Immerse yourself into a circle of wine professionals, connoisseurs, and oenophiles and you’ll generally hear something along the lines of this:
“If we just educate customers, they’ll choose better wines and naturally, they’ll gradually move up the value chain.”
I’m not saying I disagree that education is always a valuable tool – it is, trust me – it’s just that the journey of a wine consumer doesn’t always start with being more informed, it often starts by being more engaged.
That’s where our industry has a major problem.
Let’s go back to the pyramid of influence so we can see how a consumer’s passion for a product comes from increased exposure to that product. The movement up to the top two tiers is self-motivated, where the bottom two are stimulated by increased experiences, recommendations and trial.
Take a closer look.
There are four primary dimensions we use to understand the characteristics and profile of a wine consumer: Brand Attitudes and Opinions, Wine Discovery Behaviors, Wine Shopping Behaviors, and Sales.
Three of these can be found from a single type of data source. To truly unpack the trio of activities, we have to examine the minimal way the “Wine Interested” and above consumers might interact with wine — through a wine journaling app. This is where people record the wines they love, or they hate, and it’s a common tool that people use to picture scan wines to help them make a purchasing decision when they’re not sure what to pick.
Since “Wine Aware” and “Wine Engaged” are so closely bundled together in the Pyramid of Influence, this is one of the ways we can extract this consumer segment based on their behaviors.
From there, we look at the lowest and most common form of engagement, a wine scan.
A wine scan is where a consumer takes a picture of a wine label to match it for either purpose above. For a “Wine Engaged” consumer, this is the simplest weight way that they can engage with wine. It represents a series of questions like, “Is this wine good or bad?” or “Who else likes this wine?” or “What rating do other people like me give this wine?”
The second behavior from a wine journaling app is a rating. A rating, depending on the app, can be anything from thumbs up or thumbs down, to five stars, to a numerical score.
The most important aspect of the rating system is that we have to assume the user has consumed the wine before leaving their rating. This isn’t due to direct attribution and validation, but it’s a reasonable assumption that if a consumer is rating a wine, they have experienced consuming it.
The third behavior is a wine review. A review is where a consumer enters a comment or some sort of tasting notes in the app. It’s more than a rating, it’s a textual representation of what they thought of the wine – how it tasted, how it smelled, whether they’d buy it again, etc. This is a higher form of engagement, and requires considerably more investment by the wine consumer than a lightweight behavior such as a scan.
Each of these activities requires an investment of time, which, as we know from data, the theme generally is the more time something takes, the fewer people will use that functionality – unless of course, there is a meaningful exchange of value.
From a frequency perspective, think of these engagement behaviors in the following terms:
Scans > Ratings > Reviews.
Like with any tool, there are a couple of caveats to the category of wine journaling apps. With this data set, it’s important to note that it skews to lower-end consumers, by that, we mean the majority scanning wines are under $20, not necessarily our high-end wines.
Another few caveats: this is currently only a US wine consumer perspective based on a sample data set of approximately one year’s data, a snapshot in time.
Our data science team has developed a wine consumer segmentation model, in which we divided all the consumers into three key clusters of engagement and focused on the annual behaviors described above (scans, ratings, reviews).
Low Engagement: 0 – 9 scans
Medium Engagement: 10- 99 scans
High Engagement: 100+ Scans
Pairing engagement to the consumer segments results in something like this:
Low-Engaged Users = Wine Engaged
Medium-Engaged Users = Wine Enthusiasts
High-Engaged Users = Wine Leaders
As you can see, the bands expand significantly from 9 points of data to 90, all the way to infinite.
As you probably know, the 100+ scans are limited by how much a consumer would drink in a year.
As you can see, the low-engaged users represent the majority of consumers, and the cliff from low to medium engagement is even more severe than the Pareto Principle. It gets even more extreme when you consider medium to high engaged users.
As we dig deeper into the behaviors, we see even more striking fall-offs.
Only 13.6% of low-engaged users will rate a wine,, and of those, only 5.2% will write a review.
With medium-engaged users, you see a slightly higher rating percentage at 22% of rating the wine. However, there is a much higher ratio of consumers who rate and will also review the wine.
Finally, as expected, users that scan a large number of wines tend to rate and review them at a much higher frequency. They also have a much greater ratio all the way to the review.
If you line these users up to the consumer segments, the high-frequency users are the Wine Leaders.
When you look at actual chart representations of these three clusters, you can see that the precipitous cliffs from types of wine consumers and better understand how they engage with wine. These behaviors won’t change or be overcome by education, they will only be solved through engagement with wine.[Volume of Total Users, Total Scans, Total Ratings and Total Reviews by User Group]
A different view of the same data:
The future for wine industry growth is going to rely on how we create sustainable ways to engage with both current and potential customers.