Be in awe of inherent errors, take no chances with random errors, and be strict with yourself against subjective errors. Investigation is just the beginning, research is the key, Regarding market research, behavioral data analysis is very popular in the Internet age. Platforms such as social media and e-commerce have accumulated massive amounts of user data. Therefore, the argument that "Internet research is booming, traditional research is dead" was born on the Internet. However, this may not be the case. 1. Market research has and only 3 purposes.
A skin care brand received the following survey results: 64% of your users are women aged 25-35; The favorable rate of XX products reaches 85%; The conversion rate of "click-order" of home products reached 12%; "Pure natural" is the keyword with Romania Phone Number List the highest user click rate; ... After confirming that "natural" is the most concerned keyword, he developed zero-added "pure natural" skin care products. As a result, consumers are not buying it, because the smell and texture of skin care products lacking certain additives are very different from before. It turns out that the "pure natural" that consumers pursue is actually less stimulation to the skin, rather than simply removing various additives. These data have numerical values, sources, behavioral lines, and are available everywhere on the Internet.
We call them "quantitative surveys." However, such surveys do not provide meaningful insights. If you don’t know the story behind consumer behavior, even if you have a huge amount of data, it’s just a dry database that ultimately leads to a justifiable failure. More importantly, many innovative products and services do not have ready-made direct data for reference, such as DJI drones and Uber. Moreover, some products have high development costs, and cannot be developed just like software products and some lightweight products, and tested and iterated on the Internet. This kind of research problem is very common. The root cause is that researchers believe too much in the power of data and ignore the deeper insights behind the data.