Why now is a great time to move to a marketing data warehouse

Edward Ford:
Hey everyone, and welcome to another episode of the Marketing Intelligence Show.
This episode is from an earlier recording we did with Tambourine on why now is a great time to move to a marketing data warehouse. In this episode, you'll hear from Christina Davis, VP of Media and Analytics at Tambourine, and Evan Kading, lead solutions engineer and product evangelist here at Supermetrics, as they tackle the challenge of how you should be storing all your marketing data.

So, I'll hand you over to Evan and Christina for today's episode.

Evan Kaeding :
Well, thank you, everyone, for joining. We have another webinar here today with Supermetrics put on in partnership with Tambourine. I have a special guest today, so we're very excited to get into the content here. Today we're going to be talking about why now is such a great time to move to a marketing data warehouse. So, without further ado, I'll go ahead and move into the introductions. So, let's go ahead and jump in introducing myself.
My name's Evan Kaeding. I am the lead sales engineer here at Supermetrics. Been at the company for about a year and a half, but was a user of the Supermetrics line of products for several years prior to joining, having used the Supermetrics line of products for implementing small-scale marketing reporting solutions for several SMBs, some mid-size companies, all the way up to large enterprises for implementing marketing data warehouse solutions using a variety of the tools from Supermetrics.

Very privileged today to be joined by a friend of mine and a close partner in our collaboration here with Supermetrics and Tine. Christina Davis. Christina, would you like to introduce yourself?

Christina Davis:
Yes, hello, everyone. My name is Christina Davis. I'm the VP of media and Analytics at Tambourine. We're headquartered in Fort Lauderdale, Florida. I've been working with Supermetrics for almost two years now, so I am very excited to participate in today's webinar.

Evan Kaeding :
Awesome. Thanks so much, Christina. So let's go through the agenda that we have today and we can go through some of the things that we'll cover and kind of set an outline and framework for what we'll discuss today.
So first we're going to talk about what is a marketing data warehouse. So kind of go into some of the things that have gotten us to where we are today, why some of the technological advances are important for you to understand as a marketer, and why some of the barriers to entry for getting into a marketing data warehouse have dropped relatively precipitously over the past decade or so. We'll also talk about some of the main reasons why this year 2022 is really going to be an important year for moving into a marketing data warehouse here.

So we're going to talk about really three different things that I think are pretty important when it comes to making the decision about how you're going to automate your marketing data reporting. This involves storing a growing collection of marketing data, tackling the different API limitations from the different sources that you're used to working with, and then of course, just the capabilities of marketing data warehouses that make this an easier than ever job in order to unlock the power of that data as well.

And then of course we have Christina on the phone to talk about the effects this has had on Tambourines business. They came to us with a set of business problems and a set of technology problems. We were able to craft a solution that effectively helped them solve the technical problems and unlock some additional value for the business. So very excited to hear that as well.

So let's move on to talking about what exactly a marketing data warehouse is. So going back in time all the way back to prehistoric times, maybe 10 years ago, let's say you wanted to get a data warehouse.

This is going to involve placing a purchase order to a hardware company, someone like Dell, someone like HP, ordering a server, having this run inside of your business somewhere, having a team, build it, manage it, and install and configure the software on it. Also, you could essentially store that data inside of that data warehouse so that you could access that within your company network or within your office walls. If that sounds like a lot of work. That's because it is and it was right? So this is what data warehouses were prior to moving them to the cloud.

What's really changed now is data warehouses are now available in the cloud, and they're really available for mass adoption. That's really what's changed here, and onscreen is one of the flows that you can build essentially without ordering a single server, without having to get any team members involved other than yourself, you can really actually build this entire stack by yourself with a few clicks and a credit card. That's what's changed in the world of data warehousing over the last 10 years. We say it and it's really true, everyone in the world is about three clicks on a credit card away from spitting up a marketing deal warehouse.

