Global brand consultant Martin Lindstrom thinks it’s fair to say if you take the top 100 biggest innovations of our time, perhaps around 60% to 65% are really based on Small Data
Big Data, or simply defined as sifting through enormous amounts of data for patterns in human behaviour, is one of today’s hottest business buzzwords. However, global brand consultant Martin Lindstrom, ranked 18th on Thinkers 50 list of the 2015 top 50 management thinkers in the world, argues in his book, Small Data: The Tiny Clues that Uncover Huge Trends, that it is foolhardy to base one’s business strategies on Big Data alone because, at best, it is an“idealised” snapshot of what people want.
Lindstrom, who has been called the Sherlock Holmes of marketing, has snooped about people’s homes in 77 countries, 300 nights a year, in the past 15 years. Today, he counts Lego as a longtime client, alongside Nestle, Pepsi and Porsche.
In this article, I share highlights from this book and seek to share Countly’s experience with regard to some potential applications that are facilitated by Digital Analytics and Behavioural tracking. I hope these serve as inspiration as you leverage your existing Small Data to plan more effective marketing.
Definitions of Small Data and Big Data
Big Data is all about finding correlations, but Small Data is all about finding the causation, the reason why. Small Data contrasts with Big Data, which usually refers to a combination of structured and unstructured data that may be measured in petabytes or exabytes relative to Small Data which is measured in gigabytes and terabytes.
Big Data is often said to be characterized by 3Vs: the volume of data, the variety of types of data and the velocity at which it is processed, all of which combine to make Big Data very difficult to manage.
A company might invest in a whole lot of server storage, and use sophisticated analytics machines and data mining applications to scour a network for lots of different bits of data, including dates and times of user actions, demographic information and much more. All of this might get funneled into a central data warehouse, where complex algorithms sort and process the data to display it in detailed reports. While these kinds of processes have benefited businesses in a lot of ways, many enterprises are finding that these measures require a lot of effort, and that in some cases; Similar results can be achieved using much less robust data mining strategies.
Small data is one of the ways that businesses are now drawing back from a kind of obsession with the latest and newest technologies that support more sophisticated business processes. Lindstrom thinks it’s fair to say if you take the top 100 biggest innovations of our time, perhaps around 60% to 65% are really based on Small Data.
The issue here is that as we become so obsessed with Big Data we forget about the creativity. Big Data is all about analysing the past, but it has nothing to do with the future. Small Data, which Lindstrom defines as seemingly insignificant observations seen in consumers’ homes, is everything from how they place their shoes to how they hang paintings. This is emotional DNA that will help to define a hypothesis first before you start to study it more closely and find potential correlations.
Lindstrom shares an interesting story about meeting the founder and owner of IKEA, Ingvar Kamprad in one of IKEA’s Stockholm stores. He found Kamprad sitting behind one of the cash registers and checking people out. When Lindstrom asked, “Why are you doing that?” Kamprad replied, “Because this is the cheapest and the most efficient research ever. I can ask everyone why they choose it and why they didn’t choose it.”
Three Killer Small Data Applications
“Big Data is useless, it is about large datasets that represent large groups of people with a large variety of behaviour. For most companies, their primary focus is to form connections with their customers.” – Devin Wenig, President eBay (Source: McKinsey)
Wenig shares Lindstrom’s opinion and feels most companies do not want to be part of a big dataset when their customers may be looking to buy a shirt. Such a question is about small data; it’s about non-intrusive gathering of user behavioural information to unearth insights that may not be noticed by customers themselves.
Let’s build upon Lindstrom’s and Wenig’s observations as we summarise three killer Small Data Applications:
1. Leverage and own your apps’ data
Lindstrom notes that smartphones can tell an enormous amount about who we are and what we’re dreaming about. At a personal level, we are constantly creating this small data each time we check in, search, browse, creating a unique signature that provides a glimpse into our digital and physical selves.
Behavioural data from your apps’ users is a black hole or goldmine of information depending on your state of digital readiness. If you choose to use free Analytics solutions like Google Analytics or Flurry, your data is the product as Google Analytics will leverage this to enhance its Google Adwords product. Owning your own data is also a great strategy since you get access to raw, un-sampled data and provide a solid foundation for both Small and Big Data applications.
2. Master Small Data before investing in Big Data
Remember Kamprad, IKEA’s founder, sitting behind a cash register? He had noticed that customers often do not fit cleanly into IKEA’s preconceived perceptions of a target audience and such variations never show up in POS data. For example, IKEA may be very interested in digging into what furniture purchased by singles and their reasons for such purchases.
I recommend starting with what one gleans from human tics, nuances and aberrations. A deep dive into a small sample of your target audience and understanding their digital behaviour is likely to help you to identify drivers behind usage patterns. This helps you to define a hypothesis first before you start testing it.
Given that Small Data is faster, cheaper and more intuitive, it makes sense to master Small Data first prior to selectively investing in Big Data to realise incremental commercial value. As a first step, you may wish to customise your own dashboard to bring metrics that matter to your business, e.g. no. of Trips daily, Daily Revenue, Cancel Ride, Total Crashes, for a Ride-calling mobile app, to your eye level to react to these metrics more quickly.
3. Enriching online experiences
Devin Wenig, president of eBay Marketplaces, had previously shared that he feels digital shopping can be both utilitarian and inspiring. “The next wave” for retail’s future will spur inspiration and imagination, capture experiences that happens in stores, “I went to the store to buy a pair of pants and bought a shirt as well that went well with it”.
Small Data is at the centre of Wenig’s vision: Let’s begin with the end-user, what they need, and how they can take action. Focus on them first, and a lot of our technology decisions become clearer. To create a holistic customer profile is easy but to inspire a customer is far harder. For example, we can combine insights from social channels and campaigns with mobile analytics and transactional data to build rich profiles but what is the right data that we can use to inspire customers?
I recommend that businesses pay more attention to these complex but valuable questions. Even giants like Amazon and eBay are finding hard to answer. Lindstrom points out that Amazon had opened about a half-dozen brick-and-mortar stores since 2015. Amazon may also have concluded that the internet is less capable of delivering certain enjoyable retail experiences such as spontaneous conversations with fellow shoppers on what they’re reading, and having a book cover or blurb grab you as you walk down the aisle.
Small Data: The Tiny Clues that Uncover Huge Trends is a quick read, just 245 pages. I recommend you read it at one sitting and take notes on interesting points that you may be able to apply at your company. At the very least, your “observation-sense” should be raised after reading.
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