What factors should an ecommerce based business be considering when selecting a brick & mortar retail location?

Two decades after they first launched online, Amazon.com opened up their first brick-and-mortar store in Seattle recently. This is a trend. Companies such as Blue Nile, Bonobos, and Warby Parker moving from purely e-commerce to add brick-and-mortar retail. We all know the impact e-commerce has had on the way people purchase goods and services, however 91% of all retail dollars are still spent in brick-and-mortar stores. More and more companies are trying to figure out how to get a piece of that pie.

We are witnessing the transformation of the physical retail landscape.

There is abundant opportunity. Brands realize that they can provide completely unique off-line experiences to complement their online retail operations. These online only retailers are not limited by their off-line operations and technology the way that traditional brick-and-mortar retailers are. The real secret of a successful retail operation today starts in the back of the house and then moves forward. This allows us to consider much smaller, more impactful retail footprints.

Many retailers who are moving from online to offline are recognizing the opportunity to open brick-and-mortar locations that act as data-driven, 3-D immersive advertisements. If we look at some of this new breed of retail locations we will notice that they worry less about front of the house inventory. They worry more about how they can engage and merchandise differently than they do online or their competitors do off-line. Deciding on what remarkable customer experience you deliver in your retail stores should drive your location selection and retail strategy.

The way people selected locations for the last 30 years has not changed much.

Most of the the data they use are based on mass media models based on an outdated understanding of consumer behavior. They ignore the ubiquity of mobile phones, Google, social media, Amazon Prime, Amazon Prime Now, online reviews, Uber and many other technological innovations have already taken hold and influence consumer purchase habits. You wouldn’t buy advertising today the same way you did in the mid-1990’s, so why would you use the same methods to choose a location. I was shocked when I learned that high reputation firms, in the site selection industry, are still producing real estate models based on magazine subscription data.

In 1999, advertising fundamentally changed. You no longer had to target your potential customers either by the location (website) or by their basic demographics (Women, married, household income >$100,000).  You could target them by their intent for the first time. The pay-per-click advertising model was born and it allowed advertisers to show ads based on potential customers’ interests. Instead of blanketing the sports section of Yahoo! to sell baseball gloves to potential customers, you could now only show them ads to your business when they showed intent and searched for things like (baseball equipment, baseball gloves, catcher’s mitt, etc). This advertising model has delivered the most ROI to their millions of advertisers.

Two parts to deciding on a retail brick and mortar location: the science and the art.

Some retailers, like Starbucks, Costco or Walgreens employ more science than smaller operations that can’t afford to deploy a scientific approach, or, if not constrained by capital, are definitely constrained by expertise. In some sad laughable cases, retailers do a “follow the Apple Store” strategy--which is worse because they are assuming, often incorrectly, that an Apple customer is their ideal customer and they frequent the Apple store as often as they would like their store visited.

The “tip” of the science iceberg is setting minimum demographic requirements before a retailer will even consider a location. As a simple example, BlueNile might be looking for men and women from 30-45, with higher household incomes. Yet, there is so much more data available today to make a more targeted decision on location.

The problem is that this is where science ends for the majority of retail brands. If a location hits a demographic threshold, they then typically jump right into the art side of choosing a location. This is where online only retailers have a significant advantage. They can look at more varied and non-traditional kinds of data than traditional retailers and they need to leverage that type of approach.

Would you want to make decisions based on inexact demographic criteria that is projected from decade-old census data or would you want to supplement that with people’s interests and demands based on search and social data? Add in a bit of car traffic? Mobile and beacon data? Then add in a mix of actual purchase behavior and consumer spending? Do you know your synergistic and cannibalistic brands?

A simple example of how critical moving beyond demographics to psychographics is vital to success

A small restaurant sushi chain that began in Austin, TX and used one of the leading site location services. They found a location in North Austin, that matched their demographics in terms of age, population density, family size and income. The service had predicted that location would be a success for that chain. What was never part of their scope and never came up in their analysis, was how little interest the people in this new area had in any Asian food, let alone sushi. While the two other restaurants next door had lines out the door (you’d know from a search or social media analysis), this location produced about 20% of the expected revenue and eventually helped bankrupt the chain.

John Prior, IdealSpot’s VP of Data Science says “most of the analysts and retailers trying to evaluate new locations “the old fashioned way” – spreadsheets overflowing with old data and preconceived notions – and seem to still have a very limited view of the factors that might affect the success or failure of a particular brick-and-mortar location - how visible is it to traffic? How accessible? What times of day? How much disposable income do nearby residents have? Without a deep analysis, the retailer will need to make guesses about his/her customer; what types of places do they visit? With what frequency? How much money are they "ok" with spending in those neighborhoods/places?

Those are all good questions to know the answers to, but I ask myself – how do these analysts tie those data points back to actual success or failure of a business? And are they only looking at 10 or 20 inputs? This is where most analyses “hit the wall” – people will spend lots of time and money collecting piles of data, but then have no way to relate it to success or failure of their business. This is why businesses end up with locations that fall far short of their expectations.”  

Analyzing the data allowed a chain of spas to recognize anytime they were near dollar or thrift stores (within their a couple of minutes of their location) it sent their sales plummeting. We assume people who were trying to be frugal, didn’t feel comfortable indulging themselves at the spa.

With today’s big data technologies, IdealSpot.com has developed machine learning algorithms that process 15,000+ variables so that retailers can access thousands of data sources in real time to make data driven decisions for their brick and mortar locations. Online, we have been talking about the zero moment of truth, but for centuries retailers have proven it is all about “location, location, location.”

As a side note, Blue Nile seems to have chosen a great location based on the data I have seen. In fact, one strong predictor of success for an engagement jeweler is their proximity to a Bed, Bath and Beyond on Long Island, NY. They seem to have done well in their first location, hopefully they can keep it up as they scale. The brick and mortar opportunity is clear, but just like in online commerce, those that leverage the data to experiment and create great customers experiences will dominate.