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The ReTel Team

The Hawthorne Effect & You

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?

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!"

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

“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?

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

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.