Collecting small data in the world of big data

It is a chilly morning in late October in Bangalore, India. As I return back home after a short walk to the bus stop to drop my daughter off to her school, my colleague walking with me begins collecting bird feathers on our way back, of all hues and sizes. We start debating which birds have what kind of feathers, and when she is done collecting four different kinds of feathers, she stops. Another colleague urges her to collect more, but she says “four is good for today”. And she sets me thinking on what is the power of small data. While the world is raving about leveraging big data and the power of mass customization, I argue in this post about why successful firms must also invest in small data.

What is small data?

The best definition of small data comes from none other than Martin Lindstrom, who wrote a book titled “Small Data: The tiny clues that uncover huge trends”. He distinguishes big data from small data thus: “Where big data is all about drawing correlations, small data is about identifying causation” (read more here). Big data is typically collected through a variety of sources, from your credit card spends, loyalty card behavior, search algorithms, and mining of transaction data. What big data analytics can do is pretty visible and known to all of us – patterns that can aid prediction. In his book and other writings, Lindstrom write about the need to uncover the causation behind these patterns. One of the examples he often cites is how a US bank found customer churn using big data, and with the help of small data, discovered that they were moving their assets and mortgages around, and possibly leaving the bank not because of poor customer service, but they were going through divorce!

Small data for listening to customers

A couple of days back, I read an interesting article on why Amazon is opening physical stores by IMD Professor Howard Yu (read it here). In that article, Yu labels Amazon’s book stores as not so much distribution channels, but “research laboratories”. Laboratories where customer journeys are observed, what they like and how they spend their time browsing; simple things like which aisles do they reach first, do they pick up the books first or read the reviews pasted below, do customers get influenced by recommendations, and the like. Small samples, but rich inputs on causation. Retail stores have long been using small data – have you not read about why bread and staples are placed at the end of the alleys and chocolates at the check-out counters? Small data like this helps identify why certain shoppers behave the way they do, whereas big data will be good to classify shoppers into dashers, economists, the pros, and the candy store kids. [Dashers know what they want and dash in and out of the store, picking up her favorite brands/ products/ pack sizes and rushes out. Economists, on the other hand, rummages through deals and offers, and typically shops at warehouse clubs and wholesale shops. The pros are those who do considerable research on the deals and offers, analyze value for money, wait for the right time to buy (like festive seasons), and typically get the best deals. The candy-store-kid is the retailer’s delight; she behaves as the name suggests – impulsive, compulsive, and extensive shopper. Read more about it here.] On the other hand, small data will help analyze when does a typical dasher behave like a candy-store-kid. I was in Barcelona recently, and typical to my urban foreign travels, I was shopping in supermarkets. I noticed that a lot of these stores had “male zones”, where typical electronics, electrical goods, FC Barcelona memorabilia, and beer are stocked. Small data, could suggest that men would hang around the ‘zone’ till the women shop for all the essentials, and just as they reach the counter, these items are added to the cart and billed. Given the festival season, maybe even the textile showrooms of the famed Chennai’s T. Nagar might have implemented this!

Small data for innovation

There is no better use of small data, unless you listen to customers. And better still, if you could listen to your customers at the prototyping stage, well before product design and introduction. User innovation spaces provide opportunities for firms and innovators to collect valuable small data well before the product design. In fact, such small data could help innovators listen not just to the prosumers (innovative proactive consumers, who engage with the firm and are typically early adopters), but a wide variety of consumers as well. One such experiment on early-stage user innovation platform is a physical store-like service manufactory at the Nuremberg city center – JOSEPHS®.

JOSEPHS® – the service manufactory

JOSEPHS® is a unique concept, where user and open innovators could come together with real consumers, consumers who could walk-in to the store as if they shop for goods and services in the city center. The ambience and feel is designed to look like a retail store with spots housing different innovators and a coffee shop at the entrance.

Set up by the Fraunhofer IIS in collaboration with the Freidrich Alexender University at Erlangen-Nuremberg in the city center of Nuremberg city, Germany; JOSEPHS® is envisaged to be a platform for bringing University researchers, Fraunhofer scientists, innovative entrepreneurs, and retail consumers to co-create services. Much like the prototyping TechShops, MakerSpaces, HackerSpaces, or FabLabs for designing products, JOSEPHS® aims at integrating users (randomly walking in) with innovators; a micro-factory for services.

In order to attract walk-in customers, JOSEPHS® has a coffee shop at the entrance. In order to sustain the innovation and create spaces for co-creation, there is denkfabrik, a workshop space, and meeting areas.

Please visit the website of JOSEPHS® at http://www.josephs-service-manufaktur.de/en/. For more information on how the concept works, you could watch the YouTube video at https://youtu.be/eoW3zJkYqzw. [If you would rather watch it in German, please visit https://youtu.be/MIwKdYa3_9A and https://youtu.be/0ndvx-LrBBI]. If you are an academic and want to learn more about JOSEPHS® and teach about it in your class, you can download a copy of my case on JOSEPHS® from the Harvard Business Publishing for educators at https://cb.hbsp.harvard.edu/cbmp/product/IMB567-PDF-ENG.

[Disclaimer: I am a visiting professor at FAU, Nuremberg and have been involved in the conceptualization of JOSEPHS®, as well as the author of the case mentioned above. Read about my journey to FAU here. And about my course at FAU here.]

Summing up

So, why does Amazon open retail stores? How does FirstCry.com manage its online and offline ventures? Think small data. Time to integrate small data with big data to get real deep insights. In the next post, I will delve deep into the business model of FirstCry and elucidate the synergies between online and offline stores.

(C) 2016. R Srinivasan.