Tag Archives: DataViz

When should you design a questionnaire with brand colours, fonts, and formats? #MRX 

You’ve seen the commercials on TV where the host or actor discussing the fantastic properties of the amazing product is wearing clothes and accessories that match the products packaging and branding perfectly. Sometimes it makes for creepy over-branding whereas other times it makes the commercial more calm and focused. In either case, the intent is to unconsciously teach you the brand colour so that when you are in the store, the familiar colour will draw you in, consciously or unconciously.  

However, the world of research is different. Using brand colours as part of questionnaire design can significantly affect the outcome of research and whether that results in increased or decreased scores, the impact is negative. Results from surveys should reflect in-market experiences, not unconcious associations of brand colours. If you plan to measure brand recall, awareness, purchase, attitudes, or perceptions within the the general population or within category users, particularly if you want to compare with other brands, never brand your questionnaires with brand colours, text styles, or formats. Questionnaires formatting should be neutral in all ways such that unconconsious recollections won’t be created. 

So when is it appropriate for questionnaires to use brand features in the design? When can you use your brand’s colours and fonts and styles to pretty up what can be generic, boring pages?

When you’re contacting existing clients or customers to ask about a specific purchase experience or brand experience. That’s about it. 

In such cases, the bulk of the questionnaire will focus on the specific experience with the specific brand. There may be a couple of generic introductory questions, but 90% of the questionnaire will focus heavily on your brand, your employees, your shelves, your website, your selection, etc. There is no point in creating a sense of blind review or uncontaminated response because the brand must be revealed early and significantly. 

If you’re not sure which way to go, there is a very simple solution. Never brand your questionnaires unless there is no way around it. Better safe than sorry. 

Want more questionnaire tips? Have a peak at #PeopleArentRobots, available on Amazon.  https://www.amazon.ca/dp/1539730646/ 

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Viz-Fest Day 3 – Data Visualization and Dashboards #MRX #NewMR

Live note-taking from Viz-Fest, November 2016. Any errors are my own.

Tom Schlak, E-Tabs, Data Viz for the Visually Impaired

  • Most important information visually displayed to achieve objectives and can be monitored at a glance, Charts, graphs, icons, mostly visual
  • How can data visualization be made accessible to someone who is blind
  • Viewing a musical score versus listening to a score, “seeing” a chart can’t be replicated
  • What are the alternatives, Maybe use speech assistive technologies
  • 5% of the world has some visual impairment, 11.5% for people over 50 years of age
  • Good designs benefit everyone even fully sighted users
  • Text and fonts – font size on screen nothing less than 12 to 16 points, Helvetica, Arial
  • Font styles more complicated, must consider legibility and readability, legibility is individual characters, readability is overall appearance including spacing and formatting, good fonts have better spacing, less fancy script, less detailed, filled lettering, less heavy, less thin, less serif, Use display fonts carefully because they aren’t accessible
  • Contrast – sometimes corporate colours are a problem but readability must come first, use an online tool like adobe color CC, let’s you choose colors that match your target color or complement, matters for typography too, forget white on yellow, yellow on red, grey on green
  • Clean user experience – avoid cluttered dashboard, eliminate non-data pixels like background images and watermarks, large logos, etc, think about data-to-ink ratio, avoid dark gridlines or over-labeling, don’t fill up backgrounds just because you can, gradients of grey can be difficult to differentiate, use colors like red and green appropriately, only highlight what must be highlighted, flatten the design by removing shadows and 3D elements that make a flat image look shiny
  • Eg Microsoft logo went from shiny colored circle to four simple boxes
  • What about color deficiency, blindness – 8% of men and .5% of women have deficiencies, the worst forms can distinguish 20 hues but people without can see about 100 hues
  • Avoid red green charts as this can hide the data
  • Traffic light indicators are common, red green yellow, and should be used with caution, consider using shapes as well, arrows, checkmarks, faces, and maybe use them only for one state, just the positives or just the negatives
  • Use cross hatching or fill patterns so you don’t rely just on color
  • Use Colblinder online to test colors, light colors can lose their differentiation, text labels will help, try also ColorBrewer online
  • Much of this applies to good design anyway
  • Is it still beautiful is someone can’t see it? Communicate to everyone

