Industry Trends

Competing with Amazon. By Inches. eSite Redefines Trade Area Analysis

Competing with Amazon. By Inches. eSite Redefines Trade Area Analysis

Trade area analysis has been traditionally encumbered by “pen, paper and spreadsheet” techniques that are woefully inadequate to the “Amazon Challenge.” The placement of retail stores must be precise or a business will lose precious customers that still shop offline.

eSite Analytics allows businesses to locate stores with pinpoint accuracy while gaining critical insights into lifestyle demographics, and the locus of retail activity door-by-door and inch by inch.

A “Sixth Sense” for Trade Analysis

A core focus of trade area analysis is to visualize the center of retail gravity within a region. eSP helps clients visualize the difference between retail and commercial trips to trade areas, understand how the trade area changes during the day, and the impact of demographics.

For example, while businesses must locate close to where people live, humans today are always on the move. In fact, this “mobile lifestyle” of the modern consumer is perpetually peripatetic. eSP is designed to take the guesswork out of knowing where customers are and, more importantly, where they are going to be.

Locating Where Customers Will Be

Current trade area analysis shows were customers are today. As Wayne Gretzky said, however, success means skating to where the puck will be. eSP provides that kind of insight with a database engine designed to crunch trip data from over 2 billion trips in aggregate, and thousands of discrete data points in minutes.

What’s more, unlike traditional trade analysis which is based on legacy counts that can be several years old, the eSP platform accesses data on a rolling 12-month basis, with fresh data being uploaded every quarter, so that customer data is never more than month old. 

Pyramids and Polygons

eSP platform tools will analyze information based on object-oriented, dynamic mapping. The advantage they provide over traditional hierarchical approaches can be explained by the difference between a pyramid and a polygon.

A pyramid is a classic “top-down” analytical paradigm. Analyzing trade areas with this traditional approach “ladders” customers into a hierarchy-from best to worst and vice versa. This assumes that retail activity is linear, which is frequently untrue.

The advent of Amazon has made the retail experience non-linear, multi-modal and dynamic. A brick and mortar retailer must therefore exploit the smallest activities that occur between the intersections of online and offline activity in a trade area. This is where polygons make a difference.

Using a sophisticated API, eSP imports data points within seconds to build a “shape shifting” polygon that crawls over the trade area to show hot spots of activity. eSP uses a single polygon to cover the entire trade area instead of building multiple polygons that can increase complexity and inefficiency during analysis.

Even more importantly, the new polygon solves the age-old problem of store cannibalization-because clients can now predict how the traffic to one location will meaningfully affect the traffic to another. The eSP polygon allows the viewer to spot the big picture and the small details all at once down to 138 inches.

To view the eSP platform in action, please contact eSite at 843.881.7203.

For interviews, contact Chris Birt at ABd Public Relations at 612.220.3800 or 

eSite AnalyticsCompeting with Amazon. By Inches. eSite Redefines Trade Area Analysis
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Regression Analysis. Good for Hairlines. Bad for Retail.

Regression Analysis. Good for Hairlines. Bad for Retail.

And now for a little bald-faced honesty. While it might still be useful as a tool for middle aged men studying the retreat of a once-promising hairline, regression analysis has outlived its usefulness as a retail planning method. In fact, it’s about as modern as a Corner Barber Shop for the data-driven business of today.

For years, regression analysis has been the preferred site selection tool for corporate planning staffs with cookie cutter stores. It can be used to predict sales in a proposed new location, based on existing data sets.

The danger is in the details, though. A data set that is based on mid-market locations ranging from Minot to Moline, for example, won’t be as reliably accurate when trying to forecast results in Manhattan. And the larger the variation in market size, the greater the potential for widely missing the forecast in a proposed new store.

The regressive approach may still be efficient for sleepy locations where time stands still. The rest of the retail world, however, now requires ensemble planning.

ensemble models provide more accuracy than traditional regression methods

Ensemble analytics can generate dynamic, real-time forecasts from a variety of high quality “streams” to plan with pinpoint accuracy. Because this model is flexible, not static, site planners can accommodate a wide ensemble of variation in their plans.

