Predictive analytics are dominating the marketing ecosystem now, but do they give marketing professionals more insight into customer preferences than good old-fashioned human feedback? That depends both on the quality of the Big Data that generates analytics and the customer feedback intake mechanisms.
Predictive analytics are often presented as a cure-all for companies seeking to understand their customers better and improve sales. They can be incredibly useful—though, as a recent Gartner study points out, there is no magic bullet since every buying cycle is unique. That's one of the reasons it's so important to find a balance between Big Data and human feedback: Understanding the customer is essential for delivering a positive customer experience and driving sales.
But how do you gain insights into customer preferences?
Often on the opposite side of the ledger from Big Data proponents reside those who say it's always better just to ask customers what they want instead of trying to parse immense data sets to identify preferences. Those who are in the "customer knows best" camp insist that company growth depends on soliciting customer feedback and building a strategy around better meeting customer needs.
The truth is that both analytics and feedback are important in the quest to identify customer preferences and deliver a positive experience.
Why Companies Fail to Understand Customer Feedback
Companies that don't have a robust analytics capability are at a huge disadvantage in today's marketing ecosystem, and businesses that fail to fully understand customer feedback are also at risk.
One reason companies fail to find opportunities when they look through feedback for ways to increase efficiency is that they too often rely on a single department's account of the customer experience: sales. The sales teams' perspective is incredibly valuable, but now it's possible to gain customer feedback during the sales process and analyze technology breadcrumbs for a more accurate account of what's happening in real time.