Marta Blankenberger, redaviZ, Data Driven Infographics

  • Infographic is visual representation of data to share information quickly and clearly
  • Need to think about position, size, shape, color
  • Icons help us understand and interpret information
  • Combine a doughnut chart with an icon, or combine a bubble chart with a map behind it
  • Masking – cover or uncover part of a chart but placing a shape in front of it, try that with charts, grey out a section or tone down the color of part of it
  • Combine icons, put stars over circles to mask different areas
  • Try using an arrow as the bars in a bar chart, just paste the image into the bar
  • Try putting a grey bar behind the bars to represent the maximum of the bar
  • Try using copied icons as the bar (e.g., 5 stars, 8 stars, 3 stars), you can use masking to turn 5 out of 10 into 5.3 out of 10
  • Use conditional formatting of data labels, change the color/shape/size of labels if they are less than a specified value, or put checkmarks as the data point

The last few not quite so live blog posts: Empathy, Digital Context, and Visualization #ISC2015 #MRX

MRALive blogged from the 2015 MRA Insights & Strategies Conference, June 3-5, 2015 in San Diego. Any errors or bad jokes are my own.

 

The Use (and Misuse) of Empathy in Market Research: Tom Bernthal, Founder and CEO, Kelton Global

  • journalists try to make people comfortable enough to tell their stories
  • insights industry has really involved in the last 12 years to bring in the skills of journalism and story telling
  • stories are more powerful than data
  • smart brands build empathetic bridges between businesses and customers
  • successful companies today don’t just sell a great product, they sell a great experience
  • what your company does doesn’t matter if people never find out about it
  • customers need to crave your product
  • Warby Parker, Tom Shoes, Dollar Shave Club, Travel Hack – all do this well
  • Head is understanding, Heart is feeling and you need to do it with the ears eyes and heart of another
  • Gut is your instinct
  • Mikes Hard Lemonade was build from gut, and the guys name wasn’t Mike, Mike was the most popular man’s name at that time
  • 1 – make room for emotion, provide psychological air, connect people like me, foster conversations that reveal deeper emotions and experiences. community style research helps with this, allows people to talk amongst themselves as they would normally do with their friends. don’t be afraid of silence, when the room goes silent there is pressure to fill that space. often the respondents will dig a bit deeper to give you more if you give them the space
  • 2- dare to be wrong and challenge what you know, empathy has a narrow field of vision. research is to formulate hypotheses. you need to look off center. it’s not as simple as understanding one single person. it’s everybody around that one person.
  • 3 – connect at the scale, explore the ordinary. we borrow from the tragedies of others to make our empty days feel better.
  • 4 – know when to zoom in and zoom out, integrate approaches. empathy doesn’t increase as the size and scope of the problem grows. we have more empathy for a single person than for hundreds. empathy lacks foresight. it latches on today not tomorrow.
  • 5 – don’t let the facts kill the story, let the story carry the facts. the story is a machine for empathy. powerful tool for imagining yourself in other people’s situations.
  • 6 – have a little empathy. tell a story your audience can hear. consider stakeholder mindset when crafting your story. the risks of poor storytelling are high. tune out if too long. dismiss if too broad.

 

After Omni-Channel: Preparing for Digital Context: Stacey Symonds, Sr. Director, Consumer Insights, Orbitz, Martie Woods, Lead Strategist, Thought Leadership, Stone Mantel

  • consumers are expecting to reduce the gap between thinking and doing – consumers will almost always give up information about their behaviour if they thin the information will reduce steps required and help them accomplish a goal quicker
  • consumers surround themselves first, then make all sorts of micro purchases
  • now, we buy a brand, and then we buy all the accessories that we didn’t realize we needed
  • people create their own organization structures, like how your browser opens to a saved set of tabs and you automatically go to amazon to buy books
  • the home screen of your phone is what you used most often, it’s hard to get on someone’s home screen
  • consumers are seeking to maximize their attention, the more empowered people are to accomplish more in a short time, the more people meander. rarely do they do one thing at a time. so what are people doing while they interact with your brand? it may hinder the activity but consumers don’t mind.
  • while working, 79% of people email, 45% do social media, 46% listen to music, 61% are texting, 36% are banking, 30% shop, 45% are life managing
  • the journey is less about a linear path and more about a constant state of moving
  • digital supports modes – consumers develop patterns for their activities, a general pattern for focusing and getting things done
  • reading mode or working mode or exploring mode or learning mode – you need to know what mode people are in so you interact with them in the right mode
  • when you shop for clothes, you might be in planning mode or sharing mode or speedy mode
  • consumer behaviour demands more than omni offers, thinking on channels must evolve. we need to focus on digital context. its about how mobile media, data, sensors, and location all come together.
  • consumers never use the words omni and channel.
  • pillars of digital context include the environment, tools, and modes

