sponsored by: supported by - rsr research report brian kilcourse and paula rosenblum, managing...

Advanced Analytics: Retailers Fixate On The Customer

Benchmark Report

Brian Kilcourse and Paula Rosenblum, Managing Partners

March 2015

Sponsored by:

Supported by:

Executive Summary

Key Findings

Over the past decade the drumbeat sounding the importance of customer centricity in retail has

grown louder and louder. And while high performance computing is enabling faster analysis of

transactional and non-transactional data than ever before, real questions remain about new kinds

of aggregated analytics; all is not sunshine and roses.

Some highlights of the report include the following:

• Customers aren’t getting any easier to understand. In fact, within the Business

Challenges section of this report, retailers report that understanding consumers’ paths to

purchase is more challenging than ever. As always, however, Retail Winners are

approaching the issue differently. This analysis begins on page 6.

• We also see an interesting shift in retailers’ perception of the Opportunities derived from

better use of BI and Analytics (page 9). The most frequently cited opportunity has flipped

from “more intelligent allocation based on customer insights” to gaining a better

understanding of the customers in the first place. This is an important distinction, and

shows real progress on the road to relevance.

• However, Winners are experiencing a real internal issue with personnel. On the opposite

side, lagging retailers are weighted by budgetary constraints. Find out how each plans to

overcome these inhibitors in the Organizational Inhibitors section, beginning on page

13.

• While analysis of customer behavior and cross-channel purchase decisions is by far the

most important objective retailers hope to achieve with their BI & Analytics capabilities,

few are satisfied with what they can do now. Find out what technologies retailers crave

(and value) most in the Technology Enablers section, starting on page 17.

Based on our data, we’ll also offer several in-depth and pragmatic suggestions on how

retailers should proceed. These recommendations can be found in the Bootstrap

Recommendations portion of the report.

We certainly hope you enjoy it,

Brian Kilcourse and Paula Rosenblum

ii

Table of Contents

Executive Summary ........................................................................................................................... i Research Overview ......................................................................................................................... 1

Can We Have Too Much Of A Good Thing? ............................................................................... 1 Building A Better Mousetrap ........................................................................................................ 2 Retail Winners And Why They Win .............................................................................................. 3 Methodology................................................................................................................................. 4 Survey Respondent Characteristics ............................................................................................ 4

Business Challenges ....................................................................................................................... 6 Those Pesky Customers .............................................................................................................. 6 Change Is Hard ............................................................................................................................ 7 Summing Up The Challenges ...................................................................................................... 8

Opportunities ................................................................................................................................... 9 Understanding The Focus On The Customer .............................................................................. 9

Notable Differences On Opposite Sides Of The Pond ........................................................... 10 Keeping Data vs. Experience In Perspective ............................................................................. 10 Is ‘Good Enough’ Really Good Enough? ................................................................................... 11

Organizational Inhibitors ................................................................................................................ 13 In Aggregate, Existing Technology And Budgetary Constraints Rule ....................................... 13 Winners Have A People Problem, Laggards Lack Understanding ............................................ 13 UK &EU Have Infrastructure Problems, US Lacks Talent And Support .................................... 14 Clean The Data And Help Us Aggregate It ................................................................................ 15 Winners Crave Data; Others Looking For Simplicity & Proof Points ......................................... 15

Technology Enablers ..................................................................................................................... 17 Beyond Transactional ................................................................................................................ 17 Winners And Technology ........................................................................................................... 18 What’s The Holdup? .................................................................................................................. 20 A Way To Go Faster? ................................................................................................................ 21

BOOTstrap Recommendations ..................................................................................................... 23 Don’t Forget About The Bottom Line ......................................................................................... 23 Give More Thought To Purpose-Built Tools .............................................................................. 23 Look For Ease Of Use Tools To Avoid Looking For Scarce Talent ........................................... 23 Ease Of Use Should Translate Into Shorter Time-to-Value ....................................................... 23

Appendix A: The BOOT Methodology© ........................................................................................... a

Appendix B: About Our Sponsors.................................................................................................... b Appendix C: About RSR Research ................................................................................................... c

iii

Figures

Figure 1: Trying To Understand The Customer Trumps Other Analytics ........................................ 1

Figure 2: We Have Something – It’s Just Not Enough .................................................................... 2

Figure 3: Retail Winners Really Do Think Differently ...................................................................... 3

Figure 4: Understanding The Customer .......................................................................................... 6

Figure 5: The Raw Data Is There .................................................................................................... 8

Figure 6: Maturing Markets Bring Maturing Opportunities .............................................................. 9

Figure 7: Laggards Still Catching Up In Recognizing Opportunities ............................................. 10

Figure 8: No Real Consensus On BI Methods .............................................................................. 11

Figure 9: Work-In-Progress ........................................................................................................... 13

Figure 10: A Problem With People And A Problem With Money ................................................... 14

Figure 11: Overall: Technology Infrastructure And Processes Help Most .................................... 15

Figure 12: Differences Are Not Subtle ........................................................................................... 16

Figure 13: Opportunity Gap ........................................................................................................... 17

Figure 14: Improving On The Basics ............................................................................................. 18

Figure 15: The Potential Value Of BI & Analytics Technologies ................................................... 19

Figure 16: What’s The Holdup? ..................................................................................................... 20

Figure 17: Time Is The Most Precious Resource .......................................................................... 21

Figure 18: Getting Visual ............................................................................................................... 22

1

Research Overview

Can We Have Too Much Of A Good Thing?

