If you haven’t been docked in a space lab for the past 30 years, I’m sure you’ve been asked to complete a customer satisfaction survey, after a recent shopping experience, whether online or offline. In fact, even in space, it’s probably not a stretch to assume that NASA conducted a few with their astronauts to gain feedback on behalf of their product partners. Perhaps Cottonelle or Charmin wanted to measure astronauts’ satisfaction regarding their toilet paper products in space. Ok, maybe this is not a clean (pardon the pun) example, but you get the point.
Customer satisfaction surveys are all the rage for a reason. After all, businesses want to remain competitive, increase their customer retention, provide better service and ultimately increase their profit margins to stay in business. To achieve these goals, businesses need proper feedback from their customers. Unfortunately, customers’ attention spans are rapidly diminishing. The typical survey response rate is often below 2%, but the problems with customer satisfaction surveys don’t stop there.
The reality is that a small number of people who participate in customer satisfaction surveys are likely doing so in response to a particular product or, in most cases, a particular bad experience. In essence, the overall survey results can be largely skewed. Yes, many experts will point to their top 10 lists of possible solutions. However, the big elephant is still in the room.
So, what’s going on? A well-respected colleague and industry expert (Bill Fonvielle) suggested that at least part of the problem is that customer satisfaction is an abstraction and not a thing itself. He went on to quote the late, renowned marketing professor Richard L. Oliver who made this statement – “Everyone knows what [satisfaction] is until asked to give a definition. Then it seems, nobody knows.” In Bill’s view, when people say on a survey that they were satisfied, they are saying that the experience was okay, and nothing more. They are not saying that they were thrilled, excited or delighted. People are either satisfied or dissatisfied. He compares it to being pregnant. No one is a little pregnant; either you are or you are not. He went to say that rating scales invent distinctions that may not make sense.
What is the message for businesses contemplating a customer satisfaction survey? A better path begins with asking customers to tell you what their expectations are, and using expectations to measure, not customer satisfaction, but your performance in meeting or exceeding customer expectations.
The past few years have been a whirlwind for the autonomous automotive industry. To date, Google’s self-driving cars have driven over 2 million miles and Tesla’s 90,000 cars are equipped with AutoPilot enabling the vehicles to maintain speed, change lanes and even park without any input from the driver. What Tesla advertised as simply a driver’s assistant, is being used as much more than that. As an article on Wired.com explains, “The Internet is awash in videos of people sitting in the backseat and sleeping, and ignoring Tesla’s TOS requirement that they maintain control at all times.” Videos such as these have prompted back-lash, suggesting Tesla may be moving too fast. Elon Musk, Tesla and SpaceX CEO, disagrees. Instead of slowing down, Musk continues to move forward with Tesla’s autonomous endeavors, promising that “he’ll produce a Tesla that can drive itself from Los Angeles to New York City, no human needed.” Technology that seemed fit only for movies such as I, Robot a few years ago, is closer than many people realize. Begging the question, are Americans ready for this level of automation?
It’s not only tech companies such as Google and Tesla leading the way. Uber is now knee deep in the mix and making a name for itself in the driverless car industry. As of August 2016, Uber deployed a fleet of autonomous vehicles driving the streets of Pittsburg. Bloomberg reports that “Unlike Google and Tesla, Uber has no intention of manufacturing its own cars, Kalanick says. Instead, the company will strike deals with auto manufacturers, starting with Volvo Cars, and will develop kits for other models.” For the time being the cars are being supervised by humans, but expect to be completely autonomous in the future. However, Uber’s initiatives might be premature.
On December 14th of this year Uber’s fleet of self-driving cars began shuttling passengers around the streets of San Francisco. Within a week of the launch, there were already several safety concerns. One of the vehicles apparently ran a red light and caused a near-collision. Uber claims that this incident was caused by human-error, meaning the driver behind the wheel, though witnesses dispute this fact. Additionally, these autonomous vehicles raise alarm regarding their inability to make right-hand turns without crossing into bike lanes, potentially leading to fatalities. The state of California has threatened legal action unless Uber removes its self-driving cars from the roads until the problems are addressed and the proper permits are acquired. According to an article published by The Guardian, even though Uber openly admitted to the vehicles having a problem with crossing bike lanes, they will not be pulling their cars from the roads. A statement released by Uber VP of Engineering, Anthony Levandowski, explains, “…we respectfully disagree with the California Department of Motor Vehicles legal interpretation of today’s autonomous regulations, in particular that Uber needs a testing permit to operate its self-driving cars in San Francisco.” He claims that because all their self-driving cars are still equipped with a driver and “are not capable of driving ‘without…active physical control or monitoring’” that they do not require a permit. Uber has instead informed all drivers that “they should take manual control when turning right in a street with a bike lane while engineers try to fix the vehicles’ programming.”
