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

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“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:

  1. Design the objectives and processes for decision-making
  2. Find the right people for the team
  3. 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:

  1. 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.
  2. 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:

  1. 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.
  2. 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.
  3. 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!

Research Connections: Is It Safe To Come Out Now?

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ImageThe “brain trust” of the chain store research industry gathered again for the annual ICSC Research Conference in Chicago Sept. 30-Oct. 2.  Among the 235 attendees, there were many new brains and some notable absences (you know who you are).  I have to say that this was a well-run event with excellent sessions and the usual stimulating dialog (hats off to the organizers and the attendees who shelled out big airfares and hotel rates).  My only real complaint was the quick removal of coffee at the breaks.

I was able to personally connect with about a third of the attendees and it’s clear from those conversations that the shakeout of the past few years has created winners and losers.  Most of the large players are on the sidelines for expansion, but busy dealing with closings, relocations, and remodels.  Retailers and restaurants with strong concepts and balance sheets continue to feed on the soft real estate markets, though many report firming prices for better locations.

Lo and behold, some companies are having trouble finding analysts to support their growing levels of activity!  One theory is that the layoffs and malaise of the last few years has dried up the labor supply, especially for people with 3-5 years of experience (think about which 3-5 years those would be:  2007-2009).

There seems to be an increased appetite for predictive analytics and technology to support the market planning and site selection process.  There were more vendors exhibiting this year and the traffic around their booths was steady throughout the conference.  Maybe the tools will reduce the need for entry level analysts and mitigate the labor supply problem.  However, this will only work if field real estate reps are diligent in collecting and loading validated location data (competition and site characteristics) so that maps, reports, and models will portray an accurate picture of the deals they are evaluating.  It remains to be seen whether the “dealmakers” will actively and reliably play this role.

I also heard stories from people in larger companies about the consolidation of real estate research activities under the finance groups.  As much as we appreciate the importance of the viewpoint provided by finance people (highlighted in the session on integrating finance with research), there is a lot more to market planning and site selection than numbers.  This will become even more evident as the “omnichannel” customer experience evolves and collaboration between marketing, assortment planning, and store development muddies the water further for analysts.  Some of the recent staff reductions have left companies without much “institutional memory” of the existing stores and what makes them tick, which could impair the vision of executive teams as they prioritize their investments in remodels and new development.  Navigating with the rearview mirror is only going to get harder as new distribution paradigms emerge, and finance will be the last to know what’s going on.

One more observation:  I think that those who have attended this conference for many years need to work harder at welcoming the newer attendees and building relationships with them.  I remember the first few conferences I attended in the 1990’s and how difficult it was to meet new people because everyone seemed to be catching up with their old pals and hanging out.  A few kind souls went to the trouble to seek me out and introduce me to their friends so that over time I have gotten to know many people.  It’s very easy to focus on the people you know and ignore the new folks, so this requires a conscious effort on the part of all the veterans to make it work.

Feel free to post your comments about the conference on this blog and share your insights.

The Value Proposition for Chain Store Predictive Analytics

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How many times have you seen a “Value Proposition” slide with statements like these?

  1. “We are experts in “best practices” in chain store real estate analytics and we can help you join the 21st century.”
  2. “Our scientists have developed proprietary algorithms and methods that produce the most accurate predictive models possible.”
  3. “Our technology platform will deliver user-friendly maps, reports, and sales forecasts to everyone in the enterprise who wants them; with a 90 day deployment.”

Why wouldn’t these value propositions of “better” and “faster” be compelling for everyone?

In theory, they would.  But every chain store company has a point of view about its real estate program, and if vendors don’t understand it, they will never sell them anything…that works!

Let’s flip this around and look at it from the viewpoint of the chain store real estate executives.  They will invest in predictive analytics when they see the value of a proposition, not the proposition of a value. 

Most companies have a “committee meeting” where real estate deals are considered and approved.  There is usually a representative from each of the departments that has a stake in the quality of a new store, which is all of them:  finance, merchandising, marketing, operations, and of course, real estate.  Do they lose sleep over “best practices?”  Are they fascinated with mathematical models?  Do they enjoy rolling out new software applications? NO, NO, NO!  They didn’t become senior executives by contemplating their navels and fantasizing about perfection.  They know how to get stuff done. Why do they even want to talk to a vendor who offers chain store predictive analytics?  

