small data - insights

“Big data is about finding correlation, while small data is about finding causation” – Martin Lindstorm.
 
Marketing is obsessed with harnessing the power of big data – rightly so. Big data is extremely valuable in understanding patterns at scale, helping brands take advantage of trends and for marketers to drive better ROI. But this laudable effort should be done in conjunction with the reverance for ‘small data’.
 
Small data and the role of insights in marketing is different from that of big data and provides valuable inputs into marketing that big data, by definition, cannot. 

1. “Why” is the single biggest question that big data cannot answer and requires small data. For example "Churn Prevention" is one of the most common applications of big data. Predicting patterns of behavior of customers who have churned helps create 'flags' that signal when the behaviours occur for other customers. But this does not solve for why churn may be occurring in the first place. To solve for this we need to understand the context of the churn behaviour. If the behaviour pre-churn is identified at scale is a complaint registered then the nature of the complaints might point to a solution to prevent churn. If the pre churn behaviour is isolated to certain segments/ life-stages then it might make sense to determine if the organisation is primed to serve customers when they move life-stages.   
2. Big Ideas come from Small Data : Observing people using your product, talking to them about your product and how it intersects with their lives can help you discover new ideas for your brand that when scaled can become an enduring asset. 
3. Small Data Feeds Big Data : Sometimes micro observations are important to understand tipping points. A high abandonment rate on a website can be captured, analysed ad infinitum by big data. But a survey conducted specific to understanding the reasons and nuances of abandonment can be more revealing and effective. Built over a series of such events small data improvements/ hypotheses can be scaled into big data observations and rules.

 
Small data and big data are both valuable but different. Using both intelligently Vs choosing one should be the way to go. Small data should feed valuable hypotheses/ ideas and inspire while big data should discern patterns @ scale and inform / prioritise.​

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