Uber’s current safety issues are only one of many obstacles associated with the future of driverless cars. The security risks accompanying autonomous vehicles are vast. Forbesreported that “Last year, security experts proved in a controlled test that they could use the Internet to take control of a car as it was driven down the road. Fiat Chrysler Automobiles consequently recalled 1.4 million vehicles to fix the software defect enabling hackers to control multiple vehicle functions.” It is one thing to have your computer hacked, it is another thing entirely to abruptly lose control of your 4 ton vehicle, moving at 70 mph down the highway. The U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) are continuously working to improve cybersecurity to protect against such breaches, but it is an ever-changing environment that requires constant innovation to stay ahead of potential threats.
Even with the security and safety threats constantly being assessed and addressed, many Americans still aren’t convinced. According to the J.D. Power U.S. Automotive Emerging Technologies Study, only 1 out of every 5 consumers was interested in a fully autonomous vehicle. However, as history has shown, the advent of new technology is often met with uncertainty. The fact remains that many people enjoy getting behind the wheel. A day may come in which autonomous cars are deemed superior drivers in every sense. Therefore invalidating human’s right to even operate motorized vehicles or will at least be limited to designated locations. However, given the 130 year love-affair between Americans and their automobiles, it seems unlikely that they will let go of the wheel without a fight.
Driving a car is not only a pleasurable experience for many, it’s also a source of income. An article published by The New York Times notes that “Millions of truck and taxi drivers will be out of work, and owing to the rise of car-sharing and app-based car services. Consumers may purchase fewer vehicles, meaning automakers and their suppliers could be forced to shed jobs.” However, it’s important to remember that there was once great outrage surrounding mechanized looms and the fact that they would steal jobs from weavers. Innovation and change will continuously be met with skepticism, and may disrupt the status quo, this does not mean it won’t lead to positive outcome in the long run.
Whether society is prepared for autonomous vehicles and all that comes to follow or not, the future seems to be undeniable at this point. There will continue to be obstacles and the occasional public outcry, but driverless cars and the technology that accompanies them will continue to progress. What seemed like science fiction only a decade ago is nearing fruition. A recent article by Forbes offers a more specific timeframe, explaining that “technology proponents are predicting autonomous vehicles will be a reality by around 2020 – just over three years from now.” So, buckle up, because the future of transportation is coming up fast.
A year ago, we published a series of best practice for questionnaire development. This year, we would like to highlight some common mistakes that research practitioners should avoid, to ensure their data collection effort is not wasted. We are calling them The Seven Deadly Sins Of Questionnaire Design. The Seven Deadly Sins emerged from an ecclesiastic era, and since then have evolved as broader ethic markers for those who prefer disinfected consciences. Gwyneth Paltrow lost her head over them in the movie Seven, and the secular world has incorporated them as business credos (an example being The Seven Deadly Sins of Management, from the Harvard Business Review).
As long as people are dropping the ball in their professions, the Seven Deadly Sins work as a values template. They certainly work in market research, specifically when it comes to designing online survey questionnaires. Without a heavenly questionnaire, a survey will plunge into the deepest recesses of hell.
Below are the SevenDeadly Sins of Questionnaire Design that every researcher should avoid.
You are passionate about your project. You let that lust pollute your wording, even allow bias to possess the questions like a Linda Blair dream. Sometimes you don’t even know you’re doing it! As one research expert put it: “Bias is the mortal enemy of all surveys, and as a survey creator, it’s important to guard against it to make sure you get reliable results.”
To avoid this Hades, keep your language neutral and dry; employ a sensible number of opt-outs and open-ended questions; and make sure you use a second and even third set of eyes while crafting questions.
You crave that data or have a reprehensible voracity for it. You write an extremely long questionnaire, which ultimately results in crappy data collection. You ignore the fact that respondents don’t care much for long, boring surveys, or your greediness for the data causes tunnel vision in your data collection methodology.
As some of our own research has shown, respondent fatigue sets in after 20 minutes of a survey. This may result in respondents exerting less effort and spending less time thinking about their answers as they journey deeper into the survey. A survey over 35 minutes is an indicator that your craving for data is approaching its peak with little consideration for the survey participant.
Some had predicted that online surveys completed from mobile devices would approach 50%. In an era where mobile devices are displacing computers, long survey questionnaires are just a sin.
Put it simply, edit, edit, edit! Mistakes won’t make you look slothful to respondents, just demonic. Furthermore, on the side effects of sloth, a good research practitioner explained:
But the problem is rarely “bad respondents” – instead the problem is lazy researchers. When people discover that the survey they just agreed to take is boring, tedious, repetitive, or too long, they either quit altogether or they stop providing good answers. As I’ve stated many times in the past, when it comes to data quality, the burden should always be on the researcher first.
You want to defend your research project or the sanctity of your data. Full of wrath, you will strike down upon thee with great vengeance and furious anger those unsuitable respondents who access your holy survey. Okay, maybe not Ezekiel 25:17, but you’re going to place a lot of screeners and trap questions to weed out the unfaithful.
Recent studies indicate the methodology of trap questions for surveys may not be as effective as originally thought. In reality, trap questions might have unexpected results—such as shifting the thinking of respondents to critical thinking from “optimal thinking” (that is the state of mind they reason as they normally would in daily life, which is typically necessary for reliable data).
