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!

The Value Proposition for Chain Store Predictive Analytics

Posted on September 13, 2012 by Jim Stone

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

“We are experts in “best practices” in chain store real estate analytics and we can help you join the 21^st century.”
“Our scientists have developed proprietary algorithms and methods that produce the most accurate predictive models possible.”
“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:

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.
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.
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.
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

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!

Decision Support Systems for Chain Store Real Estate

Posted on June 20, 2012 by Jim Stone

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 →

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 →

Chain Store Planning: The Missing Link

Posted on January 23, 2012 by Jim Stone

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

Posted on January 18, 2012 by Jim Stone

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.

Resolving the Restaurant Discount Dilemma

Posted on November 14, 2011 by Jim Stone

I attended the Restaurant Finance and Development conference in Vegas a week ago and it was interesting to listen to the “hot” concepts talk about the reasons for their success.

“Great food, great service, clean restaurant” is the standard line that you hear from those who are growing units and sales. Who can argue with this recipe for success? Easy to say, hard to execute, and it’s really just a way to respond politely without giving away any trade secrets.

Sounds like the baseball player interviewed after the game: “We were confident that we could do what we had to do. We knew that if we kept our focus we could win the game.” Thanks, dude. I hope the other teams didn’t hear you give away your secret on national TV.

But there was another interesting recurring comment from some of the hot companies: “We don’t discount. We know that coupons are the road to ruin and we’re fortunate that we haven’t had to cheapen our brand through promotional discounts.” Continue reading →

Analytics vs Modeling: “Democratizing” Decision Support

Posted on November 2, 2011 by Jim Stone

Mistakes are expensive. Everyone wants a model to help them avoid mistakes and repeat successes.

We want a good business model that provides a framework for success. If the business model works and we stick with it, we will make money.

In the chain store business, we want to make good site selection decisions. Avoid bad real estate; pay the right price for good real estate. We want a sales forecasting model that will help us estimate the top line number to plug into our pro forma operating model for a store (which is based on our business model, of course). Continue reading →

Anatomy of a Coffee Purchase

Posted on October 13, 2011 by Jim Stone

A mathematician is a device for turning coffee into theorems. -Paul Erdos

We have a natural tendency to oversimplify things because it’s easier to make sense of simple things than complex things. The map on the right represents the simplest view of the relationship between a store and its customers. The two-mile radius around the store is a reasonable estimate of the trade area for the store, which means that most of the customers will be inside the circle. If this is true 90% of the time, we don’t care about the details of how customers make their choices. But what if only 70% of the customers actually are in the circle? 60%? 40%? At some point we have to consider a more complex set of factors that determine our true trade area and whether it contains enough potential customers to make a store successful. Continue reading →

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