Over the past decade the drumbeat sounding the importance of customer centricity in retail has

grown louder and louder. The introduction and adoption of mobile computing only escalated the

thrumming, and the possibility of actually analyzing the “Big Data” generated from customer

movement across digital channels has brought that rhythm to a fever pitch.

In fact, retailers have always had boatloads of data to sift through. As recently as 2006, Walmart’s

data warehouse was considered the largest non-military data warehouse in the world1.The real

challenge was more about finding the computing power to get relevant answers quickly, and for

most retailers, finding the organizational desire to move from sku-level details to aggregated but

actionable information.

Moore’s Law seems to have solved the problem of computing power; as we’ll see later in this

document, high performance computing is enabling faster analysis of transactional and non-

transactional data than ever before.

The question of organizational will to look at aggregated data remains a subject for debate. While

it appears as though a generational shift in retail management has created a fertile environment

for new kinds of aggregated analytics, all is not sunshine and roses.

As we looked at the data gathered from this year’s benchmark survey on Business Intelligence

and Advanced Analytics, a theme started to emerge. We became concerned that retailers are

putting so much mind-share into analyzing customer behavior that many are missing other

opportunities. Figure 1 shows the core of our concern.

Figure 1: Try ing To Understand The Customer Trumps Other Analyt ics

Source: RSR Research, March 2015

1 Wal-Mart’s Data Warehouse, SCODAWA 2006, Vienna University of Technology, June 2006

42%

48%

52%

81%

40%

49%

38%

17%

18%

4%

10%

3%

Forensic examination of breach data

Cross channel promotional effectiveness & priceelasticity

Data security (such as “HoneyPot trap” analysis to uncover attempts at unauthorized access to

internal systems)

Customer behavior and cross-channel purchasedecisions

Relative Importance Of Analysis Of Various Types Of Non-transactional Data

Very Important Some Importance Little/No Importance

2

Despite the frenetic “race to the bottom” we’ve seen across the retail landscape, less than half of

respondents believe understanding the impact of their cross channel promotions is very

important. Similarly, despite the pummeling that the retail industry has taken on data security

issues over the past three years, only half believe analytics are extremely important in

discovering unauthorized access to systems and less than half believe there is a lot of value in

forensic analysis of this data.

Responses are consistent across all retail functional areas. In fact, most troubling of all are the

responses of those characterizing themselves as “Executive Management.” Only 36% believe the

analysis of cross channel promotional effectiveness is extremely important, and a shockingly low

27% believe forensic examination of breach data is extremely important. This data doesn’t imply

retailers aren’t grudgingly spending the money on better technology. It does mean their priorities

are laser-focused: perhaps too laser-focused.

Of course customer shopping patterns are important. But one would expect the retail executive to

be more eager to explore ways to improve the bottom line through stemming the tide of data

breaches and improving the effectiveness of promotions.

Building A Better Mousetrap

Retailers have been testing the waters of analytics for a long time, but we can see some dramatic

changes afoot (Figure 2).

Figure 2: We Have Something – It ’ s Just Not Enough


Almost one-third are re-thinking their analytical engine options for customer behavior and data

security, and fully 41% are contemplating new ways to analyze the effectiveness of cross-channel

promotions.

22%

22%

26%

38%

25%

41%

30%

30%

25%

20%

22%

16%

12%

9%

11%

11%

17%

8%

12%

5%




internal systems)


How Is Non-transactional Data Being Used?

Implemented/ Satisfied Implemented/ Considering Change

Budgeted Project Planned/ Not Budgeted

No Plans

3

This speaks to the rapid rate of technology change. Historically, retailers were loath to replace

relatively new technologies until they reached “a certain age.” But this data point in particular is

fascinating and tells a new tale: cross-channel analysis of any sort was virtually absent until the

mid-2000’s yet almost half our respondents are contemplating a refresh.

RSR believes this is part of the next dramatic shift in the retail business model. The enterprise is

becoming more scientific, more numbers-oriented, and paradoxically, more focused on

employee-enablement.

In the remainder of this document, we’ll outline the challenges driving this shift, the opportunities

retailers see coming out of those challenges, and internal roadblocks that get in their way. While

technology-oriented responses will be scattered through the report, we’ll dedicate one section to

solely focus on perceived value vs. usage and implementation plans. Finally, we’ll end with

recommendations to help retailers move forward.

Retail Winners And Why They Win

In our benchmark reports, RSR quite frequently cites differences between retailer over-

performers in year-over-year comparable sales and their competitors. We find that consistent

sales performance is an outcome of a differentiating set of thought processes, strategies and

tactics. We call sales over-performers “Retail Winners.”

RSR’s definition of these Winners is straightforward. Assuming industry average comparable

store/channel sales growth of 3.5 percent, we define those with sales above this hurdle as

“Winners,” those at this sales growth rate as “average,” and those below this sales growth rate as

“laggards” or “also-rans.”

To give the reader a flavor for the differences in Retail Winner thought processes, we took a

deeper dive in to the data presented in Figure 1, relative importance of non-transactional data.

The differences are fascinating (Figure 3).

Figure 3: Retai l Winners Real ly Do Think Dif ferently


35%

43%

46%

74%

52%

54%

60%

90%




internal systems)


Percent Citing Non-transactional Data As "Very Important"

Retail Winners All Others

4

Invariably, we find that these differences in thought processes translate into business results.

While it may not be easy to find a direct causal link, the correlations are unmistakable.

Methodology

RSR uses its own model, called the “BOOT Methodology©

,” to analyze Retail Industry issues. We

build this model with our survey instruments. See Appendix A for a full explanation.

