Being a real estate
director for a national chain retailer can be fairly disheartening these
days. Oftentimes, driving around a city for days only yields that each
corner of every “Main & Main” is already taken.
intensifies, the best spots dwindle away — and the cost of picking a
“failure” site grows — innovative retailers are forced to find new ways
of ensuring each of their sites will be winners.
Every retailer can
point to a collection of both “slam-dunk” and “disappointing dog” stores
in their portfolio. Why do some succeed while others don’t meet
expectations? There may be thousands of possible factors which influence
sales, but for each concept there are 20-50 unique critical success
factors which really drive results. New technologies are enabling savvy
retailers to dig through mountains of data to truly uncover what are the
reasons why sites succeed.
Armed with that
enhanced understanding of their retail concept, chains are building
statistical models which help them improve their hit rates
significantly. The models, when used as a supplement to existing site
selection techniques —and paired with the expertise of the real estate
director — can help a chain grow more profitably:
- The models can
sharply limit the number of “mistake” stores a chain builds, thereby
saving potentially millions of dollars in closing costs and headaches.
- Models can boost
the average sales at all new sites which they open.
- They can be used
by several departments within the firm to make their jobs easier too.
For these reasons,
retailers have begun using these statistical models as tools.
“I’m a strong
believer in models of this sort and have watched their sophistication
improve over the last ten years, so that you really can’t live without
them,” says Dan Clark, CFO of California Pizza Kitchen. “Not only did we
build them once, but we updated them later based on further learnings.”
Yet there remains
an air of mystery about models. They may seem like complicated “black
boxes” which spit out conclusions through indecipherable methods. And it
can be difficult to find retailers willing to share details about their
practices. One real estate director at a prominent national chain
declined to be interviewed for this article, pleading “I don’t want to
give you any information about how successful our site model is because
I don’t want to give any incentive for our competitors to go out and
have one built too.”
models essentially add a touch of science to the subtle art of real
estate; they become one additional tool in the chain’s arsenal of
weapons. They aren’t correct in their sales predictions 100 percent of
the time, but they can approach 90 percent accuracy levels. So they tell
powerful, compelling stories about several aspects of a chain’s
model-building effort consists of two main components: Uncovering the
critical success factors which lead to high sales or failure, and
building a model based on these factors.
1) Uncovering the critical success factors
Most firms think
they know their criteria for success (e.g., “75,000 residents within 3
miles, HHI $40K+, and Median age = 32”). But often those goals have been
derived only anecdotally rather than through rigorous proofing. They may
become outdated as the chain grows. And it can be tricky to make clear
decisions based on them. What if a site meets only five of the seven
criteria? Is that good enough?
A more helpful
approach is to start with a blank slate and then perform a thorough
analysis of the existing stores to find commonalties within the slam
dunk stores and within the disappointing dogs. A chain can be surprised
to learn what they thought was important turns out to be only vaguely
relevant, or that previously overlooked factors can be crucial.
currently Real Estate Manager at Extended Stay America, Inc., helped
create Brinker International, Inc.’s models when he was in their real
“In many ways
[building the model] was a good objective process to go through,” he
says, “because it helped us look at things in another light, perhaps
find things we hadn’t seen before. It was just a real good, analytical,
check against our decision process.”
When examining the
existing stores, a retailer can’t simply lump all of their sites into
one analysis. The factors which drive sales are different at a
super-regional shopping center than at an office park or a residential
neighborhood. So they must segment existing units into appropriate
groups and build separate models for each.
Pier 1 Imports has
created multiple models they find useful. Rick Blackwelder, Pier 1’s
vice president of real estate and development, says that analyzing basic
demographics gave inconclusive answers, so a key was to “start looking
at customer profiling and market segmentation. We broke our 700 stores
into three basic categories. We found out the makeup in the trade area
around our existing stores and really came up with a great model that we
use, which has been very very helpful to us.”
efficiency, the retailer should compile information about every
conceivable factor which could influence sales. PC-based GIS systems
enable them to call up several thousand demographic variables for
existing sites at the touch of a button. Retailers then look for
patterns between those data and the sales figures. But they can’t just
stop there — retailers need to test variables of all four types:
(e.g., number of residents, income, housing values, lifesyle
- Business climate
(e.g., competitive information, daytime pop, and types of businesses
in the area)
- Site specific
(e.g., square footage, ease of access, location within the shopping
(they may pick a terrific shopping center, but if the store is poorly
managed then all the good site work is wasted).
analyses narrow down this list of 1,000+ variables to those which are
truly the most important for each concept.
