Below is a message from Gwynnie Bee CEO Christine Hunsicker:
I wanted to share some details about our merchandising testing and why we do it.
Our goal is to carry the best possible mix of brands and items that will make our overall member base as happy as possible. There are hundreds of brands we could carry and thousands of items we could buy from those brands. Since the buyers have a limited pool of dollars, they need to be smart in how and what they buy. The last thing we want to do is disappoint our members by having too much of something and not enough of something else. In order to maximize the allocation, the buyers need to make the best decisions they can.
In order to remove human bias, we take a data-driven approach and take into consideration things like:
- How popular is a brand?
- What styles are people closeting?
- How does a style fit?
- How durable is the style?
- How did people rate the style?
- How many people purchased the style?
- Does the brand help attract new members?
- Does the brand impact retention of current members?
While some of the data can be collected relatively easily, the last two points are not so straightforward. Acquisition and retention metrics require looking at data over time, which then introduces “noise” into the data.
For example, let’s say we see retention rate go up when we launch Style & Co. That would seem to indicate that we should buy more Style & Co but it’s not that easy. In August, we launched our Loyalty Rewards program, released a new Android app, increased our success rate of sending out prioritized items, altered how we visually merchandise the garments and know that teachers ramp back up to use the service heavily.
In order to understand the impact of solely the brand or the style, we have to construct tests that allow us to control for all of the “noise” that is going on at any given moment in time. To do this, we randomly segment the customer base into group A and group B. Each group sees the same number of styles per launch. Some styles are common to both groups and others are different for both Group A and Group B.
Currently, we are testing the impact of workwear and 3 specific new brands. For instance, in a launch of 9 styles, everyone would see the same 5 styles and Group A would see 4 test styles (Enlo, Vince Camuto, and Kasper) while Group B would see 4 styles from brands we already carry. And yes, that means Group A is not seeing 4 styles, and Group B is not seeing 4 styles. However, once the test ends, everyone will have access to these styles and brands. If the test is successful, we will then make these new brands part of the regular rotation.
We believe that running these tests before committing millions of dollars on a new brand is the best thing to do for the member base. If there were a better way to do this and get accurate results, we would do it. If we don’t get accurate results, the assortment could shift dramatically in 6-9 months in an incorrect direction based on dirty data, ultimately disappointing our members.
But we also know that we need to maintain our customer experience. Therefore, we have to find a compromise. Today, that compromise is a New Arrivals board on Pinterest that gives members direct access to all the launched styles, regardless of group. We know this won’t satisfy everyone and is not frictionless, however, it is the best path we can offer while maintaining the integrity of the test and data.
We continue to explore all possible avenues that allow us to gain data, while better serving our members. At the same time, I am completely open to suggestions that meet statistical rigor. The long-term benefits are significant for the entire membership, and that is what we are optimizing for through these tests.