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Transcript of The intelligent retailer's world of insight(1)
The Intelligent Retailer’s World of Insight
Benchmark Report 2011
Brian Kilcourse and Paula Rosenblum, Managing Partners
November 2011
Sponsored by:
Supporting Sponsors:
i
Executive Summary
In an era of continued global economic uncertainty, rapid response to market conditions is
increasingly important. Once disparate departments within the retail enterprise now need to
respond as a single organism. An important tool to enable this responsiveness is an Enterprise-
wide BI strategy. The need has grown and more retailers are moving in the direction of putting
one in place. The value of this enterprise-wide strategy is to ensure that each department is
operating from the same set of data, delivered at the same time. Delivery mechanisms can and
will likely differ depending on the physical location of the data consumer, but the data itself is
consistent across channels, geographies, departments and roles.
Business Challenges
In the five years that RSR has been conducting benchmarks on the subject of BI, retailers have
consistently expressed a need to move more quickly. The need for speed remains the most
frequently cited business challenge driving new BI and Analytics initiatives. But the challenge is
different for Retail Winners compared to all other retailers. While average and laggard performers
aren’t getting the information quickly enough, most Retail Winners are getting the information
quickly, but are unable to react to what it reveals. Additionally, more real-time information on
relevant and personalized cross-sells, up-sells and hot promotions, along with actionable
information about customer complaints, should be deliverable - but the industry, for the most part,
is lagging.
Opportunities
Most retailers share the same desire to retain customers longer, and as a result have shifted the
focus of their BI efforts to the stores. Winners additionally focus on improving their ability to react
more quickly to supply chain disruptions outside the “four walls” of the business. Non-winners put
more faith in opportunities that are inside “the four walls” of the business, after the receipt of
goods.
Organizational Inhibitors
For most retailers, siloed information contained in existing “legacy” transactional systems is by far
their biggest operational impediment to delivering new generation BI capabilities. But retailers
also complain more that it’s hard to quantify the technology ROI for new BI capabilities. To
overcome this inhibitor, many are turning to pilot projects to prove the value of new BI and
Analytics capabilities.
Technology Enablers
Retailers understand that without a robust technology infrastructure, transforming mountains of
transaction and customer data into useable metrics is almost impossible. While the “plumbing” for
BI is being put into place, retailers are excited at the prospect of bringing consumer-grade
usability into the enterprise. But today’s reality is different: while desktop scorecards and
dashboards have clearly become more ubiquitous, a surprising percentage of C-level executives,
store managers and other retail executives are still predominantly getting their analytics through
“Flash” reports.
BOOTstrap Recommendations
RSR’s recommendations to retailers regarding next-generation BI and Analytics are as follows:
ii
1. Get an enterprise-wide BI strategy in place. Such a strategy will have these critical
components: executive commitment; an infrastructural plan for creating, retrieving, updating,
and deleting “big data”; a wireless plan for the stores; a roadmap that insures a step-wise
approach to implementation, and modern “delivery vehicles” for actionable information.
2. Prioritize those who most need real-time information, and information that is most valuable.
Temper the enthusiasm created by consumer-oriented smart mobile technologies with
appreciation for the underlying complexities.
iii
Table of Contents
Executive Summary ........................................................................................................................... i Research Overview ......................................................................................................................... 1
Why Did We Undertake This Research? ..................................................................................... 1 Traditional Approaches and Conventional Wisdom Now Fall Short ............................................ 2 Guidelines Used for Describing BI in this Report......................................................................... 3 RSR’s Methodology and “What’s a ‘Retail Winner’ Anyway?” ..................................................... 4
Defining Winners and Why They Win, and Why Laggards Fail ............................................... 4 Survey Respondent Characteristics ............................................................................................ 4
Business Challenges ....................................................................................................................... 6 Can’t Get Information Fast Enough or Can’t Act on What They See .......................................... 6 Delivery Mechanisms Lag ............................................................................................................ 7 The Data Delivered Remains Somewhat Pedestrian .................................................................. 8 Despite the Challenges, Opportunities Abound ........................................................................... 9
Opportunities ................................................................................................................................. 10 Pushing Reaction Time To The Front Of The Process .............................................................. 10 Getting Smart In The Store ........................................................................................................ 11 What About New Demand Signals From Social Media? ........................................................... 12
Organizational Inhibitors ................................................................................................................ 14 Siloed Systems Supporting Siloed Business Units .................................................................... 14 Status Quo ................................................................................................................................. 15 Pilot Projects Gain Favor ........................................................................................................... 16
Technology Enablers ..................................................................................................................... 18 There’s a Lot of Plumbing Under those Dashboards ................................................................. 18 Beyond the Excitement and Promise, What’s the Reality Today? ............................................ 19
BOOTstrap Recommendations ..................................................................................................... 21 1. Get an Enterprise-wide BI Strategy in Place ......................................................................... 21 2. Prioritize those Who Need Real-time Information Most ......................................................... 21 3. Temper Enthusiasm with Appreciation for Complexity of the Task ....................................... 21
Appendix A: RSR’s Research Methodology .................................................................................... a Appendix B: About Our Sponsors.................................................................................................... b Appendix C: About RSR Research .................................................................................................. d
iv
Figures
Figure 1: Not Your Father’s Uses for Business Intelligence ............................................................ 1
Figure 2: Enterprise-wide BI: 80% are doing SOMETHING…. ....................................................... 3
Figure 3: Either We Can’t Get the Data or We Can’t Do Anything about It ..................................... 6
Figure 4: Smaller Retailers Challenged to Recognize Best Customers .......................................... 7
Figure 5: Delivery Vehicles Lag for Everyone but Consumers ........................................................ 8
Figure 6: Pedestrian Data Yields Sub-optimal Results ................................................................... 9
Figure 7: Nimble On The Buy Side? .............................................................................................. 10
Figure 8: Getting Back To The Store ............................................................................................. 12
Figure 9: Not Getting All The Signals - Yet ................................................................................... 13
Figure 10: Legacy .......................................................................................................................... 14
Figure 11: Frog In a Kettle? ........................................................................................................... 15
Figure 12: “Try It, You’ll Like It!” .................................................................................................... 16
Figure 13: Infrastructures Matter ................................................................................................... 18
Figure 14: Delivery Mechanisms all Sound Really Appealing… ................................................... 19
Figure 15: …but Reality Lags Behind ............................................................................................ 20
1
Research Overview
Why Did We Undertake This Research?