We've talked about how easy it's to get one of these set up, but what exactly is a data warehouse? So for those who aren't familiar, for those who might be using a Supermetrics product, something like Supermetrics for Google Sheets or Supermetrics for Data Studio (Looker Studio), a data warehouse is made up of many different tables. Tables are just like spreadsheets. They have columns, they have rows, and they can be used to store and house your data much like Supermetrics for Google Sheets, and Excel can move data into these tables. We can also automate the movement of data into these data warehouses.

Now, these data warehouses, they live on the cloud. Again, you don't have to provision your infrastructure. It's very easy to get these set up. All of these cloud providers are lined up around the block ready to sell you warehouses, and in many cases, they're competing on price and they can be priced very attractively. Just on screen here, we have BigQuery provided by the Google Cloud team, Azure Synapse on the Microsoft side, and then Snowflake, which actually runs across all three different clouds, Amazon, Google, and Microsoft.

So no matter what cloud technology stack your company is using, I'm sure your vendor has a data warehouse option that can be used. Of course, the data sources you're familiar with on the left can be used to basically load that data into your data warehouse where it can then be visualized and have that data analyzed using one of the data, common data visualization and BI tools. So, this is what a data warehouse is. It allows you to store a growing collection of data. So regardless of how much business data your company has, whether it's marketing data, whether it's sales data, whether it's operational data, all of this can grow into a marketing data warehouse or into a data warehouse in general. And really, it's one of the best solutions for building out a company repository for your critical information for doing that historical analysis. So that's what a data warehouse is.

Let's talk about why this is something that's good to at least examine if not to implement here in 2022. And this is really what I'd say is kind of the meat of the presentation. This is what has changed pretty dramatically over the course of the last couple of years. Of course, here at Supermetrics, we have been in the business of building connectors to these different APIs over the course of, like I said, the last five or 10 years.

We have a really good understanding of these APIs, and if you've been paying attention like we have, you'd might notice that some of these API limitations have begun to actually become significantly more restrictive. Facebook ads, good example, one of the first connectors we ever built. For the longest time, you'd been able to access the lifetime value from your Facebook ads. However, with recent API changes, you're only actually limited now to 37 months. For those of you who say, well, 37 months, that's three years and some change, that's plenty. I'd encourage you to think again because over the last couple of years we've had what some would consider once in a lifetime scenarios, once in a lifetime situations, and it can be important to establish a historical baseline for your business continuity so you can understand if your marketing campaigns are actually performing well based on a historical standard.

Things are changing in the advertising world as well. The way that cookies are being exchanged is going to be changing the way that I dfas in individual mobile advertising device IDs are being exchanged. That's also been changing, and so there's a lot of things that need to be taken into account when you're measuring the performance of your media. These are things that change structurally over time, and it can be very difficult to analyze your performance if you don't have that historical benchmark data. Similarly, Facebook organic, again, used to be a lifetime. API is now limited to 24 months, and then the elephant in the room that everyone is talking about but not doing a whole heck of a lot about is Google Analytics and GA4 (Google Analytics 4).

We're fielding lots and lots of questions from customers about when should I get on GA4 (Google Analytics 4), and how should I start implementing it. The right answer is really as soon as possible and with a minimum possible setup. The reason is because this is one of the most restrictive APIs we have seen here. GA4 (Google Analytics 4) has a limitation of 14 months of historical data, which is really just a couple of business cycles depending on the products that you're selling here. So you've got a lot of incentive to store that data for year-over-year analysis, especially if you operate in a seasonal business where cyclicality is important for measuring and understanding. This is really just the tip of the iceberg.
Of course, at Supermetrics, we have over a hundred different connectors. Many of them have different API storage lifetimes.
One notable addition to this that I won't put on screen yet because it's not officially released, but we're going to be releasing an Amazon ads connector as well for our data warehousing line of products. This API actually has a limitation of what is 60 days, two months of data essentially. And so if that's not an incentive to start storing your data using some kind of mechanism, I don't know what is.

