Krista Seiden, KS Digital – Digital Analytic Trends: A Changing History and What's Next

January 16, 2020

Learn from Krista Seiden, Founder & Principal Consultant at KS Digital, how the digital analytics industry has seen a lot of changes in recent years. Seiden will discuss:

  • Shifting trends in the analytics industry
  • New tools like Google's App+Web Tracking

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Hi everyone, this is Krista Seiden, founder and principal consultant of KD Digital, and I am so excited to be back today at ObservePoint's Virtual Analytics Summit. Thank you so much for having me you guys. Today I'm really excited to talk to you about analytics trends and what's next. So let's dive right in. So who am I? Like I said, I'm the founder and principal consultant of a analytics consultancy called KS Digital. Prior to that, I was VP of product marketing and growth at a company called Quantcast. And before that I spent almost seven years at Google, as the analytics advocate for Google Analytics as well as a product manager on Google Analytics. I'm a co-chair of the San Francisco chapter of the Digital Analytics Association and a huge advocate for women in analytics. You can find me on Twitter, @KristaSeiden, and on my blog at

You may actually recognize me more, however, from a bunch of videos you might've seen online. So these are the videos of the Google Analytics Academy. I spent many hours writing and filming this content to help teach analytics all over the world as well as a bunch of smaller, quick tip series videos. So if my voice sounds familiar, my face looks familiar, that's probably where you've seen me before. Okay. So let's dig right in. Today I'm going to talk about some industry trends. I've spent the past couple of months doing some research on kind of the different analytics platforms people are using and how they're thinking about analytics, and then what's next. And specifically this is going to be what's next when it comes to Google and their take on analytics, and I don't think we'll have time for Q and A here, but you know, just in case we have that too.

All right, so let's look at the industry trends. So analytics platform research results. Now this all started out when I sent out a tweet asking about, what is your primary form of analytics tracking? Is it an Adobe or Google Analytics? Something smaller, like an Amplitude, Mixpanel, or Heap? Or are you doing things in-house? Doing it yourself with a data warehouse or log files? Now overwhelmingly, the answer here was I'm using a major platform. I'm using Adobe or Google Analytics. Now, this isn't surprising for a couple of reasons. One, the free version of Google Analytics is on so much of the internet. But two, it's because this was my tweet, and I happen to have a large Google Analytics following. So you know, unsurprisingly, a lot of people replying probably are using Google analytics. I wanted to get a little bit more scientific here.

And so I actually put together a much longer form research in a Google Form that I sent out by Twitter and LinkedIn and Measure Slack. And I pinged a bunch of other analytics companies asking them to share it as well, because I wanted to make sure that it was a representative sample, not just across the board of analytics companies, but also globally. And so that is the research that we're going to dig into next. So like I said, a bunch of questions in this research, but I started with that same question, what is your primary analytics platform? Now I got about 250 respondents here, and I broke it up into a major platform, a niche platform, or an in-house or combination of in-house end platform. And so as you can see, Adobe Analytics, Google Analytics, GA 360, IBM Coremetrics, still a couple of holdouts there, fall into the major category, and that's the majority of respondents, 80%. But we do actually see more here that are using a niche vendor or in-house.

So niche vendors being Amplitude, Heap, Mixpanel, Snowplow, AT Internet, and a few others. In-house can actually be some kind of a combo solution as well using BigQuery or you know, Google Analytics plus BigQuery or something else to really get more powerful with your data. I do have to note here, I had a couple of angry emails from some lovely French people saying that AT Internet was a major vendor. I decided to keep it in the niche category, because it wasn't quite there. But there was a number of respondents coming in from the AT internet side that I do think we're maybe a little bit over weighted in this survey. So I've tried to call that out when I see it.

Okay. So the next question I asked is, if you are using a major vendor, which one? Now, unsurprisingly the free version of Google Analytics is number one here. And like I said earlier, it's on so much of the web, but it should be coming up in the survey results as the number one result here. But I think surprisingly for me at least, GA 360 was pretty close behind. I expected to see a lot more difference in those numbers. But perhaps a lot of agency people answered the survey, and so you know, they might have multiple clients, some using free, some using GA 360, and they just chose to answer as GA 360. A number of people using Adobe analytics and two holdouts using IBM digital analytics.

