Customer Feedback Sorted Automatically
First, collect the data…
Next, analyze it…
Then automate how you handle the feedback.
The latter is the latest move enabled by Qrvey, which harvests in-app feedback data for analysis and action. AutomatiQ, launched this week, automates this process, which was manual for users before this feature was added to Qrvey’s platform.
“The platform is not an application,” said Arman Eshraghi, founder and CEO of Qrvey. “In an application, you target a use case to automate. Our objective is to let the user use our platform to define the processes we are trying to enrich.” he said.
Feedback is the third step added to Qrvey’s platform, the first two being data collection and analysis. Prior to today’s announcement, users had to wade through piles of feedback data to understand the customer and then craft a response, which may not reach the customer in a timely manner.
Automation allows the user to set up a rules-based response system to suit the situation, Eshraghi explained. For example, a user may have a customer response query set up with feedback coming in on a scale of 0 (No) to 10 (Yes).
Anyone responding with a 9 or 10 is a promoter for the product, he explained. You can send them a request to write a review about the product. Those responding with a 7 or 8 are having a problem with the product, so maybe the user sends them a coupon to try it again, hoping for a better customer experience. And those rating the product with a 7 or less should have their feedback routed to customer service, just to find out what is the problem with the product.
Automation also speeds dialog. Manually sorting through feedback may add days or weeks before a customer hears from someone. Getting back to a discouraged customer a week later results in a dialog after the contest for that talk as lapsed.
How the feedback is done is also crucial. Pop-ups and e-mails may not be enough. What the user wants to achieve is a dialog with the customer, and that requires laying down a logical road map of questions and responses, Eshraghi explained. This capability can be enhanced via APIs, linking Qrvey with the machine learning component of artificial intelligence.
Smart feedback can be guided by machine learning by having the app ask the right question of the consumer for every answer they give. An answer helps design the next question, Eshraghi pointed out.
The next big step for Qrvey is to use its knowledge of user cases and craft templates and turnkey solutions for each case, Eshraghi said. That should speed the time it takes to construct a solution that solves his problem. “The user logs on and sees the things that are most important to them,” he said. According to Eshraghi, Qrvey is looking at its market, figuring out which solutions it can sell directly, and where re-sellers can come in handy selling to market niches they know better.