We know that consumer purchase decisions are often made quickly and subconsciously, but there are opportunities where it’s possible to influence a consumer’s perception of a brand. People often make buying decisions by using all five of their senses and once product designers discover what each of these sensory influencers are, they can develop packaging that strategically speaks to consumers at each stage of the decision-making process. It’s ultimately about designing a complete experience–one that supports the brand every step of the way.
At my company, we developed the 4sight Sensory Lab, pictured above, to uncover these answers. Here, for example, cold beverage drinkers known to prefer their drinks not simply cold, but chilled to the perfect temperature, are taken through a progression of exercises that mimics the various points of contact that consumers have with a product.
We identify which bottle shape, size, color, material, and texture promises that sense of cold refreshment at first glance. As the test subjects move closer, details such as condensation and frost become evident and when they are handed several bottles, each chilled to the exact same temperature–but made of different materials, textures, shapes and finishes–they provide feedback on which one feels like just the right cold.
In the Sensory Lab, our process helps us ensure that at each stage of interaction with a brand, consumers receive the right information, enabling them to see, feel, hear, smell, and taste the value of the product. Here, we’ve identified the six stages that lead to a first purchase or a repeat purchase:
This is the first impression at a distance, seeing the product in someone else’s hand, on the shelf, or across the room. It’s the first visual promise of what a product will do for your senses. For Pom 100% Pomegranate Juice, the distinctive profile of the bottle featuring those fully rounded spheres, allows the distinct dark red color of the juice to catch the attention of a shopper. It promises a bold, robust taste. A new entry into the tequila segment, SX Tequila chose a distinctive, curvaceous bottle with smooth lines and frosted texture to communicate the sense of a smooth-tasting, chilled beverage.
Here, consumers take a closer look and this is where details begin to hint at tactile sensations. Flowing details etched into the structure of the Aquafina water bottle strongly suggest the refreshment that the product provides.
Orangina, meanwhile, promises its fresh orange flavor through a dimpled finish on the bottle that suggests you are consuming straight from an actual orange.
Next, consumers make that first physical contact and combine the visual with the tactile experience. When grasped, the gentle curvature of the Febreze bottle and the angled spray head convey the soft and pleasant aroma that will fill the air. The smooth, diagonal neck on the new Miller Lite Bottle promises a refreshing flow of beer while the bold taper from the neck to the body provides a strong and confident grip for the hand. Adding the texture of the hops etched in the glass provides further engagement.
When the consumer makes a physical step towards consumption or use of the product, there’s another opportunity to solidify your brand’s perception. When the foil cover is peeled off of a can of San Pellegrino, it offers the sensation of actually peeling fruit. It also incorporates a crinkling sound, which adds to the sensory experience at opening.
The point at which the product is consumed or used and here, all five senses can be at play.
A smooth metal tip on Clinique’s Even Better Eyes product provides a refreshing and reviving cold sensation on the skin. For Gerber Good Start, the designated scoop holder on the side of the container provides for a clean usage experience and preserves the product for future consumption, as fingers do not contaminate the powder.
There’s another opportunity to create a pleasant user experience when the product is disposed of or put away for later use. Wrigley 5 Gum incorporates a lock feature and embossed details to convey a secure and clean resealable pack. The Oreo cookie package also utilizes the sense of sight with a resealable film to promise lasting freshness. Once the film is replaced after each usage, it recreates the look of a fresh, unopened package.
In The Sensory Lab, we’ve gleaned significant insight into how the five senses influence consumer decision-making at six pivotal points. Incorporating a similar approach in your design process will help insure your package effectively communicates key brand attributes at each and every point of influence.
[Image: Shopping via Shutterstock]
Here is the link to the original article: http://www.fastcodesign.com/3024657/6-tips-for-making-a-powerful-first-impression?partner=newsletter
If you were forced to rely on only two target audiences to guide all your future design work, I’d strongly recommend using astronauts and toddlers. Fortunately, the connection between them goes beyond the design of their underwear to the nature of perception and expertise, and in what we treat as valid data, and what we choose to ignore as “noise”–the extraneous details, out-of-category input, the anecdotal tidbits. As it turns out, noise is much more valuable for useful design insights than you might think.
