Data Analytics: Limitations in Assessing Consumer Insights

consumer data, data analysis

When making an online purchase you must have noticed a list of related product appearing on the website based on your interests, likes and search queries. Similarly, a message popups on your screen showing purchase suggestions based on the cookies in your browsing history, or an email lands in your inbox suggesting other products on the basis of your previous purchases from an online store. Businesses are paying attention on user behaviors and purchase patterns more than ever to understand consumer preference and propose a better solution; the process is termed as data mining.

Data isn’t just the information a user inputs when signing up on a website with an intention to purchase, but it includes online behavior like product categories a user browse through, search for or clicked into. Consumer data is collected in the last few years than ever before, and the trend is expected to rise in 2016. Marketers are struggling to utilize this huge volume of information to create more targeted and smarter marketing campaigns. However, in this article we will see some limitations of data what it fails to inform businesses about their customers.

Targeting the Right Audience

Consumer data can help you target the right audience with information on previous purchases, search queries and browsing product categories. An article published by New York Times in 2012 on data-driven marketing where a company collected data of users in their second trimester and suggested post pregnancy products like lotions, diapers, feeders etc. unfortunately it ended up with an embarrassed father finding about his college student daughter pregnancy because of direct marketing messages for pregnant women.

Another problem with data-driven marketing is that it cannot assess if the users are purchasing for themselves or someone else. For instance, purchasing kitchen items for your housewife doesn’t make you a chef. Also there are purchases you make once in a long period of time, so you don’t necessarily need related products. Like, if you are buying a new refrigerator you don’t necessarily need other appliances. So, data is accurate to the point of suggesting related products but it may not asses the actual consumer or actual needs.

Assessing the Actual Cause

Data can only asses a user’s preference from purchase patterns or level of interest from repeated searches, yet its fails to identify reasons why a certain event occur. For instance, a teenager may follow a soccer league and watch matches on ESPN doesn’t necessarily likes to play soccer. Data can only correlate between certain factors and does not provide the actual cause of the event. Consumer insights are beyond repeat purchases and buying patterns, like when a teenager like soccer it fails to assess what causes him or her to like the sport, the history, taste and influencing factors from fashion to music to video games and so on. Understanding these insights and designing a marketing strategy is a n entirely different concept.

Lacks Qualitative Intuition

Data depends upon numerical information and statistics. Implying your marketing strategies on numbers does not suffice human rational. For instance if your marketing campaign shows a downward trend in customer responses you might consider suspending your campaign, however continuing your marketing efforts could affect your competition in the long run, which data fails to predict. Secondly, data analyzes trends and current situation but does not provide enough information to base future decisions. Therefore, best marketing campaigns are usually a mix of statistics and human instinct.



Shahrukh Saeed is a design savvy digital enthusiast focused to share latest tools, tricks & techniques of the web, digital marketing UX/UI & SEO with business startups and learn from experts in the field.


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