Okay. So the next question I asked is, what is your secondary vendor? And here we actually see a lot more variety in these answers. As you can see, a lot of words up on this word cloud, but if we break it down and look at the standouts, the actual first place standout here was that they're doing something in-house, doing it themselves. 32 of the respondents that said they are using some sort of secondary analytics platform were doing it in house. Next we had Mixpanel, Snowflake or Snowplow, and Amplitude, and one holdout is still using Kissmetrics. All right, so then I asked, what are the reasons that you are using a secondary platform, or why is that platform your secondary, not your primary?

So a number of different responses here. I'll just go ahead and read them through. So from a few people whose primary was GA, 360 and their secondary was Adobe Analytics. "Because my day-to-day job is done in GA and Adobe is our legacy tracking system, or there's a learning curve to learning Adobe Analytics. Flipping that around, primary as Adobe Analytics, secondary as GA 360. GA 360 is used by less analysts, or they're only using the free free version, or they're really only using it for the marketing audiences.

When their primary platform was Heap and their secondary platform was Google Analytics, they said, "well, product analytics is more important for us than marketing analytics," and this one, you know, this one hurts me a little bit, especially because I think, you know, Google Analytics has actually done a pretty poor job marketing themselves as more than just a marketing analytics product, that you can absolutely do product analytics with Google Analytics. But there's all of these new platforms that have come out and say, "well we are specifically for product analytics," and that message has really taken hold in the industry. Next, we have a primary platform of GA 360 and a secondary platform of AT Internet. And the response here was that, "well, AT Internet is not as matured, and it doesn't contain all of the features as my primary system or it lacks functions in usability or development."

Next, we have primary is Snowplow, and secondary is GA 360. And here they said, "well, there are just less use cases for GA 360. Snowplow is used for data science, data mining, and in depth analysis, and this is a trend we'll see through a lot of the data continuing through this survey. Then we had primary is Google Analytics, secondary is Hotjar. Hotjar is different kind of insights. It's used to enrich the qualitative analysis of Google Analytics in this case. And finally, the primary is do it yourself or Amplitude, and the secondary is GA. Both of these had reasons of they only used it for marketing audiences or to run ads.

So then I asked, are you satisfied with your current primary analytics platform or setup? And, you know, the majority of people here were actually fairly satisfied, I mean some were between seven and eight. And, you know, a lot of people on the upper side of the scale, 10 being very satisfied, 1 being very unsatisfied. Reasons why people were unsatisfied, slow innovation, outdated, needs more flexibility, sampling, hit limits, data silos, and data privacy and governance. A lot of those are hits at the free version of Google Analytics. Then I asked, how likely are you to consider changing primary analytics platforms in the next 12 months? Now, if you've ever been through a change of analytics platform, you know that this is a difficult and long process and not something that you want to go into with any question. And so it's actually fairly surprising to me to see that so many people are towards the higher end of the scale, meaning they are more likely to consider changing when we didn't see quite that same distribution with the satisfaction of their platforms. So I asked if you are considering changing, what is the solution you're looking to move to? So a bunch of people said Snowplow or in-house. A lots said Amplitude. Two people are moving to Mixpanel, two people moving away from Mixpanel, and a few people upgrading to GA 360. Now, overall, I saw a general trend towards either mixing your data sources and doing things in-house or streamlining into a single platform. So two kind of different strategies here that I see diverging in this data.

Then I asked, do you use a separate vendor or a platform for mobile app analytics? Now the majority said, no, we use the same platform or we don't have a mobile app. And some said, yes, we do use a separate vendor. And so for those that are using a separate vendor, I said, well, what is that separate vendor? A lot of respondents here said Google Analytics for Firebase. Now, admittedly this one might be a little bit biased in these results, considering I was the one that wrote the survey. Although, like I said, early on, I did try to get a representative sample of respondents here. But, you know, the next most popular here were Appsflyer, Facebook Analytics, and Kochava.

And then I said, are you planning to combine platforms to track web and app at any point in the future? And here, you know, the majority of people said, yes, we do want to combine those platforms and check them together.