There are other analytical ways to evaluate respondent data that don’t include placing land mines in your questionnaire.
Budgets are rarely fun, unless you’re working on the next Marvel film or you’re a Congressman(not from the state of Illinois). At the same time, a sense of greed within you assumes that the internet ought to make research economical.
Therefore, you skimp on incentives. Bad move.
Some reports claim that 175,000 online surveys are conducted a day. This volume has influenced a drop in participation rates to historic lows, which some estimates to be at 2%. On the other hand, studies have shown that the proper incentive will have a positive effect on survey response rate. We touched base on this topic on a previous blog.
This form of avarice can be avoided by rewarding your respondents properly for their time, and never assume they care about your brand as much as you do.
Okay, you’ve done all this work and soon respondents will joyfully complete surveys, while vying for an iPad, that trip to Hawaii or Starbucks gift card. You say to yourself: “this is a lot for a 40min IDI or telephone interview and it’s all from my blood, sweat and tears”.
This attitude of envy will harm your research project. You’re not just envious, you lack empathy—the key ingredient for a successful online survey questionnaire. As stated in a previous blog, empathy is significant.
“Companies need to have more empathy for the research participant. The person(s) who writes the survey instrument should ask themselves if they could sit through that survey for 25-30 minutes. Companies should make surveys fun and engaging, regardless of the topic. They should test their surveys over and over again to identify the fatigue points in the survey. This is usually the area where data integrity is compromised.”
This quote addresses all the other deadly sins, mind you, because they all overlap. It certainly overlaps with:
Pride is also known as vainglory. It basically means you think you know better than everyone, including study participants. Pride has become a positive quality in western culture, but don’t let it fool you.
Pride also tends to stifle the ability to be open-minded. With all the tech innovations changing market research this year, such as eye tracking technology, social listening etc., don’t let your pride assume your ways are absolute and unchangeable.
The opposite of the Seven Deadly Sins are the Seven Heavenly Virtues. We’ll discuss them pertaining to online surveys in the future. Right now, though, avoiding the above list will likely create a paradise for your next online survey undertaking. Hell might not freeze over, but neither will your data.
Emotional intelligence, although not to be confused with IQ or being emotional, is defined as the ability to be intelligent about your emotions. It consists of motivation, social skills, empathy, self-awareness and self-regulation.
Numerous studies have shown that the brain is built to adapt in response to good or bad experiences more than any other organ in our body. In other words, Emotional intelligence can be acquired and increased over a period of time.
Some Research scientists have been calling for Emotional Market Research and it is safe to say that the time has finally arrived. As consumer decision-making becomes more emotionally-based, successful brands will identify and utilize emotional values as strategic foundations for meaningful positioning, differentiation, and more authentic storytelling.
The future of business will be based on having a strong emotional connection with the consumer. Brands that adapt their research agendas to get a better understanding of the role that emotions play have a powerful advantage.
Examples of Emotional Market Research
WebCams: Three years ago, there were a handful of companies that provided or even scratched the surface of emotional research. Companies like Affectiva and RealEyes were the two dominant players in the space. Today, it is one of the top 5 emerging research methods, according to the latest Greenbook Industry Report. Webcams seem to be one of the more popular methods –typically placed in remote panels of users viewing ads or products in their homes or offices—they detect facial expressions and then provide what they call “emotional analytics.” In essence, the technology unearths the authentic feelings of individuals in real-time and intensity-level.
Eye-tracking technology: Ranked 9th on the list of emerging methods and becoming widely adopted by many research firms, these technology records conversations alongside an automated system analyzing eye movement. From a selling standpoint, eye-tracking software deciphers a potential customer’s preferences in regard to webpage layout, brand placement, or even the product itself. Some studies have eye-tracking technology correctly identifying the honesty levels of subjects at an 83% accuracy level.
In a way, eye-tracking technology is a form of online survey, albeit in a different language, able to measure the intimate tastes of respondents. In fact, online surveys and eye-tracking technology could be a marriage made in marketing heaven, as their union truly focuses on a key issue in any manner of research sampling: honesty.
Using big data, transaction data and social data along with conscious and unconscious mind shopping behavior data presents a new single view of how marketers may be able to influence behaviors.
Ultimately, the goal is to develop novel marketing models to integrate the best from big data analytics—as well as influence based on how brain stimuli relate to perception, memory, and decision-making. Big data may provide information on “what” people did, but neuromarketing gets to the “why” they did it according to swaying stimuli.
There are other, smaller examples, such as utilizing GPS technology to record the actual movement of shoppers instead of relying on their memories later on in a study. QuestionPro has added a number of innovative tools to the mix as well, including “Live Discussions”, which harnesses feedback, using a custom, real-time, qualitative platform to probe deeper into a respondent’s mind. Conversational Form, which is currently in beta, combines Artificial Intelligence and innovative techniques to humanize the survey experience in a chat-like conversation to capture better user responses.
While there is still a lot of work to be done when it comes to emotional research, the technologies are still developing and methodologies are being perfected. However, the subconscious resistance to emotional research remains. Not embracing this form of research, however, could negatively affect customer experience, which has a huge impact on overall business revenue.