There are a couple of other structural limitations to these APIs that are, I think worth talking about. Not just historical retention limitations that you're seeing here, but some structural limitations just in terms of how quickly can these APIs respond. Are they able to actually give us the data that we need in a timely fashion for users of Data Studio (Looker Studio)? When you're querying that data, all of that data has to come directly from that API and if that API isn't performant or isn't able to serve up that amount of data in a short amount of time, you're going to get your reports that are spinning. And so for customers that do need to do long-term historical analysis might have other issues associated with large amounts of data, you can run into issues when you're using products that are connecting directly to those APIs.

So all of these are reasons why it's important to start considering the implications of a marketing data warehouse for your reporting. Of course, these are Supermetrics products that we all know at love. They have a lot of really good use cases that don't involve, don't involve using large amounts of historical data or don't necessarily involve using a high degree of complexity, but these are cases where some tools are better suited for other jobs. And so if you have a marketing data warehouse dealing with a high amount of data, dealing with large amounts of historical data, these are really what marketing data warehouses are built for here.

So one of the things, like I mentioned here, is storing that growing collection of data. I like to show this slide here and I realize this kind of looks like one of those slides that consultants put in front of you when they're trying to sell you something.
So I understand what these kind of marketing data and maturity diagrams evoke in terms of senses and fields, but overall, I like to bring this in just because it kind of shows the evolution of the data journey with Supermetrics. Many of you on the webinar today are already using Supermetrics, so thank you for your continued business and support, and you've already made it to level four where you're automating the recording on a user level.

Now we have many businesses, many customers who are automating their reporting, building out those dashboards and reports for the individual users and stakeholders that need to manage that business data and make decisions based on that data. Then some are even going the extra mile and automating the business reporting as well. But what we find is that when you're using these products for building out large and automated business reporting sophisticated suites for business reporting, it can be difficult to manage the scale, especially if there's large data volume. And so the best tool for storing a large collection of growing data isn't necessarily going to be in a Spreadsheet. It's going to be in one of these cloud data warehouses. And again, like I mentioned, the complexity and the cost associated with implementing a data warehouse was previously so high that it wasn't worth the struggle. But now that the complexity and the cost has come down, achieving level six automated reporting on a user and business level with a marketing data warehouse is much more achievable and probably closer than you might think. So let's go to the last piece here that I think is important to emphasize is that it's now easier than ever to unlock your data's potential.

So with the advent of these cloud data warehouses, you have the ability to unlock your data's potential in a variety of different ways. I'll talk about number one, just being able to plug in any kind of BI tool to your data warehouse and start to build visuals and dashboards based on that data. So there's a number of good tools in ecosystem PowerBI we see quite often. Data Studio of course itself can plug into Google BigQuery and other data warehouses as well. There's also Qlik and Tableau that are also popular in the marketplace. So choose a BI tool, hook it up to your data warehouse, and you can start building those dashboards and visualizations, sharing those with your customers and with your internal stakeholders. It's easier than ever to do that than it is today, especially since all of that data is on the cloud.

Number two, we actually have a newer kind of entry, newer entry it on the ecosystem here where we have reverse ETL vendors or data activation vendors as well. We actually had a separate webinar just a couple of weeks ago, specifically about some of the use cases around reverse ETL and data activation where you can take the data as it sits in your data warehouse and actually push that out to different platforms. So push that data out to your email marketing automation tools where you can help your marketers essentially build out campaign flows that are more reactive and can help essentially drive upsells and different conversions in different ways based on the data that's inside of your data warehouse. Similarly, pushing things like lead scores into your CRM so that your sales team can prioritize their outreach. There are a lot of good use cases for marketing data warehouses and the reverse ETL there, and this essentially makes it a great target to unlock that potential.
I'd say the third piece here is really that you can essentially have that data in a single spot where it can then be accessible to anybody in the organization. So once you get the privacy and security controls up and running, you can ensure that this data is provided internally to your analysts, of course, externally to your clients and any stakeholders that might need it as well.
So just the ability to share this data across the organization so that everybody's reading off the same page. That can be a pretty significant driver in terms of delivering value to the organization with a data warehouse.

So in the context of these different, in the context of these different benefits, let's recap here.

