In a recent Chain Store Age article, adidas shares how it reduces fraud in employee and administration losses following its implementation of Oracle solutions. Adidas shared their experience at Oracle Industry Connect. You can download the presentation adidas: Measuring and Managing Loss to Preserve Profit from the Oracle Retail RACK. 

Now available as a cloud service, Oracle Retail XBRi Loss Prevention Cloud Service captures all POS transactions and then administers advanced business analytics that apply a laser-focused look at key loss patterns. Designed to be completely agnostic to the POS solution and source data, XBRi integrates with both Oracle and third-party POS solutions – even multiple solutions – giving retailers flexibility and freedom of choice. The cloud service shifts funding from a potential capital investment in software and IT infrastructure to an operational expense. 

To learn more, be sure to visit us at the Oracle Retail Booth #1227 at #NRFProtect this week in Long Beach, CA. 


Tuesday May 12, 2015

Insights from OIC: Oracle Helps Retailers Turn Today’s Most Disruptive Trends to Their Advantage

In late March, retail executives gathered at Oracle Industry Connect 2015 to share perspectives. Here is a glimpse of what you missed from the sessions....

Retailers at the recent Oracle Industry Connect sessions talked about numerous “forces of disruption” that are changing their business and creating new opportunities.  Setting the tone for two days of presentations by retailers, for retailers, Jill Puleri, Senior Vice President and General Manager of the Oracle Retail Global Business Unit, talked about what retailers are doing to thrive in the midst of new market opportunities.

The most disruptive forces, said Puleri, are often consumer-driven and offer subtle but important insights.  Among them:

● People using their mobile devices differently: “Instagram has a 25% greater engagement rate than Facebook, which tells you that visual is more engaging than text on these devices,” said Puleri.

● Greater willingness of consumers to reveal where they are to third parties: “People are using Uber to hail a cab, which says the ‘creep factor’ about revealing their locations is lessening,” she said. “That’s important because people are keeping their mobile devices within one meter of their body for 23 hours a day.”

● However, people are still wary about data breaches, which have not been limited to retail but have spread to health care and other industries.

● With same-day delivery, “Amazon has set the bar,” said Puleri. Even though the e-tailer loses money on many of its lower-value shipped orders, it has raised consumer expectation levels about service. In addition, the growth of third-party companies handling the “last mile” of delivery have the potential to disintermediate the customer’s loyalty to the retailer. 

Puleri revealed results from New Consumer Study: Retail Without Limits that surveyed 5,000 consumers in 10 countries: 83% insist on the adoption of new technology by retailers, “Because they want to use that technology in their shopping process,” she said. “In addition, 70% rate stock transparency to be very important in e-commerce. Consumers don’t understand that this is a hard thing for many retailers to accomplish; they just want it. And 50% of respondents expect to use their mobile devices for product research, which points to the need for responsive design” that provides optimal experiences on different mobile devices.

More than 160 Oracle customers shared their success stories at OIC. Apparel retailer Lilly Pulitzer’s CIO Keary McNew revealed that Oracle helped the company implement responsive design for its e-commerce offerings last year, and that the retailer would launch a new mobile app for iOS integrated with the Oracle Open Commerce platform in May 2015.

For sports apparel retailer LIDS, the Oracle Retail Locate solution provides visibility into 800 of its stores’ inventories to the retailer’s e-commerce website, and also gives in-store associates tools to find items on the shelves at other stores, and also to arrange for these items to be shipped to different stores or to customers’ homes, according to Vice President of Information Technology Larry Havlik.

Offering these customer-friendly services can create additional challenges to how retailers operate. “In many cases, process changes are harder than technology,” said Puleri. When instituting ship-from-store, for example, “questions arise such as, Where do stores get packing material? What happens when someone cancels an order? Oracle is documenting these processes from retailers all over the world.”

