It’s rare you would ever come across a marketer or executive who isn’t well aware of just how critical it is to use data to make decisions. But, knowing that you need data and knowing what data you need are two very different things. It’s much harder to understand what information you need, how to contextualize it, interpret it meaningfully and, ultimately, use it to make meaningful decisions.
What’s the Harm in Continuously Collecting and Keeping Data?
More data is better, right? It makes sense—the more you have, the more you learn and the more you improve. Unfortunately this isn’t always the case. Many companies and leaders have found themselves data rich and insight poor. A company’s ability to compete is increasingly driven by how well it can leverage the right data, apply analytics and implement new technologies.
Adding to this problem is data’s lifespan. The mantra, “don’t throw data away because we don’t know what’s important,” doesn’t apply to all situations. Like any trend, data becomes no longer relevant, inaccurate or outdated the further you are from when it was collected. Too often people struggle to make immediate decisions because they don’t have the right data they need in front of them. As a result, an opportunity is lost.
But you can fight back against analysis paralysis. Here are two questions to keep in mind so you don’t freeze up.
How I Collect Data Isn’t Really Important, Is It?
It really is. When it comes to choosing the right tools and techniques, there are no silver bullets. Uncovering meaningful insights and understanding an opportunity or challenge fully means using the right data collection methods and techniques. It’s important to retain an agnostic approach and work with measurement experts who can gauge the benefits and considerations of a particular method.
In market research firm CMB’s brand tracking work, whether starting a program or rejuvenating an initiative that is no longer useful, CMB takes time to determine which KPI’s and which methods will yield the most meaningful insights. They use multiple advanced analytics techniques including Chi-square Residuals and EMPACT to identify who and what the brand stands for relative to the competition, and BayesNets, TreeNet, Stuctural Equation Modeling and TURF to prioritize the brand elements that most motivate desired outcomes. Each has their benefits and limitations—so all of these must be considered from the start.
Am I Factoring In Context When Interpreting Data?
“Contextualization is crucial in transforming senseless data into real information—information that can be used as actionable insights that enable intelligent corporate decision-making,” writes Alissa Lorentz in Wired. If there’s no context, there’s no telling the type of data, when and where it was stated (and by whom), any outside factors that may impact the data and so on. A doctor wouldn’t make a diagnosis without first understanding the complete history of their patient. The same is true for data.
Depending on the type of data you’ve collected and your goals with that data, analysis can be very complicated or very simple. The analysis method that you will use depends on the type of data you collect and the indicators you are using.
In segmentation work CMB did with a popular millennial makeup brand, they were able to integrate data from social media—including popular forums, YouTube beauty videos and Twitter—to deepen the brand’s understanding of their target audience and better position the launch of their new palettes.
Let the Data Drive Decision-Making
Companies are leveraging data to automate processes, optimize selling strategies and enhance the overall efficiency of their businesses. In doing so, this data also helps companies achieve competitive advantage, reduce the cost of operation and drive customer retention.
No matter how much data you have, if it can’t be turned into a compelling narrative then you’re just wasting resources. Learn more about how you can harness the power of data to make critical decisions that drive growth.