Enolytics Case Study: Direct To Consumer Data for a California Winery

A few weeks ago, a winery in California raised their hand and offered to contribute to the Enolytics data model.

“Great!” we said, and started working with information about their DTC program that they sent via Excel spreadsheets. We built an interactive dashboard that visualizes their data in twelve different sheets, from Key Performance Indicators (KPIs) to Year over Year comparisons to Average Pricing by Varietal. 

What’s cool, and useful, about the presentation of the data in this way is that each of those sheets can be sliced by variables such as Order Type (Amazon versus Tasting Room versus Website), Month, and Bottle Size. Infographics are generated from within the dashboard in real time, so they can immediately compare performance scenarios in rolling twelve-month periods.

Easily viewing monthly real time actionable business insights means that the winery can quickly take action when an issue or opportunity arises.

Which is a very useful tool to add to your kit.

Building the dashboards, however, is a capability that we are not seeing among many wineries.

(Is it something we can help YOU with? Let us demo the dashboards for you, and find out.)

Now let’s take it a step further, beyond the scale of individual producers.

The pain point for this particular winery is price elasticity. They need to understand what happens when wineries similar to theirs raise their prices.

It’s a tricky question, and it takes data to answer it.

Now that we’ve built the framework, we’re in a better position to integrate similar data from a variety of wineries in order to yield insights in response to the question.

We’re better able to help an increasing number of individual wineries and, as we do so, we become increasingly able to develop cohorts of wineries, which will yield valuable business intelligence for the industry.

A Note on Privacy: You’ll see that the graphic included with this post (Average Pricing by Varietal) is de-identified. You purposely can’t tell which winery’s data you’re seeing, because that winery naturally wants its data protected. When we integrate multiple data sources, we anonymize the data. In direct engagement with wineries, obviously the data is transparent.

Thank you, as always, for reading and I look forward to your questions and responses —