Ok, I couldn’t help myself here. I’m playing off the title of a great satirical book by Al Franken, a comedian who later became the Democratic Senator for the State of Minnesota. According to the experts, and Franken would agree, we’re all a bunch of liars. If you don’t believe me, google it. The only item in dispute is the number of times per day we lie. The range seems to be anywhere from twice a day to 200 times per day. That’s a lot of lying…
“Honey, do I look fat in this outfit?”
Dr. Phil(Sorry, but that’s my source) describes our lying in terms of personal relationships. He lists several reasons for this behavior, including our attempts to:
– Avoid punishment
–Gain an advantage
Fortunately, many of our relationship lies are white lies. They’re told to spare the feelings of our loved ones and to keep us out of the doghouse (or off the couch).
But our personal relationship lies are only one part of our lying behavior. We lie to coworkers and strangers, too. Consider our recent presidential election. Almost all polls predicted a Hillary victory.
Pollsters and pundits alike have been scratching their heads since Nov. 8th and there are multiple theories:
The first theory suggests the pollsters did not have representative samples to begin with. It seems unlikely the major polls got this wrong – many of them have been conducting these polls for decades. It will be interesting to see if any of them change their selection methods before the mid-term elections.
There is some dispute about whether the overall voter turnout was greater in 2016 than in the 2008 or 2012 general elections (apparently the experts do not agree on the number of eligible voters in the US.) But there is no dispute about the demographics – a greater percentage of Republicans voted in 2016.
This one is fascinating – pollsters cheat, too. When an organization’s polling results are vastly different from others’ results, they might skew their own numbers!
This one could have been the main reason the pollsters got it wrong. There is a common belief that many respondents failed to admit their preference for Trump.
In all likelihood there were multiple factors contributing to the failure to accurately predict the presidential outcome. I suspect the 2016 election will become a case study for college students in the years to come.
What does this have to do with consumer surveys? Everything.
If you have a large population to choose from, the first three of the above challenges are fairly easy to control. You can increase the sample size if the response rate is low and you can ensure you aren’t skewing your own results.
But what about the lying?
Do our customers lie on customer satisfaction surveys? If we believe the psychology experts, the answer is ‘yes.’ But why? There is no reason to lie on a customer satisfaction survey. Nothing ‘bad’ will happen to you if you say you will buy a particular product or service in the future even if you have no intention of doing so; Nothing bad will happen if you ‘break up’ with a brand during a rant then continue to buy their products/services the following day either.
Some consumer behavior doesn’t make sense, or does it?
After a long, stressful day at work are you more likely to take your frustrations out on a brand that cheerfully asks you to complete yet another short surveywhen all you want to do is order a pair of shoes and go home?
Part of the problem with customer satisfaction surveys is the shear volume we’re constantly exposed to. Sometimes they’re like a bad horror flick – they’re everywhere. This ultimately results in survey fatigue for the consumer. The reality is that today’s shoppers want personalized experiences – they assume their favorite brands should already know what they think and want.
Another problem is respondents’ attempts to ‘gain advantage’ – the lie we use in our personal relationships. Many people will provide ‘good’ feedback because they believe it will increase their chance of getting some future benefit. This is especially true when the brand offers an extremely attractive incentive for their feedback. I’m not implying the respondent should not be incentivized, but the reward should not be the key driver, especially in the case of customer sat surveys.
Finally, some customers are people pleasers – they tell us what they think we want to hear.
Does this mean we shouldn’t conduct customer satisfaction surveys?
Not at all. But it means we need to be smarter about how we use them. Customer satisfaction surveys will continue to provide us with valuable information, but they need to be used in conjunction with other methods of gathering feedback.
One phrase we often hear repeatedly from research practitioners is that: “Survey participation is declining and online data quality continues to plague the industry.” After investing a great deal of time, resources, and effort, they are often unsatisfied with the quality of data collected for their research. After all, the research is meaningless if the survey results are inadequate. “How can I effectively increase survey participation and data quality,” they want to know, “without extensive data scrub?”
The first area of focus is often the data collection methodology. The next area of focus, naturally, is the instrument – the actual language used in the survey, particularly for online, direct mail, or mobile surveys where no other guidance is available. Survey incentive is usually the last variable that companies look at as a means to boost response rate or to address data quality issues. Given consumers exist in a culture driven by rewards, it should be natural for survey respondents to expect an attractive incentive in exchange for their time – and rightfully so.
Unfortunately, research practitioners and panel companies alike undermine the significant role that incentives play when it comes to data collection. In fact, some researchers view survey incentives as something that could potentially create bias in their data collection efforts, based on the assumption that respondents will not provide honest answers to survey questions and are only driven by the reward. Although this is not completely incorrect for a small number of research participants, it is, however, not the norm.
As stated earlier, our society is already reward driven. Just look around – in business, in commerce, in our day-to-day life. They are passed off to staff and packaged in wellness programs that encourage pedometer steps and healthy eating habits. They are plaques presented to sales reps reaching quotas. They are the points we earn, the loyalty cards we shuffle in our wallets and the frequent flier miles we stockpile. Even our bonuses and raises are forms of reward and incentive.