 

Best Practices for Data Visualization and Presentation Design: Erik Glebinski, Manager, Consumer Insights, Pepsico, Kory Grushka, Partner, Work Design Group

  • [font size on the title slide are GREAT!]
  • [wow. room is completely packed, not a spare seat and the entire back wall is full of standers, and the aisle is full of sitters!]
  • ‘look at how much data i pulled’ – too much data on one slide
  • ‘the novelist’ – people who use a paragraph of text on the page
  • ‘the repeater’ – does the same point on multiple slides in slightly different formats
  • ‘the sleeper’ – people who use the same chart on every single slide
  • ‘the cartoonist’ – uses clipart in anyway everywhere
  • ‘the cliffhanger’ – someone who uses unnecessary chart builds
  • typography, colour, simplicity, cohesion
  • serif or sans serif – serif has the little decorations on the end of the letter, like times new roman, useful for large passages of text, for reports, because those lines create connections between letters
  • sans serif have grown in popularity because internet is short form content which is better suited for focusing on a single word or a few words
  • warm colours stick out more than cool colours, highlight in orange or red
  • be minimalist with colour, use it sparingly, it should be used for a strategic purpose, stick to 2 or 3 colours. lots of colour kills hierarchy and makes it look cluttered. bias to fewer colours.
  • don’t colour every bar in a bar chart differently
  • apple is a case study in flat design
  • less is better – Dieter Rams
  • if you have any bias, move to simple minimalist edited down side
  • keep font, formatting, and colour consistent from slide to slide, use one colour on an entire slide where it makes sense
  • infographics are design heavy and data light, focus on narrative and story
  • data visualizations are data heavy and design light, often done by dumping data into programs that plot, designer makes sure it looks good
  • 3 ways to evaluate – clear, insightful, beautiful – does it make sense given the subject, is it legible, is there a value add or is it just a spreadsheet, does it look sophisticated
  • bar graphs – space between the bar should be half the space of the bar; where category names are really long flip the chart to horizontal so you can read from left to right
  • line charts – the less lines the better, 6 or more lines is cumbersome so split them up into multiple charts; label the lines themselves on the chart
  • pie charts – consider donut chart if you need several pie charts; start your largest slice at 12 o’clock, 6 slices or less is best so aggregate the smallest ones if you can
  • presentation design
  • pure cinema concept of Alfred Hitchcock – push the unique concepts of the film, use storyboarding – do your own storyboarding even if its stick figures and squares and lines that look really stupid
  • use one idea per slide, limit all unnecessary information, you don’t need every datapoint but just the ones that make the point, visually show them less and communicate more
  • use layering – use multiple slides, highlight one point on one slide and a different point on the next slide eg, grey out several lines
  • white space is your friend, eyes are drawn the point that matters
  • slides shouldn’t talk – avoid too much text, 4 or 5 bullets with 4 or 5 lines; don’t use narrative, this isn’t a book, don’t use sentences that required reading

 

Visualizing Big Data: Social Network Analysis by Michael Lieberman #CASRO #MRX

Live blogging from the CASRO Digital conference in San Antonio, Texas. Any errors or bad jokes are my own.CasroDigital

Visualizing Big Data: Social Network Analysis”
New open source programs, such as NodeXL, a free Excel back-end module, are making the visualization and analysis of social network data more accessible and robust. This presentation will provide the fundamentals of Social Network Analysis (SNA), provide sample Twitter and Facebook maps, and show how they may be used for enhancing marketing research on the socialmediaosphere.

