“What if” – Moving from Realistic to Real

Chain store companies are increasing their use of predictive analytics in many areas including market optimization, sales forecasting, direct marketing, and localization of product offerings.

The value of simulating decision outcomes before investing financial and human resources is compelling.  Therefore, much attention has been focused on algorithms and user interfaces that make it possible for analysts and executives to compare alternatives using “what if” scenarios.

So here’s a big fat “WHAT IF”:  what if the data that are used in the models are not accurate?  What if the location of your existing stores or competitors don’t reflect “ground truth?”  The answer is, you will get maps, reports, and sales forecasts that appear REALISTIC but they are not REAL.

After years of resistance, chain store executives are becoming eager to use more “science” in the planning and evaluation of store locations.  What does “science” mean in the context of a complex system like the retail marketplace?  It’s certainly not a set of well-defined cause and effect relationships that can be predicted with precision, such as the movement of the planets.

A better description would be “fact-based” decision-making, which means capturing relevant and accurate data about the marketplace and inferring conditions that are favorable for the operation of chain stores.  These facts include trade area demographics, proximity of sister stores and competitors, and the quality of the site.  It is certainly possible to create mathematical models that simulate the interaction of these factors in order to forecast sales, but the ultimate decision to approve an investment requires the experience and judgment of experts who use modeled estimates with other sources of facts and opinions.

Regardless of the specific approach to making decisions, if the “facts” are not accurate, mistakes can happen.  Models will generate inaccurate estimates, and experts could be misled by maps or reports that have existing stores or competitors in the wrong locations.


It’s not easy to capture and validate accurate location information, especially when the database covers a large geographic area such as the entire US.  When you are designing decision support systems that rely upon these data, invest in content and processes that will make your software and models give you reliable answers.

Here are some of the keys to good data quality:

  1. Research the sources of content including demographic data and business locations and compile a list that compares the quality and price so that you can find the right combination for your needs.  Some data sources are VERY expensive and not a lot better than some that are more affordable.  Others are VERY cheap, but you get what you pay for.
  2. Design a process for getting your staff to update and correct business locations when they find differences between maps or reports and what’s in the real world.  Sometimes it’s necessary to designate a “chief editor” for changes to make sure that locations are not duplicated or changed incorrectly (e.g. new longitude coordinate is missing the negative sign).
  3. Select a software platform that allows you to make the changes yourself rather than relying upon a vendor to change them.  Ideally you should be able to have changes synchronized across all platforms and devices, whether desktop, web browser, or mobile (eg smartphone or iPad).
  4. When you are getting ready to spend a lot of time on a market plan or site evaluation, spend extra time validating the locations (existing stores, traffic generators and competitors) in that area.  It’s not practical to try to validate the entire US at once, with the exception of your own store locations.

ImageYour models, maps, and reports will only be as good as your data.  Before you spend $100,000 or more on a system to support your real estate planning and site selection, make sure that you are powering it with good fuel!

The Whole World’s Watching

We’ve been hearing about global expansion by  chain store retailers and restaurants for years.  Most people probably think that in many parts of the world this is an “imperialistic” initiative by US and European companies.

My blog statistics indicate that there’s global interest in chain store development from local people. Continue reading

Climbing the Stairway to Wisdom

There’s a broker who’s sure that all sites turn to gold
And she’s selling the stairway to heaven.
When she gets there she knows, if the sites are all sold
There’s a vacancy coming to save her.
And she’s selling the stairway to heaven. 

– Led Zeppelin (adapted)

One of the popular metaphors for decision-making in the information age is the “Knowledge Hierarchy.”  It is based on the idea that we start with raw data and gradually process it through stages until it becomes wisdom suitable for making good choices.

Some people apply this to professional development over the career of a chain store real estate executive.  When you’re young, you simply see data, but as you get more experience and insight, you are able to use your wisdom to evaluate sites.

The fact is, each site must be evaluated through a process that starts with raw data that is enhanced with verification, context, and benchmarks until it is ready for the application of human wisdom. 

The human brain uses pattern recognition and analogies to analyze complex decisions.  This is why “analog stores” are popular in site selection.  If we can find existing stores that are similar to a proposed location, we can adjust the details and apply our knowledge of its sales performance to the new site. Continue reading

What Gets Measured Gets Done

Everyone knows the value proposition for chain store real estate research:

“If you avoid closing one or two stores it pays for itself.”

So why are people so hesitant to invest in analysts, predictive analytics, and information systems to support better real estate planning and site selection?

I’ve been asking this question for nearly 20 years, and I think I’m finally beginning to see the answer.

It’s about measurement.