In marketing, rewards are indispensable tools. We donate portions of proceeds to causes. We employ games, contests, points, and loyalty cards – all to motivate specific behaviors. Incentives help us broaden word of mouth marketing, increase revenue, shrink advertising cost, expand into new markets, and keep customers coming back for more. Marketers use incentives because they work. So why should this be any different for market research?
Without data, there is no research; without respondents, there is no data; Panel providers must incentivize their respondents fairly and act as good ambassadors for their panelists. Research practitioners, on the other hand, must be realistic and understand that the world has changed. Volunteer survey participants are almost extinct. High earning CEOs or influential individuals receive monetary incentives to give lectures, speeches, or to provide their expert opinions to various organizations. Their opinion is never questioned due to the value of their speaking fees. Survey participation and data quality will continue to plague the research industry until research practitioners understand the value that rewards play in our daily lives. It may be too late for them to realize that without incentives, there are no respondents.
Comedian TJ Miller, of HBO’s Silicon Valley, performs a standup in which he tells of an entertaining, yet extremely terrifying time in which he suffered a life-threatening brain malformation. He was in the middle of pitching a movie idea when he collapsed to the floor while seizing, and was rushed to the hospital. His story continues, he explains that he suffered from an arteriovenous malformation (AVM) hemorrhage, which is essentially an abnormal connection between the veins and arteries.
When Miller awoke from his coma in the Cedars-Sinai ICU neurology ward, he found a nurse standing over him saying, “Your doctor cannot be here, but a proxy will be here in just a bit.” He then explains how he was given little to no information about his condition. Next thing he knew, what looked like an iPad on a Segway rolled in the room and on the screen, was his doctor, who was video calling from a different location. This robot-doctor then began explaining Miller’s condition and how lucky he was to be alive. Miller, who at this point is more shocked about a robot wheeling into his room and diagnosing him, asks his doctor the humorous yet, understandable question of “…Am I in the future?”
While TJ Miller had only been in a coma for a few days, his question of “Am I in the future?” is certainly one that most of us would wonder. The idea of autonomous Segway-like robots wheeling around hospitals, video-calling to doctors across the country or around the globe sounds like something out of Star Trek or a Kubrick film. These proxies that Miller mentioned are known as telehealth robots and are being integrated into all aspects of hospitals. NBC’s, Julia Boorstin, describes how they can allow a stroke victim to be assessed by a specialist when every minute counts and there isn’t a specialist at the hospital. Telehealth robots increase the standard of care and allow more patients to be seen in less time, thus cutting down on the over-crowding and potentially saving money.
Additionally, robots are seeing an increase in popularity in rural hospitals for more than just improved patient care. For instance, Hamilton County Hospital in Kansas was very close to shutting down when telehealth robotics were brought onto the scene. Their chief executive, Bryan Coffey explained that “we brought in a telemedicine robot and started seeing an 180 (degree change). There’s been a 40 percent increase in (patient) volume and we’re consistently, month over month, 15 percent in growth.” Their investment of $36,000 for the robot yielded a substantial return on their investment leading to greater patient throughput.
These robotics are also being utilized by graduate nursing students at the University of Alabama in Huntsville (UAH). UAH and other universities that have introduced similar programs are leading the way in terms of telehealth education. They are utilizing this technology to train students from off-site facilities as well as providing online students more of a presence in classrooms and hospitals. The nursing dean at UAH, Marsha Howell Adams explains, “It will allow our graduate students in our nurse practitioner pathways to actually be responsible for the management of the patient care in a simulation scenario.”
The full benefit of the telehealth industry has yet to be seen. In Miller’s case, while he awoke confused to what seemed to be a futuristic robot-doctor, there’s no doubt that the rapid care offered by telehealth robots quite likely saved his life. Telehealth allowed his AVM hemorrhage to be discovered in a timely manner as opposed to being discovered on the autopsy table. Through the advent of technology, a hospital visit can offer immediate care from a specialist on the other side of the country, allowing top-rate care in less time, all with the help of an autonomous robot on wheels – perhaps this is the future.
However, in many ways, it seems natural for robotics, science, and healthcare to merge and progress in this manner. A more noteworthy display of futurism emerges when seemingly far off technology leaves the scientific sphere and spreads into the social and political sphere.
Though perhaps not as visually shocking as a free-roaming robot on wheels that Skypes to doctors in remote locations, the artificial intelligence system known as MogAI is a game changer with far-reaching effects.
MogAI was created by Sanjiv Rai in 2004 and has accurately predicted the past three Presidential elections. According to a CNBC report, it gathers data from over 20 million points from around the internet, including Google, Facebook, Twitter, and Youtube, in order to create its predictions. MogAI works by monitoring social media and internet user engagements to anonymously gather information. It not only accurately predicted the election results, but also noted the fact that Trump would surpass the number of engagements that Obama had in his peak of the 2008 election, long before anyone could have made that prediction.