First, the astronauts. One little-known quirk of the Apollo moon landings was the difficulty the astronauts had judging distances on the Moon. The most dramatic example of this problem occurred in 1971 during Apollo 14, when Alan Shepard and Edgar Mitchell were tasked with examining the 1,000-foot-wide Cone Crater after landing their spacecraft less than a mile away. After a long, exhausting uphill walk in their awkward space suits, they just couldn’t identify the rim of the crater. Finally, perplexed, frustrated, and with the oxygen levels in their suits running low, they were forced to turn back. Forty years later, high-resolution images from new lunar satellites showed they had indeed come close–the trail of their footprints, still perfectly preserved in the soil, stop less than 100 feet from the rim of the crater. A huge, 1,000-foot-wide crater, and they couldn’t tell they were practically right on top of it. Why?
It should have been easy for them, right? These guys were trained as Navy test pilots; landing jets on aircraft carriers requires some expertise in distance judgment. They also had detailed plans and maps for their mission and had the support of an entire team of engineers on Earth. But their expertise was actually part of the core problem. The data their minds were trying to process was too good. All of the “noise” essential to creating the patterns their minds needed to process the data accurately was missing. And patterns are the key to human perception, especially for experts.
Consider everything that was missing up there. First, there’s no air on the Moon, so there’s no atmospheric haze, either. Eyes that grew up on Earth expect more distant objects to appear lighter in color and have softer edges than closer things. Yet everything on the Moon looks tack-sharp, regardless of distance. Second, the lack of trees, telephone poles, and other familiar objects left no reference points for comparison. Third, since the Moon is much smaller than the Earth, the horizon is closer, thus ruining another reliable benchmark. Finally, the odd combination of harsh, brilliant sunshine with a pitch-black sky created cognitive dissonance, causing the brain to doubt the validity of everything it saw.
Ironically, that kind of truthful, distortion-free data is usually what experience designers want to have as input for their decision-making, no matter what they’re trying to do. We tend to believe that complex systems are the tidy, linear sum of the individual variables that create them. But despite the pristine environment of the Moon, the Apollo astronauts were repeatedly baffled when it came to simple distance and size perceptions, even after each team came back from the Moon and told the next team to be aware of it.
Meanwhile, the toddlers I mentioned earlier provide a corresponding example of the power of patterns in perception. When my first child was about 4, we came across a wonderful series of picture books called Look-Alikes, created by the late Joan Steiner. Each book has a collection of staged photographs of miniature everyday scenes like railway stations, city skylines, and amusement parks created entirely from common, found objects (see some examples here). Without any special adornment, a drink thermos masquerades as a locomotive, scissors become a ferris wheel, and even a hand grenade makes for a very convincing pot-belly stove. The entire game is to un-see the familiarity of the scene, and identify all the common objects ludicrously pretending to be something other than what they are. There’s no trick photography involved, but you can look at each picture for hours and not “see” everything that’s right there in front of you. You know it’s a trick, but you keep falling for it over and over.
The really amazing part is that the toddler, a true novice with only a few years’ experience in seeing, completely understands the scenes she’s looking at, even though every individual piece of “data” she’s looking at is a deliberate lie. Yet the pattern of data that creates the scene is “perfect.” We already know what those scenes are supposed to look like before we even see the book’s version of them, so we unconsciously project that pattern onto what we’re looking at, even to the point of constantly rejecting the contrary data our eyes are showing us. There is in fact no amusement park in the photograph I called an amusement park. But I see it anyway.
In data-processing parlance, the signal-to-noise ratio of the moonscape was perfect (actually, infinitely high), and zero for Look-Alikes pages (the whole joke is that there really was no signal there in the first place). Yet a toddler can read the noisy scene perfectly, and the seasoned test pilots were baffled by the noiseless scene. How can this be?
The lesson is that patterns drive perception more so than the integrity of the data that create the patterns. We perceive our way through life; we don’t think our way through it. Thinking is what we do after we realize that our perception has failed us somehow. But because pattern recognition is so powerfully efficient, it’s our default state. The thinking part? Not so much.