There are several possible reasons.  First, the end game is getting and satisfying customers.  The real estate strategy and program must clearly “map” to this goal.  That means that a store, restaurant, or service center should:

  • Be Convenient
  • Have what the customer wants
  • Make it fun and easy to fulfill their needs

Convenience is based on the location of the store, which includes what it’s near, its visibility, and its accessibility.  This is the focus of the real estate decision, and the profitability of a store is heavily dependent on location quality.  Anything that helps the chain acquire better locations is worth consideration, whether it’s data, software, training or predictive analytics.  Chances are that some combination of these things can contribute to better locations. The challenge is assessing the needs of the organization and designing a plan that blazes a clear path from the current situation to a desired state that increases the number of happy customers and drives profit.

At any given point in time, the executives in a company will have the ability to visualize a better state that is achievable from where they are.  The plan must be manageable over a one to two year period, because business must continue while changes are implemented.

The trick is to foster a dialogue that leads to a needs assessment, a game plan, and a set of expectations that represent a compelling return on investment.

What are the ingredients in such a plan?  It varies dramatically from company to company.  Here are some factors that influence the best approach to good real estate decisions:

  1. Breadth of customer profile.  If 80% of your sales come from girls between the ages of 14 and 18, you need to make sure that the trade area of a store has enough teenage girls.  If your customer could be anyone, successful stores might have many profiles, and the analytics required to figure this out are more tedious and complex.
  2. Size of the store.  If you are opening 50,000 square foot stores, you will be investing more capital, opening fewer units, and evaluating fewer opportunities, so you will be able to justify more field research.  This means that you don’t need to rely as much on screening tools and complex multi-user systems that are designed for fast decisions.  If you are opening 1,200 square foot stores, it’s about screening out the dog locations to allow enough time for the ones that deserve serious consideration.
  3. Format of the store.  Predictive models for enclosed malls are very different from models for open-air centers or street retail.  Malls are mostly about the quality and tenant mix of the mall, but open-air centers require a careful analysis of the trade area demographics and competitive landscape.
  4. Decision styles of senior executives.  Some companies place greater emphasis on quantitative analysis than others, and you can’t easily change this.  There are many winning combinations of people and tools, art and science, book smart and street smart.

Each chain store company has limited time and money to invest in store location decisions.  Predictive analytics that make a difference will be discovered through a dialogue among experts who know the business and the tools and are committed to working together to find the unique value proposition that maximizes ROI.

Predictive analytics can help increase average unit volumes significantly over time.  Here are some of the ways predictive analytics empower decision-makers to see more clearly:

  • Provide access to more facts to support judgments
  • Reveal patterns in the facts that create insights
  • Simulate decisions before actually executing them
  • Establish a common “language” of factors important to decision-making
  • Benchmarking with a checklist of factors to consider before making decisions
  • Visualization of facts and patterns as a catalyst for interactive exploration and discussion among decision-makers (e.g. “paperless” real estate committee meetings)
  • Maintaining an objective record of decision logic for learning and improvement

These are propositions of value, and when applied to a specific company, they may become part of a value proposition!

“What if” – Moving from Realistic to Real

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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.

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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!

Decision Support Systems for Chain Store Real Estate

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During the past year, the Chain Store Advisors blog has covered a variety of topics under the broad rubric of real estate planning and site selection.  Many of these ideas have been combined in a white paper released last month called “A New Approach to Best Practices in Real Estate Planning and Site Selection.” Sounds like a typical boring white paper title, but don’t be fooled!  The “new approach to best practices” is really a rejection of the very idea of best practices in chain store real estate analytics.

There are best practices for simple tasks and information systems such as backing up your data or verifying the accuracy of your locations.  However, for situations and systems that are complex such as the retail marketplace, it is impossible to create standardized methods and practices because the “cause and effect” relationships between things are too hard to predict and they are constantly changing.  A phrase used by people in complex systems theory is “emergent practices,” which describes ways of approaching and solving problems based on recurring patterns in data and collaborative decision-making.  Some of these approaches are described in the white paper. Continue reading →

The Whole World’s Watching

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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 →

The Biggest Name You’ve Never Heard Of

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I’ll bet you’ve never heard of Jack Valtrade.

Jack is one of the most successful commercial real estate operators in the eastern US.  He owns 25 shopping centers, a 200 store retail chain, a leasing company, a property management company, and a consulting business.  His net worth is estimated at $500 million, all self-made.

Chain Store Advisors interviewed Jack last month to gain some insights into his success.  “I have an unusual background,” he said.  “My dad is a PhD Economist with the Census Bureau and my mom teaches Geographic Information Systems at Princeton.  My first job was working for a commercial real estate broker when I was 16.  I put myself through M.I.T.  doing feasibility studies for a shopping center owner, and then got my Master’s degree in Statistics with a concentration in Consumer Research.” Continue reading →

Climbing the Stairway to Wisdom

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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

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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

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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.