We’re learning the hard way that big data isn’t always better. And it doesn’t always translate to accurate insights. In fact, studies show a wide trend of mismanagement when inexperienced teams try to interpret tons of data. Most organizations are still in the transition phase of implementing a data-driven business model, and for many, that means starting small is better.
Consider two well-intentioned teams: One has triple the amount of customer behavior data, but no infrastructure to draw concrete insights from it. The other has limited data, but a skilled team and appropriate systems in place to take actionable steps on all of it. The first team is unfortunately in over its head, while the second team is thriving – by thinking at a more granular level.
Narrowing down your focus to a few key areas that drive performance is better than trying to achieve everything at once. By examining companies who’ve used data analysis successfully in recent years, we can see how a narrow focus is actually what brought them success.
Marketing expert Martin Lindstrom published his 7th book, Small Data: The Tiny Clues That Uncover Huge Trends, after noticing that the big data craze was overshadowing the potential that small bits of information have. In the book, he presents a series of case studies revealing how “small data” saved big companies.
“Big data” means large data sets that have different properties from small data sets and requires special data science methods to differentiate signal from noise. In other words, more correlations without causation leading to an illusion of reality.” – Michael Walker
Lindstrom’s take has been called more human-centric’ than traditional approaches because he encourages brands to pay attention to individual customer interactions. He advocates for smaller sample sizes and making data-based decisions only if it aligns with values and vision.
While it sounds obvious, it’s an approach that could easily fall to the wayside in the trek toward more and bigger data. Small organizations are often led astray from this tried and true approach of leveraging one-on-one interactions, instead feeling pressured to invest in technology that is too much too soon. And when different types of data insights are suggesting contradictory truths, it gets even more difficult.
LEGO was on the verge of bankruptcy in 2002 until a small insight revealed what drove families to purchase their toys. They’d been assuming that in the ‘instant gratification’ age, kids wouldn’t want to spend time building complex toys. Big data insights indicated that simpler toy sets would increase the company’s sales, but the idea proved false when sales plummeted further.
In an attempt to salvage the company, LEGO researchers went to visit customers in person, and one child’s story revealed an insight their current data wasn’t showing. The time it took to create a specific outcome was actually a source of pride for the kids – They didn’t mind the complexity, in fact, they preferred it.
The company recommitted to what they did best – toys with smaller, more complex pieces and saw a resurgence in sales. The Lego Movie was another result of these customer conversations that put LEGO back on the map.
We often hear about how small organizations can leverage big data, but rarely do we hear about how huge organizations leverage small data in pivotal ways. You may be tempted to think that large-scale quantitative data is best in every circumstance, but Lindstrom’s case studies prove otherwise. LEGO’s openness to exploring more qualitative data through personal interactions got them back in touch with their audience, saved their company financially, and helped them create a truly personalized customer experience. Win-win-win.
There is value in big data, but there is also value in small data, especially when it comes to improving your customer experience. Organizations that can pay attention to the little things and not get swept up in every big data trend are more likely to stay grounded and connect to their audience consistently.
As with any tool, data is only useful if the users know how to handle it. If there’s anything Lindstrom’s small data approach teaches, it’s that your data must be high quality – based on true connections and interactions with customers rather than impersonal assumptions. There’s so much data in the world, but not all of it is of equal value – and some if it may even contradict your next best step. If you’re working to revamp your customer experience and haven’t had success with traditional approaches, thinking small may be the answer.
Just because big data doesn’t serve every situation doesn’t mean you have to throw the baby out with the bathwater. AI and analytics can go hand-in-hand with this approach to ‘small marketing.’ With the one-on-one capabilities AI enables, you can exercise precise customer segmenting and glean insights from small groups of your audience. From here, you’re empowered to connect with each demographic authentically. This ongoing cycle of gathering small data, gleaning insights, and taking small action steps creates sustainability and clarity in your business strategy.