In other remarks, Mike Webster, SVP and General Manager of the Retail and Hospitality Global Business Units at Oracle Webster noted that in addition to its technology offerings, Oracle has deep retail expertise that it makes available to customers. “Omni-channel is an enterprise opportunity that involves getting to a single enterprise view of inventory, customers, orders, price, and promotion,” said Webster. “Focusing just around the edges won’t help: the conversations now are about what retailers need to be thinking about in planning, supply chain, e-commerce, point-of-sale, business intelligence, and customer engagement to support omni-channel. We’ve built that out into our Oracle Retail Reference Model.

“That’s important because ultimately omni-channel isn’t about channels, but about how we bring the power and the process of the entire organization to deliver a differentiated customer experience,” he added.

Dive into the research a little further: Read the full research report


Friday Oct 26, 2012

Analytics in an Omni-Channel World

Retail has been around ever since mankind started bartering.  The earliest transactions were very specific to the individuals buying and selling, then someone had the bright idea to open a store.  Those transactions were a little more generic, but the store owner still knew his customers and what they wanted.  As the chains rolled out, customer intimacy was sacrificed for scale, and retailers began to rely on segments and clusters.  But thanks to the widespread availability of data and the technology to convert said data into information, retailers are getting back to details.

The retail industry is following a maturity model for analytics that is has progressed through five stages, each delivering more value than the previous.

Store Analytics

Brick-and-mortar retailers (and pure-play catalogers as well) that collect anonymous basket-level data are able to get some sense of demand to help with allocation decisions.  Promotions and foot-traffic can be measured to understand marketing effectiveness and perhaps focus groups can help test ideas.  But decisions are influenced by the majority, using faceless customer segments and aggregated industry data points.  Loyalty programs help a little, but in many cases the cost outweighs the benefits.

Web Analytics

The Web made it much easier to collect data on specific, yet still anonymous consumers using cookies to track visits. Clickstreams and product searches are analyzed to understand the purchase journey, gauge demand, and better understand up-selling opportunities.  Personalization begins to allow retailers target market consumers with recommendations.

Cross-Channel Analytics

This phase is a minor one, but where most retailers probably sit today.  They are able to use information from one channel to bolster activities in another. However, there are technical challenges combining data silos so its not an easy task.  But for those retailers that are able to perform analytics on both sources of data, the pay-off is pretty nice.  Revenue per customer begins to go up as customers have a better brand experience.

Mobile & Social Analytics

Big data technologies are enabling a 360-degree view of the customer by incorporating psychographic data from social sites alongside traditional demographic data.  Retailers can track individual preferences, opinions, hobbies, etc. in order to understand a consumer's motivations.  Using mobile devices, consumers can interact with brands anywhere, anytime, accessing deep product information and reviews.  Mobile, combined with a loyalty program, presents an opportunity to put shopping into geographic context, understanding paths to the store, patterns within the store, and be an always-on advertising conduit.

Omni-Channel Analytics

All this data along with the proper technology represents a new paradigm in which the clock is turned back and retail becomes very personal once again.  Rich, individualized data better illuminates demand, allows for highly localized assortments, and helps tailor up-selling.  Interactions with all channels help build an accurate profile of each consumer, and allows retailers to tailor the retail experience to meet the heightened expectations of today's sophisticated shopper.  And of course this culminates in greater customer satisfaction and business profitability.

Sunday Feb 19, 2012

Shopping Habits

I recently read an excellent article from the NYTimes called How Companies Learn Your Secrets in which the author describes how retailers try to understand and shape our shopping habits.  Its a rather long article, so I'll do a bit of summation.

Recall when you first learned to drive how much concentration was required to back out of the driveway.  But now it's a fairly simple task that takes little thought.  That's because the brain has been taught this task, and it's very repeatable without expending tons of effort.  In other words, it's become a habit.  Habits are composed of three steps: cue, routine, and reward.  A large portion of the shopping we do is habitual, like grocery shopping.  There's very little complex decision making, and much of the in-store marketing is ignored.  Enter the toothpaste aisle, snag your brand, and check it off the list.  So how is a retailer to grab a shopper's attention to break out of the habit loop?

