Shinesty is among the quirkiest brands on the web. The company sells clothing, costumes and general fun for those who want to stand out at a party. One look at their website, Instagram feed or Facebook page and you’ll see, this brand has a fun edge.
Shinesty is also a Panoply customer and uses our product to coalesce and transfer data from dozens of sources.
Today, our customer spotlight keys in on Bob Vermeulen, Shinesty’s Director of CRM. We had a chance to sit down with Bob and discuss his background, his role at Shinesty and how he’s pushing data intelligence at the small but data-rich internet retailer.
Tell me more about yourself and Shinesty!
I’ve been at Shinesty for a few months. My background is in big data and running direct marketing analytics for 20+ years. Shinesty is about four years old and we’re growing really fast. In 2018, our company realized they had an increasing need for business intelligence insights.
What is data and business intelligence like at Shinesty?
When I was first brought on, Shinesty used RJMetrics, which is a great solution for, say, a 5-20 member company that just needs basic information about their sales - but that tool doesn’t have the flexibility we needed for this stage in our business. For example, with RJ, you have limited access to your own database, they do their own data model. As a result, as a BI pro, I’m severely limited as I can’t write back to the tables and you can’t make calculations or changes to the data model without jumping through hoops with their developer team. With that process, I get put into a queue - with the speed of business these days, I needed another solution.
For example, if I’m running an analysis and I put a request in the queue - by the time the issue is executed it’s been one to two weeks and we’ve moved on to wanting other types of insights/reports.
So, Shinesty knew they needed a different solution and I was brought on board to help that transition. We’ve gone down the path of choosing a three-vendor stack (at least for the time being). We have data integration and warehousing with Panoply, supplemental ingestion via Fivetran, and Looker as our data visualization tool.
There were a few things that initially attracted us to Panoply.
- The vast amount of connectors available into the data warehouse
- The fact that they’re all included in a predictable price
- The warehouse was super easy to setup and data is immediately available
Panoply has done everything to make it easy to spawn a warehouse, import data and get to work. The ease of use with Panoply is outstanding.
What decision factors led you to Panoply?
Immediately when we went through the trial with Panoply - we also tried Snowflake and Fivetran’s BigQuery solution. And by far - Panoply was the easiest.
We were literally up and running within minutes of signing up - having data being piped in was amazing. Also, the fact that you didn’t have to have a database administrator (DBA) background was perfect for us.
The competitors have their pluses and minuses but we needed something quick, easy, and affordable. Panoply checked all three of those boxes. With Panoply - things were so easy. You simply type in your username/password/host address and boom - data was in the place it needed to be, instantly.
In our case, Shinesty is a company of 30 people—we have no IT person—we have one Shopify technical resource and I run BI...so our tools need to be easy for us to do our jobs.
Tell me about your data sources.
Our data needs started on the marketing side with ads and web analytics and now that we have a bonafide data stack in place, we’re adding new data sources all the time (operations, merchandising, app performance, etc.). In Panoply, we have data coming in from from:
- Google Analytics
What are the goals for your data stack?
Our biggest goal is democratizing data - giving dynamic dashboards, insights and the ability to explore date to as many Shinesty employees as possible. We’re a small yet nimble company and we have goals we need to meet - and putting data into the hands of those daily decision makers is huge to us. After that it’s speed to insights and the flexibility to do more complex analyses.
Now that we have more data integrated, we can use insights to better target and mix our paid budgets. Soon, we will we better understand our various customer segments across touchpoints, as well as predictive analytics so we can be even more forward looking.
Before, we had basic analytics, but now we can track which channels are performing best - and whether people coming through those channels are lower or higher lifetime value targets for Shinesty so we can invest or course correct according.
In the new future, we’ll start tracking which inputs yield the highest value customers for us and which signals predict product success. These are things we could never imagine with our old solution. Also - we’d like to create propensity models and add those to our emails and customize our web page based on your buyer profile. But, to do any of these things, we need a flexible data environment - that we have now with Panoply - which allows us to plug in web tools/services without involving IT or change requests.
What metrics or formulas do you track an ongoing basis?
Before, our business channels and marketing spends were largely tracked at a high level. More detailed analytics had to be pieced together from one spreadsheet model to the next.
As such, we pulled metrics from the tools themselves (such as Google Analytics and Facebook) and imported them into spreadsheets. Now, we’re looking at 75% of the same numbers but in an automated, scalable way and our product and channel managers can spend far more of their time making smarter decisions because they’re not copying and pasting data into spreadsheets.
Which channels perform best for Shinesty for attracting customers?
We’ve developed a strong mix at this point. Though I’ll say the nature of our products and marketing biases us toward more visual channels (social, email, etc.). With the data, we can see the inflection points of which channels are attracting the right customers right now. In general, our focus is on entertaining our customers, that way when they’re interested in purchasing a product we haven’t burned them out.
What has been the biggest ‘aha’ moment since adopting Panoply?
I just got back from a two-week vacation. For me, the biggest aha moment was when no one has mentioned Panoply (as a problem) at all since I’ve been back. The tool is performing and doing its job—materialized views are making our queries/visualizations fast and efficient. The fact that Panoply just works is huge for me and us as a company.
We’re excited to look at the new data we’ve realized and see what else we can do with it.