If You Ask The Wrong Question You Will Get The Wrong Answer

Posted on January 24, 2013 by Jim Stone

“I need to get more scientific about site selection. What software should I buy?”

Given the massive closings of stores since 2008, many chain store executives are asking this question today in order to avoid a repeat performance as they start growing again.

The problem is, it’s the wrong question!

About 20 years ago it dawned on me that most real estate investment decisions were made using gut feel fueled by the desire to close deals, often in the face of contrary evidence. For those old enough to remember, this was in the wake of the late 1980’s Savings and Loan meltdown, which seemed like a pretty serious crash until the Subprime Mortgage debacle of 2008 (estimated cost $7.7 trillion versus $1.5 trillion for the S&L Crisis).

I set out to create software that would help decision-makers gain fast access to market knowledge and insights that would increase the financial performance of their investments (this seemed easier than making software that would reduce greed on Wall Street or incompetence in government).

It took me another 10 years to realize that I had underestimated the importance of the decision-making process itself! The best tool is of no value in the wrong hands. The selection of research and predictive analytics tools is actually the LAST step in the quest for better real estate decisions, which should follow these steps:

Design the objectives and processes for decision-making
Find the right people for the team
Acquire the research and predictive analytics tools to support the people and processes

Each of these steps is disruptive to an organization. However, buying software is probably the least disruptive, so that’s where many companies choose to start! Gary Cokins, a veteran in the area of analytics and organizational change, says this:

“Organizations seem hesitant to adopt analytics. Is this due to evaluation paralysis or brain freeze? Most organizations make the mistake of believing that applying analytics is 90 percent math and 10 percent organizational change management with employee behavior alteration. In reality it is the other way around; it is more likely 5 percent math and 95 percent about people.” (source: http://www.informs.org/ORMS-Today/Public-Articles/February-Volume-39-Number-1/Obstacle-course-for-analytics)

This explains why it is difficult to sell analytics, and it’s interesting to see how consultants and software vendors have adapted. Here are some examples:

If a company is not willing to significantly change its process, they will not get much value from predictive analytics. Therefore, they won’t want to pay much for it because the ROI will not be sufficient.Hence, some vendors optimize their product offering to be inexpensive, which usually means reducing the quality of the modeling services and the software deliverable that contains the model.Unfortunately, there is no such thing as a cheap model that is also reliable.
Some companies do not have the expertise in-house (at any level) to understand predictive analytics, which includes data quality, modeling methods, and the interpretation of results.In order to “meet the customers where they are,” some vendors create “black box” models with stunning graphics and user interfaces that insulate the customer from the messy, elusive realities of the complex retail marketplace.
Although video games can be incredibly realistic, they are still not reality.

Everyone wants a quick, easy solution. If you try to lose weight with diet pills, you’re not really dieting. If you want to be more scientific about site selection, take the time to look at your decision-making processes, staffing, and finally, your predictive analytics tools. Depending on the number of stores you are opening and the size of the investment, you might choose one of three approaches to making better real estate investment decisions:

Hire a consulting firm to build a model and provide an overall market strategy and an analysis of each site that you are seriously considering. If your level of activity is not high, this might be more cost-effective than creating an in-house staff and equipping them with the tools.
Develop an in-house research team that can support the decision-making process, and provide them with models and tools built by outside firms who are experts in this highly specialized discipline.
Start with outsourcing and gradually build an in-house capability over time as the economies of scale justify the investment in the fixed overhead of people and tools.

Mapping and reporting tools are very good and affordable today, and every company should have the ability to analyze markets and sites with these visualization tools and generate descriptive reports.

However, predictive analytics require much more expertise to build and use. If you aren’t ready to change the way you work, and invest in proven modeling methods, save your money for when the time is right!

“What if” – Moving from Realistic to Real

Posted on September 4, 2012 by Jim Stone

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:

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.
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).
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).
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.

Your 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!

Climbing the Stairway to Wisdom

Posted on March 20, 2012 by Jim Stone

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 →

Effective Training: If It Was Easy Everyone Would Be Doing It

Posted on August 24, 2011 by Jim Stone

I have become obsessed with the realization that chain store operators are leaving billions of dollars of sales on the table by failing to properly train and develop their talent in the real estate teams (total sales of US retail establishments is around $4 trillion according to the 2007 Economic Census published in 2009).

Why is this? Laziness? Ignorance? I don’t think so. Some of the most clever AND street-wise people I’ve ever met are senior executives in chain store companies. I think that the training challenge is relatively new and requires adapting to new market conditions. It’s the natural evolution of the chain store business. Sears built an empire with selection (“Sears Has Everything”). Wal-Mart revolutionized retailing with their supply chain management. Apple has seemingly cornered the market on “cool” and “easy.” Here are some of the driving factors that have increased the priority of training from low-moderate to high: Continue reading →

Doers and Viewers: Division of Labor in Real Estate Research

Posted on August 3, 2011 by Jim Stone

After 3 ½ weeks, my blog page called “Site Selection Surprises: Stories from the Field,” has more than twice the average page views of the other blog articles. What’s so compelling about this article? The stated purpose of the page is to provide a forum for chain store real estate dealmakers and analysts to share stories of success and failure in order to build our experience base for evaluating future deals. Makes sense, who wouldn’t want that?

Continue reading →

A Vision for Profitable Chain Store Development

Posted on July 28, 2011 by Jim Stone

I have had the good fortune to get a close up view of many chain store operators in action over the past 20 years. It’s amazing to see the wide variety of approaches used to find, open, relocate, and sometimes close stores. There are many different org charts and reporting structures that sometimes place the real estate function directly below the CEO and in other cases reporting to the CFO or VP Marketing. There are Real Estate Research Directors who have large staffs and tight controls over deal approval as well as companies who give the dealmakers responsibility for research. Continue reading →

Different Problems Different Questions – The Challenge of Context

Posted on July 19, 2011 by Jim Stone

Years ago I was trying to sell site evaluation software to the commercial lending group at Freddie Mac. They had fifteen underwriters around the country with huge piles of loan requests on their desks and very little time to analyze each deal. The person I was working with described his problem like this:

“There are only about ten criteria we need to evaluate in order to approve or reject the loan. Unfortunately the ten criteria are usually different for each deal!” Continue reading →

And now, the rest of the story…

Posted on July 13, 2011 by Jim Stone

The retail marketplace is a complex system that is constantly changing and is full of surprises. Sometimes these surprises are positive; and other times they are not! It never ceases to amaze me how many different combinations of factors lead to success and failure in chain store performance. Complexity drives statistical models crazy (as well as the people who use them). There seem to be as many exceptions as there are rules, and the best way to understand these exceptions is by listening to experts tell their stories about what happened.

AND NOW… I’m pleased to announce the unveiling of a new page in this blog:

“Site Selection Surprises – Stories from the Field”

Please click on the new page above for the rest of the story…

Connections: the Key to Success in Chain Store Real Estate

Posted on July 7, 2011 by Jim Stone

Not too many years ago, chain store real estate was almost entirely a “people” business. The ICSC was the formal organization that provided regular gatherings among landlords, tenants, brokers, and all the other suppliers to the shopping center/chain store industry. “Going to Vegas” has become an annual pilgrimage for dealmakers since 1986 (the first convention was held in New York in 1958). The telephone and the automobile were the primary research tools. The research department was often located at one of the many bars, restaurants, coffee shops, and golf courses across the country. Continue reading →

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