It’s important to note that these results were not expected. MogAI was not simply going along with media trends or regurgitating expert opinion. This is something different.
So why is MogAI more effective than traditional methods of prediction? It is not limited by human bias or the hesitancy attached to telling a stranger who you are voting for. MogAI monitors social media platforms, uses algorithms and improves with time. This program’s name is referencing Rudyard Kipling’s character, Mowgli, from The Jungle Book. MogAI, like Mowgli learns from interacting with its ever-changing environment.
Per CNBC, Rai explained that, “While most algorithms suffer from programmers/developer’s biases, MoglA aims at learning from her environment, developing her own rules at the policy layer and develop expert systems without discarding any data.” In other words, MogAI is not limited by the human error – it finds the best way of gathering data and does so without interruption. MogAI sifted through reactions of videos of the election process on Facebook and YouTube. It monitors likes and dislikes. It analysis metadata. It considers the opinions of those who only speak up only when hidden behind their online cloak of anonymity. Artificial intelligence systems like this display the pulse of our nation in a way that we have never been able to do before and this form of tech innovation will eventually affect our day-to -day activities in areas that we possibly can’t imagine.
Artificial Intelligence has enormous growth potential and a number of companies in various industries are already adding AI to their playbooks – manufacturing, retail, healthcare, technology, transportation and more. Even in market research, an industry that is often driven by empathy and emotional intelligence, is giving AI some strong attention. Of course, AI is not yet able to address the human aspect of research but some companies are looking beyond these challenges. QuestionPro, a Research Software provider, has recently launched “Locus” – An Artificial Intelligence bot that can help understand what type of survey the user is interested in to help them develop the survey instrument. While “Locus” is still in Beta, it’s a testament to the growing adoption of AI, to improve customer experience. Vivek Bhaskaran, CEO of QuestionPro, explains that “Much like a conversation, Locus will continuously be “trained” to become increasingly effective at communicating with users and maximizing both the quality and efficiency of their QuestionPro experience. Eventually, the Artificial Intelligence Bot will boast even more capabilities within the product.”
The applications for this and other AI prediction systems are staggering. By analyzing data from places such as Facebook Live conversation feeds or Google analytics and then completely anonymizing this information, data predictions can offer fewer privacy violations and more accuracy than ever before in history. It is estimated that the Artificial Intelligence market will be worth $16.2 billion dollars, by 2022. If this estimate is accurate, this 62% compound annual growth rate from 2016 to 2022 is staggering. While a major part of this growth is from the healthcare industry, AI is already making its presence felt in other sectors, as evident by the use of SIRI and Google Assistant in mobile technology. The reality is that AI is already here and its future looks bright. Whether it can take us where no human has gone before remains to be seen.
As the presidential race draws to a close, there are numerous polls from diverse sources available to the public. However, there is a lack of consistency between many of the polls. Is Hillary up by 3 points in Florida or is Trump up by 2 points? Whose poll is right and whose is wrong? Like many questions in politics, it depends.
All political polls are based upon some assumptions about who is actually going to vote. This is called a model of the electorate. Having a correct or incorrect model will determine how accurately a poll will predict the outcome. Social scientists who argue for a pure random sample can really mispredict an election if they do not take into consideration data collection methodologies. One example is the recent U.S.C. Dornsife/Los Angeles Times Daybreak poll, which has been getting a lot of slack by pollsters, due to its outlying poll results. Some make the argument on how the data was weighted; others blame a 19 year-old Trump supporter for skewing the poll results. While those are both legitimate points and probably contributed to the skewed poll result, sample and data collection probably played an even more significant role in this issue.
Dr. Jim Kitchens, a research practitioner with over 30 years experience in political polling suggests that “ Weighting works as well as setting quotas, within a reasonable limit. If the sampling source (list, panel, etc.) is good, you should be close to your quotas and it may require some weighting.” In other words, weighting alone is not the issue nor is that 19 year-old Trump supporter. By applying quotas in the sample, this would ensure that enough Republicans and Democrats were represented. Thus, minimizing the risk of working with a toxic sample.
The Romney campaign failed to call Ohio (the entire 2012 election for that matter) correctly because they were dependent upon telephone-based data collection. Even merging in cell phones, this methodology will skew a sample toward older voters, white voters, and Republicans. They assumed many of the younger voters and minority voters who supported President Obama in 2008were not going to vote because they did not find them on the telephone. This was a mistaken assumption. However, if a pure random sample is taken from an internet panel, it may skew the sample toward younger people. This, again, boils down to data collection and sample.
The key is to set quotas from two or three critical groups based upon past elections of a similar nature. The most critical factors for politics are party affiliation, race, age, and gender. According to Dr. Kitchens, “there are two ways to construct a model: (1) quotas during the data collection or (2) mathematical weighting based upon the assumed turnout. Either method is methodologically sound and will work.”
The problem for political polls is that no one knows whose model is right until the election is over. Even Nate Silver, who is regarded as a god among pollsters now because he accurately predicted the winner in the 2012 Presidential election for every 50 states, including the District of Columbia, has had his critics.
