Qualitative Analytics: Why numbers do not tell the complete story?

Data scientists love numbers, yet not all data is numerical. Qualitative analytics should not be ignored, especially given the unique value it provides.

Quite often, the focus of analytics efforts is highly skewed towards quantitative aspects and the qualitative efforts are either negligible or replaced by the executives’ “gut feeling”. It is imperative to understand how such a casual approach can hurt your business.

The analysis of qualitative data through the methods generally used for qualitative research is referred to as Qualitative Analytics. The differentiation between Qualitative and Quantitative analytics is not always obvious. For example, during Text Analytics, measuring the frequency of certain words would be considered Quantitative Analytics; whereas exploring the contextual meaning of popular words would be considered Qualitative Analytics. In other words, Qualitative Analytics includes the analysis of context, human behavior, emotions and other factors that are hard to digitize without losing any meaning.

Qualitative Quantitative Research Qualitative analytics is a very powerful tool for exploratory research – the earliest phase of analytics. It is also a great tool to bridge the gap between insights provided by quantitative research, and provide in-depth understanding of the underlying reasons and motivations for a phenomenon. Pragmatic qualitative research is done through a variety of methods. While some of them are simple such as Surveys and Interviews, others are highly advanced such as Ethnography and Phenomenology.

One of the most common myths about qualitative analytics is that it is useful only for academic researchers in selected fields such as social sciences and neuroscience marketing. The truth is that - almost all business problems have a qualitative aspect, and thus, quantitative analysis alone would never be able to tell the complete story.

As an example, let’s consider web analytics. All leading web analytics software have excellent quantitative analytics capabilities. They can easily, yet precisely, provide information such as: number of users who visited the website during a given time period, geographic locations from which the website was accessed, time period for which user stayed on the website, etc. However, when it comes to the “true business value” questions (such as why did the customer not make the purchase? what factors influenced the customer’s buying decision? how did the customer feel after the purchase?) these web analytics software, at-best provide vague answers.

So, if you are narrowly focused on the clickstream, you will never be able to comprehend the thoughts going through customer’s mind as she moves across the “conversion funnel”. So, when you think of sales, instead of focusing on the “click” event, think broader about the interaction and overall experience. One of the most popular business Key Performance Indicator (KPI), Customer Satisfaction can never be measured through quantitative analytics alone. No amount of quantitative guessing can match the business value of “voice of customer”.

As the CMOs increasingly drift from Customer Relationship Management (CRM) to Customer Experience Management (CEM), there is an increasing need for holistic understanding of customer experience across the complete sale cycle. This can be achieved only through deploying qualitative analytics and integrating it with quantitative analytics to provide 360-degree data analysis.

Let’s face it. Quantitative analytics still needs more manual intervention and the results are often fuzzy. In absence of a clear-cut approach and thus automation, it is not as time and energy efficient as the traditional quantitative analytics. But, qualitative analytics is still indispensable as it provides deep, actionable insights about the ‘why’ and ‘how’ aspect, which often gets ignored as we continue to be inundated with the ‘what’ ‘where’ and ‘when’ of statistics.

With the rapid pace of technology advancement in recent times, it now seems that the software tools used for academic purpose are ready to deliver on the business analytics needs of the industry. MAXQDA, ATLAS.ti and NVivo are some of the best qualitative research tools, with extended capabilities of semi-automating the pattern recognition from content (such as subjective answers, interview transcripts or video recordings).

If you are a business manager, it is high time to think again about your analytics dashboard and whether qualitative analytics are getting your appropriate attention.
Anmol Rajpurohit
Anmol Rajpurohit is an intern and blogger at KDnuggets, and a visiting student researcher at UCLA REMAP. He is a B.Tech. graduate in Computer Science from India and is keenly interested in research and development work in the field of Big Data, Data Mining, Social Analytics and Computer Networks.