Here’s how the argument goes:

Summary (simplified):  If we invest in better research, we can use a predictive model to estimate sales performance for proposed sites.  If we know the population and income of the trade area, the distance to our competitors, and the quality of the site, we can use the weighting factors in the model to calculate the sales potential.  We’ve heard that it’s possible to have a model where the estimate will be +/-  20% reliable at least 80% of the time.  In the past we have only been +/- 20% correct 65% of the time.

Therefore, the avoidance of bad stores alone will more than cover the investment in people and tools to build and use the predictive model. Continue reading

Here We Go Again

The economy is showing signs of perking up.

Although retail store closings are expected to continue at a strong pace in 2012 (see http://www.forbes.com/sites/erikamorphy/2011/12/31/2012-another-bad-year-for-store-closings/), many retailers and restaurants are staffing up for growth and looking for deals.

Is this the pregame warm-up for the next cycle of boom and bust?  Or maybe, just maybe, there’s a smarter, more disciplined group of decision-makers running the chain store companies today.

As I mentioned in an earlier post, the chain store industry doesn’t have well-defined best practices in real estate planning and site selection.  In order to begin a dialog on this subject I have created a “rubric” that describes various practices at different levels of effectiveness that can be used as a self-assessment tool for people in the chain store industry (click here for the Best Practices Rubric).

There are only nine items on the rubric, so you should be able to complete it in 5-10 minutes unless it causes you to think hard about what you’re doing, which would be a good thing!  Simply circle the description of the practice that best describes your business and when you’re done, add up the points as indicated at the top of each column.

The total points will range from 9 to 36, assuming you complete the rubric.  Based on my experience and exposure to chain store companies over the past 18 years, I would interpret the scores as follows:

0-15 points:  If you are still in business, it’s because you have smart, street-wise people              and probably aren’t doing very many deals each year.

16-20 points:  You are about average for the industry, which is OK if you don’t mind being average.  However, you won’t be able to stay average without improving, given the the struggling economy, growth of “omni-channel retailing” and the new breed of tech-savvy competitors.

21-27 points:  You know that you’re doing a good job because you have made a conscious decision to create good decision-making processes supported by good people and good tools.  You are already thinking about how to stay ahead of the game and work smart, not just hard.

28-36 points:  The business practices of your company are truly “best practices” and you represent the elite of the industry.  You might be reluctant to share what you do because you know that you have a serious competitive advantage.  However, you will probably talk about it anyway because of the positive energy that market leaders generate with employees, investors, customers, and partners.  We thank you for your leadership!

I look forward to your public and private comments on the rubric and the general subject of best practices in real estate planning and site selection.

2012 Predictions for the Chain Store Real Estate Industry

I guess it’s a good time to do a “2012 Predictions” post.

I’ve been talking to a lot of people about best practices in real estate planning and site selection during the past year.  Many of my observations and insights have appeared in this blog since May 2011.  Looking ahead to the next year, there is evidence that some of the “forward looking” comments from last year will begin to appear in the practices of leading companies in 2012.

Retail store sales during the 2011 holiday season were flat compared to last year (there are so many stats I don’t even want to quote one).  The bigger news was the increase in online sales, and the biggest news of all was the increase in purchases from mobile devices such as smartphones and tablets.  This puts “teeth” in the trend toward “omnichannel retailing” that signals a new paradigm in consumer behavior and requires a new approach to retail real estate planning.

Here are two general predictions about the chain store real estate business for 2012:

#1 Betting on Clicks or Bricks

Given the continuing weakness in the economy, capital investment in store development will be more carefully scrutinized by senior management than ever before.  Given the growing success of the online and mobile channels, there will be more conversations between The CEO, CFO, CMO (Chief Marketing Officer), and CDO (Chief Development Officer or VP Real Estate in most companies).

New questions will be on everyone’s minds and lips.  Instead of “how many stores should we add this year net of closings,” people will be asking “how much can we grow sales without adding stores?”  Another way to ask the same question might be “what’s the ROI on store development compared to promotion and discounting through other channels?”

These questions will lead to some innovative planning and business models that will look very different from the traditional approaches that have not changed much in the past few decades.

#2 Performance Improvement

Investments in technology to improve real estate planning and site selection have grown dramatically in the past five to ten years.  Most of the larger chains and many smaller ones have a Real Estate Research Director or Senior Analyst who is responsible for delivering strategic plans and sales performance estimates to the decision-makers.  They have armed themselves with mapping and demographic programs, databases, and analytical tools based on everything from excel to sophisticated statistical models.  Almost everyone agrees that research can improve performance and that the investment in analysts and their tools is worthwhile.

