The Inside Track

ReTel's Blog Has Moved!

2010-06-22T14:09:00.002-05:00

To our avid readers - we have integrated our blog into our website, so we will no longer be updating the blog you're reading right now.

Please bookmark our blog's new address at the following link:

http://www.reteltechnologies.com/news/retel-blog/

Thanks for your continued interest!

The ReTel Team

The Hawthorne Effect & You

2010-06-01T16:40:00.006-05:00

In our last blog post, we spoke briefly about The Hawthorne Effect and how ReTel’s surveillance auditing services can be used as a mechanism to trigger it. In the post, we’ll dig a little bit deeper into the origins of The Hawthorne Effect, how it works, and case studies that reveal its power in operationally-driven environments, such as quick serve restaurants (QSRs) and convenience stores.

The History Of The Hawthorne Effect

At its simplest, The Hawthorne Effect can be described as a change in the performance of subjects under observation, simply because they are aware that they are being observed. In studies performed in the 1920s, researchers were baffled when upticks in performance during a study suddenly disappeared when the study was complete. As it turns out, the test changes made to the observed participants environments had only a nominal effect on their behavior; rather, it was the observation itself that truly had an impact on their performance.

How It Works & Why It Works

In many instances prior to observation, participants in studies were either unaware of their performance and therefore unable to understand whether it was good or bad, or they were aware of their performance but made no special effort to improve it because there was no means of measuring it, and therefore no incentives or punishments based on that performance. Once observation was established, however, participants became more aware of their behaviors, modifying it either explicitly or unknowingly to a higher level of performance. As those early studies showed, as soon as the observation or measurement mechanism was removed, performance soon slipped back to previous, lower levels.

Examples of The Hawthorne Effect in the QSR and Convenience Store Industries

Perhaps the most well known use of The Hawthorne Effect in these industries is with drive thru timers. Prior to the existence of drive thru timers, franchisors and franchisees had no way of understanding speed of service at the drive thru. By default, then, they had no way of providing employees with timing benchmarks or awareness of their performance at the drive thru.

With the installation of drive thru timing devices, certain chains saw an overall reduction of up to 29 seconds per order during peak rush times. Chains such as McDonald’s estimated that for every 6 seconds saved at the drive thru, unit sales increased by as much as 1%. It’s easy to see the impact that an improvement like this can have on a high-volume business such as a QSR.

What is interesting to note about these drive thru timers is that they do nothing else but provide highly visible evidence that the drive thru is under observation for speed of service. It is simply by knowing that they are being measured that the drive thru crews increase performance, which therefore increases sales.

Applying the Power of The Hawthorne Effect Elsewhere

ReTel’s advanced auditing technologies allow organizations to put the power of The Hawthorne Effect to work anywhere in their organization. Similar to the above example, ReTel’s customers have been able to realize significant gains in performance simply by measuring and providing awareness of measurement.

Do You Know Who Your Heroes Are?

2010-05-04T13:09:00.001-05:00

Over the past decade or so, the installation and use of surveillance as a management tool for restaurants and retailers has become commonplace. However, there are still a few businesses that are just now adding CCTV as a method to better understand their businesses.

There are two things that typically occur when a business installs surveillance for the first time. The first is an immediate jump in employee and business performance, as the Hawthorne Effect takes hold (more on this in a later post). The second thing that happens is either an explicit or implicit rejection of the practice by the business’ employees. We’ll focus on the latter for this post.

The primary reasons for employee rejection of surveillance are two-fold. First, dishonest employees will recognize that the presence of surveillance can potentially reveal this behavior if they continue it, which can lead to negative consequences for them. Second, the general perception of business surveillance is that it is only used negatively for punitive measures. Unfortunately, this second reason for rejection is often untrue.

The best business owners truly use surveillance as a management tool – and that means using it both to discover opportunities for improvement, as well as best practices and great performance that should be rewarded and encouraged. As we’ve spoken to numerous quick serve restaurant (QSR) and convenience store owners, managers and employees, we’ve discovered an interesting problem that faced almost all of them: the best employees would leave because they felt unrecognized. Not only that, but they felt that their opportunities to shine were often sullied by the behavior of other employees who were less inclined to perform.

For owners and managers who actively used surveillance to identify their best employees and reward them, good employee retention became a breeze – and it had an impact on the rest of the employees as well, driving performance higher thoughout the organization. Of course, adding ReTel’s ConstantAudit service has helped many of these owners and managers discover these star employees even sooner, and track better performance more often.