In our surveys, we continue to find the kinds of differences in thought processes, actions, and

decisions cited above. The BOOT helps us better understand the behavioral and technological

differences that drive sustainable sales improvements and successful execution of brand vision.

Survey Respondent Characteristics

RSR conducted an online survey from December 2014 – February 2015 and received answers

from 113 qualified retail respondents. Respondent demographics are as follows:

• Job Title: Executive/Senior Management (C-level or VP) 12%

Middle Management (Director, Manager) 34%

Individual Contributor and Other 54%

• Functional Area of Responsibility:

Executive Team 10%

Merchandising 21%

Marketing 16%

Store Operations Management 19%

eCommerce/Direct Operations 6%

Logistics/ Supply Chain 6%

Information Technology 14%

Finance 14%

• 2013 Revenue (US$ Equivalent)

Less than $50 million 4%

$51 million - $249 million 3%



$1Billion to $5 Billion 31%

Greater than $5 Billion 20%

• Products sold:

Fashion/short lifecycle 20%

Seasonal 18%

Basic/replenishment 34%

Perishables 12%

Consumer Electronics/ Durable goods 18%

• Headquarters/Retail Presence:

USA 75% 81%

Canada 0% 33%

Latin America 0% 17%

5

UK 13% 28%

Europe 8% 25%

Middle East 1% 12%

Africa 1% 8%

Asia/Pacific 1% 15%

• Year-Over-Year Sales Growth Rates (assume average growth of 3.5%):

Worse than average (“Laggards”) 7%

Average 50%

Better than average (“Retail Winners”) 43%

6

Business Challenges

Those Pesky Customers

An executive of a large U.S. based chain once jokingly remarked that “Retail would be an easy

business if it wasn’t for those pesky customers.” But those customers aren’t getting any easier to

understand. Since RSR’s earliest studies on the subject of Business Intelligence & Analytics,

we’ve seen a steady progression in retailers’ attitudes about the importance of knowing who their

best customers are and how they get to the point of making a purchase. In fact, when we have

asked retailers to identify the top three business challenges that drive interest in the greater use

of BI & Analytics, “customer issues” have consistently been the most frequently cited challenge:

2008 We can’t identify our best customers to offer special incentives to them while they are shopping

42%

2010 We don’t know what customer sentiment is until we can see it in sales

58%

2012 Shifting from product focus to customer focus 50%

2014 We need to understand consumers' "paths to purchase" 55%

But consumers are changing faster than retailers; when we asked the question about top

three business challenges this year, retailers responded that understanding consumers’ paths to

purchase is more challenging than ever (Figure 4).

Figure 4: Understanding The Customer


39%

31%

47%

49%

33%

43%

55%

22%

38%

42%

43%

46%

51%

58%

Customers are less loyal

Our hyper-competitive and dynamic market creates theneed to model and forecast different scenarios

Information-empowered consumers are more demanding

Consumers expect to have instantaneous access toinformation about products and services wherever they

are

Competitors use customer information as a competitivetool to win more "share of wallet"

Sudden changes in consumer trends and demand - weneed to react more quickly

We need to understand consumers' "paths to purchase"

Top Three Business Challenges Driving Interest In Expanding The Use Of BI And Analytics

2015 2014

7

But what is interesting about this year’s responses compared to 2014 is that the focus has subtly

shifted away from giving the right contextual information to consumers when they demand it, and

more towards using information gleaned about consumers in order to react more quickly to

changes in demand.

Clearly, retailers generally want to be agile enough to respond to shifts in consumer demand long

before they are reflected in sales. But when we looked inside the responses by performance, we

discovered that a more agile response to shifts in consumer demand is the top concern

particularly for Retail Winners (63%, compared to 43% of all other responses). Average and

under-performers on the other hand are far more concerned that “Competitors use customer

information as a competitive tool to win more ‘share of wallet’” (54% compared to only 35% of

Winners). If there’s any one winning behavior to be gleaned from virtually every RSR study ever

published, it’s this: Winners focus on the customer, while laggards focus on the

competition.

Another nugget from responses to the Business Challenges question was revealed when we

looked at them by company size. Although this year’s study revealed a lowered concern that

“Information-empowered consumers are more demanding” (43% compared to 49% in the 2014

study), retailers that generate more than the equivalent of $1B in sales remain much more

concerned than their smaller competition (47% compared to 31% of all others). This points to the

continuing challenge for larger retailers to strive to personalize the shopping experience by

providing relevant information along consumers’ paths to purchase. That can only be

accomplished by understanding the context of each purchase based on stated and implied

preferences, which brings us full circle back to the need to capture more information about each

consumers’ paths-to-purchase and to act on insights gleaned from that data while the customer is

still engaged. In essence, this challenge drives the entire agenda for 21st

Century retailing:

to bring the store to the customer - anytime/anywhere.

Change Is Hard

External business challenges create internal operational challenges. As we mentioned earlier in

this report, Winners stay focused on consumers, while others tend to look over their shoulders at

what others are doing to win market share. Winners want to move faster, but what may initially

seem like alarm bells going off in the headquarters of average and underperformers starts to look

more like an excuse for inaction when we examine what those retailers consider to be the top

three operational challenges (Figure 5).

Respondents of all stripes expressed concern that they “can’t see customer sentiment until it is

reflected in sales”. This isn’t a new concern and is indicative of the fact that for many years

historical sales have been used as a major predictor of future demand. Certainly since the advent

of sku-level scanning at the Point of Sale, item movement has been the primary proxy for

consumer demand, and retailers’ systems and processes are aligned around using that data.

Looking back at past RSR studies, we can see just how intractable this operational challenge is.

