Three Pitfalls in Collecting Consumer Insights and How to Avoid Them
Consumer insights are an efficient way to transform user-generated content into actionable insights. This data can transform business strategy, marketing, product innovation, and customer service. However, it is a good idea to have a diversified approach to research and balance qualitative and quantitative data.
What Are Consumer Insights?
Consumer insights express customer feelings, opinions, pain points, and preferences. This data used to be collected by focus groups, customer surveys. Nowadays brands use alternative data collection in textual form composed by people on the internet in the form of reviews, social media posts, and comments on websites. When these user-generated texts are analyzed with a consumer insight tool they are transformed into data that can be used for quantitative analysis.
For instance, Revuze analyzed customer reviews of classic cleaning brands Clorox and Lysol. Although the words of the reviews were the starting point of the analysis, algorithms assigned a rating to the text that measured sentiment and attitude.
This process was done in several categories, including leakage, whitening, smell, and ease of use. In the final analysis, each category was assigned a percentage depending on how important customers felt they were. Feelings of consumers could be translated into numbers, rankings, and percentages, and the result was the ability to take qualitative information, transform it, and use it as quantitative data.
 However, looking exclusively at numbered data has its pitfalls as listed below.
#1. Data Alone May Not Provide Enough Context
Consumer insights may unveil the fact that more customers prefer a certain brand for its ability to whiten clothes, as in the Clorox and Lysol above, or for its fragrance, but the missing context prevents making hasty assumptions based on the data.
For instance, without looking at the actual text, it might be hard to determine how much the customer valued each metric. If a product had an 80% satisfaction rating but was the smell was mentioned in just 22% of the reviews, it may be difficult to determine whether the smell was relatively unimportant to the consumers or if it was something they were willing to overlook in that product compared to other features, but they would like the product even more if it had a different fragrance.
Also, the numbers alone can’t tell the reader what features the customer would prefer to see, for instance, would they like the product better if it had a floral fragrance or something pleasant but more abstract, like mountain spring? A lot can be learned from a chart with numbers, but other options that are not listed in the categories may not be measured and remain overlooked.
#2. Data Gives the Whats Rather than the Whys
Sometimes it is important to know what customers want, how they shop when they like to make purchases, but it can also be useful to understand why. For instance, if the pandemic had not been widespread news and if people were suddenly purchasing protective face masks in a specific region, the numbers alone may indicate that selling masks is good business.
However, without the additional information that there is a contagious disease in the area, it may be hard to know how long to sell these protective masks (perhaps there is a spill in the area that is being cleaned up soon). Also, marketers may miss out on listing related products, such as alcohol gel and vitamin C and zinc to boost immunity if they don’t know why so many people are purchasing these masks.
Looking at news reports, reading some of the eCommerce reviews and social media posts rather than relying only on data generated by consumer insights software will add some causes behind the data and may provide a more meaningful interpretation of the numbers.
#3. There Are Some Exceptions to the Segmentation Rule
Segmenting your customer base into various groups facilitates targeted marketing techniques. Personalization is the goal, but since it is hard if not impossible to create a marketing strategy for every individual customer, segmentation and pitching products to consumer personas is the preferred method.
However, it is important to remember that segmentation isn’t foolproof. It is easy to make assumptions based on conventional wisdom about certain demographics. For instance, if in general people who live in a certain region of the country tend to prefer sports, beer, and action movies over wine, theatre, and independent films, the temptation may be to phrase emails and make references to things the majority of readers in that demographic will relate to.
That may mean that you may lose the interest of some email subscribers or social media followers who don’t have the same tastes as the majority in their demographic. That may be fine if appealing only to the majority will generate enough sales, but it could mean losing a valuable niche market that may be interested in a speciality product. Segmentation is important but it should be revised now and again.
More Than Just Data
Data is valuable in measuring progress, assessing consumer sentiment, and examining information quantitatively. However, there are times when it is beneficial to look also at qualitative factors and to put data in perspective.
Searching for the motivations, the sentiments of those overlooked by segmentation, and the contexts of buying decisions will refine marketing strategy and product development.
