Posted By Aliza Pollack,
Tuesday, June 30, 2020
Journey Mapping: Big Picture Thinking
I sit here writing, waiting for inspiration to hit: Where is that pithy line that usually finds its way to my brain? The one that sets up the essay/article/slide so well? This is what I do: I take big ideas and craft them into compelling packages to showcase their meaning. But times are intense. And inspiration isn’t so light and sparkly right now. My mind shifts to the bigger issues at play. As I ponder the pandemic, and the stark lifestyle changes it has brought on—civic uprisings, Black Lives Matter, pain, unemployment—it’s hard to deny how interconnected we are, and how vital it is to consider the context when trying to understand a problem.
This attitude translates to work. Often, before COVID-19, a business problem would reveal itself and one team would own it and the solution-finding process. Collaboration across teams can easily be stymied by the rush of business life, with looming KPIs, clogged calendars, quarterly reports, changing leadership and multiple hashtag and mottoes like, “move fast and break things.” The world, and the people in it, are complex. Thinking and working contextually is fundamental. It encourages collaborative work and holistic solutions. Enter journey mapping: a framework that sheds light on the full customer experience. The behaviors, attitudes, delight, and pain points that the customer encounters on the way to your product/category/service/experience. When executed well, journey mapping can coalesce often siloed consumer-facing teams and inspire a more nuanced marketing and product development road map.
Here are my four steps that might help you make this fit within your organization:
Benchmark: Root the team
As with all fact-finding missions, before starting consumer fieldwork, gather your major stakeholders in any way possible (e.g., individual interviews – face to face, phone, short workshop, Google doc, survey) and build alignment.
- Download what is known across teams (marketing, product, CX, data science, etc.): ingoing hypotheses, perceptions of the journey, CRM survey open-ends, personas/segments we want to pursue, competitive analysis, data science.
- Identify what is unknown: What confuses, what are barriers to entry, underlying motivations, who is the real customer, duration of this journey, perceived competition, biases.
- Agree on what success looks like: How should the final deliverable look, what does it need to achieve, what will this work impact within the organization—communications development, product development/refinement, innovation, all?
Discovery: Center on your respondent
- Who will you talk to? Think through the key identifying variables of your broad user base: demographics, frequency of usage, awareness of category, awareness of brand, etc.
- Go into discomfort zones: Journeys capture the full lifecycle which extends beyond purchase. Talk with power/passion users, latent users and rejecters.
- Focus on the individual: I strive for pristine data, so choose one-on-one interviews (video, F2F) rather than focus groups. Use your analysis to uncover patterns.
- Kickstart participant memory: We’re asking people to recall experiences, which are inherently flawed (humans forget). Help them shore up memories with real-life artifacts: calendar entries, receipts, credit card slips, social media posts…. these items spark authentic stories and emotions.
Analysis and visualization: Show your story
Most likely, you’re sitting on a trove of data (yikes!) with a need to synthesize in both meaningful and compelling ways.
- Plan ahead: Talk with your client in advance to decide on the best form of deliverable(s). Guide them toward what’s possible. They also might want to validate quantitatively, so talk through how you can be of service to bring it all together without losing the high touch of qual.
- Sparring partner: It’s likely that you will be lost in the data weeds. Pluck someone from the team, the office (the street?) to share your findings. Relaying the story to a stranger reveals its strengths and weaknesses. If you can’t answer their questions, there’s more work to be done.
- Bring on the designers: Unless you’re design-gifted, work with a professional. They’ll elevate your product.
Action planning: Move them to the next step
While this isn’t integral to the journey map process, it’s an important part of your client’s path. I try to bake it in to the workflow. You’ve started the project with full team inclusion; now help them all put this valuable information to use.
- Can you share it to the full team, followed by a Q&A session?
- Can you conduct a workshop/sprint to inspire some new ideas which they will prioritize?
- Can you overlay it with jobs-to-be-done (JTBD) framework so the team can see how their respective plans match/meet where users are, and how they feel in that moment.
Through a rigorous process, fed by varied disciplines/teams, journey maps help you pull back to see the sum of all parts.
Aliza Pollack runs research projects to root brand initiatives in real insight. Her work is human-centered, not consumer-oriented. Any brand, no matter how loved, is a fleck of dust in our lives. To resonate, it needs to know how people live, their ambitions, fears, and inspirations. I love digging for those nuggets.
