Monthly Archives: September 2012

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!