One trick is to identify "teachable moments" when a shopper is out of their routine and susceptible to influence.  It turns out that Target is very good at this.  They analyze their customer data to determine when events such as a new job, graduation, home purchase, and marriage have occurred and then do target marketing (pun certainly intended).  After all, those life-changing events can extend change to shopping habits, which will pay off handsomely over time.

Of course the big kahuna of life-changing events is the birth of a baby.  That information is available from public records, so many retailers use the opportunity to mail lots of diaper and formula coupons to new mothers.  If they can establish a relationship with Mom first, they have a better chance of retaining her and her family for a long time.  So to beat the competition, Target wants to market during the second trimester before the other retailers pile on.  But how the heck can that be done?

Diapers and formula are dead give-aways that there's a newborn, so work backwards and examine the products purchased by women leading up to the big event.  It turns out they buy lots of lotions and start switching to scent-free versions of detergent and soaps.  They buy vitamin supplements, cotton balls, and nursery furniture.  Target has gotten so good at their pregnancy prediction scores that they can often determine the due date and sex of the yet-to-arrive baby.

The article goes on to relate a story about an angry father walking into a Target store demanding to see the manager.  He was upset that Target was sending his teenage daughter coupons for baby supplies.  The manager apologized, and followed up a few days later to apologize once again.  However, it was the father that ended up apologizing because his daughter was in fact pregnant.  Oops.

As you can see, this has the potential to be a public relations nightmare, so Target wisely mixes in other coupons alongside the baby products.  This hides the fact from pregnant women that they're being targeted, and doesn't raise alarms with the boyfriends and husbands that are still in the dark.

Oracle plans to release the second module of our Retail Analytics family this year.  Its called Oracle Retail Customer Analytics.  'Nuff said.

Tuesday Oct 18, 2011

Moneyball for Retail

Back in 2003, Michael Lewis wrote Moneyball:The Art of Winning an Unfair Game which was also released last month as a feature film staring Brad Pitt.  The story focuses on the Oakland A's baseball team modernizing its scouting methods to be less subjective and more analytical, allowing it to compete with better funded teams.

Just like it’s not fair that the Oakland A’s $41M budget had to compete with the Yankee’s $125M budget, many retailers find it difficult to compete against Walmart, Target, and Amazon, companies that spend an enormous amount on IT. But it’s possible to follow Oakland’s lead and compete on analytics.  Retailers that better understand their customers will have an advantage, sometimes regardless of the prices they charge or the products they carry (although you really need all three to be successful over the long-haul).

Aileen Lee of Kleiner Perkins Caufield & Byers presented on this topic at the Web 2.0 Summit, which was covered by Richard MacManus in this article.  (Read the article for some real-world retail examples.)  Even small retailers can gain a competitive advantage if they (1) collect the right data, (2) analyze it correctly, and (3) act upon it quickly.

Traditionally retailers didn't do this type of analysis because the data just wasn't available, but since the advent of e-commerce much more data has been collected.  Other sources include loyalty programs, and more recently, social networks.  Retailers have optimized supply chains, new store locations, and even pricing, but the next generation of analytics will focus on individual consumers, understanding what they want and what offers will influence their purchase.

There are lots of ways to attack the problem, and one that's extremely scalable leverages Oracle's engineered systems as described by Jean-Pierre Dijcks:

Not every retailer needs this much analytical horsepower, but imagine if answers were available at the speed of thought.  Privacy aside, the possibilities to personalize the shopping experience are tremendous.

Wednesday May 25, 2011

Oracle Retail Merchandising Analytics Released

Today Oracle Retail announced availability of a new product called Oracle Retail Merchandising Analytics, the first of several BI applications planned for the retail industry. To further describe the product, I've asked Mark Lawrence, the brains behind ORMA, to explain the strategy and why this approach is different than what came before.


It's probably safe to say that those reading this blog are all too aware of retail's "data rich but information poor" reputation, and that today's competitive pressures are forcing the industry to compete on analytics. You can't improve on something if you don't measure it and monitor it, right?