This just might be why online grocery shopping has yet to really take off. The average large U.S. supermarket offers about 50,000 SKUs, yet a weekly grocery shopper can easily get a complete trip done in about 30 minutes. We certainly don’t feel like we’re making 50,000 yes/no decisions to make that trip, but in effect we actually do. Put that same huge selection online, and all of those decisions are indeed conscious. Even though grocery shopping is a repetitive, list-based task, the in-store noise of all those products that aren’t on your list give you essential cues to finding the ones that are, and in reminding you of those that were not on your list but you still need. That’s even before you get to the detail level, where all the other sensory cues tell you which bunch of bananas is just right for you. So despite all the extra effort and hassle involved in going to the store in person, it still works better because of, not in spite of, the patterns of extraneous noise you have to process to get the job done.
To account for the role of noise within the essential skill of pattern recognition, we need to remind ourselves how complex seemingly simple tasks really are. Visually reading a scene, whether it’s a moonscape, a children’s book illustration, a grocery store, or a redesigned website, is an inherently complex task. Whenever people are faced with complexity (i.e., all day, every day), they use pattern recognition to identify, decipher, and understand what’s going on instantly, instead of examining each component individually. The catch is that all of the valuable consumer thought processes we want to address–understanding, passion, persuasion, the decision to act–are complex.
However, the research we use to help us design for these situations usually tries to dismantle this complexity. It also assumes a user who is actually paying attention, undistracted, in a clean and quiet environment (such as a market research facility), and cares deeply about the topic. Then we “clean” the data we collect, in an attempt to remove the noise. And getting rid of noise destroys the patterns that enable people to navigate those complex functions. So we wind up relying on an approach that does a poor job of modeling the system we’re trying to influence.
The challenge is to overcome the seemingly paradoxical notion that paying attention to factors completely outside our topic of interest actually improves our understanding of that topic. Doing so requires acknowledging that our target audience may not care as much about something as we do, even if that topic represents our entire livelihood. It requires a broader definition of the boundaries of what that topic is, and including the often chaotic context that surrounds it in the real world. It also requires a more than casual comfort level with ambiguity: Truly understanding complex systems involves recognizing how unpredictable, and often counterintuitive, they really are.
This is why ethnographic research is so popular with all kinds of designers. The rich context ethnographies offer is full of useful noise; the improvising people do to actually use a product, the ancillary details that surround it, and the unexpected motivations a consumer might bring to its use. These are all easier to access via a qualitative, on-location approach than they are via a set of quantitative crosstabs or sitting behind a mirror watching a focus group. It’s also a powerful human-to-human interface, in which the designer uses his innate pattern-recognition capability to analyze patterns in user behavior.
What often gets overlooked is the role noise can and should play in quantitative research. Most designers’ avoid quantitative research because of the clinically dry nature of the charts it produces, and the often false sense of authority that statistically projectable data can wield. However, only quantitative research can reveal the kind of perceptual patterns that are invisible to qualitative methods, and the results needn’t be dry at all. The solution is to appropriately introduce the right kind of noise to quantitative research, to deliberately drop in the necessary telephone poles, trees, and haze that allows those higher-level perceptual patterns to be seen and interpreted.
Fortunately, there’s already a model for this. When analog music is digitally recorded, some of the higher highs and lower lows are lost in the conversion. Through a process called dithering, audio engineers can add randomized audio noise to the digital signal. Strangely enough, even though the added noise has nothing to do with the original music, adding it actually improves the perceived quality of the digital audio file. The noise fills in the gaps left by the analog-to-digital conversion, essentially tricking your ear into hearing a more natural-sounding sound. The dithered audio really isn’t more accurate, it just sounds better, which is more important than accuracy. Returning to our opening examples, the moonscape was in dire need of dithering, while the Look-Alikes scenes were already heavily dithered. And the real world in general is heavily dithered.
So, for quantitative research aimed at guiding the design process, the trick is to value meaning above accuracy. Meaning can be gleaned via the noise you can add to the quantitative research process by including metrics outside the direct realm of your topic area. It means considering what else is adjacent to that topic area, acknowledging the importance of respondent indifference as well as their preferences, and recognizing what kind of potentially irrational motivations are behind the respondents’ approach to the topic, or the research itself.