So we talked about mitigating the effect of API limitations, not just historical limitations, but also the limitations associated with API performance. We can also talk about managing large data volume here, so large data volume, of course, if your data volumes are significant, it's going to take the underlying APIs, Facebook APIs, the Google APIs, so on and so forth, a long time to return that data and that can result in a slower performance. Now, we also talked about extending functionality, whether that's through visualization, through extending that to individual members of the business or potential clients, and then also reverse ETL. So we've got a lot to like here in terms of what is actually involved in using a marketing data warehouse in terms of what the benefits are.

What I'd love to do now is help let Christina go ahead and introduce Tambourine to us so we can learn a little bit more about their business and how they were able to use a marketing data warehouse to uncover quite a bit of customer value in terms of this data. Christina, over to you.

Christina Davis:
Thank you, Evan. Before I tell you how Supermetrics helped us unlock our data, I'm going to tell you a little bit about our business. We're a digital marketing company serving over 800 hotels and resorts and tourism destinations worldwide. We set out to increase bookings and revenue for our customers and our core services and offerings include setting the standard for hotel, website design and integrated marketing solutions.

So we had a number of problems that we needed to unlock and Supermetrics understood our problems.
The first one being our portfolio was growing and our data size outgrew Google spreadsheets. And so we started to come across a ton of challenges. And also not every ad platform had seamless and reliable connectivity with spreadsheets in our BI solution with hundreds of clients.

We needed a solution to easily integrate our portfolio data to view industry trends. And so we were kind of stuck in not being able to see how the industry was performing and leaning on other companies to tell us that. So we also, it was very time consuming to manually report paid immediate performance for our clients. So we shared these problems with Supermetrics, and I'll let Evan tell us how Supermetrics helped unlock our data.

Evan Kaeding :
Yeah, super. Thanks for the overview, Christina.

So, let's talk a little bit about the solution for Tambourine that we ended up devising. So we had a number of different data sources in play. Some of the main ones were Facebook, Google, Bing in this case, and then LinkedIn Ads. And so a couple of the things that we kind of did is we took an inventory of all of the different data sources that were important for Tambourine. We took a look at their existing infrastructure, and what we found was there was actually an incredibly robust solution stack that was built out on top of Google Sheets.

And I'll tell you what, I've seen a number of sophisticated spreadsheets, and Tambourine really had, like I said, very sophisticated spreadsheets, but the problem was the data volume was just too much, right? You would open the Google sheet, the wheel would spin, sometimes you'd be able to get the data you needed, other times it wouldn't. And this became an administrative burden for all of those involved. And so it became really important for us to say, okay, let's pick a technology. Let's pick a tool that's going to ensure that we can actually scale this, not just for the existing clients, but for the growth that Tambore has experienced. I saw on the previous slide 800 clients, I'd say that I've seen that number increase pretty significantly just in the time that I've been working with Christina. So that's exciting to see by itself.

But we wanted to build something at scale, not just something to maintain the existing clients, but also something for when tambourine triples or quadruples in terms of their client base, they can take on new clients without having to worry about the scale of the marketing data stack. So we decided to use BigQuery simply because them and their team were pretty closely integrated to the Google stack. They used Google Cloud in a lot of their internal functionality. So BigQuery was the obvious choice here. So we underwent a modeling exercise where we helped to build out schemas for all of the different data sources they worked with.

We transformed a lot of their queries that were built in Google Sheets into moving this data into Google BigQuery, and then helped ensure that all of the data was lined up in terms of ensuring that the different accounts that were being used were being provided to, were being mapped to the correct, the correct resorts, the correct destinations, the correct hotels, restaurants, those kinds of things, all of the different pieces of tambourines portfolio.

And so what we essentially built inside of this data warehouse was a managed data model where Tambourine could then do two important things. They could build dashboards for streamlining their client reporting, so build the dashboard, they could be templatized and rolled out across all of their different clients, saving their team several hours a week or per month in terms of making sure that we're getting a streamlined data access pattern for all of their clients. Number two, and this was actually really cool as well to see, we actually helped Tambourine leverage their data to build an industry trends dashboard.