So if you have unsung heroes, use every tool that works to discover them and recognize them. If you don’t have the tools to do this, then get them. The impact on that individual, as well as your entire organization, is well worth the effort.

The Camera That Cried "Wolf!"

2010-04-12T10:15:00.003-05:00

You may be familiar with the classic tale of the boy who cried wolf. As a shepherd, he spent all day watching his sheep graze peacefully, yet on occasion, would cry "Wolf!" to get a villager or two to run down to help. When they arrived, he'd yell "Gotcha!", laugh, and the poor villager would sulk back to the village, angry that they wasted their time on his joke. Of course, we all know what happened next. By the time an actual wolf came around, and the boy cried "Wolf!", no one came to his aid. The result? No more sheep, one very satisfied wolf, and an out-of-work shepherd.

If this boy was a video analytics system, he would have what we call a poor signal-to-noise ratio.

Signal-to-noise is one of the general terms used to describe how often an automated alerting system returns a true event versus a false event. It's also what typically fails these systems in the real world, as system operators learn to ignore alerts because the majority of those received tend to be false. In the surveillance world, automated alerting has primarily been used for video analytics and surveillance system "health check" programs that check assets for operation and uptime. Here's an example of how they fail because of signal-to-noise issues.

Let's assume I manage an installation of 100 cameras in a medium-sized corporate facility. I assign one guard per shift to sit in an on-site central monitoring station to watch screens, get alerts, and be prepared as a first responder in case of an event.

First and foremost, I have a health check running on my surveillance system. If a camera goes down, the guard protocol is to receive the alert, check the monitor, verify the problem, and fix the problem (or call the surveillance installer to fix it). I have one of the best in breed health check systems. That means that each camera only generates 2 false positives a day. Over 100 cameras, that translates to 200 false positives a day. Let's also assume that there are 5 real problems mixed in there. This translates to a signal to noise ratio of 1:40. For every 40 false events, there is 1 real event. The result? When the health check cries "Wolf!", no one responds.

Next, I have video analytics on 20 cameras outside monitoring a virtual tripwire to indicate a perimeter violation. Here's where it gets even more challenging. Next to and around the areas that these cameras monitor, I have branches swaying in the wind, animals (maybe even an occasional wolf) going to and fro, and the occasional early morning jogger who seems to like jogging along our fence. Therefore, on a daily basis, each camera is sending 50 alerts of a tripwire violation - that's 1000 a day! And, on a daily basis, the perimeter is never violated. Not even on a weekly basis. Or monthly. Actually, last I remember, there was a break-in...two years ago? You get the picture.

So what is the solution to the problem of "The Camera That Cried 'Wolf!'"? Unfortunately, many industry experts will tell you that even the best analytics and health check systems are still a ways off from effectively lowering the rate of false positives that they generate. That's why ReTel is developing applications that will work right now with our proprietary two-layer auditing system to solve these problems and more. The broadest description of what we are developing would be a video noise filter that separates bad from good, returning only signal to the end user.

And our goal is not to replace analytics or health checks, but to make them better and more usable, so that they become acceptable features of an organization's security and surveillance system. Bias is already creeping into end users' opinions on analytics and health checks, which can make future adoption difficult - even after all the wrinkles are ironed out. That is unfortunate, because they can be truly useful tools to help manage an organization's security and surveillance.

After all, when that real wolf comes, you want to make sure that there is someone there to hear the warning!

Crowdsourcing the Singularity

2010-03-31T23:33:00.009-05:00

“Come on. Learn, goddammit.”
- David (Mathew Broderick) to Joshua, War Games, 1983

I’m reading the Singularity is Near by Ray Kurzweil. For those of you who’ve never heard of the Singularity, “it’s a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed.”

According to Kurzweil, we’re currently living in the 4th Epoch and are rapidly approaching the 5th Epoch, which is the Epoch where all of the Singularity goodness begins.

"Looking ahead several decades, the Singularity will begin with the fifth epoch. It will result from the merger of the vast knowledge embedded in our own brains with the vastly greater capacity, speed, and knowledge-sharing ability of our technology. The fifth epoch will enable our human-machine civilization to transcend the human brain's limitations of a mere hundred trillion extremely slow connections."