Business Intelligence and its resultant analytics have come a long way in retail over the past five
years. These changes are enabled by faster hardware and informed by new data and user
interfaces emerging from the consumerization of IT. New, simpler to use tools and techniques are
being used by retailers track and monitor performance.
Specifically we find BI-generated reports, dashboards and alerts:
• moving out of the glass house into the hands of decision-makers
• shifting from long lag-time look backs to near-real-time feedback loops
• becoming more granular and detailed
• shifting focus from solely within the enterprise to 360 degree views – from source to
consumption
This is evident in retailers’ assessment of BI value (Figure 1):
Figure 1: Not Your Father’s Uses for Business Intel l igence
Source: RSR Research, November 2011
While retail over-performers (the group RSR calls “Retail Winners”) have a slightly different focus
than the aggregate, the overall response pool calls out the importance of getting information
faster and places a greater focus on evaluating the entire value chain, from source to
consumption.
29%
36%
36%
38%
39%
42%
42%
57%
62%
40%
41%
54%
45%
36%
29%
40%
30%
32%
31%
23%
11%
16%
25%
29%
19%
12%
5%
Enable a “360 degree” view of our business (customers, suppliers & partners, internal operations)
Maximize the value of our investments in inventory
Help plan product assortment, allocation, pricing andpromotions
Help optimize supply chain performance
Match internal process performance metrics tocustomer satisfaction metrics to assess the value of…
A tool to manage “exceptions” as they are happening, not after-the-fact
A tool to support more timely responsiveness todemand changes
Understand customer behaviors in order to executeour business strategy and build loyalty
Track key performance data to control our internalprocesses and compare actual performance against plan
Value of BI to Support Business Processes
Very Relevant Somewhat Relevant Little to No Relevance
2
Retail Winners tend to be more outwardly focused than their peers:
• 40% of Retail Winners find 360 degree views of the business to be very relevant vs. 15%
of all other respondents
• 69% of Retail Winners believe understanding customer behaviors to help build business
strategy is very important vs. 35% of all other respondents
• Almost half (47%) of Retail Winners believe in is very important to match their internal
performance metrics with customer satisfaction metrics to evaluate their business vs.
only one quarter (25%) of all other respondents
Clearly in an era of continued global economic uncertainty, rapid response and outwardly facing
metrics are increasingly important.
Traditional Approaches and Conventional Wisdom Now Fall Short
Retailers and economists have long used metrics like consumer confidence and the fluctuating
price of oil and other commodities as a predictor of demand. They have also used their own
products’ past performance as prelude to the future. But the Great Recession and the uncertain
economic years that followed have shown these forecasts to be unreliable for retailers at all levels
of performance1.
Similarly, conventional wisdom long held that all reductions in payroll-to-sales ratios in stores
were good reductions. However, as the web and other selling channels have become more
convenient, the lack of helpful staff in stores has become more obviously inconvenient for
shoppers who have found their voice in Social Media, and found alternatives through mobility.
RSR’s research has shown payroll-to-sales ratios are finally stabilizing2, but tools are clearly
needed to insure that in-store payroll is acting as productively and as frequently in customer-
facing roles as possible.
In the face of so much uncertainty, and with the need to respond as a single organism rather than
as a set of disparate departments, the recognition of the value of an Enterprise-wide BI strategy
has grown and more retailers are moving in the direction of putting one in place (Figure 2).
1 Twenty-first Century Merchandising Takes Hold: Benchmark Report 2011, RSR Research, August 2011 2 The 21
st Century Store: The Search for Relevance, Benchmark Report, RSR Research,
June 2011
3
Figure 2: Enterprise-wide BI : 80% are doing SOMETHING….
Source: RSR Research, November 2011
The value of an enterprise-wide strategy is that it insures each department is operating from the
same set of data, delivered at the same time. Delivery mechanisms can and will likely differ
depending on the physical location of the data consumer, but the data itself is consistent across
channels, geographies, departments and roles.
Guidelines Used for Describing BI in this Report
We’ve found differences in terms used by retailers and vendors when describing BI and analytics.
To set a level playing field, we make the following distinctions:
• Many people consider the terms Business Intelligence (BI) and Analytics to be
interchangeable. For our purposes in this report, we will take this route. BI churns data
and produces outputs. Those outputs are “analytics.” For our purposes, they both fall
under the topic “BI.” “Advanced” analytics offer the ability to optimize pricing, model
customer behavior, segment customers, forecast demand and more. As part of an
enterprise BI strategy, these advanced analytics should be reviewed distinctly from
reporting and are beyond the scope of this document.