The experience of Duluth Trading Company remains an excellent touchstone for the ensemble based approach—and a poster store for progress.

Duluth Trading started to work with eSiteAnalytics in 2016, leveraging its Trailblazer™ retail analytics tool. With a 360-degree look at spatial analytics, eSite helped its client to identify sites and marketing opportunities that best support its customer base.

Unlike regression analysis, eSite provides Duluth Trading with an over-arching analysis that accommodates multiple inputs and customer channels (online, retail and outlet) to profile customers and locations. The company then uses that data to determine who their customers are and where they will be. The result is more accurate forecasts – and better information to use in making location decisions.

Better yet, eSite accommodates data beyond the physical world to assess online channels and other data to unmask perceptible “shifts” in sentiment and other factors that can make or break a store. It is ultimately this ensemble approach that gives Duluth a real-time view of how to strategically optimize their presence within current and future markets.

Leaving their competition more time to consider how to cover up those bald spots. Stay tuned.

eSite AnalyticsRegression Analysis. Good for Hairlines. Bad for Retail.
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Customer Viewpoint: The Psychology of Retail Analytics

Customer Viewpoint: The Psychology of Retail Analytics

Talk about a retail revolution. When customers buy wine from Firstleaf, they don’t sample, sip, sniff or study wine labels and tasting notes. They simply take about 10 seconds to fill out a short questionnaire and then—voila!—a customized shipment is on its way. After the first box arrives, buyers are encouraged to sign into their accounts and rate the wines. That feedback is used to generate a second shipment, which is also rated, and on it continues until the wine novice becomes a wine lover and loyal customer.

One luxury magazine said this interactive shopping experience makes the word “algorithm” sound sexy. Another called it “Netflix for wines.”

It’s precisely the sort of data-driven business intelligence that’s transforming how consumers interact with brands. Even as people debate the FBI’s right to hack into iPhones and Apple’s protection of personal data, millions of consumers are willingly offering up all kinds of personal information in exchange for better shopping experiences.

The question is:

How is your brand leveraging it?

All that data—geospatial data, traffic data, demographic data, psychographic data, transactional data, reviews and ratings—empowers us to put customers at the core of retail decisions in ways that weren’t possible a mere decade ago. From merchandising to marketing to site selection, we have opportunities to provide customers exactly what they seek, often before they even realize they want it.

But what is it that makes people comfortable inviting retail brands into their personal lives? What can a company do to ensure its best customers are feeling well-cared for…instead of big-brothered?

If you’re knee-deep in the process of aggregating and analyzing, it’s time to take a step back and look at the actual psychological motivations behind many of these buying behaviors.

Why the Paradox of Choice Makes Customers Welcome Algorithms

One of the world’s experts on choice is Sheena Iyengar, a professor at Columbia Business School. In one of her famous studies, grocery shoppers were presented with two alternating sampling stations. One included 24 jam flavors. The other offered six. Although the larger sampling station attracted 20% more traffic than the small one did, only 3% went on to purchase jam. But when people were presented with just half a dozen flavors, 30% bought at least one of them.

While shoppers could have selected from a wide variety of jam flavors, they were 900% more likely to make a purchase when asked to make a decision based on just six. Researchers call this The Paradox of Choice.

Although these initial findings have been challenged in recent years, countless examples still show how limiting choice can be a powerful influence on buying behavior.

For example, consider the case of online clothing retailer MM.LaFleur. The company does not offer a wide array of items or change inventory by season. Instead, female customers choose from a notably small collection of mix-and-match pieces intended for the workplace. They can even skip the shopping experience altogether and opt instead for a bento box—a compartmentalized container of products designed to come together as a complete set of workweek outfits.


Nearly 40% of first-time customers place their second order within four weeks. According to a Fast Company report, “Revenue grew nearly 600% in 2015 from a year earlier, and is projected to reach $30 million in 2016.”

MM.LaFleur hasn’t just identified that its core customers are paralyzed by choice. They devised an entire retail model around the concept.