In our 2012, 2014, as well as in this year’s study, a virtually identical number of retailers identify

this as a top-3 operational challenge (37%, 39%, and 38%, respectively).

Winners are now trying to get past that challenge and focus on learning how to align

merchandising promotional activities with consumer trends on their paths to purchase, before

they are reflected in sales. Non-winners quickly fall back onto now-familiar excuses for lack of

progress; current systems make it hard to develop alternative forecasts to more effectively

8

anticipate and respond to consumer demand, and operators can’t respond quickly to changes in

demand anyway.

Notwithstanding the differences in how Winners and others see their operational challenges, most

retailers agree that one important challenge has diminished in importance – the availability of the

information stored in their operational systems (Figure 5).

Figure 5: The Raw Data Is There


As we’ll discuss further in the Technology Enablers section of this report, retailers have been

chipping away at their technology portfolios to get at the actionable data locked up inside their

transactional systems. This is important because it underlines a point made in our Introduction -

that retailers have the data to analyze – it’s just a question of what they are trying to understand.

Summing Up The Challenges

From their responses, one could conclude that retailers’ business and operational challenges can

be summed up simply: retailers know they need to glean information about customer

preferences as early as possible to enable more agile responses to current market

conditions. Where each retailer takes it from there is dependent on whether it thinks and acts

like a Winner or not. Winners seek to change the dynamic of their internal Merchandising,

Marketing, (and by extension) Supply Chain operations. The goal is to use customer-centered

insights to forecast demand, make better merchandising and marketing decisions, and adjust

quickly when those forecasts are off. Non-winning retailers fret about their competitors, their

legacy systems, their siloed operations, and their people’s ability to react quickly enough.

Worrying is wasted energy – it’s better to act. In fact, those pesky consumers demand it.

39% 24%

6%

45%

20%19%

2012 2014 2015

Information Is Siloed In Our Operational Systems

Winners Others

9

Opportunities

Understanding The Focus On The Customer

This year, we see an interesting shift in retailers’ perception of the opportunities derived from

better use of BI and Analytics. The most frequently cited opportunity has flipped from “more

intelligent allocation based on customer insights” to gaining a better understanding of the

customers in the first place (Figure 6).

Figure 6: Maturing Markets Br ing Maturing Opportunit ies


Given retailers’ intense focus on the customer, this actually makes a lot of sense. The difference

is subtle, but important.

Last year’s top response presumes products have been selected and purchased, and the

company is deciding locations and channels where demand is strongest. This year’s response

pulls back in time: before buying new product, before allocating it, retailers want a better

understanding in general of what will please their best customers. And having gained that

understanding, then they can deliver relevant products, services and marketing messages to

improve customer productivity.

If retailers can actually execute on these opportunities, they will find themselves with satisfied

customers, and far fewer markdowns.

21%

29%

26%

39%

44%

55%

38%

40%

23%

25%

27%

39%

41%

42%

48%

57%

More effective corporate planning

Ability to identify more opportunities to optimizeoperations

Improved reaction to supply chain shocks

Quicker reaction to sudden shifts in demand

Better "what if" capabilities for matching demand toassortment, price, and promos

More intelligent allocation and optimization of productsbased on customer insights

Deliver relevant products, services and marketingmessages to win more "share of wallet"

Gain a better understanding of who our customers are,their buying habits and their preferences

Top Three Opportunities From Greater Use Of BI And Analytics

2015 2014

10

Notable Differences On Opposite Sides Of The Pond

While we didn’t find many substantive differences between Retail Winners and others, we did find

some interesting differences between US and UK/EU-based retailers.

• More US-based retailers see a top-three opportunity in using advanced analytics to

create “what-if” scenarios. Differences were significant: 46% vs. 29% respectively. RSR

believes, in this regard, the US is likely more mature in its usage of advanced analytics.

• More UK / EU-based retailers see quicker reaction to sudden shifts in demand than US-

based retailers (50% vs. 37% respectively). In prior years, US-based retailers were very

focused on this challenge. RSR believes the Great Recession and its fragile economic

aftermath have inured US–based retailers to this challenge, and so as a response, it is

now less highly prized. Economic conditions across the pond remain fragile.

• Surprisingly, UK/EU-based retailers are more bullish on improving their reaction to supply

chain shocks than US-based retailers (38% vs. 24%). US retailers have worried over port

congestion and dockworker strikes multiple times. We can only assume that it did not

make the top-three as often because they feel somewhat powerless to control those

shocks with or without analytics.

Keeping Data vs. Experience In Perspective

For the past two years we’ve asked retailers to rate each of their organizations’ reliance on data

vs. Experience and Intuition. We’ve noticed, particularly in our merchandising studies that

retailers are becoming almost too numbers-oriented, and forgetting that there’s an art to buying

and also a sense that experience brings wisdom and intuition to all departments.

Our contention is that a combination of data and experience/intuition is the best way to go

for most departments in the retail enterprise. Apparently, for most departments in the

enterprise, a majority of Retail Winners agree (Figure 7).

Figure 7: Laggards St i l l Catching Up In Recognizing Opportunit ies


56%

38%

32%

34%

44%

49%

44%

51%

39%

43%

44%

45%

49%

50%

54%

59%

Store Operations

Marketing

Finance

IT

Sourcing/Procurement

Merchandising

Direct Channel Operations

Product Development

Percentage Of Respondents Using A Combination Of Data & Experience / Intuition

Winners Others

11

It’s interesting to note that the most significant differences are in product development, direct

channel operations (eCommerce), IT, Finance and Marketing. In these departments, Winners are

more apt to report they use this combination.

