A PERENNIAL PROBLEMThe National Retail Federation, in

conjunction with the University ofFlorida, has been conducting an an-nual National Retail Security Survey(NRSS) since 1991. The results ofthe survey give a pretty clear picture

One of the most significant devel-opments in retail company manage-ment over the past 15 years or sohas been the increasing use of infor-mation technology to run the busi-ness as a systematic, organic whole.The correlation of data from functionslike inventory control, POS, merchan-dising, and so on allows manage-ment to monitor performance on adaily basis, and to correct problemswhen they begin rather than monthsafter the fact.

Data, collected and used this way,is what is often called business intel-ligence. Fed through the right combi-nation of trend-tracking and visuali-zation tools, business intelligencecan enable managers to spot unusu-al activity at the very beginning. They

can then choose to encourage it (ifit’s a good trend) or get it quicklyunder control (if it’s a bad one). Re-tailers are beginning to use businessintelligence to combat what is univer-sally agreed to be a bad trend —shrinkage.

AUGUST 2006 BUSINESSOBJECTS.COM B1

HOW DO YOU DEAL WITH CRIME? THE WAY SERGEANT JOE FRIDAY DID:

GIVE US THE FACTS, MA’AM. JUST THE FACTS.

That said, however, it needs to benoted that a shift is taking place inpilferage patterns. For employeetheft, NRSS figures show a 40% de-cline in average dollar loss per casebetween 2003 and 2005. During thesame period, there has been a 220%increase in dollar loss per case ofshoplifting. If this trend continues atthe 2004-05 rate of change, for thisyear the per-case dollar loss fromshoplifting will be 30% larger than theper-case dollar loss from employeetheft, whereas in 2003 it was 85%smaller.

ORGANIZED RETAIL CRIMEThese numbers cannot be ex-

plained by an upsurge in brazennesson the part of the general population;something new is going on. Duringthe course of a session at the recentNRF Loss Prevention Conference,two LP investigators from Safeway’sNorthern California Division dis-cussed what, exactly, that somethingnew is.

“For years,” said Senior Investiga-tor Celia Kettle, “we convinced our-selves that shrinkage was due prima-rily to a combination of bad inventorypaperwork and employee theft. Sho-

of just how intractable the problem is. Fifteen years ago, in 1991, the surveyshowed inventory shrinkage to be 1.79% of total U.S. annual retail sales. Lastyear it was 1.6%. It has been as low as 1.54% (2004) and as high as 1.95%(1994), but it’s always somewhere between 1.5% and 2% of total sales. (Andnot just here: a study from ECR Europe and University of Leicester, Shrinkagein Europe 2004, shows overall stock loss amounting to 1.84% of total Europeanretail sales.)

It’s also always a lot of money: total dollar loss to U.S. retailing from shrink-age in 2005 was just under $37.4 billion. Although it affects all retailers, someare more susceptible to shrinkage than others. In the U.S., retailers sufferingabove-average losses include accessories, home centers, craft/hobby stores,supermarkets and drug stores, and apparel shops. A long list of retail cate-gories report below-average losses; at the bottom are liquor stores, warehouseclubs, furniture stores, and camera shops, where items tend to be either largeor carefully guarded.

PERCEPTION VS. REALITYRespondents to the 2005 NRSS sur-

vey named employee theft as the sin-gle largest cause of shrinkage, at47.6%; this was followed by shoplifting(32.6%), administrative error (14.6%),and vendor fraud (5.2%). These per-centages have not shifted radicallyover the years; employee theft contin-ues to be the biggest problem, despite(and possibly, to some extent, becauseof) which, retail loss prevention activi-ties tend to focus much more heavilyon shoplifters than on the associatestaff. (This is apparently even more thecase in Europe.)