After spending many years building a homegrown Enterprise Data Warehouse (EDW) at Circuit City (eh-hem, great BI was unfortunately not enough to save the company), I was hired by Oracle to lead the creation of a next-generation BI solution for retail. One that would leverage the full Oracle BI/DW technology stack, storage-to-scorecard, yet not necessarily require that full stack. One that would be optimized for Oracle's retail apps, but designed to integrate with non-Oracle data sources as well. One that would not only address retail enterprise needs, but those of the full corporate enterprise. One that was modularized so that it could serve as a retailer's EDW or that could augment an existing EDW with one or more specialized data marts, perhaps enabling a next-gen EDW via incremental data mart implementations. One that could surface BI, properly-filtered, to the right people, at the right time, using the right delivery method whether it be mobile, dashboards, or objects embedded in a planning or operational app. One that I would have wanted to employ at Circuit City, had it been available then (reminds me of my former dream of the "BI guy" saving the company and retiring early on stock options...).

So, Oracle Retail Analytics, with the first of five planned modules just launched last month, embodies all of those things. That first module, Oracle Retail Merchandising Analytics (ORMA,) is now Generally Available, is built on Oracle database 11gR2 and includes packaged integration using Oracle Data Integrator 11g with Oracle's merchandising product family, expansive Oracle BI 11g metadata and reporting, and a data model that is based on Oracle BI Applications 11g to enable cross-domain, retail + ERP/CRM analytics.

Each module is "plug-and-play" in that it includes packaged integration with the associated Oracle Retail applications, fully physicalized data model, and Oracle BI metadata and reporting. What I really like about the strategy is the ability to choose among 5+ retail BI modules and 25+ ERP/CRM BI modules to meet the unique needs of your particular retail enterprise, yet deploy that selection on a consistent and cohesive framework, and do so incrementally if desired.

Want to combine, say, Merchandising with Customer, Loyalty, Finance and HR to turn data from Retail, Siebel, EBS and Peoplesoft into information to drive business decisions? Want to, say, compare labor costs (HR) with sales per employee (merchandising)? Perhaps you have these Oracle apps and want to include supply chain BI coverage but don't own Oracle's supply chain apps? Oracle Retail Analytics is designed to also accept data from non-Oracle sources yet preserve the majority of packaged ETL transformations (ETL tends to consume 60-80% of the effort that goes into developing a BI/DW solution, and we want to pass as much of that value along as we can regardless of data source).

What also really excites me are the possibilities when running Oracle Retail Analytics on Exadata. While we've baked-in plenty of features to enable optimization of both loads and queries on Exadata, we've been careful to ensure great performance and scalability regardless of chosen platform (Exadata is optional). Since we've had the good fortune of being able to design from the ground-up using the very best and latest Oracle tech, at times we've felt like kids in a candy store. Designing "from the ground-up" has also enabled some features that otherwise would be difficult to design in a performant manner, like "as-is/as-was" reporting for the product and organization dimensions - allowing users to account for changes to these dimensions when assessing historical performance. So, as items are reclassified, or stores open, close, or move to new regions, reporting is done based on the dimensions as they were, and/or as they are.

Using Oracle BI 11g, Oracle Retail Analytics enables more than just viewing reports. It enables deep analysis including data mining (detailed, transaction-level data is retained) and in-context and embedded actions - so we have the ability to initiate an action right from a dashboard or report. These actions can include things like triggering a workflow to order more stock, or kicking off a promotion based on events or metric thresholds being crossed. Or, they can be simple things like notifying people of key information, guiding someone to do further analysis. We call this 'Closed Loop Analytics' - because it enables closing the loop between insight and action, and Oracle Retail Analytics is designed with this capability in mind.

If you're at Crosstalk in June, attend my session to learn more. --Mark

About


David Dorf, Sr Director Technology Strategy for Oracle Retail, shares news and ideas about the retail industry with a focus on innovation and emerging technologies.


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