The only inverted response is in Store Operations, where Winners are far less likely to support

using a mix of data and intuition. They are more likely to support running store operations purely

on intuition than others (30% for Winners vs. only 10% for others). We can only assume that

Winners have effectively communicated the “what” of their expectations, and leave the “how” to

the stores themselves. This will become a problem if and when retailers use in-store

tracking tools like wi-fi and iBeacon to present offers to consumers. In that event, store

layouts will need almost military precision, and that is unlikely to occur in an environment that

supports experience-driven layouts.

And so we can say that the opportunity here is to combine information derived from data and

experience to create a more compelling, efficient, and yes, customer-centric retail enterprise.

Is ‘Good Enough’ Really Good Enough?

BI and analytics have evolved significantly over the past decade. From data warehouse

“appliances” to high-performance purpose-built databases and hardware, the opportunity to dive

deep into the data has never been greater. Yet as we can see in Figure 8, far fewer retailers are

taking advantage of those tools than one would expect.

Figure 8: No Real Consensus On BI Methods


The seeming drop in enterprise BI platform usage is driven by UK and EU-based retailers, with

only 13% selecting it. The same is true of the seeming increase in using operational systems and

applications to derive analytics. They are both driven by the higher percentage of UK/EU

17%

17%

20%

12%

11%

22%

9%

12%

17%

18%

19%

26%

We have a dedicated team of analysts that use ahigh-performance BI toolset

We use an enterprise BI platform on top of adata warehouse

We extract data from our operational systemsinto spreadsheets

Our applications have BI & Analytics capabilitiesbuilt into them

We use an enterprise reporting tool for ouroperational and financial reporting needs

We use the reporting functions that are builtinto our operational systems

Current Primary Method For BI Reporting And Analytics

2015 2014

12

respondents. Still, the data is underwhelming. Retailers are unified in what they want to do, but in

almost complete disagreement on how they can get there.

RSR believes this approach (using whatever is built-in), while inexpensive, is generally sub-

optimal. What made sense even five years ago no longer will suffice. If retailers are serious about

getting deep into customer data, a purpose-built data warehouse with analytics both built in

and discoverable is a must.

We rarely exhort retailers to change technology horses, but in this case, the disconnect between

the desire (understanding customers and their paths to purchase) and the reality (disjointed tool

sets) cause us to say “it’s time to take another look.”

With these defined as the opportunities, it’s time to take a look at what holds retailers back.

13

Organizational Inhibitors

In Aggregate, Existing Technology And Budgetary Constraints Rule

Overall responses sing a familiar refrain: new technology initiatives are hamstrung by existing

infrastructures and it’s hard to get the budget to move forward in any case (Figure 9).

Figure 9: Work- In-Progress


But the data gets a lot more interesting when we look at differences based on performance and

geography.

Winners Have A People Problem, Laggards Lack Understanding

Considering what we’ve learned so far about the tools retailers are using to deliver their BI and

Analytics and combining that with the pride that comes with outperforming one’s peers, it’s not

surprising to see a general unwillingness to change among Winners. It’s also not surprising to find

others, not as flush with success, worrying over budgetary constraints. This data is evident in

Figure 10.

20%

22%

24%

26%

27%

28%

29%

30%

37%

39%

It's hard to quantity ROI for new BI & Analyticscapabilities

Lack of talent that know how to leverage BI andAnalytics

Complexity of the existing tools

Lack of executive support

Availability of analytical talent

The volume of data is overwhelming our ability tosift through it

The corporate culture values experience andintuition over "fact based" decision making

We have a limited understanding of how to usenon-transactional data from digital sources

Budgetary constraints

The data has to be "pulled" from our operationalsystems

Top Three Organizational Inhibitors Standing In The Way Of Making Better Use Of BI & Analytics

14

Figure 10: A Problem With People And A Problem With Money


What’s more surprising, however, is Retail Winners’ belief that they need people on staff who

know how to leverage BI and analytics. The presentation layer of BI and analytic solutions are far

easier to use today than they were even five years ago. Still, at least Winners know what they are

looking for, even as a third of them report organizational resistance as a top-three concern. We

saw virtually no interest among any of our respondents in outsourcing the function. They want to

own their own analytical talent.

Even more fascinating, we see a symptom of what RSR calls “magic bullet syndrome” in these

responses. Even though all respondents overwhelmingly believe that BI and Analytics using non-

transactional data will unlock the secrets of customers’ paths to purchase, almost 40% confess

they have a limited understanding of how this would actually work. We would venture to say that

without this understanding, if the budget was available for new purchases, hopes for the magic

bullet would be dashed.

UK &EU Have Infrastructure Problems, US Lacks Talent And Support

We found some interesting differences in inhibitors across geographies:

• Fully half of retailers from the UK and EU report they have a problem “pulling” data from

operational systems, vs. 38% of US-based retailers

• Retailers from the UK and EU are more concerned about complexity of tools (29% vs.

24%)

• Retailers from the US are more concerned about finding talent to leverage BI and

analytics (25% vs 13%)

• Retailers from the US are more concerned about lack of executive support for new

initiatives (29% vs. 17%)

39%

43%

26%

25%

11%

19%

29%

31%

35%

38%

We have a limited understanding of how to usenon-transactional data

Budgetary constraints

The volume of data is overwhelming our ability tosift through it

The corporate culture values experience andintuition over "fact based" decision making

Lack of talent that know how to leverage BI andAnalytics

Significant Differences In Organizational Inhibitors: Winners Vs. Others

Winners Others

15

The implication here is that our US-based respondents have cleaned up their underlying

infrastructure more, while our UK and EU respondents struggle more with their technology base.

