Using A Data-Driven Approach To Unlock Customer Insights

Using A Data-Driven Approach To Unlock Customer Insights

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From companies offering complete transparency to anticipating their needs and offering an immediate solution, there’s no question that today’s consumers expect a lot from brands. From their perspective, they hold more power than ever before and are well within their rights to demand that businesses meet their needs in a predictable yet customized way. 

This is a tall order for businesses, but one that many can achieve by using available data in more meaningful ways. Using data effectively is so important that organizations that perform and leverage behavioral analysis of their customers achieve 85 percent higher sales than those that don’t use this tool.

Roadblocks to Using Data More Effectively

Despite the large amount of customer data available to the average company, most use only a small percentage of it when trying to connect with customers on new offers. For example, companies may keep data unnecessarily siloed, automate processes sporadically, and use inefficient legacy systems. Dashboards and models provide outdated information and the primary processes of the business require too much manual intervention.

Another common problem is that businesses don’t have a clear picture of the outcomes they’re trying to reach by optimizing customer data. Unfortunately, the result of all this inefficiency include leaving highly valuable data untouched and reaching disappointing sales figures. 

Knowing this, some companies choose to spend more on analytics tools that enable them to better target customers and understand market conditions. However, brands that perform the best don’t necessarily spend money on upgrades. They learn to analyze customer data more efficiently and obtain improved and more measurable value from it.

Understanding and Employing Cohort Analysis

Most business professionals have heard the term cohort and understand it to mean a group of people in a unique environment who eventually come to share several common characteristics. Cohort analysis, then, simply means to study the collective activities of a specific cohort. A cohort group typically includes several individuals who took a specific action around the same time or share one or more personal attributes. Cohort analysis can be useful when analyzing a single cohort or when comparing different cohort groups.

An example of a cohort could be a group of several people who purchased a new piece of technology within days of it becoming available to the public. Studying what this group has in common, such as higher income, the desire to own the best technology, and types of positions held, can provide valuable insights into how members of the same cohort might behave in the future. Essentially, a business is completing behavioral analytics by collecting this data in the first place and then measuring it against specific timeframes.

In a business setting, analyzing separate cohort groups is far more effective than looking at all customers together. Dividing customers into their biggest defining demographic like income or education level over a set timeframe is an example of cohort analysis in action. Brands can further divide their cohort analysis into one of the following:

  • Time-based cohort, which works by reviewing a specific month, quarter, year, or whatever other time segment works best for the company.
  • Segment-based cohort, which focuses on one or more specific characteristics identified in the dataset.

With these customer insights uncovered, brands are in a better position to proactively offer customers solutions to problems they didn’t know existed yet.