1stdibs, the leading global marketplace for vintage, antique and contemporary design, has posted the results of its annual Interior Designer Trends Survey, completed by hundreds of interior designers around the world. The data reflect the tastes of design experts, informing the industry and consumers of the interior trends we will see in 2020. The findings indicate a focus on creating one-of-a-kind spaces through the use of unique, antique or customized products; a growing preference for sourcing items from local artisans and makers; green as the most on-trend color of the year; nature motifs; and an increase in the use of digital platforms for furniture purchases.
Simple Ways to Start Your Analysis
Looking for a quick way to get started understanding the results of your research?
Projects that are survey research-based can be daunting. So can projects that involve the analysis of sales, promotional activity, advertising, or other marketing-related activity.
We live in a world of complexity and big data. Simple guidelines and a keen eye can reveal patterns that you might have otherwise overlooked. Here are a few tips to help you start analyzing your project:
Take a walk through your data.
Scroll through the data and see where values “pop” – that is, where are they high and where they are low? Do your tables flow in the same way that you think about your business? If so, you will begin to see numbers that imply relationships. As a result, visual outliers can become major insights.
Compare those who are interested versus not.
In the research business, we refer to this as “acceptor-rejecter” analysis. If, for example, you have a five-point purchase scale, group the “fours” and “fives” and compare them to “ones” or “twos”. Throw the neutrals in with the rejecters to compare positives vs. everyone else. Are there larger differences? If so, What do you infer?
Mine the gap.
The benefit of acceptors vs. rejecters is that you are looking more vs. less extreme. The difference between them is valuable in identifying a compelling story. Typically, this is done in the form of point gaps. A large gap between acceptors and rejecters points to an insight.
Sort your data.
If you have attributes of various features or benefits, sort them from high to low and compare the acceptors and rejecters. Or compare demographic groups, such as Millennials vs. Baby Boomers. Sort them on ratings or point gaps. Larger point gaps can identify attributes that are choice drivers.
Think linearly.
Array groups you are interested in analyzing by order of magnitude. For example, a variable like education is easy: college educated vs. not. For income, create low, moderate, and high income groups, and compare across. The same is true for other continuous variables, like age. Be clever, use medians and not means.
Your eyes will easily see patterns, especially if interest is correlated with your dependent measures.
These little baby hacks will get you on your way!