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

“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!

The Value Proposition for Chain Store Predictive Analytics

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!

Decision Support Systems for Chain Store Real Estate

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

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

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.

Chain Store Planning: The Missing Link

The 4 P’s of Marketing (Product, Price, Promotion, Place) have been around since the 1950’s.  For chain store operators, PLACE is more critical than for other types of businesses.

A better term for PLACE is DISTRIBUTION, but of course it starts with “D,” so it never made it into the 4 P’s.  DISTRIBUTION is a term used to describe what is commonly called “the supply chain” and the facilities involved in distribution are “the supply chain network.”  These facilities include factories, distribution centers, and retail stores. Continue reading

Breakthrough in Market Planning

I attended a franchise trade show recently and visited with a company that was selling multi-unit territories that had been pre-defined based on the expected number of development opportunities and their approximate locations.  The map they had on display looked something like the diagram on the left below, where the green and orange boundaries represent territory boundaries and the black dots are target locations for stores.  The franchise rep said that the target locations had been generated by a sophisticated modeling program and then field-validated by the real estate team.

A more traditional territory plan is shown on the right, where each target store location is a point representing the ideal center of the trade area which is a polygon showing the primary trade area.

I certainly commend this operator for using “best practices” in market planning by laying out their territories in advance and proactively offering them to prospective franchisees. 

What’s interesting about the map one the left is the way that the boundaries are just large enough to surround the points.  This means that a franchisee can select locations up to the edge of the boundary without worrying about encroaching on the stores in the adjacent territory because there is a “buffer” between the stores built into the market plan.  If the buffer is based on the appropriate level of spacing between stores based on the density of the area (e.g. urban, suburban), then it’s a great planning tool.

The problem with the traditional approach shown on the right is that franchisees in adjacent territories might select a location closer to the same edge of the territory and have significantly overlapping trade areas.  One solution to this problem is tocreate a second zone within each trade area that limits the range of choices for a site and creates a buffer similar to the one built into the map on the left.

This is the first time I’ve actually seen a map like the one on the left in a live sales situation and I believe it’s an important step forward in the state-of-the-art in market planning.  It certainly doesn’t eliminate every issue that can arise between franchisees in adjacent territories, but it establishes some clear expectations at the beginning of the process that reduces the chances of conflict at some point in the future.

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!

Do We Have Industry Standard Practices in Retail Real Estate?

Why does any industry have standard practices?  Every business is different, even within an industry, so what’s the point of trying to standardize?

Industry standard practices are a lot like the rules in a game.  They provide a systematic, consistent, and proven framework within which the players develop their strategies and exercise their skills.  Having rules doesn’t make you a winner, but they make it possible to become a winner by promoting the following competitive strengths: Continue reading