The question remains:  how much of the potential value from research activities is being realized?  Is there room for improvement?

My prediction for 2012 is that investments in analysts and technology will continue, especially for companies that have been slower to get on the analytics bandwagon.  However, the new focus will be improving the “people” side of research.  This starts with benchmarking business processes against best practices in the industry and extends to consistent training programs to align the deliverables from research with the decision requirements of everyone in the real estate process including senior management, dealmakers in the field, and people in other departments such as marketing and merchandising. 

There’s been a lot of talk about “enterprise GIS” and “localized assortment planning” in the past few years.  The reason this is on my list of predictions is that all of the prerequisites to actually implement such performance improvements are now in place.

a)       The technology is good and has become affordable, especially with “cloud computing” which allows companies to build a platform gradually with variable costs instead of committing to a huge IT infrastructure investment to get started.

b)       The need to integrate real estate planning with other channels at the highest level is crystal clear based on the growth in online and mobile sales and the social media phenomenon

c)        The large capital investment required for bricks and mortar stores has become harder to justify in a weak economy that shows no signs of bouncing back quickly as in past cycles.

By this time next year there will be some great case studies of companies that are actually doing these things and reaping the financial benefits (although it might take another year to quantify this in a convincing manner).

In the interest of accountability, I have marked my calendar to follow up on these predictions one year from now.  I hope I can find some good cases to talk about then or we may not have a very happy new year in 2013 and beyond!

The Three Biggest Mistakes in Retail Real Estate

Are you tired of hearing people use the phrase “location, location,location?”  It is an insult to both the speaker and the listener.  It’s like saying the most important thing in pro sports is winning, or that the most important outcome in investing is making money.

Now that we’ve survived another round of massive store closings and bankrupt retail and restaurant chains, let’s see if we can shed some light on the path forward for real estate planning and site selection.

I would argue that mistakes are made at three levels:  market selection, market planning, and site selection.  All three are critical to the success of a chain store real estate program and the types of mistakes that cause pain and death are different for each. Continue reading

Real Estate Research Knows the Score!

I just got back from the annual research conference of ICSC (International Council of Shopping Centers) in San Diego.  It was the same bunch of people with a few new faces, but the topics and conversations were very different!

Three years ago everyone was sighing with relief that online sales were not completely replacing bricks and mortar stores.  Social media was the personal ads in the local underground newspaper.  Web-based demographics and reporting were designed for running trade area reports in your hotel room.

This conference is clear evidence that the real estate research profession is keeping up with the changes in the marketplace!  First of all, hats off to the program committee that developed the topics and arranged the presenters.  I found myself having a morning conversation with one viewpoint and an evening conversation with a very different viewpoint!

There were three major takeaways from this conference:

  1. Most companies are trying to move real estate research tools to the web and provide access to dealmakers.  This was clear in the “Best Practices” session and the game plan is not just to provide maps and demographics, but analytics as well!  The need for “a single version of the truth” in data management was a recurring theme.
  2. Social media are generating critical data about the “voice of the customer” and changing the way we look at customer profiling, marketing, and merchandising.
  3. The bricks and mortar store is no longer the only way that shoppers can have a powerful shopping experience with the merchandise.  High bandwidth on computers and mobile devices (including tablets) are making it possible to create rich virtual applications such as Me-tail (http://metail.co.uk/how-it-works/) that will continue to feed the growth and market share of online sales.
It’s amazing to look at the last 20 years in the real estate research profession and compare the rate of change in practices and trends in the past three years to the previous 17 years.  However, I’m very pleased to see that the more senior members of the group are not being stubborn and sentimental about the past, but embracing the exciting changes and reinventing their practices to be useful and relevant.  Maybe it’s driven by concerns about job security, since many of us will be working into our 70’s to pay off college loans.  Or maybe we realize that technology may change the way we do things at an unprecedented pace, but the “art” of real estate planning and site selection is based on experience, and we will all have more of that as we get older!
Stay thirsty, my friends.

The Voice Of The Customer: Can You Hear It Through The Screaming?

Listening to the “voice of the customer” has been the hallmark of successful retailers, restaurants, and service businesses for years.  Technology has greatly increased our ability to capture and analyze customer data.  With the explosive growth of Facebook, Twitter, and other networks, social media are becoming the loudest, clearest, most current voice of the customer yet!

The challenge today is finding the “signal” in the data so that we can reduce the voice of the customer to actions such as real estate planning, site selection, marketing campaigns, and assortment planning.  With data warehouses full of customer transactions, household files with demographics, and feeds from social media, it’s harder to understand what the customer is saying than to guess the Dow Jones average by walking onto the trading floor of the New York Stock exchange! Continue reading