In another study by Iyengar, this same idea was shown to impact how Americans save for retirement. When participants were offered two funds, participation hovered near 70%. When 59 funds were offered, participation dropped closer to 60%. And the more choices that were available, the more likely people were to completely avoid stocks or equity funds—not the kinds of decisions, Iyengar said, “that any of us would recommend for people when you’re considering their future financial well-being.”

Iyengar’s research helps explain why consumers so often respond favorably to limited choices.

Still, retailers do have a fine line to walk. In an excellent article from Adobe, the brand’s director of product marketing, Kevin Lindsay, argues that there “needs to be a balance between choice and hyper-relevance:”

With increasing demands on time and discretionary spending (plus consumers’ sheer intolerance and impatience surrounding anything that’s not highly tailored) personalization is not only a powerful tool, but a necessary one—nearly 9 in 10 consumers say customized experiences influence their buying decisions.

Lindsay goes on to describe the process of buying a pair of sneakers, and how using data to limit options can increase the likelihood of a purchase… as long as it doesn’t cross the boundary between freedom of choice and big brother-style customization.

These sentiments were reinforced in an Infosys omni-channel retail study. In that survey, 59% of shoppers who experienced personalized promotions and recommendations believed it noticeably influenced purchasing.

What these recent examples and countless others teach us is that many customers welcome data-driven limitations that make shopping a more pleasant, less overwhelming experience.

How the Path to Purchase is Becoming a Path to Purpose

Another area of developing research reveals the significance of authenticity. In one study, consumers were found to choose brands that engage them on their passions and interests 42% more often than they do those that simply urge them to buy a product or service. As digital platforms and social networks change the relationship between brands and consumers, the traditional path to purchase is becoming a “path to purpose.”

Starbucks’ achievement plan, McDonald’s pay with lovin’ campaign and Dove’s real beauty crusade all leveraged this growing sense of connection between customers and altruistic brands.

Retailers that rely too heavily on transactional data are getting a very small part of the story. Where do your customers spend their time? What’s important to them? What causes do they get behind? What stages of life are they in and how is that impacting their hobbies, interests and values?

For example, the following narrative is extracted from one of the dozens of customer segmentations in eSite Analytics retail analytics tool TrailBlazer:

[This group will] volunteer for social causes, vote Democratic and march in protests to protect the environment. They belong to arts groups that support dance, symphonic music and opera. Globally-minded, they’re interested in other cultures and champion human rights abroad. While they want to succeed economically, they don’t want to work for organizations with weak ethical reputations.

Based on this description, which of the following two brands do you think this group of young professionals would be more likely to buy from? This one:


Or this one?


You’d expect them to go with the latter, right? But while some specialized brands can get away with catering to one classification type, as the YogaSmoga website does, most will need to cater to multiple subsets. For example, here’s another customer classification, again taken from TrailBlazer:

[This group] likes to shop, though they’re price-sensitive. They like to stretch their money, typically waiting for sales, patronizing factory outlets and heading right to the clearance racks.

This group of customers would most likely respond more favorably to the promise of “up to 70% off.” But what if the brand analyzing these groups is not a clothing retailer but rather a fast-casual restaurant that serves all-natural beef burgers and supports local farms? In that case, both customer segments would show up in geographic reports identifying pockets of key audiences.

This is where the ability to both acquire and dissect the data around core customers’ interests can be an incredibly powerful tool. It allows a company to create a start-to-finish path connecting all interactions with the brand, from on-the-ground and in-store experiences to online engagements.


Now that you’re thinking more about the paradox of choice and your customers’ paths to purpose, how will you begin to apply these philosophies to your own growth strategies?

P.S. TrailBlazer™, our 360 degree retail analytics tool, provides of a lot of granular-level customer insights like the ones described above. Request a live demo here.

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eSite AnalyticsCustomer Viewpoint: The Psychology of Retail Analytics
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The Fastest-Growing Fast Casual Restaurants of 2015

The Fastest-Growing Fast Casual Restaurants of 2015

Fast casual is officially the growth engine of the U.S. restaurant industry.

According to a recent report of 500 chains, sales for fast casual brands has grown by 12.8%—nearly double the rate of the next-largest increase from any other restaurant segment.