Michael Lieberman, Founder, Multivariate Solutions

  • SNA – social network analysis – patterns of connection when people follow, reply, and mention one another on internet communications like twitter
  • Jacob Moreno did a chart of football in the 1930s. NSA uses it today to map terrorist networks. Embedded image permalink
  • Why SNA? How to improve the network, uncover patterns in relationships, follow the paths of information, for quant or branding research
  • Excel, open source system to map twitter, facebook, flickr, youtube, voson, wiki data. It’s easy but there is a learning curve. NODxl
  • Clients don’t want to learn about regression, they want to learn what to DO
  • Need to know – how many people can this person reach, how likely is this person to be the most direct route between two people in the network, how fast, how well connected to other well connected people
  • Degree, Betweenness, Closeness, Eigenvector (influencer)
  • Everyone really cares about who the influencers are but we learned yesterday that influencers are the worst responders
  • Brand maps have many islands – lots of people who don’t talk to anyone else. And then clusters of people who focus on specific topics. Auto companies need to know who the unconnected people are to pull them into their own groups.
  •  Broadcast map is good for celebrities like Lady Gaga.
  • Twitter is unstable, try one every day for 30 days. Hyperlinks are far more stable over time.
  • See some examples here  

Other Posts

Pie-Packing by Mario Klingemann: More fascinating pie chart art

I am definitely not a fan of modern art but for some strange reason, I really appreciate this artwork. These are more stunning examples of pie charts done correctly. If you are this talented, you are welcome to create all the pie charts you want!

Check out all of Mario’s artwork here.
Pie Packing Mona Lisa
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The Starry Night Pie Packed
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The Girl with a Pearl Earring Pie Packed
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Now click on this image to see what Mario says about his reason for creating it. “But when I look around what is being done in data visualization today I have the suspicion that in many cases the design is more important than the actual information and that the use of data is more an excuse to justify the use of aesthetics.” Hmmm, seems I’m not alone!
Dada Visualization I

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#MRA_FOC #MRX Effective Data Visualization by Naomi Robbins, Part #1

nbrgraphs

I was caught between a pillow and a soft place this morning with a choice between a session on social media and the other a session on charting. But, as a fan of Edward Tufte, a legendary charting specialist, I couldn’t resist attending Naomi’s data visualization session.

She began the session by testing the room lighting to see if the colours on her presentation would show correctly on the screen, something I can appreciate having  given presentations myself where a variety of colours ended up looking the same. It is something everyone should do particularly if you are presenting charts. If your labels, gridlines, or distinguishing chart features don’t show up, you might as well not do the presentation at all.

Here are just a few of my favorite points:

  • The best chart is the one where the information is detected most quickly
  • If perceiving the information is not important, then a pie chart is fine, e.g., when the chart is used as decoration
  • The way you read a chart depends on which software you use and labeling the data points does not make a bad chart ok. See the chart below to see if you can determine what the data points are. Does the line match up with the front of the bar, the back of the bar, or neither!
  • Graphs are to show relationships and trends, not exact numbers. If you need exact numbers, then use a table. Hence, bar charts do not need numbers.
  • All bar graphs should start at zero because bars reflect length which has a zero.
  • Alphabetical order is rarely the best way to order data.
  • There is no substitute for colour.
  • People know what number comes between 88 and 90 so you don’t need to label every point.
  • When we use error bars, we often use 68%. But 68% makes sense in a table for self-calculation. Doesn’t 95% make more sense in a chart?
  • Museums want to show data honestly and accurately. Corporations….. have other ideas. 🙂

Naomi presents in a style reflective of a professional statistics geek with tons of charts and examples and I got quite a kick of the morning session. She showed us a lot of tricks that I like to play on my colleagues such as having them guess chart values on really bad charts. She showed us a number of charts that I have never seen before and am now anxious to try. She showed many examples of bad charts turned good with just a couple minutes of work. She provided a set of notes that is probably the best set I have EVER come across. She suggested that though Edward Tufte is a charting genius, he is not the only expert in charting and she introduced us to William Cleveland, one of her favourite experts.

This slideshow highlights just a few of the huge range of charts that Naomi highlighted. You really need her commentary to see just how funny some of the charts are but I’m sure you’ll enjoy them anyways.

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Conversition Strategies Social Media Research: By researchers, For researchers
conversition strategies social media research by researchers for researchers

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