At Method, we’ve developed a technique for observing these perceptual patterns in quantitative data by using perceptions of brands far afield of the category we’re designing for. Essentially, it’s a dithering technique for brand perceptions. This technique often displays an uncanny knack for generating those hiding-in-plain-sight aha moments that drive really useful insights. There are doubtless many other approaches you can employ once you make the leap that acknowledges the usefulness of noise in your analysis.
But no matter what format of research you use in your design development process (including no formal research at all), there are some guidelines you can follow to allow the right amount of useful noise to seep into your field of view, so that your final product does not wind up being missed on the moonscape of the marketplace:
Recognizing that you’re not the center of your target audience’s universe allows you to understand how you fit in. Be sure to take honest stock of just where your target audience places your topic area on their list of priorities.
No matter what metrics you’re using, consider looking several levels above them–or next to them–to identify patterns that are impossible to see when you’re too close to the subject.
How familiar is your target audience with your subject? Are they experts or novices, and how are you defining that? Generally, the higher the level of expertise, the higher the dependence on pattern recognition. Novices carefully and slowly compare details; experts read patterns quickly and act decisively.
No matter where your data comes from, think about what has been omitted. Was that distracting noise that was tossed, or crucial context?
By taking a look at the entire picture–instead of isolating a single data point–you open up opportunities for understanding the motivations, reasons, and outlying factors that impact data. Contrary to popular practice of stripping out noise, noise is in fact critical to the generation of deep insights that allow us to design better and more effective brands, products, and services.
[Image: Supermarket via Shutterstock]
July 31, 2013
By Jonathan Wai
At 16, Albert Einstein wrote his first scientific paper titled “The Investigation of the State of Aether in Magnetic Fields.” This was the result of his famous gedanken experiment in which he visually imagined chasing after a light beam. The insights he gained from this thought experiment led to the development of his theory of special relativity.
At 5, Nikola Tesla informed his father that he would harness the power of water. What resulted was his creation of a water-powered egg beater. Tesla, who invented the basis of alternating current (AC) power systems, had the unusual talent to imagine his inventions entirely in his mind before building them. He was apparently able to visualize and operate an entire engine in his mind, testing each part to see which one would break first.
Thomas Edison—famous for developing the light bulb and more than 1,000 patents—was fascinated with mechanical objects at an early age. He once said: “To invent, you need a good imagination and a pile of junk.” He wasn’t joking. In his lab he wanted to have on hand “a stock of almost every conceivable material.” According to an 1887 news article, his lab was stocked with chemicals, screws, needles, cords, wires, hair, silk, cocoons, hoofs, shark’s teeth, deer horns, cork, resin, varnish and oil, ostrich feathers, amber, rubber, ores, minerals, and numerous other things.
Einstein imagined with his mind. Tesla imagined with his mind and built with his hands. Edison imagined with both. They all had extraordinary spatial talent—“the ability to generate, retain, retrieve, and transform well-structured visual images.”
Spatial thinking “finds meaning in the shape, size, orientation, location, direction or trajectory, of objects,” and their relative positions, and “uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions.” Spatial skill can be measured through reliable and valid paper-and-pencil tests—primarily ones that assess three dimensional mental visualization and rotation. Read more about examples of items that measure spatial skill here.
But despite the value of these kinds of skills, spatially talented students are, by and large, neglected. Nearly a century ago, a talent search conducted by Lewis Terman used the highly verbal Stanford-Binet in an attempt to discover the brightest kids in California. This test identified a boy named Richard Nixon who would eventually become the U.S. president, but two others would miss the cut likely because the Stanford-Binet did not include a spatial test: William Shockley and Luis Alvarez, who would go on to become famous physicists and win the Nobel Prize.
Today talent searches often use the SAT and ACT which include math, verbal, and writing sections, but do not include a spatial measure. All of the physicists described above (and Tesla who could do integral calculus in his head) would likely qualify today at least on the math section, and Edison would likely have qualified on the verbal section due to his early love of reading. However, there are many students who have high spatial talent but relatively lower math and verbal talent who are likely missed by modern talent searches and therefore fail to have their talent developed to the extent it could. Also, because colleges use the SAT and ACT for selecting students, many high spatial students likely do not make it onto college campuses.