• Our definition of “real-time BI” means “as real-time as it needs to be”. As we’ll see later,
in many instances, retailers are receiving information faster than they can actually use it.
In our view real-time BI delivers actionable information into the hands of decision-makers.
With these nuances explained, we’ll move on to the details of the report.
5%
15%
35%
17%
28%
Very low priority or no plans
We see the value, but it's not at the top of ourpriority list
We're working on putting one in place
We've have one in place for less than twoyears
We've had one in place for longer than twoyears
To What Extent Does Your Company Have Enterprise-wide BI Strategy in Place?
4
RSR’s Methodology and “What’s a ‘Retail Winner’ Anyway?”
RSR uses its own model, called the “BOOT,” to analyze Retail Industry issues. We build this
model with our survey instruments. Appendix A contains a full explanation of the methodology. In
a nutshell, the BOOT consists of four parts:
• Business Challenges – the external challenges a company faces.
• Opportunities – the ways the company perceives it can overcome those challenges
• Organizational Inhibitors – the internal barriers the company faces that may prevent it
from taking advantages of the opportunities it sees
• Technology Enablers – assuming a company can overcome its internal issues, the
technology tools it can use to support taking advantage of the opportunities it identifies
Defining Winners and Why They Win, and Why Laggards Fail
In our surveys, we continue to find differences in the thought processes, actions, and decisions
made by retailers who outperform their competitors and the industry at large – Retail Winners.
The BOOT model helps us better understand the behavioral and technological differences that
drive sustainable sales improvements and successful execution of brand vision.
Our definition of these Retail Winners is straightforward. We judge retailers by year-over-year
comparable store/channel sales improvements. Assuming industry average comparable store/
channel sales growth of two percent (the bar in a post-recession world is relatively low), 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.” Because there
have been so many strong retail “comebacks” post-recession, we also identified those whose
comparable increases exceeded 10%. It is consistent throughout much of RSR’s research
findings that Winners don’t merely do the same things better, they tend to do different things.
They think differently. They plan differently. They respond differently.
Laggards also tend to think differently. They may have spectacular vision, but often fail on
execution. They may forget the power and breadth of choices today’s customer has. They fail to
re-invent themselves when it becomes obvious their existing business model is no longer
working. They don’t change their business processes in an effective manner, and so they either
eschew technology enablers, or don’t gain expected Return on Investment on those they DO buy.
In good times, they skate by: in tough times these weaknesses come back to haunt them.
Survey Respondent Characteristics
RSR conducted an online survey from July - October 2011 and received answers from 95
qualified retail respondents. Respondent demographics are as follows:
• Job Title:
Senior Management (CEO, CFO, COO) 23%
Vice President 32%
Director/Manager 27%
Internal Consultant 6%
Internal Staff & Other
12%
• 2010 Revenue ($ Equivalent):
Less than $249 Million 32%
$250 - $999 Million 9%
5
$1 - $5 Billion 26%
Over $5 Billion
18%
• Selling Format:
Fast Moving Consumer Goods 38%
General Merchandise and Apparel 46%
Food Service/Hospitality 16%
• Headquarters/Retail Presence:
United States 61% 67%
Canada 7% 26%
Latin America 2% 20%
Europe 11% 27%
United Kingdom 4% 21%
Asia Pacific 11% 31%
Middle East 1% 11% Africa
2% 10%
• Year-Over-Year Comparable Store Sales Growth Rates (assume average growth of 2%):
Worse than Average (Laggards) 16%
Average 19%
Better than Average (Retail Winners) 54%
More than a 10% Improvement 11%
6
Business Challenges
Can’t Get Information Fast Enough or Can’t Act on What They See
In the five years that RSR has been conducting benchmarks on the subject of BI, retailers have
consistently expressed a need to move more quickly. In 2007, this was at least somewhat
influential to more than 90% of survey respondents. This year the need for speed remains the
most frequently cited business challenge (Figure 3).
Figure 3: E ither We Can’t Get the Data or We Can’t Do Anyth ing about It
Source: RSR Research, November 2011
But we’ve also seen a shift this year. While just more than half of respondents are not getting
information to merchants quickly enough, just under half of respondents get the information, but
can’t act on what they receive. The organization’s ability to respond lags its ability to inform.
This is most evident when looking at Retail Winners vs. the rest of the respondent pool. Sixty
percent of average and laggard performers aren’t getting the information quickly enough, and
59% of Retail Winners are getting the information quickly, but are unable to react.
27%
60%
27%
60%
47%
33%
33%
10%
28%
31%
48%
48%
48%
59%
17%
37%
33%
52%
46%
41%
48%
Logistics managers don’t get information fast enough to minimize the impact of supply chain
problems
Can’t identify our best customers to offer special incentives to them while they are shopping
We struggle to match inventory to demand
Merchants don’t get information fast enough to react to differences between what they thought
would happen vs. what is actually happening
Marketing doesn’t know what customer sentiment is until we can see it in sales
Can’t support customer cross-channel activities very well
We can’t act quickly enough on the information we receive
Top Three Business Challenges that Create Interest in Using Near-real-time BI
All Respondents Winners All Others
7
More significant differences emerge when looking at Retailers across different revenue bands.
The largest retailers, those with annual revenue over $5 billion are caught in BOTH conundrums.