- Ray Kurzweil, The Singularity is Near

The singularity will begin when we build intelligent machines like Joshua from War Games. Combining human understanding with silicon’s raw processing power will result in an explosion of innovation and progress. This argument makes sense. However, I think Kurzweil may have missed an intermediate step between the 4th Epoch (today) and the 5th Epoch (the beginning of the Singularity). Let’s call it Epoch 4.5.

Epoch 4.5 is when our brains’ “mere hundred trillion extremely slow connections” will help computers transcend early stage artificial intelligence. We’ll do this by greatly expanding crowd-aided artificial intelligence, resulting in computers that provide a Joshua-like user experience. The crowd will support, train and augment these computers’ intelligence in real-time on a vast scale.

On a small scale, Mechanical Turk is already doing this, which is why Amazon’s tagline is - “artificial, artificial intelligence.” Here are some other real world examples:

reCaptcha uses CAPTCHAs to digitize books. Every time you enter a CAPTHCA to register for a website or buy something, you’re helping a computer understand text.

Google has created an ESP game where two people look at a picture and type in words that describe the picture. When the words of two players match, the players get points. Google then uses those words to index the images for Google Image Search. -

As crowdsourcing becomes more prevalent, larger pools of on-demand workers will be available. The computers of Epoch 4.5 will leverage this ever present crowd to address their intellectual weaknesses.

As a small thought experiment consider an advanced Roomba. Let’s call this Roomba SuperRoomba. SuperRoomba’s AI isn’t much better than Roomba’s; however, SuperRoomba is connected to the crowd. When SuperRoomba’s AI is stumped, it queries the crowd for help. Here are a few examples:

One of my former Long Island neighbors orders a SuperRoomba. SuperRoomba carries the latest speech recognition software, but he still can be confused by a nasally, Long Island accent. When presented with a confusing word, he sends the offending audio clip to a worker in the crowd. The worker corrects SuperRoomba’s understanding, training him to recognize “Dawg” as “Dog”

My old neighbor outfitted his SuperRoomba with a security camera. While video analytics can detect motion and a small subset of activities, it can’t tell the difference between a real threat and my neighbor’s dog. While patrolling, if SuperRoomba encounters sudden movement, he sends a snapshot to a worker, who evaluates the threat level.

Where will this crowd come from? To make Epoch 4.5 a reality, millions of workers will need to be available to serve these requests in real time. The best source for this labor is the same labor pool that currently staff factories. Factories using tens of thousands of workers construct complex products in mass every day. In a similar manner, online information factories could process computer requests. There are certainly enough underemployed people in the world to staff these virtual factories. According to the UN, 1.4 billion people make less than $1.25 a day. Most of these people work in terrible conditions and would jump at the chance to work in a safe office environment.

SamaSource, a non-profit that brings crowdsourcing work to African refugees, has already shown a demand for this work, as well as the ability to reach these workers. Imagine the impact if an additional 1.4 billion brains came online to guide, correct and train the next generation of artificial intelligence. We could provide safe and decent-paying jobs to the world’s poorest and simultaneously bring about a revolution in computer intelligence.

Is That a Bomb in Your Underpants or are You Just Happy to See Me?

2010-02-05T19:35:00.002-06:00

There was a lot of uproar over the "Underpants Bomber" in the days after Christmas. The talking heads on the Sunday morning shows pounded the TSA hard and wanted to know how this guy got through. It isn't possible to point a finger at one reason. Tons of small mistakes led to the security breach. My sense is that most people feel airport security is ineffective.

Recent research backs that feeling up. A test at O'Hare found that TSA agents only found 60-75% of planted guns, knives and bombs (all fake of course). It's nearly 10 years after 9/11 and we're still not very good at airport security. Why? Well, according to research by Harvard Prof. Jeremy Wolfe, it turns out that human brains really suck at visually searching for rare events. When an item shows up only 2% of the time, which is the frequency of contraband in luggage, people miss the item 30% of the time. However, if the item appears 50% of the time, people miss the item 7% of the time.

One solution to the problem would be to insert staged contraband images into the image feeds so that 50% of the images contain a gun, knife or bomb. If TSA had done this last year, they would have intercepted 4,504,455 more prohibited items (most of it probably 6 oz bottles of hair gel).