Customer Journey maps
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Posted By Michael C Sack, Co-Founder/Owner/Moderator/Interviewer,
Friday, June 5, 2020
“Visual Data” Is NOT Data Visualization
An average person is exposed to over 10,000 image-based impressions a day. By contrast, the average person takes in less than a thousand words and a dozen numeric comparisons per day. We receive essentially ten to 100 times more image impressions than other types of information. This balance of information types is consistent with how the human mind works.
The first decision that a human makes is Momma versus NOT Momma. In the first twenty-four hours after birth, a normal baby can tell its own mother’s face from all other faces. The distinction is made on visual clues like eye and hair color, the shape of the face generally and the shape of the nose, mouth, eyes, and other facial features, specifically. The recognition is a visual discrimination.
Momma vs Not Momma
Then think about a child before they form a sentence, generally 2-3 years old, i.e., sometime in their second year. This is over 500 times later than their first visual discrimination. Then consider how long it is before they can do addition, subtraction, multiplication, and division. It is thousands of times later than the first visual discrimination.
If you think this may be a biased comparison, try this experiment. I have a granddaughter who is nine months old (though she was two months premature). Nonetheless she just said her first word (da-da) and began to crawl in the last two weeks.
I put her in her little play area and placed one cookie at one end of it and four cookies at the other. She went for the four without hesitation. She cannot say four or count to four. She does have a visual construct that allows her to judge MORE versus LESS. We all know more cookies are better, even a 9-month-old. (Note: my daughter only let her nibble on one cookie with her one tooth.)
Our minds are visual, first and foremost, and do not rely on words and numbers to navigate our worlds. If we did, we might not make it to adulthood. Our verbal and numeric skills would come too late. Our visual recognition of threats and opportunities begins far earlier.
Our Industry Has it Backward
The mind operates on visual information more than ninety percent of the time. According to the Massachusetts Institute of Technology (MIT), ninety percent of information transmitted to our brain is visual and the human brain processes images in 13 milliseconds—60,000 times faster than text.
The consumer insights industry gathers 99 percent-plus verbal and numeric data. It is even worse than that. We also ask our questions verbally and numerically. Of the three data types, this makes it the hardest for respondents to answer.
What Are Visual Data?
How can we turn the visual messages into measurable data? The answer is simple and complex at the same time. We need to receive and record the visual information in the same way the brain does.
In Thinking, Fast and Slow, Nobel Prize winner Prof. Daniel Kahneman identifies System 1 thinking as “the brain’s fast, automatic, intuitive approach…” and that “intuition is nothing more and nothing less than recognition.” BUT RECOGNITION OF WHAT?
Our Mind Operates on Visual Structure
Here is an image that represented a breakthrough insight in the appliance industry. The research was about stovetops. The study found that consumers wanted a stovetop that took full responsibility for their safety and protected them from heat and harm. This image summarizes that result, really: snow and a stovetop
The image to the left is how we think we see things, but that is only the conscious (System 2) view that considers the content as image recognition. The brain first sees it as shown below. This is a (partial) System 1 view of the same image.
Neuroscience is the fastest growing segment in the industry, and it observes the process of the brain’s System 1 recognition. Yet it does not tell us what is being recognized. Visual Semiotics does.
Visual Semiotics is the science of Visual Data. In the example, blue and white cause some of the signals in the brain that neuroscience monitors as secure and isolated/safe. Shapes cause some of the signals in the brain neuroscience monitors as separate/protected. Physical context (like distance, dominance, proximity) cause some of the signals in the brain neuroscience monitors as in charge/responsible. We are aware of four other symbol types that complete the System 1 decoding of images.
This Is Not New!
The knowledge of visual constructs shaping our thinking was discovered in the 1960s by psychologists working in the Tavistock Centre (Clinic) in the U.K. They were working to try to develop a better treatment for autism.
They discovered that autistic children broke the world down into fewer symbolic visual structures (“constructs”) than other people. Autistic children also made some constructs totally dominant (being able to see through a window did not differentiate it from a door). Learning how each child decoded the world visually was the key to learning to communicate with them. This also taught the clinicians how the rest of us visually deconstructed the world.
What is relatively new is the language for describing this process, Systems 1 and 2. Dr. Kahneman named it and showed its relative influence on decision-making and economics about fifty years after the process was discovered.