2) Building the models
Once a retailer has
fully evaluated what makes their concept tick, they weave those
learnings into a predictive tool. There are four major types of
statistical models; some solutions may integrate the results from more
than one of these methods:
- Analog models
rate potential sites on a numeric scale. That rating is then compared
to existing sites with similar scores — the sales at the new site
should be analogous to those at the existing units.
models are equations which assign weights to site data to forecast a
sales figure directly.
- Gravity models
are spatial analyses which focus on the number, location, and drawing
power of competitors in the region.
- Neural network
models have historically been used for pattern recognition
applications such as fingerprint matching. But firms such as Neilthall
Associates are now applying this technology in retail not only to
inventory replenishment systems, but also to site selection.
The end result of
any model-building process is a series of equations which the retailer
can run easily — when coupled with data — to help predict sales.
Once the models are
in place, the retailer may be astounded at how useful they are not just
for real estate purposes, but for several different departments within
the firm. Senior executives, therefore, often initiate the
model-building process. Allyn Taylor says about his experience at
Brinker: “Not only was (Norman Brinker) one of the model’s biggest
supporters but he was probably one of the biggest instigators. He was
constantly wanting to learn from past experience. One bad restaurant can
suck the profits away from five to ten good stores, so it’s just
critical to eliminate those mistakes.”
the organization include:
Selection. At their basic level, models help chains decide between
two seemingly-equivalent sites.
Selection. Armed with a sales forecasting model, retailers scan an
entire city at once to find the likely hot pockets for their concept.
Once they have determined how many units a city can support and what
its predicted sales are, they compare this information across cities
to find the best ones for their future expansion. “We’ve looked at
some markets and intuitively said ‘this just doesn’t feel right, we
probably shouldn’t be here’,” says Bob Goehle, vice president of real
estate at Staples, Inc. “But then the model supports otherwise,” and
the site proves successful. Pier 1’s Blackwelder agrees. “There are
some markets that we thought of (entering) but now have backed away
from based on this model as well,” he says.
By refining the customer profile, the chain can create detailed maps
to find the buyers. “Knowing who our customer is and where they live
is far and away the most important thing we’ve gained from this
(model),” says Blackwelder. “It’s helped us in our advertising
approach —we do monthly insert mailings and we use this (model) to
help target where those mailings go.” Marketers also appreciate the
higher “hit rate” the real estate departments achieve because they no
longer have to devote so much time to “cleaning up” as many “mistake”
Resources and Operations. The sales models also “benchmark”
existing units. By comparing actual revenues to those predicted by the
model, retailers highlight which stores are underperforming
expectations — possibly due to subpar management. CPK had one such
unit, and as Dan Clark says, “When we strengthened the management
team, the awareness and sales (moved up to where the model had
expected).” Conversely, if a store is exceeding model predictions,
they may have discovered a new Operations techniques which other
stores in the chain can copy.
Planning. Higher hit rates permit a chain to grow faster. Also,
knowing how many units are feasible in each territory promotes
efficiency in the budgeting and long-range forecasting process.
- Wall Street.
Stock analysts love models and carefully laid-out development plans.
When a CEO tells the investment community about the success of their
model, it can set the groundwork for continued confidence in the
growth plans for the company. And for private companies, having this
type of analytic system in place can help secure loans and private
- Mergers &
Acquisitions. When a retailer first eyes a potential merger
partner, they may think that the target has dozens of attractive sites
which are performing well currently. But will those same sites
continue to perform well when converted to a different concept? By
plugging those sites into their own site selection model, a retailer
can pick its partners more carefully.
models can be built in-house, or there are now consulting firms with
different specializations to guide retailers through this process. The
price ranges from a one-time cost of $20,000 to an annual fee well above
$100,000. But as Blackwelder says, “the cost per store is really
minimal. And consider that one bad choice or one right choice pays for
the whole thing for a year.”
Because these tools
can be so helpful throughout an organization, the savvy real estate
director enlists the support for a model-building project from
colleagues in other departments. They can spread the cost of the project
as well as the benefits. As Bob Goehle at Staples says, “Our model is
really shared by every department in the company, and our research
department gets pulled in a lot of different directions” to make use of
With the models in
place, retailers will not just pick better sites, but they will also
outsmart their rivals.
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