At ReTel, we've experimented with similar techniques to improve the visual search capabilities of our auditors. Understanding the way the brain sees is a big part of successfully leveraging the crowd to analyze video.

More Tales of Drive Thru Debacles

2010-02-03T11:51:00.007-06:00

I received a lot of feedback on yesterday's post from friends and colleagues who had experienced similar activity in the past, which has prompted me to further explore how pervasive the practice of "pulling" customers at the drive thru is, and the ultimate effect on consumer experience at quick serve restaurants (QSRs) in general. One of the best articles I found is at The Consumerist, appropriately entitled "Not So Fast Food." Apparently, many Burger King franchisees face this problem, and it can have a huge impact on customer loyalty.

Now, one of the most damaging things I've heard about why this practice is terrible for customer satisfaction is as follows: once the customer is out of the timing loop, the crew loses any incentive to rapidly deliver the order to the customer. They're simply no longer being tracked, so there is no impact to them connected to timeliness. Now, as the owner of that restaurant, imagine if this happened just once to one of your most valuable repeat customers who dines weekly at your location. Imagine if it happened twice. With average ticket sizes approaching $6+ these days, that would be a total of ~$1250 lost annually from just that customer if they started going to your competitor instead.

Read the Consumerist article here.

Games People Play

2010-02-02T12:32:00.004-06:00

As we continue to meet with more franchisees with a deep concern for the quality of their customers' experience and their stores' operations, we hear more and more stories about how important policies are used and abused to skew the data that operators rely on to better run their stores.

Just yesterday, we met with a multi-unit Burger King franchisee who pays very close attention to speed of service at the drive thru (and wishes he had a way to track speed of service at the counter - he does now with ReTel). He continually faces a frustrating situation, however, with his employees' attempts to continually "game" the drive thru timers.

As nearly all franchisees who operate stores with drive thrus know, speed of service is critical to attract additional customers to the line of cars waiting to order. And, as nearly all franchisees know, employees who want to game the drive thru timer will have customers pull forward to the parking lot to wait for their order to be delivered. This causes the customer to leave the drive thru timing system, and therefore incorrectly closes the timing loop early to artificially lower drive thru times.

This practice is pervasive, and it ultimately ends in massive customer dissatisfaction as they realize the quick speed of service they expected was, in fact, not really there. As evidence of this, I came across a very interesting consumer post on PlanetFeedback today. In the post, the consumer entered an empty drive thru, placed her order, and was still asked to pull forward to receive her order as the location had not prepared enough food in advance to fulfull the customer request. Read the full post here (as well as the comments below her post) to get the full gist of this customer's dissatisfaction with this practice.

An Anecdotal Example of the Impact of Slow Speed of Service

2010-01-25T15:43:00.004-06:00

This weekend, I traveled with friends to Lake Tahoe for a weekend of great skiing. As is our tradition, we stopped at the In N' Out Burger in Tracy, CA for burgers and fries on the way up...and, of course, on the way down as well.

For those outside of California, In N' Out is famous for two things. First, their food is of exceptionally high quality for a fast food restaurant. Second, their service is exceptionally slow for a fast food restaurant. However, the former trumps the latter, as any In N' Out you visit is constantly full of loyal patrons patiently waiting for their order to be delivered.

As we rolled up to In N' Out for the second time on that trip, my friend Adit chimed in. "Hey everyone," he said, "Let's get our food from the drive thru, and then take it inside to eat." When we asked why, he explained that the drive thru service is much faster, and that he didn't want to wait the extra few minutes to receive his order should we complete our transaction start to finish inside.

Now, this illustrates something that is very interesting to us at ReTel. The importance of the drive thru has been acknowledged in the quick service restaurant (QSR) industry for quite some time. In fact, for many chains 60% or more of their revenue is from the drive thru versus the counter. As a result, timing at the drive thru has become a must have for any well managed QSR. After all, a visibly shorter line leads to more customers entering that line instead of choosing a drive thru elsewhere.

But what about Adit's example? Does slow speed of service inside the house lead to more customers lining up outside the house at the drive thru? And does one less customer inside, and one more outside, potentially drive away another customer at the drive thru because the line now appears to be too long to join?

It's an interesting question to ponder. We're in a position now to help illustrate the impact of speed of service inside the house. Can we connect that to speed of service outside and the potential impact of customer acquisition at the drive thru? We'll see.

