Those of you familiar with Klaviyo are familiar with analytics screen here. You can see it’s a pretty typical one and there’s really not that much that you have available to you within the Performance Dashboard. You have a little bit around conversion and campaign performance.
When you get to Custom Reports, you can see it’s really a glorified export tool.
So, while it’s called a Custom Report, you’ve got pretty limited amount of information, which is why we developed in Nautilus a pretty robust analytics tool for Klaviyo and we’ve segment things out by Campaigns and Flows.
Get a sense of some of the types of things that you can see here.
First, there’s a Campaign Dashboard which, let’s say, we’re looking at the last 30 days here and what you can start to see is just to get a sense of a lot of the High Level metrics that most marketers and email marketers want to take a look at – Sends vs Delivered, Unique Opens. Obviously a little bit different now with iOS 14 and some of the changes that Apple has made but everything clicks.
You can see down to Revenues, Revenues & Recipients, Conversion Rate, Average Order Value (AOV), First Time vs Repeat Revs, which is frankly not even available in Klaviyo.
On any of these you can click through and that will then lead you to a report where you’ll get a sense of all the campaigns that ran in that date range, and then all the metrics that were on those graphs, you’ll have them by campaign, including the List & Segments. You’ll be able to see all of these all the way across.
You’ll also be able to do something that you can’t do in Klaviyo, which is export all the raw data. One thing you cannot see in any of these reports is what are the orders that have been attributed. You get some basic level information around AOV and Count of Orders, but again, when you go into some of these campaigns, you realize that there’s very limited information for you to QA, which is why, again, we’ve made everything within Nautilus.
You can export all the raw data, see all the actual orders, dates, and, obviously, email address of the customers.
That’s the most basic level just right off the bat. Once you start to drill down, you’ll be able to see things that I think you would expect to see within Klaviyo’s reporting. Everything from Subject Line analytics.
So you can look at it at the Summary Level, as well as the Campaign Level. You can look at Send Time, as well as, Domain Level Performance.
I think about these as pretty basic and fundamental when it comes to Email Analytics, yet they’re very, very difficult to get access to if or you have to do it either manually or via CSV files versus in Nautilus, we bring in everything overnight.
So everything’s ready for you in the morning as of the day before.
You’ll see similar types of reporting for Flows just like we have for campaigns. You’ll actually be able to break down Flows – By Email. You can see not just the effectiveness of a flow, but all the emails within it. Then we start to get into some more interesting ones. You will start to get a list and segment history over time.
We have one here called Customer Life Cycle. This also gives us a flavor of how we start to marry data between Klaviyo and Shopify. For example, for anyone whose profile was created in the last 30 days, you’ll get a sense of how many people were added to your list during that date range.
How many of them are Non-Purchasers that have purchased once, twice and three times. What percent of them have moved on from a Non-Pro from, basically, Non-Purchaser to Purchaser.
To each successive state, you’ll see Total Revenues, things like AOV for these different buckets, LTV of these different folks. Then you’ll also get a sense of what kind of activity these had in different buckets. So you know certainly as people are further down the funnel probably means you have a higher chance of connecting and re-engaging with them. Versus if someone said no activity then, obviously, you got to get back in front of them and you got a very low probability of getting them to order.
Within the Lag report, that’s a similar one but that provides a different view, which is essentially how many people have been added to your list. That’s the same one we just looked at, but you’ve got it broken down by month. In this case, you’ll also be able to see how long did it take from the time their profile was created, essentially, when they were added to your list.
So when did they convert? So, let’s say, for the folks that were in April 5th through April 30th, 16,000 of them had been added to the list of which 7,700 of them have not purchased at all.
Then you can see the bulk of the purchasers came on day zero, which effectively means they created their profile the same day concurrent with purchasing. Then about another 10% beyond that have ordered in the following 30 days.
Obviously, as you go further back, you’ll get some trends to see if you’re not converting people. If we go back even to the last 90 days, we’ll be able to get a sense, let’s say for February folks, 50% of the list has converted on day zero, and then you can get a sense of how many more you getting over time.
So, effectively, how good is your nurture sequence? And if you don’t convert someone on day zero, are you able to convert them at all?
So these are some of the types of things that we can do with the Nautilus. Again, there are Campaign Insights, Engagement Metrics. You can look at top products and you can start to drill down into all of the raw data so that you can have validation and understand what’s going on at the order level as well.
