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Retail real estate decision-making comes out of the silo


By Dean A. Stoecker, CEO, Alteryx, LLC

The “prediction gap” of expert opinion that endlessly disagrees between a rising economic recovery versus continuing recession illustrates the complexity and volatility of the marketplace, and how little room there is for retail chain real estate investment mistakes.

Site selection decisions – some of the most risky for retailers – have traditionally been made by separate divisions or within isolated decision-making silos using disparate data sources. Store location strategy typically started with real estate considerations like cost per square foot. From there, decisions trickled down from the C-level (who first consider balancing investment portfolios and managing shareholder expectations), to operations (who must then plan, staff and manage logistics), to merchandising (who must stock, display, position and promote), to marketing (who must research, segment and target the customer base to bring the right buyers in the door with the right media buys). Buying/leasing a new property and then allowing these decisions to flow downstream is no longer sound business practice.

There’s been talk about bringing decision-making out of these silos, but until now, it was almost impossible to walk the walk. The technology simply did not exist to bring together disparate data resources like POS feeds, customer data, demographics and geospatial business intelligence. There have been advances, but the solutions have never been flexible, comprehensive or fast enough.

But today, with the advent of cloud computing and the integration of nearly real-time data integration and analytics, the opportunity for every retailer to walk the walk has arrived. Every department in every retail organization now can participate in these critical decisions, and share in all the risks: real estate, marketing, merchandising and operational considerations should be optimized into every step of every project. This phenomenon is redefining best business practices for every multi-site retailer, large and small.

While their business models may be different – from coffee shops to apparel to convenience stores – the national and regional retailers that are going to survive and thrive are finding ways to take advantage of this depressed economy to expand their market presence with profitable new sites while capturing an increasing share of the customer’s wallet. The progressive early adopters using today’s advanced software tools and analytics are viewing their businesses holistically. It’s no longer just about ‘where’ a store is located. It’s also about how much it will cost to get product to the store, how much it will cost to buy media to drive the right consumers to that store, what competitive threats loom for each location, and how each store’s performance can be measured and managed according to shifting market conditions.

Unifying these information assets across all departments allows managers to immediately view and understand:

  • The square-foot costs from a real estate perspective;

  • The supply chain forecasting and support costs from an operations perspective;

  • What marketing support costs it will take to bring in the available target customers; and

  • Localized merchandising and replenishment programs to optimize retail floor space management efforts.

In essence, all costs of doing business in a specific location can now be considered collectively, before closing on any new store site. Store performance goes up, risk goes down, and metrics become clear and measurable.

The race to find the right real estate is exacerbated when managers have to spend valuable time interacting with different technologies and static datasets to analyze the potential revenue and targeted market for each prospective site. Third-party data found in spreadsheet and mapping systems is often required for site location profiling, but this data does not have the flexibility to allow managers to make the changes and update facts about proposed locations. As a result, static datasets and an inability to update the process by users in the field make site location analysis inconsistent from one location to another. This variation in data consistency from market to market and within markets hinders the decision-making process and undermines the real estate manager’s ability to develop repeatable results.

The tools are there to process point-of-sale data and to drive real estate site selection and forecasting models in ways that have never been done before. The analytical process around customer profiles has advanced to the point where each department can independently analyze their own profiles and categories within a store, thus breaking down the decision-making silos across operations and merchandising barriers to holistically assess not only real estate decisions, but single store site and individual manager performance.

By quickly creating complex model calculations for identifying new locations, retailers can input additional factors and preferences like urban storefronts, shopping centers, end-caps or pads, free-standing buildings, parking, view corridors and zoning laws.

A web-based system can be used to input parameters and identify scenarios in preparation for field visits. Managers from sales, operations and real estate can enter the applicable site specifics and choose the reports they want in just minutes, as opposed to hours.

The data can then be taken to the next level once the store location is selected so they know what inventory to stock and individual customer profiles can be created for each specific store and market by region. In addition, they are able to utilize operations space data, combine it with the customer profile, and make recommendations on merchandising.

This kind of decision-making is allowing investors, operations managers, marketers and realtors to drive their businesses by looking forward as a team toward their most lucrative opportunities. This new standard rises above the old model where individual departments could only look back at demographic data (almost always many months out of date), what mistakes were made, and why, and try to assess the future from there. It doesn’t require a crystal ball. We have the tools today to walk the walk and ensure a profitable future.

Dean A. Stoecker is president, CEO and founding partner of Alteryx, LLC. Alteryx hosted, on-premise and hybrid SaaS solutions integrate spatial intelligence into enterprise workflows, seamlessly scaled across local, regional and global markets.

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