As this rapidly-growing concept continues to outpace other restaurant categories, dozens of brands are filling in the gaps between fast food and casual dining—and earning places in the hearts of hundreds of thousands of food-loving, health-conscious spenders.

After another big year for fast casual, there’s still a lot of revenue potential to be realized in this segment. As we come to year’s end, it’s a good time to look at some of the top winners of 2015.

Fast-Growing Fast Casual Chains

In one recent comparative analysis, Shake Shack topped the charts for same-store sales growth, revenue per square foot, the number of units added and profit margins.

The high-end burger chain has “amassed a cult-like following because it hasn’t attempted to grow too fast or deviate too far from its stated mission,” says one expert in Entrepreneur.

At a time when many fast food stores are closing, Shake Shack openings are being championed and celebrated. Lines continue to snake out the door of nearly every location on a daily basis.

Another high-growth, fast casual concept is The Habit Burger Grill, which also ranked remarkably high in same-store sales growth and revenue per square foot.

And then there’s Zoe’s Kitchen, the Mediterranean-inspired fast casual concept which saw total revenue jump 29.4% to $56.4 million following the opening of ten new restaurants during the third quarter of 2015.

These certainly aren’t the only fast-growing fast casual concepts out there, but they’re definitely some of the most prominent ones to watch as we head into 2016.

So, what’s contributing to their success?

Put simply, there’s nothing casual about these fast casual brands. Last December, Market Realist was already reporting on the tremendous development of this sector:

“Fast casual is in a fast-growth stage and is eating away market share from several other restaurant formats. There are many reasons for this growth, some of which include shifting demographic preferences in taste, food quality, and food ordering methods.

A year later, that growth continues to skyrocket. These brands continue to be several steps ahead of the latest customer preferences and habits.

Lucky guesses?

Not even close. By segmenting customers, many of the fastest-growing fast casual brands know exactly where, when and how to appeal to high-spending customers.They aren’t aware just of the growing trend toward healthy alternatives to traditional fast food. They know precisely who wants it and where they’re located.

Are you thinking of launching a new fast casual concept or growing an existing one?

Stay tuned! We’ll be sharing our analysts’ favorite tips for segmenting customers and discovering your brand’s DNA. Sign up for our newsletter and we’ll deliver them straight to your inbox.



eSite AnalyticsThe Fastest-Growing Fast Casual Restaurants of 2015
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Is Your Retail Model Keeping Pace with the Latest Developments in Retail Modeling?

Is Your Retail Model Keeping Pace with the Latest Developments in Retail Modeling?

In this post, we’re going to take a look at the retail model of the future, and how to do it today!

We’re talking about the process retailers use to select sites and optimize networks of stores. This method allows growing brands to remain relevant as consumer expectations evolve. As one industry executive said:

“Changing consumer shopping tastes and expectations are quietly transforming the retail industry. The shopping model of the near future is poised to look radically different from just a decade ago, and retailers that don’t keep pace with changing tastes are setting themselves up for a rude awakening, at best, or extinction.”

Retail modeling lets you predict future store performance and outcomes in a fast-changing landscape.

But there’s a little secret most site selection consultants won’t tell you:

Most retail models are biased, and by themselves incomplete.

If you want to achieve reliable sales forecasts, you have to use combinations of multiple models to reduce the potential for biases that can (and often do) impact results.

By using combinations of multiple models—instead of a single, subjective approach—you get a more complete picture of the potential sites that will perform well for your brand.

At eSite Analytics, we call this collective approach to retail modeling Ensemble Modeling™. Our ensemble models include a combination of different models, including:

  • Analog
  • Naïve Bayes
  • Decision Tree
  • Neural Networking
  • Linear Regression
  • Spatial Regression
  • Logistic Regression Gravity

It’s a great way to get more reliable sales forecasts and better plan location-based messaging.

Interested in learning more? Ask us for a 15-minute demo of eSite Analytics’ exclusive Ensemble Modeling™ approach.

eSite AnalyticsIs Your Retail Model Keeping Pace with the Latest Developments in Retail Modeling?
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