Crowdsourcing and San Fran Awesomeness

2010-01-21T11:24:00.003-06:00

Last night I went to the Crowdsourcing Work Meetup event in my new hometown - San Francisco. The energy, passion, and excitement in this city is incredible. Chicago never could have staged this event. I feel really lucky to be here. ReTel is at the geographical and ideological epicenter of a brand new technology that is going to change the world. My co-founder, Adam Rodnitzky said it best - "This is turning into a movement." He's right. The wave is building and it is going to change lives all over the world.

Lukas Biewald, the CEO of CrowdFlower, moderated and organized the event. He's a great guy, and if you're interested in crowdsourcing, you should read his blog - http://blog.doloreslabs.com . The other speakers were great as well. Here is a list:

•Aaron Koblin, a local artist, who created The Sheep Market, Bicycle Built for Two Thousand and Ten Thousand Cents projects.

•Leila Janah, a social entrepreneur who runs Samasource, our fantastic non-profit partner.

•Sharon Chiarella, VP at Amazon in charge of Mechanical Turk.

•Panos Ipeirotis, NYU Stern school professor who writes one of my favorite blogs on crowdsourcing and Mechanical Turk.

If you want too see an amazing visualization of the power of crowdsourcing, go to Aaron Koblin's website - http://www.tenthousandcents.com. It's amazing.

If you want to get into the underlying coding and theory behind crowdsourcing, go to Panos Ipeirotis blog -http://behind-the-enemy-lines.blogspot.com.

If you want to learn about how crowdsourcing is going to save millions from poverty, go to http://www.samasource.com.

Ok, I've got to go. I've got a board meeting this afternoon. :)

ReTel Technologies Chosen as 2009 AWS Challenge Finalist

2009-11-12T17:09:00.004-06:00

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

2009-09-07T17:51:00.003-05:00

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

2009-04-24T12:38:00.006-05:00

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?"

2009-03-22T21:53:00.002-05:00

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

2009-03-16T17:30:00.005-05:00

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

2009-03-02T16:56:00.003-06:00

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

Follow ReTel on Twitter

2009-02-26T15:22:00.003-06:00

As a technology-focused company, it makes sense for us to leverage technology when and where possible.

Hence, we have joined the throngs on twitter, and will be posting our 140 characters or less thoughts on our industry and the world at large.

Just click on the button below to start following us now.

2009 In-Store Research Usage & Attitudes Survey - Your Participation Requested!

2009-01-21T11:49:00.006-06:00

If you work in retail, CPG or FMCG manufacturing, QSR or retail-focused consulting, we would appreciate your participation in our

2009 In-Store Research Usage & Attitudes Survey.

This anonymous survey is designed to capture and quantify the prevalent in-store research methods used, the needs that drive in-store research, industry participants’ attitudes towards in-store research and future trends towards in-store research.

At the conclusion of the survey, results will be sent to all participants, with a summary of key indicators released to relevant media outlets.

The survey typically takes less than 5 minutes to complete. To participate in our anonymous survey and receive a complete set of results, please click on the following link:

http://www.reteltechnologies.com/limesurvey/index.php?sid=21687

Thank you for your participation, and please note that ReTel Technologies will not contact you for any commercial purposes unless you specifically request more information about solutions from ReTel.

Us And Them

2009-01-07T13:21:00.007-06:00

Besides being the title of a great Pink Floyd song, "Us And Them" is a very a propos title for our latest blog posting.

Our position as a leading provider of on-site video analytics gives us a unique view into the entire video analytics ecosystem. Lately, we've started to understand at a much deeper level where we fit, and how our approach to video analytics compares to existing technologies in terms of providing business intelligence, whether for retailers, quick service restaurants or other types of physical locations.

The emerging picture we see is that there are a number of security analytics softwares that are being marketed as suitable for business intelligence (BI) analytics. The key features that attract retailers to these software suites is that they are relatively inexpensive and integrate easily into digital video surveillance systems. Yet, fundamentally, these security analytics are ill-suited for retail BI at best, and at worst deliver incorrect, flawed data that leads to damaging business decisions.

Consider a simple task that many security analytics tools perform for BI purposes: people counting. From a video analytics perspective, this is simply noting a large-scale change in recorded pixels within a trafficked area. For the purposes of security analytics, the software is typically looking for what would be considered an "exception" - that is, a person where one should not be. This works great in a security system where a single person is that exception.