This year, several assumption pollsters have to consider include:
Will the minority voters turn out for Hillary Clinton at the sample level they turned out for Barack Obama?
Will Donald Trump be perceived in such a negative way by Republican women that they will either vote for Hillary Clinton or stay home?
With both candidates having a majority of voters view them unfavorably, will turnout among all voters decline? Low turnout usually means an older, more conservative electorate.
Will the outrage from Hispanic leaders toward Donald Trump actually drive a significant percentage of new Hispanic voters into the electorate?
Every poll has to be built upon the assumed correct answer to these questions. So, it will be election day before the argument about whose polls are correct can be answered.
While we may not know whose poll is right or wrong until after the 2016 Presidential Election, I’m sure Mitt Romney would agree with the following statement: Get your sample and data collection methodology right!
American voters are heading to the polls in November to determine for the 58th time their country’s president for the next 4 years. While presidential elections are known to provide good theatre, the 2016 Presidential Election has been filled with some interesting twists and turns, and is undoubtedly one for the ages. The Republican nominee, Donald Trump, whose loose cannon speaking style has created a firestorm even among his own party, is viewed as a social media scandal machine. On the other side, we have Hillary Clinton, a former First Lady, Senator of New York and most recently Secretary of State, who simply can’t seem to shake some of her old ghosts – email issues, wall street and even issues with her own foundation. With less than 5 weeks left before the general election, these two candidates are keeping voters on the edge of their seats.
In order to gauge the pulse of American voters heading into the general election, we conducted two surveys to compare how the opinion of the general US population differs from those in a battle ground state like Florida. The surveys were conducted using qSample’s likely voter panels. More than 450 respondents participated in each survey, with an even split on party affiliation. The data gathered has provided critical insight regarding where voters stand on various issues, when it comes to these two candidates.
The data reveals that despite lack of experience and a series of faux pas made in his presidential campaign, Florida voters are leaning towards Trump (50%-38%) in this upcoming election. However, Secretary Clinton is holding on to a slight lead among the general US population (41%-37%). The data also shows that more than 20% of respondents cited patriotism as the reason they would vote for Trump. While Floridian voters think Trump cares more about the country than Clinton, they also indicated that she has better economic and foreign policy experience than Trump (17%). When comparing the data, both audiences seem to agree on this point – Clinton has more political experience, whereas Trump seems to care more about the country.
Voters from both panels were asked which candidate they feel is more qualified to be president and their feelings on the current state of the country. Florida voters indicated that Trump is as qualified to be president, with both candidates splitting the votes at 42%. This number is not surprising, since Floridians typically vote Republican in general elections. On the flip side, general US voters give Clinton the nod as most qualified to be president by 44%.-37%. Roughly 40% of US respondents indicated that the country is heading in the right direction or about the same direction. Despite a strong job market, universal healthcare and a strong economic recovery, a large portion of the respondents (58%) indicated that the country is not heading in the right direction.
When it comes to how respondents stay up to speed with this year’s election, both panels indicated that they follow the presidential election mainly via television (38%). Print media is not yet dead, at least for presidential elections. More than 21% of respondents in our survey indicated that they follow the election by reading newspapers, followed by social media outlets.
If the trend from 2012 continues, it appears that voters from the battleground state of Florida are leaning to vote Republican in the 2016 Presidential Election. While it will be a tight race, the data shows that the general population will help Hillary Clinton break that glass ceiling.
Many in the research industry banged the mobile drum as if mobile data collection would replace online in only a few years. This mindset, like other delusions of grandeur, ignored the clear challenges ahead for such a victory. Mobile data collection certainly managed some progress in the space and according to the latest GRIT industry report published by Greenbook.org, it is producing some respectable numbers. However, it still has a long way to go; furthermore, it fell short of other outlandish claims like its potential to end email. The emergence of new research methods without question tears market share away from online data collection, but is that enough to say email is dying?
The tyrannosaurus and email belong to the same species according to many analyzing the impact of email in the age of mobile apps, omnichannel integration, and other competitive technology. Though other options certainly perform well, and possess growing user bases, email remains king with no signs of slowing down.
Users find email, as allegedly the most used application type, trustworthy, valuable, and comfortable in contrast to other options. It offers the versatility and ease of use other applications, even the most well-engineered, do not.
In the age of choice, with so many competing technologies and services, a stalwart like email endures attacks from all sides. Email stands in the way of new media organizations and application organizations that see an enemy rather than a resource to leverage, a view likely resulting from the dramatically different profit margins of email and SaaS. This article explores the state of email both as a channel and web application type, and its relationship with users.
The Birth of Email
The concept of mail, passing messages verbally or in written form, likely began ages ago. Documented evidence reveals Pharaohs utilized couriers to deliver documents throughout Egypt. Other regions eventually caught up to the infrastructure and theory of Egypt employing their own mail systems.
The United States’ mail system began in the late 1600s with personal mail delivered by associates and assistants. In this era, a governor established the first formal post system, between New York and Boston. 5 It took roughly over 200 years for the postal system of today to develop from poorly-constructed, individualized systems relying on various transportation methods. These cobbled together systems relied on odd combinations of coaches, horseback, steam ships, trains, and hot air balloons.