So of course I asked, well, what solution are you planning to use? Overwhelmingly, again, Google Analytics App and Web, the new combined solution from Google Analytics. And this one might be a little over indexed due to who asked the survey, but based on the comments, I actually think that this is fairly representative, because the commentary around this was along the lines of, you know, Google just released this new thing. I'm really excited to see what it does, or I'm going to watch it and see if this is going to be a real solution for us, or I really hope that this new thing from Google is going to help solve, you know, these cross-platform, cross-device issues that we have. You know, other singnotes here, AT Internet. I think that one might be a little over indexed. But Snowplow. Doing something in-house, and Adobe. So my last question of the survey is, what trends are you seeing in the industry?

So the most common theme was GDPR, ITP, CCPA, etc. These new regulations coming out that are making it difficult to track and use data and this move towards a cookie-less world. Now, certainly this is difficult for everyone. Everyone is trying to have to, you know, figure out a new way of tracking that is compliant and good for the user and helps you still maintain the data that you need to operate.

The next thing that I heard was, I've heard a few people say they've built their own platforms and are now moving back to Adobe or Google. It turns out that maintaining a roll-your-own is hard. I've definitely heard this from people. Then segment Snowflake and Amplitude are pushing the trend towards more access to the raw event data, rather than living in the UI and schemas of the platform. Now this is a big one. I saw this come up in a number of different ways in these responses as well as a few other commentaries throughout the survey. And I think these three platforms in particular are really pushing the trend, as it says here. You know, they do give you access to a lot of that raw data, or you know, the ability to somehow manipulate it that, you know, tools like Google Analytics and the free version don't, you can't get that data out and you can't look at that raw stream of hits. You can in BigQuery if you're using GA 360, but it's still not quite as easy as you would like it to be, and it's not necessarily just everything as a raw event like some of these, you know, product analytic platforms market themselves as.

Then we have the growth of big data analysis and the power of visualization tools is making it easier to combine multiple sources of data to gain a clearer picture of digital marketing and voice of customer that is beyond what a single web analytics tool can provide. Now this one really speaks to that trend of needing more tools to be able to do your job or combining a lot of different data sources together. And then in-house analytics platforms, event-based solutions that are fully customizable. So this is kind of a build on the last one that we talked about, in terms of wanting access to that event-based raw data and being able to actually customize it the way that you need. And then I think this one's my favorite, Snowflake is turning into a blizzard heading down main street.

All right, so now that we've taken a look at the industry research and the data about kind of what we're seeing in how people are moving towards new platforms or additional platforms in their arsenal, let's take a look at what's next when it comes to Google Analytics. And that is Google Analytics App and Web. Now, if you haven't heard of this yet, it is a new property type in Google Analytics that was released in the end of July this year. It's in beta so it's not fully rolled out. It is still very much kind of in its infancy if you want to consider it that way. It's gaining new features all the time, and it's getting better and better, but they are still learning and figuring out kind of what is going to make this product very useful to their customers. But if you're looking at the screenshot on my screen right now, and you've ever used Google Analytics for Firebase, you might say, oh, this kind of looks similar. And that is because this actually builds and extends on the Google Analytics for Firebase data model. And that data model is all about events. So like some of the comments that we heard, App and Web is event-based. Everything in Google Analytics App and Web is an event. Even a page view is an event. And it's all organized here in this event table where you'll see everything that's collected in this account as an event, clickable into each of those events to see more details about them.

So there's three types of events in Google Analytics App and Web. There's automatically collected events. These are things that come out of the box as soon as you use the configuration snippet to get up and running with App and Web. Things like session start or first open, screen view, page view, you know, in-app purchase, those types of things. And then there are suggested events. And these are things that Google has put together in terms of a list of all of the events by vertical that you might consider using to track additional information. Now, I actually think the term suggested events is a little misleading. I would say that these are highly, highly, highly suggested events, and that is because if you are using a taxonomy that Google understands, then Google Analytics can provide reporting on that data in the front end user interface.

If you're not, and it's all custom, you're going to have to access that data currently through BigQuery. So the last type of event, custom events does give you the ability to log any type of event that you need. But as I just mentioned, these are not going to be available, at least for now in the front-end user interface of Google Analytics. They're only going to be available via the BigQuery export, which is actually free from Google Analytics App and Web, which is a nice bonus, but it does take a different skill set, and it does take additional setup. And so if you do want to be able to analyze your events in the front end user interface, I would highly suggest that you use the automatically and suggested events that come with App and Web.

So I wanted to go over a few implementation best practices, because the way of doing analytics with App and Web has changed. It's a completely new way to think about events in Google Analytics.

