Breaking through traditional customer segmentation for a global tech giant.

Going beyond traditional demographics

Our client was part of the online music streaming team for one of the largest tech companies in the world. Their main goal was to improve their product and make it more relevant in the crowded market. The key objective for our client was to understand the behaviors and usage of the product and where any unmet needs were. The specific research questions we needed to answer were:

• Why do the client’s customers listen to music?

• Which music moments play pivotal roles in their music journey?

• When and where do those pivotal music moments take place?

• How do various music moments impact how music is consumed?

In answering these questions, the client wanted to build unique customer segments to help their product development team create new, targeted product offerings for the right audiences. The issue with traditional segmentation, though, is that researchers receive surface-level answers based on general demographics. Music crosses demographic barriers easily. So instead, what the client needed was a deeper understanding of how and why their audience listened to music, from time of day to the emotion that drives them to hit play on their device. Our AI Open-End™ technology and our experienced analysts helped their team uncover just that.

Understanding customers at a deeper level

In order to dig deep into the minds of our client’s customers, we relied on several key AI Open-End™ questions to identify the key behavioral and attitudinal drivers. The open-ended questions presented an understanding as to why people listened to music in different situations. Some of these reasons included worship, getting energized, or looking for background noise while doing a task.

We then clustered the statements crowd-sourced from survey respondents into themes, giving the client’s team an overview of the main behaviors and drivers. The themes we subsequently converted into scales were fed into a segmentation algorithm. The different customer segments emerged as compact clusters of music listeners with similar behaviors, attitudes and experiences.

Our detailed process helped us build eight distinct customer segments that matched closely with specific life occasions and behaviors. They built customer profiles based on why they listened to music, when they were most likely to listen to music, how they felt when they listen to music, and what devices they used for listening.

A better music listening experience

From this research, our client’s product development team gained a more actionable understanding of who they were building their product for. This will assist them in creating new product offerings that tailor to their various audience members’ needs. GroupSolver® gave this client the action items they needed to make a difference for their customers—while getting a high ROI on their efforts.