Seventy percent report their merchants don’t get information fast enough (vs. only 38% of
retailers with annual revenue less than $250 million), and 60% report that they can’t act quickly
enough on that information when they do get it. These are the most significant business
challenges they face, by a wide margin.
The smallest retailers, on the other hand, also can’t act on what they do receive (69%), but in
addition they are challenged to identify their best customers (Figure 4).
Figure 4: Smal ler Reta i lers Chal lenged to Recognize Best Customers
Source: RSR Research, November 2011
This is problematic, given that most small and mid-sized retailers attempt to differentiate through
knowing their customers and the products they prefer. Without a proper BI infrastructure and
tools, they may find themselves losing their most important advantage against their larger
competitors. When Amazon.com knows your customers’ preferences better than you do, a local
retailer is in serious trouble.
Delivery Mechanisms Lag
When we look at the most typical delivery vehicles used to present BI data to various
constituents, it becomes easier to understand why it’s hard to both get and react to data.
Dashboards are great tools for desk-bound C-level and Line of Business (LOB) executives and
managers, but fall short when delivered to people in the field, like store managers and
employees. And the still ubiquitous “flash sales report” typically involves poring over information,
rather than creating an instant call to action (Figure 5).
46%
60%
17%
30%
Less than $249million
$250 million - $999million
$1 Billion to $5 Billion Over $5 Billion
Identifying Best Customers as a Business Challenge (based on Annual Revenue)
8
Figure 5: Del ivery Vehicles Lag for Everyone but Consumers
Source: RSR Research, November 2011
Today, customers are the most likely recipients of mobile alerts across all revenue bands.
Obviously this needs to change. Store Managers and employees must be armed with up-to-date
information, and can’t be expected to sit at desks or pore over reports while customers wander
around the store, smart phones in hand.
Happily we are seeing many indications of pre-packaged mobile solutions coming from the
vendor community, and are hearing early use-case results and new pilots underway for in-store
employees. The explosion of tablets as an affordable form-factor is making this shift possible and
we expect to see a significant uptick in adoption over the coming year.
The Data Delivered Remains Somewhat Pedestrian
Just as delivery mechanisms have lagged, so have the data elements being delivered to
constituents. While it’s useful to know best and worst sellers, we also believe tools to identify
best customers as they enter the store or corporate ecommerce site should be part of the BI data
portfolio. As we can see below in Figure 6, however, the data delivered remains uninteresting.
We’d love to see more real-time information on relevant and personalized cross-sells, up-sells
and hot promotions, along with actionable information about customer complaints, but the
industry, for the most part, is lagging. We’ll investigate the reasons for this more in the section on
Organizational Inhibitors, but make note of it here.
46%
27%
19%
40%
45%
47%
50%
54%
43%
22%
21%
15%
21%
11%
17%
17%
6%
6%
5%
12%
4%
2%
6%
6%
2%
4%
4%
3%
9%
35%
0%
6%
4%
4%
2%
2%
24%
30%
27%
36%
32%
26%
26%
33%
45%
Supply Chain Managers
Supply Chain Partners
Customers
Employees
Store Managers
Line Level Managers
Designated Analysts
Line of Business Executives -Vice Presidents & Directors
C-level Executives
Most Typical Delivery Vehicles for BI Constituents
Desktop Scorecard/Dashboard Desktop Alerts Mobile Scorecard/Dashboard Mobile Alerts "Flash" Reports
9
Figure 6: Pedestr ian Data Yields Sub-optimal Results
Source: RSR Research, November 2011
We see no appreciable difference across revenue bands or performance level. While the industry
aspires to become more customer-friendly, it lags in delivering relevant information to those who
might help make it so.
Despite the Challenges, Opportunities Abound
Given that retailers recognize their challenges, and given the explosion of mobile delivery tools
and techniques, coupled with ever more ubiquitous “big data” hardware, we expect to see
retailers making a great leap over the coming year, In the next section we’ll identify the areas
they are most interested in exploring,
26%
26%
33%
39%
46%
46%
57%
63%
74%
Expected Receipts
Loss Prevention alerts
Customer complaints
Hot Promotions
Expected sales
Financial scorecard
Inventory exceptions (out of stock or overstock)
Performance to plan
Current sales (Best sellers/worst sellers)
Most Typical Near Real-time Information Delivered to Constituents
10
Opportunities
Pushing Reaction Time To The Front Of The Process
Most retailers share the same desire to retain customers longer, but Winners differ from others in
their thought process on achieving that objective (Figure 7).
Figure 7: Nimble On The Buy Side?
Source: RSR Research, November 2011
40%
29%
27%
60%
40%
47%
53%
57%
73%
73%
80%
36%
67%
80%
22%
35%
35%
35%
35%
38%
42%
50%
52%
54%
65%
67%
70%
73%
Reduced shrink
Reduce or eliminate re-work at stores or DC
Exception alerts point out the need for more training
Adjust space allocated for specific product in responseto sales spikes
Improving supply chain network management
Better match of labor to customer flows “just in time”
Rapid response to changes in consumer demand
Improved IT responsiveness & better systemperformance
Higher average transaction value
Improved merchandise productivity
Increased shopping frequency
Better reaction to supply chain shocks
Better “what if” modeling capabilities for matching demand with assortment, price, and promos at a …
Higher customer retention
Rate the following opportunities you see from real-time BI to help overcome those business challenges
(A Lot Of Opportunity)
Winners Others
11
Retailers want to be able to perform more “what if” analyses with their BI capabilities, but the
scenarios they are interested in analyzing differ. Winners are much more focused than their
lesser performing counterparts on improving their ability to react more quickly to supply chain
disruptions outside the “four walls” of the business. These disruptions can ultimately cause
consumer dissatisfaction. Non-winners put more faith in opportunities for a “better response to
changes in consumer demand”, and the ability to “adjust space allocated to a specific product in
response to sales spikes”. These opportunities are inside “the four walls” of the business, after
receipt of goods from suppliers.