And that's because it's an event and parameter combination. So every event that you're logging can have up to 25 parameters that you log along with it. So I want to give you a couple examples. Let's say that you are a property booking app, like a or, and somebody does a search in your app or on your website, and it comes up with a list of different properties that match their search, and any one of those properties a user can click into, and you want to track that, you want to track if they click into these properties, and you can track that as an event, maybe something called a property view. And with that, you want to attach parameters, things like the property name, the property rating, property location, and property ID, any piece of information that you want to know along with that property view event click.

There's also a number of parameters that are attached automatically. Things like page name or screen name, screen class. So you don't have to actually think about that one, that will always be there with the event that you are collecting. Another example, if you're a publisher and let's say you're the New York times, and you have an article that is showing up on the homepage and the technology page. And the "new" section of the website and you want to know how much readership that particular article is getting. Now in traditional web analytics, you would have to go into a pages report and look for the page that each, you know, the article was on and there was multiple pages, and you'd have to, you know, do some calculations to figure out the total number of page views that it was getting.

Now in this case, you would actually use an event called a content view to fire when that article is being viewed, no matter where it is. And you'd have parameters like content title, content author name, author ID again, it has that page name or screening already attached to it. So you can do analysis by the page, but you can more easily roll it up into the total content view. Because as publisher, what you really care about is how many eyeballs are on each piece of content and how you know, worthwhile or valuable that piece of content is, that author is to your site. And this actually gives you a much better way of doing that type of analysis.

Alright, so now that we've covered that, let's go ahead and talk about some of the cool new features that are coming with App and Web. So if you log into Google Analytics App and Web after you've created the property and you're in your admin section, this is what you'll see. Now a couple of things I wanted to call out here. There's this new idea of data streams, and this is how you're actually collecting different types of data into the same property. So here you can see we are on the screen for web. That's the only thing I'm actually tracking in this property right now. But under the all data streams tab, you can actually collect multiple different streams from iOS, from Android, from web, all into the same analytics property. Now the other thing I want to call out here, and this one I think is really, really cool is this idea of enhanced measurement.

Now if you click on the settings icon in the bottom right of this, it will actually pop out this screen to set up enhanced measurement and it's just a bunch of things listed out that you can toggle on or off. And what this is, is it's Google saying, hey, these are some common events that you might want to capture. Things like scrolling or outbound clicks or site search or people downloading files on your site, and we know all of the common schemas that are used to track these types of things. So if you just tell us that you want to capture this, we can do it for you. You don't need to do any sort of additional implementation here. And this is really awesome in my opinion. I think this really helps to democratize data and to make your analytics implementation that much more accessible for a much larger group of people. Now you don't have to worry if you're a tag management specialist and your bread and butter is going in and creating tags to track all of these things individually, you can still do that. There's still plenty of work for you in App and Web. But some of these more common things, I think it's really great that they're becoming more accessible to more people.

Now to implement App and Web, there are some new tag types in Google Tag Manager. So in the upper left hand corner you'll see the three tag types now for Google analytics, the first one, Universal Analytics, that's just your standard Google Analytics tag. But the next two, they have a beta label, and these are specifically for App and Web. The first one is the configuration tag, and that's on the bottom left, and that is how you actually put the code snippet for App and Web on your page. You have a measurement ID that you would have gotten in your admin settings. You set it to fire on all pages and you have App and Web now running on your site. And then the second tag there, the app and the web event, the bottom right hand screenshot. This is how you actually go ahead and configure all of the different suggested or custom events that you want to collect in App and Web. So here you can see I have an event name of signup, and I'm using parameters for subscribed location as well as a page path to fill out the value there for this particular event.

A couple of other new things to reporting are some new metrics in Google Analytics App and Web. So there's now this idea of engaged sessions, and this is essentially a session that doesn't bounce, or it doesn't bounce immediately, and so this is set up to fire after someone has been on the site for 10 seconds. Now it's kind of an arbitrary amount of time, but we did actually do some research to see kind of how long it took for people to start interacting with the site or what point we considered people to, you know, really be there to engage with your content. It looked like it was around 10 seconds. This might change, or this might become configurable down the road. It remains to be seen, but I love this idea that you can now separate it out as an engage session, because one of the questions that I get all of the time is, how do I know that my users are engaged?

