Using Small Data to Build Products Users Crave

In a world where big data and analytics is becoming the primary means to understanding our customer, the insights that can be discovered from careful observations of the user in their natural environment are often missed. In this concisely written book, you will see how Martin was able to uncover hidden truths about real people that would be impossible to find with big data alone.

Martin Lindstrom is a master of Marketing; using observations of human behavior, psychology, and culture from an outsider’s perspective to develop new concepts that captivate audiences around the world. In Small Data — The Tiny Clues that Uncover Huge Trends Lindstrom tells stories about working with companies like Pepsi, McDonald’s, iRobot, and Jenny Craig when he was hired to visit the homes of their users to gather and mine small data that ultimately turned their products around. His observational detective skills rival Sherlock Holmes, and his narrative was extremely captivating. It was a quick read that I did not want to put down.

Some key takeaways that anyone in Product Management or Marketing can benefit from are shared below, but I encourage you to make your own interpretations by reading the book yourself.

There is a lot that big data can tell us from a macro level about a segment of a population to infer characteristics about the individual, but there’s a lot that cannot be uncovered from big data alone. Big data is great at identifying what users are doing, but it cannot tell you why users are doing it.

In one case, a bank used big data to identify indicators of customer attrition. They found that customers would move around their assets shortly before canceling their bank accounts. The bank initiated a customer retention strategy that involved sending these customers a letter urging them to stay with them. What Big Data missed, however, was that those customers identified with big data were actually preparing for a separation from their partners, and the letter from the bank only added to their stressful situation. If some qualitative observations were made in conjunction with the big data trend spotting they could have avoided this awkward situation.

In another example, Lindstrom used big data and small data to identify how adolescent girls in Brazil were selecting their clothes every day to build evidence for the rebrand of a popular clothing store. Lindstrom found that that the young girls started waking up hours before school in the morning, but they were not arriving at school any earlier than before. He requested the detailed phone bills of the participants (big data) and learned that their phone usage peaked around 6 am when they were waking up. Upon interviewing the students (small data), he learned that the girls were taking selfies and sending them in group chats every morning so that their friends could approve or disapprove their outfits. He used this finding to come up with a new concept for changing rooms in the stores that offered a platform for consulting with peers on their outfits before purchasing them.

The twin self represents a person’s inner emotional age, opposed to their chronological age. The twin self often represents the deepest inner desires of who an individual wanted to be as a child, and usually is marked by a moment of transformation in early development. You can often determine a person’s twin self by identifying small clues in their behavior or in the things they purchase. In addition to our twin self, we also have different personalities to reflect our different “Brands” to different audiences. Sometimes the brands conflict with each other, but we are never as simple as we appear on the surface.

One way to identify these different twin selves or brands are to observe what people purchase and where they display them in their homes. If something is clearly visible in their living room, it may represent how they want to be perceived to their guests. If something is hidden in the “off limits” areas such as bedrooms or refrigerators, these items are more likely to represent their true self that is concealed to guests. One example in the book was how a grown man without children had toys on display in his living room. His niece and nephew only visited 3 times a year, so it was perplexing why they were out in the open. After further digging and small mining, Lindstrom discovered that he was doing this to appeal to women when they visited his home.

In another example, Lindstrom noticed that every Roomba owner he interviewed purposely displayed a portion of their Roomba from under the couch so that just a little bit was on display. He also observed that every time visitors were over, the Roomba would be on and they would treat it like a pet. It’s interesting to see how these users wanted to be portrayed to their peers as someone quirky, innovative, and emotionally attached to inanimate objects? These findings support that iRobot should cater to the cute side of their devices more than the functional side in their marketing and in their products.

Once small data is collected, it needs to be distilled and analyzed for the insights to be understood. Lindstrom outlines his process for Small Mining with 7Cs. He outlines them in the illustration below:

Try this process on some of your own qualitativae observations you’ve made in your market/product research. Sometimes, things may seem to be jumbled and non-sensicle, but with careful observation, a narrative will usually be formed.

If you are an observer that is familiar with the brand or the culture you’re investigating you will be at a disadvantage. Most of the small data clues are completely hidden from us and can only be found if something is new or unusual to you as an outsider.

Lindstrom strongly believes that there are only between 500 and 1,000 truly unique people in the world. This is such a strong statement, as it means that we are all much more connected than we think we are. With these 500–1,000 variations, Lindstrom believes that we can ultimately be divided into four main criteria, and we are all the same aside from these variables.

Martin describes how some of the most meaningful clues in his projects were almost missed. When observing home life in rural eastern Russia, Martin almost missed the use of fridge magnets to represent desire to be somewhere else in the world. It also took a lot of time to notice that despite being told the daughter-in-law and the mother-in-law in Indian homes ran the kitchen concurrently, it took a very careful observation to notice that the placement of the spices meant everything. The mother in law spices were full of vibrant colors and were placed right next to the stove top. The daughter-in-law, on the other hand were much duller in color and were also farthest from the stove top. The extremely subtle observations were major contributions to his projects but he strongly believes that he could have missed them if he wasn't as persistent and careful in his observations.

This should be a lesson in perseverance. There is always small data to be found, and with an open mind you will find it!

Although many assumptions have been made from the small data analysis present by Lindstrom, it’s clear that his deep understanding of the human psyche has lead to building successful business models to reinvent companies. It may not be feasible for us all to act as objective cultural outsiders for most of our customers, but it is a methodology that we can do our best to apply when building products that our clients will crave to use. Big data is extremely valuable, but without Small Data to help us identify the meaning behind the numbers, we may miss the most important clues that will prompt desire in our users.

Stay tuned to our channel see how observational research methodology was applied to build evidence to support our mobile strategy.

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