It’s an important distinction. While most non-winners don’t see a lot of opportunity on the supply
chain side of their business, a majority does see opportunities for “increased shopping activity”,
“improved merchandise productivity”, and “higher average transaction value”. While these are
important, they are outcomes. As we have seen in other studies, Retail Winners take an activist
role in framing their future prospects, while laggards tend to position themselves as
victims of circumstance. For over-performing retailers, that means gaining visibility as far into
the supply chain as possible to gain the lead-time they need to alter their plans and exceed
consumer expectations.
Another opportunity area also deserves mention: over twice as many non-winners as Winners
see an opportunity to use BI to better control shrink than Winners. This again points to a historical
difference between Winners and others; they have better control of shrink to begin with – thus
there’s less of an opportunity for them as for others.
Finally, while a majority of respondents see an opportunity to use BI for improved system
performance, that choice is oddly out of place with other highly ranked opportunities.
Getting Smart In The Store
In RSR’s June 2011 report entitled The 21st
Century Store: The Search For Relevance3, we
said:
“The evolution and proliferation of consumer-held technologies have brought stores to
their Rubicon. The question retailers face is no longer, “How can we make the in-store
experience as satisfying as the web?” It has become, ‘How can we make our stores more
significant than showrooms for online merchants?’”
Theoretically, that quandary is resolved through the effective use of information, specifically by
informing store-level operational processes with actionable information derived from the
company’s BI and analytics capabilities in something approaching real-time. Consumers have
information at their fingertips nowadays that often exceeds any of the information available to
store management and personnel. If that kind of pressure weren’t enough, there’s also the
challenge of running the store at optimal productivity, having neither too much nor too little
inventory, having the right assortment at the right place and time, and having the right number of
service employees on hand to meet the demands of those hyper-informed consumers. Retailers
are seeking to eliminate the lag time to action, to achieve both the goal of servicing
knowledgeable customers better, and to run a more optimized operation.
3 The 21st Century Store: The Search for Relevance, Benchmark Report, June 2011, © 2011 RSR Research LLC
12
In an apparent response to these concerns, retailers have shifted the focus of their BI efforts to
the stores (Figure 8). Whereas only last year almost ½ of retailers who responded to our study
indicated that all channels would receive equal benefit from realizing the opportunities in BI and
analytics capabilities, this year the best value is perceived to come from improving performance
at the stores, far more than the other selling channels.
Figure 8: Gett ing Back To The Store
Source: RSR Research, November 2011
This response is heavily weighted to non-winners, who overwhelming chose the store as the #1
benefactor of better BI capabilities (73%). Winners have a far more balanced perspective, but
still also give most weight to the stores (44%).
What About New Demand Signals From Social Media?
In RSR’s report entitled Social Media’s Impact on Customer Engagement 4, responses from
retailers showed us that:
“Top Winners… see Social Media’s potential to create new demand signals. Of course,
messages from various Social Media, whether in the form of Facebook postings, email
messages, blog entries, or Twitter “tweets” are not data – they are sentiments expressed
in plain (or natural) language. Until recently, there were few technical ways of turning that
4 Social Media’s Impact on Customer Engagement, Benchmark Report, May 2011, © 2011 RSR Research LLC
47%
2%
0%
11%
40%
19%
10%
2%
14%
55%
All channels can take equal benefit
Mobile Commerce
Catalog/call centers
Ecommerce
Brick and Mortar stores
What Channel Can Gain the Most Benefit from Near Real Time BI?
2011 2010
13
unstructured text into something that can be transformed into true insights. But that has
changed in the last two years as technology providers have brought natural language
processing capabilities to the market… Top Winners are aware of the opportunity that
such technologies represent, and (more than other retailers) want those capabilities to
turn customer sentiment expressed in Social Media into new demand signals.”
The question for our retailers in this study was how much progress had they made towards being
able to consume and analyze new unstructured data from non-transactional systems such as
social media to optimize their value offerings? The answer is mixed (Figure 9).
Figure 9: Not Gett ing Al l The Signals - Yet
Source: RSR Research, November 2011
Retailers’ ability to consume un-structured information from Facebook is reflective of that
platform’s overwhelming popularity with consumers. For our retailers, no other source comes
close, even though 45% of respondents say that they can now also use signals from Twitter to
glean business intelligence. But as we’ll see later in this report, it’s not at all clear that retailers
are using such sophisticated tools as natural language processors to convert unstructured into
structured data. It’s far more likely that signals from the social media “cloud” are being translated
into something usable by external sources, such as the social media platforms themselves, in the
form of statistics. While that information is useful, it’s limited by how much the provider can or will
provide.
21%
18%
28%
45%
69%
53%
59%
69%
74%
90%
Presence on commerce portal such asAmazon.com
Location based social networks, eg. FourSquare,shopkick
YouTube
Value Opportunities from Social Media Networks
Potentially at Least Some Value Actually Achieved at Least Some Value
14
Organizational Inhibitors
Siloed Systems Supporting Siloed Business Units
For most retailers, siloed information contained in existing “legacy” transactional systems is by far
their biggest operational impediment to delivering new generation BI capabilities (Figure 10). In
this regard, Winners fared only slightly better than the total response group (72%).