What if they're just coming in there and doing nothing and then leaving. And this is trying to help you answer some of those types of questions. Now there's also an engaged sessions per user. This is just an account of you know, engaged sessions over your total users, as well as engagement time. Now, if you've ever heard me speak before about Google Analytics, you might've heard me say that I hate the metric of time on site. I think it's a poorly calculated metric in Google Analytics. Now in Google Analytics for Firebase, when that came out, there was a new metric called engagement time. And what this was, was it was the actual time in the foreground of the app, and because of how people engage with apps and you generally only have one app in the foreground at a time, it was actually a very accurate way of knowing how much time that you have eyeballs on your app. And we actually use some of these same concepts to bring two browser tabs and how the browser works to try to get to a much better estimate of what the actual engagement time on web is. And so that is what this new metric is for App and Web, and I'm pretty excited about this, because I think it's a much more accurate idea of how much time you're actually getting in front of your customers.

Alright, there's also a new segment builder or audience builder here in App and Web. And one of the really exciting things to call out here, the dropdown that you see expanded, it says, within the same event, within the same session, and across all sessions. So this is essentially user, session, and hit level segmentation. Hit level segmentation has never existed before in Google Analytics. I know from my Adobe days and from my friends using Adobe Analytics, this is definitely something that's highly requested and used a lot in that platform and others. And so I think it's really great that this is coming to Google Analytics.

And then there's this new stream view report. Now, this is really cool, because it lets you look at it across both users and events and to segment down by their actual parameters that they're using on these events or doing on these events. And you can actually click in to view a snapshot of a particular user. It's randomized. You can only see it if there's, you know, at least a number of users on the site. But what that looks like is, you know, this, you have this stream of events that somebody is actually doing. You can break it down by, you know, all of the different particular values of that visit. It's pretty cool. It helps you to debug certainly what you're doing, but also to understand a little bit more of the flow of somebody through your website.

So next, and I think this is the last cool feature that I have to talk about, is the analysis section of Google Analytics App and Web. Now, if you've been a customer of Google Analytics 360, this should look somewhat familiar to you, and this is now available in App and Web, but with some additions. So the first edition is actually the pathing report. This one is near and dear to my heart. It's kind of my baby. I actually built this one as a product manager when I was at Google, and it's a much better way of doing path analysis in Google Analytics. One of the things that I used to say often when I first started doing product management at Google was that it was my personal goal to kill all of the flow reports in Google Analytics because I thought they were that bad. And then when it came time to make good on that, and I was asked to actually design what pathing would be, I was a little nervous, and I said, oh no, that's a tough job. Everybody's going to be so critical. And about a month later, I submitted my first PRD for what this would eventually become. And I think it's actually turned out pretty well. So let's go ahead and look at a quick video of how you actually interact with this.

So as you can see here, you can have multiple steps. You can change it by event name or page title and screen name, which is just kind of an event that we've made first-class here. You can expand multiple, more rows or nodes here. You can keep going out to multiple steps. I think you can go up to 10 steps right now. And really start to break this down by the user's path of what they're doing on your site. You can segment it, you can look at a number of different dimensions and metrics, and there's a lot of additional things that are coming to this report that I'm pretty excited about. Can't tell you about it now, but I think, stay tuned to see kind of what else is going to be available with this new pathing technique.

Alright, so moving on to migration best practices. So you might be saying, okay Krista, this is really cool, but I've got a full implementation of Google Analytics already. Why would I move? Or how do I even move? It's, a, you know, it's a very different platform that you're talking about. And that's true. So some questions that I've heard about this new platform. We analyze via page views and screen views, but the same concept doesn't exist in App and Web. Instead it's focused on events. There's no page view or screen view report. How can we analyze? Or we send 50 plus customer dimensions with every hit in Google Analytics. How does that actually translate to 25 parameters per event? Or we already have a robust category action label, event hierarchy set up with hundreds or even thousands of events in Google Analytics. How can we actually migrate that?

And you know, this one, I feel very personally, a lot of the implementations that I've done over the years have very robust event setups in Google Analytics using this category action label hierarchy which, if you're familiar, the combination of those three things relate to one particular event for one action on your site. It's not really reusable that like this idea in App and Web and so translating matter, migrating that over you know, it's a very different mindset that you have to wrap your head around. Okay, so let's break these down. We analyze by screen views, but the same concept doesn't exist in App and Web. How can we actually analyze? Well, each event, as I mentioned earlier, automatically receives parameters of page name or screen name, and you can analyze that by the parameter of page name or screen name to see all of the events that are associated with a certain page or screen.