Clean The Data And Help Us Aggregate It

By far, in aggregate, retailers are more eager to solve their technical issues than they are to solve

cultural and budgeting issues (Figure 11).

Figure 11: Overal l : Technology Infrastructure And Processes Help Most


But once again, we see some significant differences between Retail Winners and their peers.

Winners Crave Data; Others Looking For Simplicity & Proof Points

Winners are far more concerned about getting data from operational systems faster and cleaner

than their peers. This is their most frequently cited way to solve their organizational roadblocks

Figure 12).

They also are more interested, this year, in providing mobile access for executives. Last year, this

was farther down their list of priorities.

13%

14%

15%

20%

21%

22%

26%

30%

35%

47%

51%

Mobile access to analytics for executives

Executive mandate

Mobile access to analytics for frontline employees

Hosted solutions

Hiring new talent into the company

Hiring new talent into the company plusoutsourcing

Database technologies capable or processing "Bigdata"

Simpler analysis tools

Pilot programs to demonstrate the value

Tools that can collate all the unstructured datawe gather

Cleaner and more timely data from operationalsystems

Top Three Ways To Overcome Those Organizational Inhibitors

16

Figure 12: Differences Are Not Subtle


Other respondents tend to have more diversified opinions on how to solve their issues. There is

no real outstanding solution to their problems. Rather they look to pilot programs to solve

budgeting and ROI questions, and simpler tools to solve their cultural concerns. There is relative

consistency between their most frequently cited organizational inhibitor, the inability to get at data

quickly and their solution – getting it. After that, responses are fairly muddled.

We suspect the answers simply differ depending on the specific retailer’s culture and financial

situation.

This leads us to the question of tools. What do retailers prize? And what are they planning to do?

19%

11%

22%

28%

34%

40%

17%

43%

10%

17%

17%

23%

25%

29%

29%

63%

Mobile access to analytics for frontline employees

Mobile access to analytics for executives

Hosted solutions

Database technologies capable or processing "Bigdata" (HANA, Exadata, HADOOP, Aster, etc.)

Simpler analysis tools

Pilot programs to demonstrate the value of newadvanced BI & Analytics tools

A combination of hiring new talent into thecompany and outsourcing

Cleaner and more timely data from operationalsystems

Overcoming Inhibitors: Selected Differences Winners Vs. Others

Winners Others

17

Technology Enablers

Beyond Transactional

We saw in the Business Challenges section of this report that retailers have a hard time

gleaning insights about consumer preferences from digitally-enabled paths to purchase. Retailers’

top opportunities revolve around gaining a better understanding of what will please their best

customers, allowing them to deliver relevant products, services and marketing messages to them.

But there’s a long way to go before they achieve that objective. While analysis of customer

behavior and cross-channel purchase decisions is by far the most important objective retailers

hope to achieve with their BI & Analytics capabilities, few are satisfied with what they can do now

(Figure 13). Furthermore, other important insights that could be gleaned from consumers’ digital

paths to purchase are much less interesting or effective, particularly to non-Winners. It’s difficult

to rationalize why retailers would be so interested in customers’ cross-channel purchase

decisions while being less interested in cross channel promotion and pricing effectiveness- the

two are undeniably related.

Figure 13: Opportunity Gap


Perhaps we find the rationale for their focus on understanding consumer shopping behaviors by

examining the weight retailers give to non-transactional customer information for the essentials of

merchandise planning – assortment planning (or “curation”) and product sourcing (Figure 14).

Over 60% of retailers in this study use non-transactional customer data for product sourcing and

17%

17%

25%

35%

35%

43%

46%

74%

21%

42%

33%

33%

52%

54%

60%

90%




internal systems)


Analysis Of Non-transactional Data Gleaned From Consumers’ Digital Shopping Behaviors

(Importance Vs. Status)

Winners: Very Important Winners: Implemented/ Satisfied

Others: Very Important Others: Implemented/ Satisfied

18

attribution decisions, and over 50% use insights from that data for assortment decisions. But as

with analysis of consumers’ cross channel purchase decisions themselves, retailers are not

satisfied with their current capabilities.

Figure 14: Improving On The Basics


The favored uses of non-transactional data suggest that retailers are thinking serially about their

merchandising practices, as in, “first let’s get our assortment plans right and make better

decisions about how to source products based on where and how they are purchased: then we’ll

think about the next set of issues, pricing and cross-channel promotion effectiveness.”

Winners And Technology

Throughout this report we have seen that Retail Winners have a more favorable view of using BI

& Analytics capabilities to overcome business challenges and create business opportunities.

Likewise, we’ve seen that Winners more strongly favor using data to augment experience and

intuition throughout the business.

It follows then that Winners also put higher value on the technologies that deliver insights.

Responses to our survey bear that out (Figure 15).

That is not to say that other retailers don’t value the technologies that can deliver insights to the

business, but they do hedge their bets, particularly for the top 5 technologies (web browser

access, mobile access, data warehouse, reporting, and analytics) ranked by Winners has having

“a lot of value”, by weighting them more towards only delivering “some value”. On the flip side,

more Winners than other retailers assign “little or no value” to the bottom five listed technologies

(scorecards & dashboards, ETL technologies, analytics provided as web services, and natural

language processors).

While ETL itself is not particularly exciting to the business user, it is foundational to getting data

from operational systems into analytics engines. As such, IT departments will still likely and rightly

place it high on their priority lists. That’s the best way to overcome the internal challenge of

moving data across systems.