(Note that Construct Psychology is the basis for Behavioral Therapy. Behavioral Therapy is both the most widely used and effective psychological therapeutic method in the world. It is the only therapeutic approach known to help substance abuse, for example.)
The Visual Future
Over ninety percent of the information that helps us through our daily lives is visual. Over ninety percent of the information on the Internet is visual. The visual data available on the Internet makes what we currently call Big Data miniscule by comparison.
At present, we intuitively recognize the meaning of Visual Data. To read and write it, we need to learn a new language, i.e., Visual Semiotics. Visual Data is the future.
Author: Michael C Sack, Co-Founder/Owner/Moderator/Interviewer
Brand Kinetics is the home of quantified Visual Semiotics and Visual Data. Visual semiotics shows you how the brain processes visual information. Visual Semiotics has been validated in 56 countries and used successfully in 101. Our process has won six major awards and our projects have won over 100.
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Posted By Maria Virobik,
Tuesday, May 14, 2019
Updated: Monday, May 13, 2019
Data Visualization: 3 Ways to Make Your Qualitative Reports Pop
What Can Data Visualization Do for Us?
Data visualization—the graphical representation of information and data—can be a powerful tool in qualitative reporting. While we certainly can’t completely escape text-centric pages in our qualitative reports, graphics add visual interest and help break up the monotony of pages (or slides) of text. Done well, graphics help support qualitative findings and enable us to communicate in more interesting ways beyond words on paper (or a screen). Effective data visualization can also help readers understand concepts more quickly and easily and make information more memorable.
All the Cool Kids Are Doing it
Newspapers and other media outlets have jumped on board the data visualization bandwagon. Publications like The Washington Post, The New York Times and the Los Angeles Times employ full-time data journalists to augment their reporting. These folks take an enormous trove of data on a particular topic—for instance, the earlier start of spring in some parts of the U.S. or the confirmed U.S. measles cases by county in 2019 —and expertly slice, dice and manipulate the information into interactive graphics that communicate big ideas in an accessible and elegant way.
Data Visualization and Qual: Not a Linear Journey
Visualizing quantitative data is relatively easy. Hard numbers and percentages naturally lend themselves to visual representation. Charts, graphs and their modern equivalent—infographics—are easy to create from quant data.
Qualitative data can be harder to visualize; transforming qual data into graphics isn't as straightforward or simple. A search for “infographics and qualitative data” reveals that some people even argue that qual data can’t be turned into infographics. Take heart, however. An equal number argue that it can and provide examples to back this assertion.
But it’s not a linear journey from qualitative data to data visualization. Many of us have heard from end clients who want hard numbers or percentages included in a final report to quantify how various concepts or ideas stacked up against each other. We can explain that “qual isn't quant” until the cows come home, but clients persist in making such requests.
Instead of giving in to these requests (or refusing them outright), there is another option. We can take this as the opportunity to develop data visualization approaches that give our clients the detail they want and expect without compromising the qualitative nature of the report. A few examples follow.
Word Clouds – an Oldie but Goodie
Word clouds are a common data visualization technique in qualitative reports. Using font size (and often color), they convey magnitude of various responses, thoughts or ideas. Larger words=more popular/frequent/common. This approach works well because it’s a way to provide granular detail without showing the actual numbers behind the information.
While word clouds aren't the answer for every situation, they are a great tool, and websites for creating them abound. The PollEverywhere blog lists nine favorite word cloud generators, including Wordle and Tagxedo. A Google search for “word cloud generator” will point you to others.
Customer Journey Maps: Timelines in Disguise
Customer journey maps are another way to employ data visualization in qualitative reports. These maps are essentially timelines; a quick Google search on this term turns up many great examples that can be easily adapted to fit your particular purpose.
Here’s one example: a timeline detailing milestones in the 21st Century Conservation Service Corps history from 2010 to 2014.
The example above is organized by year, but the general format can be adapted to visualize a customer journey. Year markers become phases in the purchase journey: research, comparison, selection, purchase. The linear format allows room above and below the line for details on the individual steps consumers undertake in each phase.
Venngage is one great resource for infographic templates and tools, including many for timelines (such as the one below). They offer a couple different subscription plans. But you can peruse the templates for free, and that might be all the inspiration you need to create your own.