Nearly every standardized test given to students today is heavily verbal and mathematical. Students who have the high spatial and lower math/verbal profile are therefore missed in nearly every school test and their talent likely goes missed, and thus under-developed. What’s more,spatially talented people are often less verbally fluent, and unlikely to be very vocal. Finally, teachers are unlikely to have a high spatial profile themselves (and typically have the inverted profile of high verbal and lower math/spatial), and although they probably do not intend to, they’re more likely to miss seeing talent in students who are not very much like themselves.
So what does the research tell us? In a study published in the Journal of Educational Psychology, my colleagues and I used longitudinal data from multiple data sets across 50 years to show that spatial talent (in addition to math and verbal talent) is important for success in STEM domains. The data came from the Study of Mathematically Precocious Youth (SMPY), Project Talent, and the GRE. Of those students in the top 1 percent of spatial talent, roughly 70 percent were not in the top 1 percent in either math or verbal talent—showing a large fraction of students having the high spatial but lower math/verbal profile.
Now a new study by Harrison Kell, David Lubinski, Camilla Benbow, and James Steiger published in Psychological Science has made the connection between early spatial talent and creativity in adult life even stronger. The study, based on SMPY data, showed that spatial skill had an increment of prediction over and above math and verbal skills (assessed at age 13) when looking at scholarly publications and patents—even those in STEM.
Can We Enhance Spatial Skill?
So, can enhancing spatial thinking improve outcomes in STEM? A new study by David Uttal, David Miller, and Nora Newcombe published in Current Directions in Psychological Sciencenotes that “a recent quantitative synthesis of 206 spatial training studies found an average training improvement of 0.47 standard deviations.” The authors suggest that including spatial thinking in STEM curricula would “enhance the number of Americans with the requisite cognitive skills to enter STEM careers.”
The research is clear that spatial skill is important for STEM careers, and perhaps we can even enhance spatial skill to help more people join the STEM fields. What we need is research directed at understanding the best ways to develop the talent of students who are high spatial, but relatively lower math/verbal. Perhaps spatial video games and online learning coupled with hands on interventions might help these students.
This is what’s so great about the Maker Movement and “Why Kids Need to Tinker to Learn”: It will help encourage all students to tinker, invent, and to use their hands to make things again. Certainly the skills encouraged by the makers might be helpful to students who go on to pursue STEM careers. But the movement probably will be most effective for spatially talented students who have been neglected in our school systems.
One student who felt neglected in the school system was researcher Matthew Peterson. As a child, Peterson felt that he was drowning in words and numbers. And in many ways he was, as he was identified as dyslexic—similar to Einstein and Edison. This bothered him so much that today he has developed a way to teach math in an entirely visual manner called ST Math.
Ultimately we need to have the individual skill profile of each student matched to individualized instruction tailored to them. We need to experiment in the laboratory and classroom and conduct rigorous evaluations to find out what actually works.
Redefining and Valuing a Different Kind of Creativity
Today we idolize creative actors, dancers, artists, musicians, and writers. But when was the last time someone raved to you about a creative engineer or mathematician? Why isn’t STEM considered creative or cool? Longitudinal research has made a solid link between early spatial talent and later creativity. Yet for whatever reason, we don’t appreciate the highly creative nature of science, technology, engineering, and mathematics.
It would seem impossible to argue that the theory of relativity, alternating current, or the light bulb were not creative innovations. And yet it is easy to forget that these advances fall squarely in the STEM disciplines. Consider the device you are reading this article from right now. Spatially talented people imagined it in their minds eye and then they built it. Not everyone is going to be an Einstein, Tesla, or Edison, but if we identify the many spatially talented students who have been neglected in our school systems we might discover many brilliant kids who are just waiting to develop their creative potential. We need to help them. After all, we will ultimately depend on their visions to help create our future.
Jonathan Wai is a researcher at the Duke University Talent Identification Program and Case Western Reserve University and writes “Finding the Next Einstein: Why Smart is Relative” for Psychology Today.
Link to original article: http://blogs.kqed.org/mindshift/2013/07/why-we-need-to-value-spatial-creativity/