23%

27%

29%

34%

25%

19%

12%

12%

12%

9%

Assortment curation decisions

Product sourcing and attribution decisions

To What Extent Is Your Company Using Non-transactional Data Gleaned From Customer-facing Digital Channels For

The Following Analyses?

Implemented/ Satisfied Implemented/ Considering Change

Budgeted Project Planned/ Not Budgeted

No Plans

19

Figure 15: The Potential Value Of BI & Analyt ics Technologies


Winners especially highlight the advantage of what might be considered baseline technologies

needed to deliver deeper insights beyond what operational systems are typically able to do

(Analytics and Reporting toolsets, and Data Warehouse technologies).

At the start of this document, we observed that retailers are making use of high performance

computing platforms to manage their transactional and non-transactional data. Here we see the

value ascribed by close to half of Winners in doing just that.

23%

34%

34%

37%

34%

35%

35%

46%

42%

43%

48%

51%

54%

48%

25%

33%

35%

35%

38%

46%

50%

54%

56%

56%

58%

60%

73%

85%

Natural Language processors

Analytics provided as web services

Scorecards & Dashboards

ETL (extract/transform/load) technologies

Integration to desktop spreadsheet tools

Large scale "Big Data" technologies (such asHADOOP, HANA, Aster, etc.)

Alerts

Data visualization

Predictive modeling

Web browser access

Mobile Access

Data warehouse

Reporting

Analytics

POTENTIAL Value Of BI & Analytics Technologies (A Lot Of Value)

Winners Others

20

What’s The Holdup?

Figure 15 confirms what RSR knows about Retail Winners from other studies we have conducted:

Retail Winners have a more positive view of the use of information to create strategic

advantage. But when we look at the implementation status of Winners’ five most valued BI &

Analytics technologies (web browser access, mobile access, data warehouse, reporting, and

analytics) compared to retailers that report they are either not satisfied with their current

capabilities or haven’t yet implemented them at all vs. those that have projected budgeted this

year, we see a gap. Winners and other retailers alike assign high value yet they are not moving

very quickly (Figure 16).

Figure 16: What ’s The Holdup?


The question obviously is, “What’s the holdup?” Why aren’t retailers doing more with their

spending plans to get the value that they believe is there? For the most highly valued technology,

“Analytics”, non-Winners are actually being more aggressive than Winners, perhaps hoping to

leapfrog the competition.

We saw that the answer in Figure 11: Winners feel constrained by the lack of talent available

and others feel constrained by the lack of money. These findings demonstrate the challenge

and the opportunity for solutions providers.

Simply put, retailers are looking for help, and they are (as always) constrained by their budgets.

This comes into sharper focus when we look at retailers’ top considerations for technology. Ease/

speed of deployment tops other considerations, but implementation costs are right behind in

importance – even higher than license structure and price (Figure 17).

-8%

6%

0%

19%

23%

-9%

3%

11%

2% -5%

-20%

-10%

0%

10%

20%

30%

Web browseraccess Mobile Access

Datawarehouse Reporting Analytics

Gap Between 'Hi-Value & 'Not Satisfied' vs. 'Budgeted Project'

Winners Others

21

Figure 17: Time Is The Most Precious Resource


When we look at the importance of the various implementation considerations today compared to

a year ago, a fresh sense of urgency is revealed. While financial issues have risen in importance,

the value of time is has grown even more important.

A Way To Go Faster?

Retailers are in a hurry, but they are caught in a conundrum: they need simpler-to-implement-

and-learn BI & Analytics, but Winners in particular don’t have the internal talent needed to push

the accelerator. Rather than waiting for the situation to magically improve, over-performers are

exploring new ways to get value from the insights buried in their data: “data visualization”

technologies (Figure 18).

35%

50%

53%

56%

56%

58%

63%

68%

Library of pre-defined analysis “frameworks”

The ability to present data in a visual, graphical form (“Data Visualization”)

Time to value

Time to learn

The ability to discover new relationships betweendata

License structure and price

Implementation costs (hardware, integration,customization)

Ease, speed of deployment

BI & Analytics Techs: Implementation Considerations

5%

12%

14%

15%

15%

Time to value

Implementation costs (hardware, integration,customization)

License structure and price

Ease, speed of deployment

Time to learn

Implementation Considerations: 2015 vs 2014 (%Chg)

22

Figure 18: Gett ing Visual


Retail Winners have been much more aggressive in implementing technologies that democratize

data analysis, for presenting the results of analysis to business users, for prototyping the

business with BI & Analytics tools (for example, performing what-if analyses based on different

forecasts), and for discovering new relationships between data.

This last point is particularly relevant given that retailers in this study show a decided preference

towards understanding what customers’ digitally enabled paths-to-purchase reveal. Retailers are

still discovering the relationships between customer activities (for example, the relationship

between clicking on reviews and making a purchase decision) – but they don’t want to need to

have a Ph.D. in Math on the staff to help them learn those relationships.

Therefore, the ability of the BI & Analytics tools to represent insights visually is becoming more

important than ever – Retail Winners are most aware of the possibilities.

28%

42%

18%

31%

23%

40%

42%

38%

43%

29%

29%

27%

11%

15%

14%

25%

23%

23%

20%

6%

25%

15%

25%

10%

Others

Winners: For data discovery

Others

Winners: For prototyping

Others

Winners: For presentation to business users

What Are Your Plans For Presenting Data In A Visual, Graphical Form (Data Visualization) Within Your Company?