A company with Tambourine size and scale has a pretty considerable view over what is actually happening in the hotel industry. And so with this power, with the power of this data, they were actually able to understand some trends that were really interesting across, especially during covid times when certain things were open, certain things were closed, difficult to identify which markets are going to be the best in terms of marketing, the different attribution models associated with those. And so we help them build essentially an overview of the industry trends across different client segments, like I mentioned, golf courses, hotels, restaurants, so on and so forth, and deliver the results not only to their leadership team, but to clients and become truly thought leaders in this space. So these are kind of the architectural components of the solution here.
Much of this was designed by the Supermetrics team. We have a consulting team and professional services team that helps with the initial build, and we're able to help Tambourine build out this solution here in terms of ensuring that we're delivering the value for the different stakeholders. So in terms of the, let's see. We've got a little slide piece here. One moment please. Here we go. Okay, so if we want to just jump back to, there we go. Super. Thank you.

So now we can talk about what the outcome was in terms of the project here that we have. So we helped to build out the whole solution for Tambourine.

We now continue to manage it on a professional services basis. However, of course, our professional services team does have a really good ability to build projects and then hand them over to individual customers who can then take on the maintenance of that project themselves. So there's a couple of different ways to get started. If you have a team in-house that wants to get started themselves, that's totally fine. We can also build out the solution in a quick start guide, help train the team.

There's a variety of different ways we can get started, but Christina, I'd love to just understand from your side, can you help us understand some of the different benefits that you were able to get from the system when you were able to streamline this data, automate the reporting, and put all of this data into a centralized location?

Christina Davis:
Sure. Yeah. First off, it has been a huge time save. With automated reporting, we've reduced the amount of resources it takes to provide monthly reports for our customers. Supermetrics also helped us provide redundancy. Rather than having someone in-house managing our data warehouse, we can rely on Supermetrics and their service team to maintain our warehouse. And then today we pull industry insights and benchmarks regularly within minutes before. That was a very manual task, and this has been a huge beneficial or benefit to the hospitality industry, especially like Evan said, during covid, when we were really watching the industry take dives, and also with its rebound, we were able to understand which markets were emerging.
So here's an example of one of our pages from our dashboard where we're tracking our real-time industry trends, not only in charts, but in bar and line graphs. And so like I mentioned with covid 19, we're able to track the industry rebound as well as filter our customer data by different markets to understand what cities were emerging and what clients to get back to starting media again. And then on the next slide, we have a visualized report of our, I think it hasn't switched over yet.

Evan Kaeding :
Yeah, let's see. Oh, there it is.

Christina Davis:
So here's an example of one page from our client-facing report that we're able to duplicate and really connect back to our BigQuery data warehouse so our customers can see all their sales and marketing stats in one custom dashboard.

Evan Kaeding :
Alright, yeah, that's awesome, Christina. Yeah, I remember when we were scoping out those reports, we kind of took a look at what we needed to provide in terms of the data and the modeling that was involved there. And that was one of the things that I think was really interesting about this use case was we really wanted to dive in and understand specifically what is important in the hotel industry. You see a couple different things here like hotel ADR, getting very specific into room nights, for example.

So these are all very specific trends where when you're actually centralizing your data, you can actually get very granular in terms of the data that is being surfaced and the approach that you take here as well. So thank you for sharing the

Christina Davis:
Benefits. I would just add too that with the API limitations and having a data warehouse, that's enabled us to be able to look back to 2019 or later because for the most part, 2020 was a wash for the hotel industry, so having the power of that data at our fingertips was really beneficial.

Evan Kaeding :
Definitely, definitely. And I'm sure you and any other businesses who operate in a seasonal cycle where you might have peak times, you might have downtime, understanding the variance among those is going to be very important. And so having the longevity of that data is going to be pretty important. And I'd say, would it be fair to say, Christina, that this data has become a strategic asset for Tambourine now?

Christina Davis:
Absolutely.