Military communication, in its never-ending quest for new and better technology, spawned a plethora of tools, however, the military nor its internet birthed email. Email began humbly in the early 60s as nothing more than shared access (i.e., time-sharing) to a system, much like the collaborative software used today. People placed messages in another user’s directory through the mainframe they shared and accessed with workstations.
When the military’s ARPANET grew, like other aspects of computer use, email experienced dramatic changes in use and technology. Email, a defining moment for ARPANET, arguably saved the project and gave us the internet.
Today, email remains the most important type of application on the internet. Some believe it transformed the internet from something critical only to certain users to something the average person wanted to use.
Average Daily Email
Table 1: Corporate Emails Sent and Received Daily Per User 2012 to 2015 23
Daily Email Traffic
Sent/Received Per Day
Table 2: International Daily Email Traffic 2013 to 2015 22
The steady growth, and continued growth, of email coincides with penetration of communications technology and services. Email began with expensive computers only accessible to public organizations, businesses, and those of means; however, as the technology developed, prices fell.
The early 90s saw the internet opened to the general public as prices continued to drop, and distribution of product expanded. The internet reached substantial public penetration by the late 90s, and connection speeds grew. Mobile technology prices, as a result of Palm devices, plummeted, and found their way to the general market. Mobile devices and supporting wireless service too began to see prices dip, improved speeds, and more penetration.
The cultural response to the technology held just as much power as its progress and expansion. Though absolutely absurd, the average person was convinced (by marketing and their peers) to acquire a cell phone. This came after years of viewing the devices as practical only for critical professions (e.g., medicine and military). Mobile technology morphed into a status symbol and trend with entertainers singing about the latest devices. Individuals and organizations also promoted the idea that people without the devices were troglodytes. Despite trends and marketing, the technology did (and continues to) enhance quality of life such as supporting education, productivity, and socializing.
In this era with new connected devices continuing to emerge and support related technology, the presence of services like email only grows. Email clients for Apple mobile devices currently dominate the client market space.
A readership survey from Success magazine reveals in-person and email (40%) communication as the two most preferred forms.15 A study conducted by NewVoiceMedia found 19% of respondents considered email the most effective way of contacting businesses and solving problems.16 A MarketingSherpa study shows 72% prefer for this communication to occur via email; furthermore, email outperformed traditional media and new media. Email proved popular across almost every demographic.
An ExactTarget survey found email to be the favored channel for deal searching, sharing content from family and friends, and financial alerts.18 A recent syndicated research conducted by qSample.com explored workplace productivity tools, and more than 51% of those surveyed preferred email to in-person meetings, email besting all other forms of business communication.
The Big 3: Gmail, Yahoo, and MSN
Three free online email services currently dominate the space and public awareness: Gmail, Yahoo! mail, and MSN (an umbrella for Outlook, Hotmail, and Live mail) mail. Hotmail began as one of the first free webmail services, and shortly after launch, Microsoft acquired the service. Yahoo benefited tremendously from the dotcom era, and quickly acquired a promising company for its Rocketmail webmail service. Both companies enjoyed popularity and a high profile during their eras as fresh and appealing companies. At the time, Google remained an almost obscure metasearch engine.
Both email services generally satisfied users, but suffered from performance issues due to their simple HTML design. Google, one of the first large organizations to recognize its potential, developed a service using dynamic code now known as AJAX. Their design featured a robust service with function more like an application than a site.
Gmail boasts 900 million users as of May 2015 with MSN claiming over 400 million (as of January 2016), and Yahoo estimating 273 million (as of February 2014). Outlook’s support for over 100 languages and Gmail’s support for over 70 further extends their reach in existing and growing international markets. 26 Litmus, an email marketing testing and analytics application, analyzes and publishes email market share data. 11 Litmus data reveals Gmail holds 15% of the market while MSN holds 5% and Yahoo stands at 3%.
Users associate certain email addresses with professionalism, and consider email as essential as a phone number. A report from Visible Logic, Inc. reveals 70% of respondents believed non-domain-level email addresses made an individual appear unprofessional, “lazy,” and “cheap.” They ranked a branded email as most professional, with Gmail in second place.
The Future of Email
Email use, applications, and the needs it satisfies remain essentially the same as decades ago. Only its integration has experienced substantial change such as creating accounts, customer support, account management, and more.
Email serves a need not easily replaced because strong options (e.g., text, collaborative software, mobile apps, and social media) fail to pull users away.6 Many SaaS based research tools, like QuestionPro, collect hundred of thousand of surveys monthly, using email as a vehicle to invite users to participate in such surveys. These numbers continue to increase steadily year after year.
Of course, one cannot argue that the medium is causing some level of fatigue, among email recipients who are constantly exposed to a barrage of product offers, sales pitches, network invitations, etc. It will be up to email marketers to find a better way to sanitize and communicate their message. However, the argument that email is dying is as absurd today as it was 5 years ago when people like Facebook’s co-founder (Dustin Moskovitz) made that claim.
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