ReTel Technologies Chosen as 2009 AWS Challenge Finalist

ReTel has recently been selected as one of seven finalists for the 2009 AWS Start-Up Challenge. In the past, over 1000 companies have applied to the Amazon.com sponsored competition.

From the AWS Start-Up Challenge website, "We’re looking for the most promising start-ups that can grow into significant, meaningful, and lasting companies that leverage AWS to build its infrastructure and business." We certainly like to think that ReTel fits that description.

We're looking forward to the December 9th competition, and wish the other finalists (Bizo, FlightCaster, Gazaro, GoodData, Involver, Motally) the best of luck!

ReTel Accelerates Expansion Into QSR & Convenience Stores

It's been a while since we've written a blog post, and it's high time we updated our customers, partners, and prospects on a number of exciting events that are on the horizon for ReTel.

Most importantly has been an increased expansion into the quick serve restaurant (QSR) and convenience store industries. Over the past summer, we've been working with a number of QSR and convenience store owner/operators to refine our advanced ConstantAudit auditing system to help these owners and managers solve their biggest challenges with employee performance, operational efficiency and customer experience.

The result is a product that is truly revolutionary - one that gives owner/operators and managers in the fast food, fast casual and convenience store industries access to important data that they've never been able to easily access before. This data includes deep insight into employee productivity, ongoing analysis of in-store conditions and cleanliness, and effective measurement of service timing in key areas of the store.

The best part of ConstantAudit, of course, is that the ideas behind it aren't ours...they're our customers, and we thank them for helping us create a suite of solutions that solve big problems easily, quickly and at a low cost.

To learn more about ConstantAudit, and how it helps solve problems in your industry, please visit the product page here.



We'll stop here for now, but we promise we'll have more great news to share very soon. Please check back soon!

Customer Path Analysis on Large Samples in Retail Settings

In audit studies it may be common to follow a single shopper around to determine the path a ‘typical’ shopper may take.  This approach may provide interesting insights from a small sample that may be used to formulate hypotheses about the psychology of shopper behavior, but fails to standup to rigorous statistical analysis due to small samples and the large number of factors that may be driving the individuals shopping behavior.  The net result is insight into a behavior that may or may not generalize to a larger population or across other shopping trip variables. 

In a large sample size it becomes increasingly difficult to generalize this ‘typical’ shopper path. The problem is that at any one decision point, a shopper may find himself or herself deciding between two paths (e.g. which aisle to walk down first).  This decision may be influenced by a number of factors: gender, age, profession, time of day, day of week, shopping mission, etc.  At the next stage the shopper again makes a decision between two paths.  It is plausible to expect that the shopper will make many of these path decisions during any one shopping trip.

To illustrate these decisions I have created the tree below:

This works fine as long as the shopping trip is short for all participants, but this length is unlikely to be the case.  The number of distinct paths through the tree grows at 2ⁿ, where n is the number of levels in the tree.  This means when there are at most 10 decision points, there are then 1024 distinct paths.  At a more realistic 20 path decision there are over a million paths. This situation becomes even worse if the tree is allowed to travel back on itself (i.e. a person can circle back to a previous path).  In this case there are infinite paths as the person is allowed to travel back indefinitely.  Clearly there is no typical path in this extreme but plausible case, but only atypical paths in the strict sense.

So how do we deal with this issue then?  We can take a step back and see how other applications of path tracking deal with impossibly large samples and near infinite decision points.  One of the best examples comes from fluid dynamics.  Air is a fluid which meteorologists track the path of to predict the weather.  Rather than track each molecule of air, compare that to its demographics (its molecular makeup, ionization, etc), and its typical path (go to the left or right of this obstruction), it is far more useful to use a map of vectors (arrows) to model the flow through a decision point.

These graphs can be used to identify choke points, movement patterns, dangerous conditions, among other meteorological predictions.

In a retail setting, this same learning can be applied to the movement patterns of a large sample of shoppers.  By analyzing the flow behavior at any one point, we can get a better holistic view of the movement behavior of a shopper.  By knowing the movement patterns of a shopper (which can be further dissected by the shopper’s demographic profile) it is easier to determine why a shoppers of a certain profile move in a certain manner.

A shopper’s movement is not determined by a pre-set path, but rather by a series of decisions that we can identify at each decision point made by the shopper.  If a shopper goes down an aisle because of a POP display, it won’t be because he or she had pre-determined that path, but because a decision was made at the time when the display is seen.  In this example, it makes sense to map out these decision moments in aggregate by dimensional shopper factors in order to best describe the aggregate ‘typical’ path.

