We’ve gone on the record before to state how important it is for companies to analyze their customers’ feedback if they want to remain competitive. Your customers know what they want and what they don’t want from the companies they buy from – and they’re not afraid to share.
The good news is that more companies are implementing solutions that will help them understand all that data. However, as with any new solution, questions may pop up regarding best practices and things to avoid.
With that in mind, we’ve compiled the top questions we’ve been asked as companies embark on analyzing their customer data – and have of course included our answers:
1) Is there a specific type of customer data that provides the richest insights?
Short answer: unstructured trumps structured.
While structured data (think multiple-choice questions, ratings, rankings, and scales) is easy to analyze and produces presentation-worthy statistics, it’s not so great at capturing why your customers respond the way they do.
Unstructured data, like open-ended survey responses or product reviews, often contain those deeper and more actionable insights. Of course, the longer and more detailed each piece of data is, the better. Customer feedback that is at least a few sentences long is a richer source than a single-word response or brief tweet.
2) Apart from surveys, what are some other good sources of unstructured customer data that my company can use?
Chances are, your company has gathered customer data in at least a few of the following ways:
- Call center transcripts
- Product reviews
- Webchat transcripts
- Customer records
- Emails
These sources, and anything else that has been written or transcribed from voice to text, is a potential source of insights.
3) What is the right volume of data to analyze?
As few as 300 pieces of customer feedback (a review, a survey response, etc.) can yield statistically significant insights. The more information you have, though, the better. Any feedback analytics solution you choose should be able to handle a range of data, from as few as a couple hundred pieces of feedback up to millions.
4) How soon will we receive insights?
The time it takes for organizations to receive valuable feedback has changed drastically over the past few years. Thanks to machine learning, computers can automatically identify major themes in the data and begin returning results within a few minutes of processing the data.
5) What are some common mistakes other organizations have made when analyzing customer feedback?
- They aren’t willing to change: Organizations that get the most value from their customer data are those that are committed to taking action based on the insights they uncover.
- They don’t structure questions in a way that gets them valuable data: Avoid closed-ended questions (like “What brand do you use to moisturize your face?”) that won’t give you much insight into the why your customers choose a particular brand. Instead, prompt your customers to go into more detail using open-ended questions (“What brand do you use to moisturize your face and what do you like or dislike about it?”).
- They don’t know what they’re trying to achieve: Have a clear goal in mind before you begin analyzing customer data. This does not mean that you should have a bias about what your data will tell you; rather, you should have a direction for the analysis that is more concrete than, “What are our customers telling us?” Consider what the company is trying to achieve strategically and how analyzing customer data can support that.
6) How will I know if it works?
Success looks different in every organization. Before you take action, make sure you know what you’re trying to achieve – and what metric or KPI best represents this in your organization. Examples of such metrics could be NPS scores, the percentage of escalated calls in a call center, employee or customer churn, etc. Once you’ve settled on a metric, continue to gather and analyze customer data and to track that metric to track your progress.
To learn more about how Luminoso is helping customer experience teams act on the feedback they receive, check out solutions page, and be sure to follow us on Twitter @LuminosoTech
