How Feedbk came to be

Posted by

At retailcloud we love lists; our support team keeps a list of all features that are requested by users, our partner team keeps a list of what is important to our partners and our product team keeps yet another one on what are key industry drivers.  

Recently our AI and analytics team was playing with some models to mash up all the data and see what features should be developed based on these three lists as well as what could have the greatest impact on our customer’s bottom line.

If you have been following our blogs you will see that we, and our industry, have been talking a lot about the impact of customer retention and building brand loyalty and experiences,  and so while a feedback and  rating system was not on our product roadmap we were not altogether surprised that our AI modeling told us that an application where customers could provide instant feedback to merchants, AND where merchants had tools and insights to make customers into fans made sense.

And so feedbk (pronounced feedback) was born; feedbk is a real time rating application that allows merchants to easily obtain feedback from their customers at the Point of Sale. It keeps track of 3 metrics for a retailer;

    • NPS (Net Promoter Score) that can be used to gauge the loyalty of a firm’s customer relationships and is claimed to be correlated with revenue growth. This is done at the POS and a single question is posed to a customer who gives it a rating.
    • CXS (Customer Experience Score) that is used to gauge customer satisfaction using email receipts and other online interactions to allow customer to provide feedback on a set of questions based on their initial ratings. These are customized questions and are used to give an average customer satisfaction score.
  • CEP (Customer Engagement Priority) that can be used to provide an operator with strategies on how to interact with various customer segments – this can be done instantly using ngauge or at a later date using social media, text  or email.

This feedback is automatically combined with segmentation which is critical in allowing operators to immediately identify problem areas and rectify them; it  is also able to provide retailers with the ability to gauge opportunity costs that are related to such issues. feedbk operates entirely on the retailcloud platform so it can provide operators with actionable analytics on how to increase units per transaction, identify ideal product mixes, and in general, identify their most valuable customers.

It’s just version 1.0, but we are excited about feedbk and the machine learning opportunities that come from it. We believe that it is the first of many pragmatic analytic solutions from our AI team that will allow small and mid size retailers to quickly access where their staff can best focus their efforts.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s