If you have a problem there’s probably an algorithm to help you solve it.
As a marketer, your first question that needs a solution is something along the lines of “who am I speaking to?” Simple enough, but there are so many possible combinations that make up the “who.” To make matters more complicated, we need to further ask questions like “why would my audience listen?” and “what approach will work for them?”
The good news is there is actually an algorithm for that, and it’s called cluster analysis. Clustering uses an Unsupervised Machine learning algorithm to identify complex patterns within data sets. Ultimately, clustering can help you find similarities and associations between attributes such as demographics, purchasing behaviors, and beyond to find significant similarities among segments. It’s like next-level marketing segmentation and persona building.
While the algorithm can unlock answers, brands must know what they want to get out of the analysis. As much as you want AI to hand you everything, it can’t, and you have to feed it the right data sets you want interpreted. This means finding the right types of attributes to correlate based on your marketing goals and objectives.
Here are some examples of how brands can use cluster analysis to personalize and strengthen marketing strategies:
Perhaps you are about to launch a new product online and you want to segment your audience. By using a combination of demographic data such as age, household income, along with historical behavioral insights such as purchasing habits and preferences, you can pinpoint homogeneous segments within the market. Moreover, you can dive deeper by identifying tendencies and those that would be early adopters.
Your brand wants to find ways to increase customer lifetime value and build loyalty among current customers. Before you invest in a loyalty program or tactics that you think customers want, a cluster analysis can explore niche audiences within your customer base and characteristics that will allow you to connect with specific groups. You might find that your younger customers are frequent users or perhaps your middle-aged customers are willing to spend more but are moderate users. You now have actionable information to make better decisions.
Data Camp provides in-depth visualizations of clustering in action via Tableau. The scenario is a travel company that needs to identify markets with a high success rate for senior travel so that they can expand to new countries. Using demographic data and world indicator data, clustering finds parallels for the business.