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.
Another important component of success, demand generation is singularly well-suited for analytics for the simple reason that if you can figure out how customers hear about your company, you can more precisely target marketing efforts and advertising dollars to the most fruitful channels. But it's important to keep in mind that data quality is the key to success. For effective demand generation, companies need access to complete data, or they'll be making a decision based on only a portion of the relevant factors.
The same is true of customer satisfaction. Customer feedback mechanisms must be well-designed and comprehensive to deliver actionable data. Many companies still rely on annual surveys to measure customer satisfaction.
Though the data generated from a yearly survey can bolster the case for an executive bonus or provide a fig leaf to managers who insist that they care what customers think, annual surveys are virtually useless as a tool for measuring and improving customer satisfaction. To be effective, feedback should be solicited in a timely fashion (ideally at the point of the customer interaction) and acted upon immediately.
Both analytics and customer feedback can provide a rich portrait of customers and potential customers. In fact, companies that develop an advanced data framework convert feedback into data that can be folded into broader analytics.
Tips for Addressing Challenges
Here are some tips that can help bring the challenges involved in analytics and feedback interpretation into focus and address any shortfalls associated with current practices:
- Create a central data repository. Customers find it frustrating to have to provide the same information over and over, so keeping all data in one place can improve customer satisfaction while also giving marketers complete information to build models.
- Act on customer feedback and analytics. The focus should always be on taking action based on the insights gained, so make sure you promptly act when you get new information.
- Don't overlook the value of "small data." In the rush to capitalize on what we learn from analyzing huge data sets, we can lose sight of the value of individual customer feedback. But "small data" from customers provides an opportunity to create a brand advocate.
- Consider all perspectives. It's easy to fall into the trap of "group-think," but to successfully interpret analytics and feedback, it's important to listen to all voices. The person who goes against the grain just might have the correct interpretation.
- Create a continuous feedback loop to refine analytics and feedback. It's critical to test predictions against results. That's the only way to validate the analysis model and refine feedback methods. A continuous feedback loop improves results.
Marketing is more data-driven now than at any point in history, and marketers are becoming increasingly sophisticated about interpreting data with the help of new technologies and solutions that fuse Big Data technology with innovative analytics and visualizations to enable real-time exploration of granular data. Marketers who use analytics and fold in customer feedback are revolutionizing sales and enabling better channel decision-making.
But while embracing Big Data and associated solutions is smart, it is important to keep in mind that insights must be actionable. If you can use Big Data solutions to spot patterns and apply feedback to guide intuition, you'll find the right balance between Big Data and feedback in an increasingly data-driven marketing ecosystem.
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