Figure 10: Legacy
Source: RSR Research, November 2011
Where Winners did outshine their competition is in the second-ranked operational challenge, that
the “our operational units don’t work well together”. They are learning to work cohesively.
Twenty-five percent fewer Winners than the total response group (36% compared to 48% overall)
rated that a top operational challenge. Presumably, most Winners have addressed the
organizational challenges and varying compensation strategies that prevent line-of-business
organizations working well together.
Another important operational challenge identified by the survey respondents is that “we get
valuable insights from social media networking sites, but can’t use it for decision making”. The
response from Winners and others was consistent. Given the high potential value that retailers
30%
32%
38%
45%
48%
75%
Our IT department doesn’t get information fast enough to react to outages and other
system problems
LP Managers don’t get information fast enough to react to exceptions
We get valuable insights from social networking sites, but can’t use it for decision-
making
Our store managers don’t have the information they need to run their stores
more efficiently
Our operational units don’t work well together
Information is siloed
Identify The Top Three (3) Operational Challenges You Face That Create Interest In Using Near-real-
time BI In Your Company
15
assign to social media (Figure 9), one has to conclude that for some retailers the “signals” to be
derived from social media haven’t affected their merchandising plans yet. Social media is still in
its early days, but it’s important to look at the “other side” of that response – 62% of our
respondents didn’t choose that as a top operational challenge. Given earlier responses about the
value of information derived from social media, it’s a good bet that a plurality of retailers have
managed to eke value out of the feedback they get form consumers, however it is that they get it.
Status Quo
In RSR’s 2010 BI study, when we asked retailers specifically to identify the top three
organizational inhibitors keeping them from taking advantage of real-time BI, retailers confessed
to an inability to get data into a usable format and a lack of funds to “do the deed”.
It is somewhat surprising to see then, in Figure 11, that not much has changed, except that
retailers seem to be more acutely aware of the organizational issues that stand in the way of
delivering improved BI capabilities.
Figure 11: Frog In a Kett le?
Source: RSR Research, November 2011
Most startling of all is that retailers complain more that it’s hard to quantify the technology ROI for
new BI capabilities (23% more of responding retailers claim this as a “top 3” inhibitor than in
2010).
15%
17%
27%
29%
29%
34%
37%
46%
54%
12%
18%
20%
27%
20%
38%
30%
41%
46%
Poorly defined store-level processes
Entrepreneurial reactive culture makes it difficultto agree on standardized alerts and metrics
We have no idea what to do with the data we getfrom social network and customer feedback sites
Our technology infrastructure is difficult tochange and adapt
We don’t believe we can react quickly enough to the information a real-time BI system might tell us
Different “versions of the truth” – data in different operational systems that can’t easily be …
Hard to quantify technology return on investmentfor new BI capabilities
There are budgetary constraints to creatingintegrated processes and systems
The data we need has to be manually “pulled” from operational systems
Identify The Top Three Organizational Inhibitors Standing In The Way Of Taking Advantage Of The Opportunities
Identified2010 2011
16
Instead, given the challenges and opportunities that retailers have identified, the fact that the
“same old” inhibitors stand in the way of progress seems incomprehensible.
The answer to this paradox might be found in the challenges that retailers have been trying to
address in these times of mobile and hyper-informed consumers. Retailers have a lot on their
plates: channel integration, consumer and employee facing mobile capabilities, the reintegration
of the store into an “omni-channel” world, the rise of the CMO and customer-centric marketing
strategies. All of these are important, and investment in new BI capabilities is apparently taking a
back seat to them all.
Pilot Projects Gain Favor
Given that retailers continue to fret over the ROI for investments in ROI vs. the potential value to
be had from new BI capabilities, our respondents indicate an increased willingness to undertake
pilot projects to prove the value (Figure 12).
Figure 12: “Try It , You’ l l L ike It!”
Source: RSR Research, November 2011
18%
38%
38%
39%
41%
41%
41%
42%
55%
58%
64%
65%
51%
44%
54%
37%
44%
44%
41%
47%
40%
42%
31%
30%
31%
18%
8%
24%
15%
15%
18%
11%
5%
0%
5%
5%
Improved integration tools
Create an ROI-based business case to gain moreresources for improving BI capabilities
Bringing in outside expertise to drive internal businessprocess change
Hosted solutions (SaaS BI)
Improve our POS systems to start gathering better data
More sophisticated tools to collate the unstructureddata we gather
Wireless devices that can deliver alerts to employees inreal-time
Cheaper, faster technology
Simpler analysis tools
Improve employee training to start entering cleanerdata
Executive Mandate
Pilot programs to prove ROI business case
Rate The Value Of The Following In Overcoming The Organizational Inhibitors You Face To Implementing Capabilities To Deliver Near Real-time Information
A Lot Of Value Some Value Little Or No Value
17
While strong executive-level sponsorship of investments in BI remains a top method for
overcoming inhibitors (as it has in every prior study we’ve undertaken about BI), establishing pilot
projects to prove the ROI has risen to #1 (from #5 in our 2010 study). This rise in importance of
pilot projects is a testament to the urgency that retailers feel to get the ball rolling when it comes
to new investments in BI.
