Shinesty is one of 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 Looker and Panoply customer - and uses this data stack to visualize and coalesce data from dozens of sources.
In a recent interview with Bob Vermeulen, Shinesty’s Director of CRM, Bob shares his background, his role at Shinesty and how he’s pushing data intelligence at the small but data-rich internet retailer.
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, the Boulder-based company realized they had an increasing need for business intelligence insights.
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 instance, we were limited to only being able access our own database and were confined to the given 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. In that reality, my requests 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 platform.
There were a few things that initially attracted us to our chosen data solution.
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 from:
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 and Looker - which allows us to plug in web tools/services without involving IT or change requests.
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 - 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.
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.
We’re excited to look at the new data we’ve realized and see what else we can do with it.