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1991 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005

1.791.91 1.88

1.951.83 1.87

1.77 1.72 1.691.8

1.7 1.651.54

1.6

Employee Theft$17.8 Billion

Source: University of Florida

Admin. Error$5.5 Billion

Vendor Fraud5.2%

$1.9 Billion

14.6%

Shoplifting$12.2 Billion

47.6% 32.6%

Source: University of Florida

$1,053.24

$1,238.27

$1,762

2005

2004

2003

$854.83

$621.67

$265.40

Employee Theft

Shoplifting

AVERAGE $ LOSS PER CASE

SOURCES OF SHRINKAGE

INVENTORY SHRINKAGE AS % OF SALES

B u s i n e s s I n t e l l i g e n c e t o L i m i t L o s s

you dislike and will prosecutethievery. Among the many ways todo this are initial orientation dis-cussions, hotlines, posters, codesof conduct, newsletters, and peri-odic training programs.

• Asset control. This soundspainfully obvious, but not everyonedoes it: keep an eye on points inthe operation where pilferage cantake place. These include refunds,voids, POS bar coding and scan-ning, exception reporting, employ-ee package checks, trash removal,and interstore transfers.

• LP systems: burglar alarms,closed-circuit television systems(visible and invisible), digital video,check screening, armored cars,POS data mining systems.

GETTING RID OF GUESSWORKThese anti-shrinkage measures are

all valuable, and they are (and willcontinue to be) part of any effectiveloss prevention program. However,they all are, to some degree, basedon guesswork: we know there’s aproblem, and we think some of thismight lessen it. The role of businessintelligence in loss prevention is toreplace guesswork with information:we know what the problem is, andthis is what we’re going to do aboutit.

The Safeway team, for example,didn’t even know it had an organizedcrime problem until it became able totrack shrinkage down to the SKUlevel. To have an ongoing, accurate,actionable view of potential loss

plifting, we said, was a minor factor.We began to get a better sense ofwhat was going on about a year ago,when we set up cross-referencing ofour reports so that we had not justdollar figures but item detail on whatwas missing.”

That’s when they found out that in arelatively short period Safeway hadlost $6 million in one SKU — babyformula — entirely to shoplifting. Inthis context, in fact, “shoplifting” is amisnomer. A shoplifter is somebodywho steals something from a storefor his or her own use, and by defini-tion is an amateur. Somebody whohits six or seven stores a day andscores a half-million dollars worth ofmerchandise a week is a profession-al. And they exist; there is a wholeecosystem of shoplifting teams, get-away drivers, re-labelers (they putnew bar codes on the merchandiseto extend the expiration date), andfences.

KNOWLEDGE IS POWERHow big a problem is this? Nobody

knows. According to the NRSS sur-vey, only a third of responding retail-ers are even tracking organized retailcrime (ORC) data. Those that are, re-ported an average of 23.2 cases per$100 million in sales for 2005, withan average dollar loss of $46,353 percase. If we extrapolated those figuresacross the entire retail universe, itwould mean that two-thirds of allshrinkage — around $25 billion ayear — is due to ORC.

That’s almost certainly not true; formost retailers in most categories, themain problem is probably employeetheft, just like they think it is. Thepoint here is not to be undulyalarmist about organized retail crime— for one thing, total shrinkageseems to be about the same as al-

ways — but to observe that many re-tailers, i.e., the two-thirds that don’teven track ORC data, have no ideawhether they’re being affected by itor not.

The shrinkage data they have, inother words, tells them something iswrong (“I pay for X but can accountfor only Y”) but it doesn’t tell themwhat. This would be unacceptable inmost areas of the operation — imag-ine an inventory-management sys-tem that couldn’t tell you the differ-ence between best-sellers and deadmerchandise — and it will eventuallybecome unacceptable in loss preven-tion as well. The data-managementcapabilities available to loss preven-tion managers are evolving rapidlyinto tools that identify specific prob-lems and enable rapid, specific ac-tion against them.

COUNTERMEASURESWe’ll get back to that shortly. First,

though, let’s take a quick look at con-ventional loss prevention strategies.According to NRSS (and, in a slightlydifferent form, to the ECR Europe re-port), they are basically four:• Integrity screening. Check

out the people you hire. You dothis by verifying past employment,checking to see if they have acriminal record, conducting multi-ple interviews, checking personalreferences, checking their drivinghistory, giving them a drug test,and checking their credit.