18
Technology Enablers
There’s a Lot of Plumbing Under those Dashboards
When thinking about BI and analytics, we often look from the interface first, and then think about
the underpinnings. In fact, without a robust technology infrastructure, transforming mountains of
transaction and customer data into useable metrics is almost impossible. Our retail respondents
clearly recognize this undeniable truth (Figure 13).
Figure 13: Infrastructures Matter
Source: RSR Research November 2011
While we have some doubt that 63% of respondents are currently gaining real benefits from
Natural Language Processors, we have no doubt that retailers understand the value of getting
their disparate data into a single, usable format through data transformation tools and integration
middleware. We are encouraged to see this universal appreciation of the underpinnings of BI,
especially since at least half our respondents come from the business, rather than technology
side of the retail house.
In that spirit, it’s a bit easier to understand the over-enthusiastic response to perceived value
received from Natural Language Processors. Line-of-business executives are finally trying to
learn the “language” of IT, and while they may not have a thorough understanding of the
differences between data transformation and aggregation tools, and Natural Language
transformation tools, they “get” that the plumbing is necessary to get the results they want.
We see a similar pattern when looking at perceived and actual value of various delivery
mechanisms for BI (Figure 14).
63%
80%
97%
97%
100%
100%
Natural language processors, to convert unstructured data (e-mails, text, “tweets”, etc.)
into structured data
Integration “middleware” between operational systems
Data transformation & aggregation tools (to help enable normalization of disparate
transactional data formats into “one version of the truth”
Value Opportunities from Infrastructure Tools
Potentially at Least Some Value Actually Received at Least Some Value
19
Figure 14: Del ivery Mechanisms al l Sound Real ly Appeal ing…
Source: RSR Research, November 2011
The enthusiasm among all respondents is palpable. The iPad and iPhone have provided an
epiphany for many retailers, with notable massive purchases at mega-retailers like Lowes (34,000
iPhones ordered for employees in 2011), and Nordstrom (purchasing iPads for sales associates
to be used for both mobile check-out and clienteling). Perhaps the most interesting data point in
Figure 14 revolves around the value and usage of corporate-wide email. Only here has actual
value lived up to its potential. In fact, the world of email has matured to a point of diminishing
returns. Retailers are far more enthusiastic at the prospect of instant messaging when necessary
through either corporate or employee owned devices than perpetuating the verbose mélange of
emails that every executive pores through on a daily (or hourly) basis.
Our only caveat here is retailers’ propensity to drown themselves with information. A barrage of
instant messages can prove to be as unnerving and counterproductive as a bulging in-box.
Discipline is still needed, or new tools will turn out to be as confusing as their predecessors.
Beyond the Excitement and Promise, What’s the Reality Today?
We have no doubt that plumbing is being put into place, and we also are quite certain that
retailers are excited at the prospect of bringing consumer-grade usability into the enterprise.
After all, there are very few user manuals sent along with new “apps” for mobile phones and
tablets – why do we need training and classes in the use of our enterprise applications? Beyond
the promise, what’s actually in use today? As we can see in Figure 15, actual delivery
mechanisms are quite different than the picture painted above.
47%
54%
34%
76%
70%
73%
75%
70%
87%
71%
71%
92%
94%
98%
Integrated voice/data network at the store level
Instant messaging via the internal network
Employee owned “smart” mobile devices
Corporate-wide Email
Commercial / pre-integrated application suite
Store Manager or Employee “portals”
Company-owned “smart” mobile devices (Phones, iPad, etc.)
Value Opportunities from Different Delivery Mechanisms
Potentially at Least Some Value Actually Received at Least Some Value
20
Figure 15: …but Real i ty Lags Behind
Source: RSR Research, November 2011
While desktop scorecards and dashboards have clearly become more ubiquitous, a somewhat
stunning percentage of C-level executives, store managers and other retail executives are still
predominantly getting their analytics through “Flash” reports. Of course, in today’s real-time
world, even the name “flash reports” is a bit of a misnomer, left over from a time when they really
just referred to unaudited sales data being given to users.
The only constituent that seems to be getting the results of BI delivered to them on mobile
devices is the consumer. Thirty-five percent of respondents do deliver information to consumers
on mobile devices. We’re not convinced that this information is all analytical in nature, but
certainly it has been scrubbed for relevancy. In fact, some might argue that some of the data
being delivered to consumers, based on computer cookie analysis shifts from relevant to
“creepy”. It’s disconcerting for a consumer who has been browsing for shoes on one site to find
ads for shoes showing up as sidebar ads on their Facebook pages. Yes business intelligence
was used, yes the information was personalized, but it is not necessarily desirable. This delicate
line between relevance and intrusion will be explored extensively over the coming years.
46%
27%
19%
40%
45%
47%
50%
54%
43%
22%
21%
15%
21%
11%
17%
17%
6%
6%
5%
12%
4%
2%
6%
6%
2%
4%
4%
3%
9%
35%
0%
6%
4%
4%
2%
2%
24%
30%
27%
36%
32%
26%
26%
33%
45%
Supply Chain Managers
Supply Chain Partners
Customers
Employees
Store Managers
Line Level Managers
Designated Analysts
Line of Business Executives -Vice Presidents & Directors
C-level Executives
Most Typical Delivery Vehicles for BI Constituents
Desktop Scorecard/Dashboard Desktop Alerts Mobile Scorecard/Dashboard Mobile Alerts "Flash" Reports
21
BOOTstrap Recommendations
We’re really encouraged to see retailers’ enthusiasm for new tools and delivery mechanisms for
BI and analytics – especially given the business-base of most of our respondents. We believe
retailers can leverage that enthusiasm and create new applications to provide digestible
information to the people who need it – on retailing’s front lines. Towards that end, we present
three recommendations.