This aggregate approach provides a much richer view of the paths of shoppers through a space.  It successfully deals with the problem of large samples, and provides rich data which can be used to re-order the shopping environment to better suit the needs of the targeted demographic profile.

Looking Back: "Are We Entering A New Era of Consumer Behavior?"

On the cusp of the release of Q4 earnings statements by retail stalwarts such as Best Buy and Walgreen's, I was amused to revisit the first post that was written for ReTel's blog: "Are We Entering A New Era of Consumer Behavior?"

When I wrote that piece back in November 2008, we had just returned from the In-Store Marketing Expo in Las Vegas. We spoke with retail services vendors, retailers, industry insiders, advisors and more. Universally, there was a tangible sense that we were at the beginning of something significant - not a tectonic shift, perhaps, but a realignment that would make itself felt for retailers and retail service providers alike.

Four short months later, and the world is a different place indeed. And, to state it plainly, the answer to the question "Are We Entering A New Era of Consumer Behavior?" is a resounding YES.

Now, the hypothesis we put forward back then was that this shift in behavior would negate much of what retailers and retail-focused manufacturers knew about their customers, thereby increasing the need for new consumer insights that would shed light on new consumer behavior. And we've found that this is, indeed, true. More so than ever before, we've seen demand for our services spike as both retailers and manufacturers seek to gain new insight to retain a competitive edge in the new economy.

It's something we think will continue to occur, as even in a post-recession world, the new savings- and value-conscious American consumer will continue to challenge past notions of consumer behavior.

Blogs I Love: Shopper Culture

I always enjoy finding like-minded folks on the web - in this case, a marketing research firm by the name of "The Integer Group" out of Denver.

This is a very insightful post on opportunities gained, opportunities lost: Clorox gets premium placement by the shopping carts with every germ-conscious shopper offered a free Clorox anti-bacterial wipe to clean their cart handle.

Yet where is the accompanying display for those shoppers to take a product after they take a wipe? That's a really good question.

Read the post here.

Increasing QSR Sales at the Drive-Thru and Inside

The other day I was in “line” to buy a hamburger at a local fast food restaurant. I’ve put the word “line” in quotes, because you could not find a dictionary where the definition for “line” would match the cluster of people standing at the counter. It was difficult to determine where the line began or how many lines there were. I’m sure you have experienced this. First, you try to find the entry point to the line. Then you try to be as polite as your hunger will let you as you nudge your way to the front. It's pretty irritating, but what can you do.

After getting my food, I sat down between a window and the soda fountain and started to eat. The window overlooked the drive-thru exit, and a steady rhythm of cars passed by. I noticed that a number of drivers parked in the lot and ate their meal in their car. I also overheard a woman at the soda fountain, who had just stood in the line with me, say - “It would be faster and easier to go through the drive-thru and then come in and eat.” Her words and watching the drivers eat in their cars gave me an interesting thought: The disorderly line inside was lowering sales both at the drive-thru and inside the restaurant.

Here is my reasoning. I bet that those drivers eat in their cars to avoid the lines inside. And if the lines inside were faster and less stressful, more of them would order inside. This shift of customers would decrease the average service time at the drive-thru increasing drive-thru sales. According to a study sponsored by QSR magazine, the number of cars in line for service has a direct impact on the total time a customer spends at the drive-thru. And McDonald’s former CEO, Jack Greenberg estimates that “unit sales increase 1% for every 6 seconds saved at the drive-thru”. I would also bet that there are other customers who want to eat inside, but go to other restaurants to avoid the line. Therefore, if you add all of these factors together, improving the inside line could significantly increase sales.

So how can you increase in-store line speed and employee efficiency? One way is to use video analytics to monitor service times and provide the restaurant manager with insight into the speed of his or her team. A similar approach, using car counters, has worked well to increase drive-thru service times. According to the same QSR study mentioned above, restaurants that electronically monitor their drive-thru service speed are on average 31 seconds faster during lunch and 27 seconds faster during dinner. If we use Jack Greenberg’s estimate of the impact for this speed increase on sales, simply monitoring the drive-thru increases sales by about 5%. In all likelihood, monitoring the in-store lines would provide a similar increase.


QSR Study: http://www.qsrmagazine.com/articles/news/story.phtml?id=6764&from=rss
McDonalds: http://www.hme.com/collateral/Operations_2005.pdf

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