• Awareness. Make sure that as-sociates, new and old, know that

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THERE IS A WHOLE ECOSYSTEMOF SHOPLIFTING TEAMS, GETAWAY DRIVERS, RE-LABELERSAND FENCES.

quired alertness, or the red zone ofserious anomaly.

INSTANT REACTIONThis moves loss prevention into a

whole new realm. Once you can ac-quire, prioritize, and monitor data onareas of potential loss on a real-timebasis, you are no longer operating byguesswork and estimation. You arealso no longer one step — or two orthree steps — behind the peoplecausing the shrinkage. Instead, youare in a position to take immediate,appropriate, and effective action.

Business Objects, a globalleader in business information andenterprise information managementsystems, is working with the retailindustry to develop data-gatheringapplications such as the onesdescribed in this article. For moreinformation on business intelligencecapabilities available from BusinessObjects, and how they might be usedto help your organization’s lossprevention program, please contactRuss Hill, Senior Director Retail & Consumer Goods Marketing, 678-627-5097 or via email at russell.hill@businessobjects.com

problems, then, you need to knownot just what’s happening with inven-tory and what’s being stolen. Youalso need to know what’s happening,on as close to a real-time basis asyou can get, with:• Register/POS station re-

ceipts: average, low, high vol-umes by register, by store, by hour,by time of day.

• Refunds: as a percentage ofsales; percentage over the prede-fined cash limit; involving multiplequantities of the same SKU or ahigh incidence of the same SKU;multiple refunds for the same re-ceipt; multiple refunds with no re-ceipt.

• Credit/debit cards: occur-rences of keyed-in cards; authori-zation failures; multiple sales onthe same card; multiple refunds tothe same card; refunds made to anemployee card.

• Employees: transactions by em-ployees; ringing out their ownsales; ringing out their own returns;voids in employee transactions;employee discount sales — dollarvalue, volume, and by whom; pur-chases across multiple stores; pur-chases above a predeterminedvalue; keyed-in transactions as apercentage of total transactions.

• Voids: total number; followed bya no-sale; above a predeterminedacceptable total/price threshold.

• Exchanges: as a percentage ofsales; above a predetermined cashlimit; SKUs with a high incidence ofexchanges.

LIKE DRIVING A CARThe information we’re describing

here will consist of hundreds of thou-sands, if not millions, of data pointsin any given day. The first job of abusiness intelligence system, once

it’s in place and gathering data, is todefine normality: here’s what the op-eration looks like when nothingstrange is going on. Once you’ve es-tablished that, what you really needto know is when something abnormalis happening.

It’s like driving a car. You don’t wantan instrument constantly telling youthat the oil pressure is X pounds persquare inch at a temperature of Y de-grees. As long as the oil is doingwhat it’s supposed to do, in fact, youdon’t want to hear about it at all. Ofcourse there’s oil in there; it’s a car.The same holds true for all thesedata points: yes, yes, fine, there arePOS transactions and voids and ex-changes and so on. There are sup-posed to be; it’s a store.

So in the normal course of busi-ness, the way you want a businessintelligence system to deliver data isnot in the form of printed incident re-ports or spreadsheets. You can getthem, of course, but what you reallywant is a picture. This type of graphi-cal data delivery, drawing from theautomobile analogy, is called a dash-board. Based on the organization’sestablished history, and with busi-ness rules in place — predeterminedcash limits and so on — it shows, forany given parameter (high-valuetransactions, multiple refunds, etc.),whether activity is in the green zoneof normality, the yellow zone of re-

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ONCE YOU CAN ACQUIRE,PRIORITIZE AND MONITORDATA ON A REAL-TIME BASIS,YOU ARE IN A POSITION TO TAKE IMMEDIATE,APPROPRIATE, AND EFFECTIVE ACTION

B u s i n e s s I n t e l l i g e n c e t o L i m i t L o s s