1. Get an Enterprise-wide BI Strategy in Place
The successful enterprise-wide BI strategy will have several critical components:
• Infrastructure: Hardware is now available to support “Big Data”. Build the integration
bridges from operational systems directly to the data warehouse.
• Executive Involvement: From the responses we’ve received to our BI survey, we
believe Line of Business users are ready and willing to become engaged. They’ll even
talk about infrastructure issues, since they recognize the importance of overcoming them.
• A Roadmap: An enterprise-wide BI strategy should include a step-wise approach to
adding incremental value with BI and its associated outputs. Think about appropriate
hardware platforms, data transformation tools and techniques, and layering in reporting,
alerts, and finally advanced analytics that are retail-specific solutions.
• A Wireless Plan for Stores: Even the best insights will lose value if they’re not
delivered in a timely fashion to the people that need them in the field. The time is NOW
to put a wireless infrastructure in place. Customers can use 3G and 4G to educate
themselves. Retailers will need the wireless infrastructure for store managers and
employees. Letting customers “hop on the bus” will just be a plus.
• Modern Delivery vehicles: The days of desktop dashboards and flash reports are
drawing to an end. “Consumer grade usability” has become the order of the day. No one
gets a user manual with consumer apps. BI can be equally as simple. Plan for simplicity
as an output of back-office complexity.
2. Prioritize those Who Need Real-time Information Most
Scorecards are useful after the fact, but real-time exception alerts are most valuable to those on
the front lines: in call centers, stores and distribution centers. Giving information to those who can
actually do something with it is critical.
3. Temper Enthusiasm with Appreciation for Complexity of the Task
The consumerization of IT has given the non-technical user a real appreciation for the value of
technology tools. However, expectations may sometimes outstrip reality. There are no magic
bullets in successful retailing. Insights delivered in a timely fashion will foster success, but it will
take some time to build those insights. Brand building with words and pictures is relatively easy
compared to the collation and synthesis of mountains of data into actionable information. While
technology development cycles are faster than they used to be, populating apps with high-
powered data will take some time.
We live in very exciting times. The fact that half our respondents can deliver information faster
than their organizations can respond to it is actually a huge leap forward. Business Intelligence
and analytics will support the return to holistic retailing the RSR has been recommending for
several years. Holistic retailing in the 21st century is channel-aware but non-prejudicial (store,
22
mobile, on-line…all are equally important and synergistic), collaborative rather than siloed, and
forward, rather than backward looking, and customer, rather than product-centric.
a
Appendix A: RSR’s Research 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 follows:
b
Appendix B: About Our Sponsors
Netezza, an IBM Company, is the global leader in data warehouse and analytic appliances that
dramatically simplify high-performance analytics across an extended enterprise. Netezza’s
technology processes enormous amounts of data at exceptional speed, providing a significant
competitive and operational advantage to retailers worldwide including Catalina Marketing, Guitar
Center, Michaels, Neiman Marcus, Nielsen, Ross Stores and Yum! Brands.
With SAS’s 35 years of advanced analytics and retail domain expertise, retailers choose SAS to
drive better business results. SAS provides winning retailers with solutions for retail merchandise
planning, size optimization, localized assortment optimization, allocation, space planning and
optimization, price optimization, customer insight, social media analytics, campaign management
and advanced forecasting across the enterprise. SAS provide flexible deployment models, and
SAS retail intelligence is ramped up at your pace. Retailers turn and return to SAS because SAS
drives better results.
For further information, visit http://www.sas.com/retail/
c
Supporting Sponsors
By enabling more content, mobility and capabilities than ever before, Intel gives you the
advantage in a rapidly changing world. With advanced silicon, industry standard platforms,
modular infrastructure solutions and ecosystem support, Intel can help you deliver a more
compelling digital lifestyle. Intel, the world leader in silicon innovation, develops technologies,
products and initiatives to continually advance how people work and live. Additional information
about Intel is available at www.intel.com/go/ic.
Manthan Systems produces cutting edge analytic solutions for global retailers. Manthan's breakthrough solutions, under the brand name ARC, transform the way retailers use analytics driven decision making for strategic advantage. The ARC product portfolio spans the entire spectrum of retail decision making with role-based, pre-built applications, and includes products for merchandising analytics, financial analytics, customer centric analytics, supplier portal and analytics. These award winning products provide a significant edge to an organization’s analytical capability and maturity, and are proven to deliver unmatched business benefits in a remarkably short timeframe. Manthan’s experience spans a wide range of retail segments and formats, having transformed decision making for over 50 leading Retailers in 16 countries. For more information visit www.manthansystems.com.
For more than 35 years, RedPrairie’s best-of-breed supply chain, workforce, and all-channel retail
solutions have put commerce in motion for the world’s leading companies. Installed in over
60,000 customer sites across more than 50 countries, RedPrairie solutions adapt to help ensure
visibility and collaboration between manufacturers, distributors, retailers, and consumers.
RedPrairie is prepared to meet its customers’ current and future demands with multiple delivery
options, flexible architecture, and 24/7 technical and customer support. For a world in motion,
RedPrairie is commerce in motionTM
.
To learn more about how RedPrairie solutions can optimize your inventory, improve employee
productivity, or increase sales, visit RedPrairie.com or email [email protected].
d
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© 2011 by Retail Systems Research LLC • All rights reserved.
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