Implemented Implementing Now Planned Within 2 Yrs No Plans

23

BOOTstrap Recommendations

After years exhorting retailers to pay attention to the wants and needs of their customers, we find

ourselves in the somewhat uncomfortable position of suggesting that perhaps they’ve become too

laser-focused in that area.

Business Intelligence and Advanced Analytics afford many opportunities for retailers, and as

such, we’ll focus this section on recommending other areas to explore.

Don’t Forget About The Bottom Line

While focus on the customer and her paths to purchase will likely deliver insights into ways to

improve the top-line, bottom line concerns are equally important. This brings us full circle back to

the importance of using Advanced Analytics in other areas:

• Data breaches are a hot news topic, and it’s best to squelch them quickly. We’ve said

many times that in today’s world it’s not a question of whether or not you’re going to be

breached; You will be. The challenge is in identifying breaches quickly, and catching the

intruders. It’s no longer acceptable to have data thieves wandering around in retailer

computer networks for months at time. Analytics are critical for this purpose.

• Promotional reliance will continue for a long time, and it’s imperative to understand the

impact promotional decisions will have or even did have on the bottom line. This is a key

value driver in advanced analytics.

• Shrink takes away valuable gross margin dollars. Delivering various kinds of exception

reports to field management is imperative to keeping shrink under control, and minimizing

administrative errors that lead to margin erosion.

Give More Thought To Purpose-Built Tools

Moore’s Law has operated at phenomenal speeds. While operational systems may provide

decent analytical tools, advances in computing power have brought decision-making tools to the

masses. Think about taking advantage of those tools. They can be used “through the cloud” or

on-premise, depending on your budget.

Look For Ease Of Use Tools To Avoid Looking For Scarce Talent

We recognize the significant lack of data science talent. Most available talent, particularly in the

US, seems to have moved into financial services companies, or to the vendor community.

Retailers have the right to demand easy-to-use interfaces for their various Analytical tools. The

vendor community has started delivering these interfaces. There’s no reason not to use them.

Ease Of Use Should Translate Into Shorter Time-to-Value

An old IT joke goes: “What do you call a twenty-year-old system?” Answer: “De-bugged.” In other

words, old systems often appear to be relatively inexpensive to own. They take the strain off IT

budgets. We believe it’s time to think beyond Total Cost of Ownership (TCO). Older, stable

technologies may seem to have lower TCO, but their value is limited. It’s time to think about

technologies that can bring a company up-to-speed quickly and effectively. We are truly in a new

era, where technologies are easier to use, and require less training. This is retail’s future.

a

Appendix A: The BOOT Methodology©

The BOOT Methodology© is designed to reveal and prioritize the following:

• Business Challenges – Retailers of all shapes and sizes face significant external challenges. These issues provide a business context for the subject being discussed and drive decision-making across the enterprise.

• Opportunities – Every challenge brings with it a set of opportunities, or ways to change and overcome that challenge. The ways retailers turn business challenges into opportunities often define the difference between Winners and “also-rans.” Within the BOOT, we can also identify opportunities missed – and describe leading edge models we believe drive success.

• Organizational Inhibitors – Even as enterprises find opportunities to overcome their external challenges, they may find internal organizational inhibitors that keep them from executing on their vision. Opportunities can be found to overcome these inhibitors as well. Winning Retailers understand their organizational inhibitors and find creative, effective ways to overcome them.

• Technology Enablers – If a company can overcome its organizational inhibitors it

can use technology as an enabler to take advantage of the opportunities it identifies.

Retail Winners are most adept at judiciously and effectively using these enablers,

often far earlier than their peers.

A graphical depiction of the BOOT Methodology©

follows:

b

Appendix B: About Our Sponsors

SAS helps retailers develop a deep understanding of customer needs while simultaneously

supporting better decisions to improve processes and boost the bottom line. In this age of big,

complex data, and omni-channel retailing, SAS continues to innovate by listening to our

customers and refining our portfolio of retail products accordingly.

Our unparalleled retail business knowledge - coupled with powerful, advanced analytics – allows

retailers to anticipate customers' wishes, empowers retailers to act, and drives better results

throughout the enterprise. SAS' solutions for retail, are available through a variety of investment,

deployment and growth options.

SAS helps customers at more than 70,000 sites improve performance and deliver value by

making better decisions faster. Since 1976 SAS has been giving customers around the world

THE POWER TO KNOW®.

Learn more at: www.sas.com/retail.

Tyco Retail Solutions is a leading provider of integrated retail performance and security solutions,

deployed today at more than 80 percent of the world's top 200 retailers. Customers range from

single-store boutiques to global retail enterprises. Operating in more than 70 countries worldwide,

Tyco Retail Solutions provides retailers with real-time visibility to their inventory and assets to

improve operations, optimize profitability, and create memorable shopper experiences. For more

information, please visit www.tycoretailsolutions.com.

c

Appendix C: About RSR Research

Retail Systems Research (“RSR”) is the only research company run by retailers for the retail

industry. RSR provides insight into business and technology challenges facing the extended retail

industry, providing thought leadership and advice on navigating these challenges for specific

companies and the industry at large. We do this by:

• Identifying information that helps retailers and their trading partners to build more

efficient and profitable businesses;

• Identifying industry issues that solutions providers must address to be relevant in the

extended retail industry;

• Providing insight and analysis about a broad spectrum of issues and trends in the

Extended Retail Industry.

Copyright© 2015 by Retail Systems Research LLC • All rights reserved.

No part of the contents of this document may be reproduced or transmitted in any form or by any means without the permission of the publisher. Contact [email protected] for more information.

sponsored by: supported by - rsr research report brian kilcourse and paula rosenblum, managing...

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