The willingness to pay $100 for an interactive nightlight was deemed to expensive by management and was slanted to be scuttled. It is hard to measure the perceived value when prospective customers have not experienced it.

To convince management, that there was indeed enough demand, a last minute concept validation survey was send to a very small sample of 250. How do you communicate a well-designed product through a survey? We designed a step-wise introduction to the nightlight, presented the possible interactive features via GIFs and eventually ask pricing questions at four different points in the survey. price approach was used to design a custom concept validation survey with a sample of 250.

The step-wise approach allowed a phased introduction of the product and assessment of the perceived value at each step. Mimicking a purchase journey. One method we used to assess willingness to pay was via Van Westendorp price sensitivity method. At the end we revealed the actual price and take-up of the concepts dropped significantly but 23% were still open to purchase the.

Equipped with the data the design team went back to the CEO and got the go ahead. When GLOW light launched a year later demand outstripped supply. Further, the market uptake rate of 23% was confirmed by two subsequent studies that sourcing and marketing did independently. Ultimately the product introduced as loss-leader was so successful that Casper considered a whole line of interactive lights for the bedroom.

Can you comprehend, perhaps calculate compounding interest or returns over a few years? Most likely not. Nonetheless we confront students, who take out student loans, with exactly that challenge. We do so, while most have only a vague understanding on how much money can go how far. I gain this understanding after interviewing 10+ students at a contract position at a major bank.

This led me to give a student team the following project in the Designing with Data studio class at CCA. Can you feel compounding?
How do you translate the math behind compounding into an approachable experience.

They started with research and found that almost all student loan calculators have an alternate motivation. Rather than explaining to students the basics, they offered to consolidate your loans, etc.

The team ended up with a highly visceral design. Entering the student loan you are planning to take out and its APR the slope and timeline changes. The more you take out the steeper the slope to bike up. The team went as far as creating a prototype using Glitch.

System data, like data your phone collects about you, holds a lot of truth. The diagram of my step count shows a rough dip during the key months of the pandemic. A rather unwelcome reminder of teeth grinding times.

All started harmless with giving my students the standard task to download their step count time series. Purpose is to teach basics descriptive statistics like min, max, average, standard deviation, etc. And teach them that system data is never perfect, e.g. the day I forgot my phone at home. Compiling the steps data across the class to compare our statistics. Subsequently, we investigate possible correlations of daily step counts with other time series data; like photos taken at a given day, daily phone usage time and pandemic sheltering restrictions, to investigate possible correlations.

In the past we've shared anecdotes of what we remembered of an experience. Now we can pull our data to revisit to remember. Our behavioral data collected across digital services is a powerful descriptor and predictor, even to the point where we are not aware of it.

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Why an integrated qual-quant-qual research approach is so powerful. After a round of deep-dive ethnographic research into the needs of retirees, we've sent a survey to identify different mindsets, when it comes to mobility and social connectivity of seniors. We arrived at four psychographic clusters that we named after forrest animals the bear, bunny, deer and fox.

To integrated our knowledge from qualitative research and survey, we encouraged our interviewees to take the survey as well. This allowed us to know how they distributed across three mindset clusters. However, the fourth had no representation from our qual participants. Keeping our human-centered understanding limited to just survey data and hamper any subsequent design efforts.

The solution for the shortcoming was to use the survey as a massive screener and select a few phenotypical “bunnies”, who had opted into a follow-up interview. We've learned that the "bunnies" tended to be more introvert and rather worked in their home or garden and socializing was a difficult task for them. So no surprise, that it wasn't easy to recruit them for interviews in the first place.

Ultimately, the qual-quant-qual approach gave the team the confidence that the resulting insights on mobility and social connectivity were representative of the entire population of retirees.

Prototypes were placed for one months in 22 homes. It was paired with was paired with a benchmarking survey in the beginning, weekly tracking - Symptoms were measured on a classified scale - and an exit interview [remote and in person based on geography]. The intention was to understand if households experienced a change in their in-homce air quality. However, the prototype research revealed something striking: 

The prototypes accomplished to reduce irritants that cause asthma and allergies, in particular when placed in the bedroom. However, the conundrum was that participants did not realized that their symptoms had decline. When shown the tracking data of their symptoms dropping for everyone who had a condition, they were surprised. The insight is that when you don’t feel symptoms, you simply feel normal.

This informed the value proposition and marketing approach of normal feeling new.