Bubble Graphs – Form and Function
Bubble graphs are another idea we can borrow from data journalism. During the 2012 London Olympics, The New York Times kept a running medal count by country and visualized the data in a simple table (below). The information is clear, but the table doesn't do a great job conveying the magnitude of differences among countries.
The Times formatted the same information into a bubble graph. This approach does a much better job conveying magnitude. You can easily identify the countries that led the medal counts. Readers could hover over any circle for more detailed information, including a country’s medal count by type (gold/silver/bronze). (Visit the link below the graphic and try it for yourself!)
The same idea—sans numbers, of course—could be employed in qualitative reporting. For example, we could use a bubble graph to report the characteristics that participants want in a dog.
Readers can immediately see which characteristics were most important and which were mentioned by fewer participants. By keeping numbers out of it, the graphic remains faithful to the spirit of qualitative research.
Sky’s the Limit
These are just a few examples of how data visualization techniques can be employed to make qualitative reports more engaging and communicate findings and implications more effectively.
Here are several links to more examples; many additional resources can be found by searching data visualization:
What are your go-to data visualization techniques and tools? What works? What doesn't? If you have advice or a favorite resource to share, please leave a comment.
Maria Virobik joined QRCA in 2018 but has worked in qualitative research since 1997. After early dalliances in the advertising world, she came to her senses and has been devoted to qualitative analysis and reporting ever since. Originally from Southern California, she and her husband sold their house last year and now live a nomadic lifestyle with their two marginally obedient dogs, Lucy and Ginger Snap.
Customer Journey Maps
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Posted By Laurie Pumper,
Tuesday, December 19, 2017
The following post was written by Alice Greene of Campos, Inc. Alice is one of our speakers at the 2018 QRCA Annual Conference in Phoenix, Jan. 24-26; her presentation is titled, Using Data Visualization to Overcome the Customer Experience (CX) Memory Barrier. Alice's presentation is just one of many reasons to attend the conference! Register now: http://bit.ly/QRCA2018
As consumers, employees, students, and Fitbit-wearing human beings, we are being provided with more and more information every day about ourselves and how we benchmark against others—seemingly to no avail. We all know why: Data alone is never enough. But I have been obsessing about how data, in combination with an individual’s own interpretation of, or story about, that data, has the potential to unlock significant personal growth and societal change.
Let’s take the state of education in the United States, which continues to decline despite measurement of every kind. These days, there is particular panic about kids needing to develop the hard skills that will be needed to prepare them for the jobs and technology of the future, as well as the soft skills, like problem solving and leadership, that often depend on self-awareness and confidence.
A friend of mine who is a local elementary school principal sees a solution to these challenges in not only sharing students’ data with them, but in asking them to explain it, also. Knowing that students often learn best when they can relate a topic to their own experiences (known as constructivist learning theory), what kind of self-actualization could come from learning about themselves by relating their own data to their experiences? Rather than sharing discrete data points with students—test scores, attendance and awards numbers, detention and extra-curricular engagement statistics—what if we present these data back to students in a visual, time-series format and asked them to describe their journeys? How would they tell their story, and what could we learn that the data simply can’t say? What was happening at home, for example, or with friends, with teachers, with their health? Imagine if we could aggregate that unstructured data into actionable, system-wide insights—with benchmarks!
Consider the case of one boy (we'll call him Danny) at my friend's school, whose data was showing fantastic performance in his words-per-minute reading score. It wasn't until reviewing Danny's results with him that she learned he was developing a speech impediment–which was bad for Danny and producing a misleading measurement. In a powerful testament to asking kids about their view of benchmarks, as well, Danny was shown different types of stuttering and immediately identified his own. He covers his stuttering by avoiding the "Sh" sound, which he can say correctly, but it makes him anxious. He was able to articulate all of this which, the principal noted, was "pretty amazing." She added: "He is now enrolled in speech and his reading is much better."
So, we all know that data can’t tell us everything we need, but we don’t all appreciate how it can be used to trigger memories or sharing that can, in collaboration with the person whom the data represents, fill in a much more complete story.
This idea of “numbers and narratives” holds equivalent power in the healthcare arena. What happens when we show patients a visualization of all their touchpoints with doctors, pharmacists, and facilities over the past ten years? What will they remember? How will they fill in the blanks? And how can these insights start to solve some of the biggest challenges facing healthcare today?
QRCA Annual Conference
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