Yet from a BI perspective for retailers, the capabilities of these tools weaken significantly. Consider, for instance, a scenario where a supermarket wants to use security-based analytics to record shopper counts in aisles to create basic store heat maps and conversion rates for categories. The typical supermarket aisle is crowded. You've got a large number of shoppers, shopping carts and employees. Depending on the view of the camera, a line of three shoppers can be recorded as a single shopper. Carts can be recorded as people. And shoppers that appear and disappear from view can be counted twice or more. With security analytics systems, these errors are being captured continually, adding layer upon layer of false data. If the supermarket retailer's merchants, marketers and operations personnel use these data to support decisions that impact their business, the results can be disastrous.

Our solutions have been specifically designed as BI video analytics tools. The methodology behind our analytics excels in challenging retail and QSR environments where heavy crowds, mixtures of customers and employees and objects such as shopping carts abound. Thus, the insights we deliver in the same supermarket situation noted above would have a vastly reduced error rate. As an added benefit, our solutions layer in additional insight such as consumer demographics, consumer-to-employee ratios and interactions, and consumer engagement with merchandise, for instance.

To summarize, our ground up approach to develop BI focused analytics versus repurposing security analytics results in significant error reduction and much richer insight. Between "Us and Them," we're clearly the right choice for retailers and QSRs looking for scalable video analytics for business intelligence.

Finally, for retailers and QSRs who are curious about what we can do, why not request a free trial? All you need to do is send us a video file from your in-store surveillance system, and we'll show you what we can do.

ReTel Featured in the New York Times

2009-01-03T13:51:00.003-06:00

We couldn't ask for a better start to the New Year. Here we are in the New York Times!

Are we entering a new era of consumer behavior?

2008-11-24T17:21:00.004-06:00

Last week, we attended the In-Store Marketing Expo in Las Vegas. There were two discernable trends we noted. The first is that the latest iterations of in-store marketing technology continue to follow a few well established paths; for instance, cart devices had a strong presence (Media Cart and Concierge had significant displays), and there were some interesting new takes on digital signage that used projection and custom form factors to add the impact and engagement that have largely been missing from traditional flat panel displays.

More relevant to our business, however, was a general consensus amongst the retailers, brand managers, POP vendors and others we talked to about a very sudden and drastic shift in consumer behavior. It's no secret that the current economy is already exacting a heavy toll on retailers. But what has been surprising to many is that the change in consumer behavior has come much more rapidly than in the past, and it has been much more pronounced. With that being said, any existing understanding retailers have had surrounding how their customers shop their stores is being thrown out the window.

Browsing behavior is no longer being based on product selection, but rather bargain hunting. Extended length of shopper visits no longer maps to higher basket sizes, but rather a higher proportion of basket size tied to discounted merchandise. Action aisles and end caps are generating even more sales, and the center of the store is receiving relatively low traffic.

While this is all heuristic and based on gut observation, it is not surprising. What was surprising, however, was that these trends were being expressed uniformly by most we spoke to, and the observation was that it was a deep, acute change. How does this affect ReTel? Once the fourth quarter holiday retail rush is over, I believe that retailers are going to truly need to revisit their consumer experience as any sales lift dissipates and the real retail doldrums hit. Forward-thinking retailers will have to rethink their in-store strategy to cater to deal-driven consumers across all segments while protecting margins and average basket size per visit. Adjacencies, cross sells, enhanced customer service will all play a role. But these will require constant measurement and refinement to optimize the mix to maximize benefits to both consumer and retailer alike. We believe that our technology will be able to accelerate this process.

Welcome To The Inside Track

2008-11-24T17:06:00.000-06:00

Hello world. As one of the founders of ReTel Technologies, I am pleased to introduce The Inside Track, where we're able to share our thoughts, insights and perspectives on the world of retail analytics, measurement technologies and general start-up experiences with the world at large.

Going forward, all three founders of ReTel will be posting to The Inside Track. Over the past year plus, the three of us have been holed up in our cozy office executing on our vision of truly usable, cost efficient video analytics. That being said, there will certainly be some fascinating stuff for us to share as we continue to build out our solutions.

In the meantime, I'll kick things off with a brief perspective on the current state of in-store customer understanding, particularly within the context of the current economy. Thank you again for your interest in ReTel, and feel free to contact us with any questions or comments about our technology, or the retail analytics space in general.