Likewise, for a particular event, you can easily see all of the pages or screens that the event took place on. So as you can see, this one shouldn't be too much of a problem. It's just a slightly different way of thinking about it. But I think you actually have a lot more power with how this is organized. If you're trying to analyze by page or screen or the combination of all of the events on a particular page or screen.

So next, we send 50 plus custom dimensions with every hit. How does that actually translate to 25 parameters per event? So this one breaks down in a couple of different ways. User-level custom dimensions in Google Analytics now map to something called user properties in App and Web. I didn't talk about those, but it's another concept in how you persist something at a user level in App and Web.

Hit level custom dimensions from Google Analytics are now essentially just events, whether that's an automatic suggested or custom event in App and Web. And then one thing I didn't mention, but is important to note. You want to be very particular in how you're actually translating this. They should only be your most important custom dimensions that you're bringing over here, because you do have currently a 500 unique event maximum in App and Web. So unlike traditional Google Analytics where you have category action label, and you can have hundreds or thousands of these things, you can currently only have 500 unique events in App and Web. But as I showed in the example earlier, you would actually use that same event in a number of different places, and what's differentiating about it is the parameters associated with it, which you can have different parameters for all of those different events. And so you just have to be a little bit more thoughtful in how you put this together, but hopefully that limit is not, you know, really limiting to most people. And I do think there's probably going to be some flexibility in that total number, and it might get raised eventually or potentially with a premium product here.

I do have a work around for you. This is using a screenshot from the Firebase tag in Google Tag Manager, but there'll be similar opportunities with the App and Web tags. And that's to send all of your parameters with an event. So if you have 50 parameters, you wanted to send, use Google Tag Manager and an event to actually send them, and then merge those parameters at the additional after 25 into a second event, using Google Tag Manager to then send multiple events. And this is nice because it, you know, if that parameter limit has ever raised, you can just get rid of the merge and send all of the parameters that you're already spending with a single event.

Now, I do have one other possible work around here. I have to note that this is not a work around coming from the Google Analytics team. This is my own suggestion as a practitioner of these tools. But it's to send multiple events each with 25 parameters. So again, not an official recommendation, but if you have, let's say a form submit that you want to track, and you have a ton of different custom dimensions or parameters that you wanted to send with that. You can have your main event for form submit, and it could have all of the most important parameters on it. And then if there was some long tail details that you wanted to send and you know, perhaps it's not even with every form submit, you can just have another event, form submit details, and send all of those long-tail parameters there. So, you know, it's a work around, it's not the best solution here, but I think it's another way to be able to send as many parameters as you need to.

Alright, the last one. We already have a robust category/action/label event hierarchy. How do we translate, how do we migrate that? So the recommendation here is to really rethink that implementation plan and your data structure, and to re-tag in the new methodology. You know, as I said multiple times, it's a very different way of doing analytics. It's shifting from a page view and session based-analytics platform in what Google Analytics traditionally was, to an event and parameter-based platform with App and Web. And so you know, you really do have to put the time in to rethink how this is going to be structured.

I think there's a backup or a temporary solution that you can use to be able to bring over some of that category action label event data that you have if it's hard coded on your page, and that is to essentially send an event to App and Web called GA underscore event with parameters of category action and label that you're then pulling from whatever you see tagged on your site. I want to stress, I think this is a backup or a temporary solution. This should not be how you actually fully roll out App and Web. But if you are in the process of migrating and you don't want to lose that data, or you want to see how it comes in, you could do something like this, but it's going to be a very messy, large data dump that you're only going to be able to access in BigQuery currently. And so, you know, you really need to think about that, and try to move what you can into the suggested events within App and Web. Alright, if this was of interest to you, I've actually written a long blog series on App and Web. It's on my blog via addresses there, so you can go ahead and check out more. There's a lot more detail on App and Web and some step-by-step walkthroughs of how to actually get up and running yourself and create your first App and Web account. And I just want to say thank you again to ObservePoint for having me back at the Virtual Analytics Summit. It's been wonderful. Thank you.

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