Evan Kaeding :
Awesome. Awesome. Very good to hear. Well, I think that's most of what we wanted to cover in terms of the specific outcomes for Tambourine.

Few of the things that I guess we can just recap on here, looks like we're having a few glitchy pieces with the slide here. There we go. So a few things just to recap. So we talked about the benefits of a data warehouse. We talked about exactly what they are, how they can deliver value, and what I want to leave you here with is a checklist that you can kind of go through in your head and go through with your team and say, okay, if these are things that are important to my business, let's actually consider a marketing data warehouse and some of the benefits that can be derived from this process here. So if year over year comparisons are important for your business.

Alright, so we've got a variety of different ways in which this can materialize. Like I mentioned, if you're in a business that is seasonal or cyclical, for example, these year over year trends can be really year only sense of what is actually going to be baseline for your business, especially as things continue to evolve in the digital marketing landscape. Things like cookies, things like mobile device IDs continue to change. It can be really hard to get a sense of if your advertising channels continue to provide value at the same rate that they used to.

Number two, if you are currently using one of our products for Google Sheets or Data Studio, if these do begin to run slowly, of course we love these products that are put out by Google, but we do see customers that do have large amounts of data. And so if these do begin to run slowly, this can be a sign that, hey, maybe we need to start moving our data into a data warehouse, something that can actually become a strategic asset for the company and can scale infinitely on the back of these cheap and easy to use cloud-based servers that are now easily and readily available.

And then lastly, when these campaigns are increasing in quantity and complexity. So there are a variety of factors that go into what actually influenced your data size. Sometimes we talk to customers about how much they're spending. Sometimes we talk to them about how many unique creatives they have in their campaigns, how many different audience segments they're targeting. So it can really vary how your campaigns and your data are actually increasing. But these are all things that can ultimately increase the size of your data and ultimately make it more challenging to use kind of a point and click solution or any of our core products here at the Supermetrics on the Supermetrics line of products here. So if you're taking a look at this checklist and you're saying maybe one or two of these things are important, I'd say that this is probably a good indication that you would benefit from a marketing data warehouse.

So we're going to go ahead and proceed to the Q&A. We've had a couple of different questions come through, and so we can go ahead and move forward with these here.

So read in many places that unless you have GA 360, there is no way to export historical data from standard UA to Supermetrics work around this to be able to export all of the raw data into a data warehouse?

So I guess unfortunately that does not well, unfortunately, fortunately, that does not necessarily need to be true. So we actually helped when Tambourine became a customer, we helped them export all of their historical data back to, I believe 2019, and we started the project in later 2020. So you can actually get pretty much all of your historical data from Google Analytics. There are some rate limits of course associated with APIs, but ultimately, as long as you're persistent, you can get the data out of GA that you have.

One thing to keep in mind is that GA is going to be sunset. I believe the sunset date for collecting new data is going to be in July of next year, but you'll still be able to retain access to your old data by I think the end of 2023. But as soon as that passes, all of that data is no longer going to be accessible. So ultimately the answer is yes, you can use supermetrics to extract and store an archive, all of your data from GA, regardless of whether you have a GA 360 subscription.
Now, of course, like I mentioned, there are data volume considerations to take into account. GA does have some peculiarities around the API, but broadly speaking, if you have a specific case that you're interested in understanding, can I get this metric? Can I get this dimension? Can I get this combination of metrics and dimensions? I'd encourage you to take up a chat with one of our product specialists and we can customize this solution for your needs for Google Analytics as well.

So we have another question here asking, do you help all of your clients set up the stack?

Yeah, so when referring to the stack, I believe this person is referring to the marketing data stack. And so the question here, the answer to this question is yes. So we have a professional services team here at Supermetrics. So we help not only with configuring Supermetrics on the data warehousing side, we're moving that data into the warehouse. We can also help with data modeling.

We can also help with dashboard customization and visualization. So these are all part of our professional services offering. We want to make sure that our customers are successful, and we thankfully have a team of, I think at this point, five or six different consultants who are all ready to help in these marketing data projects. So lots of different ways that we can help depending on what the specific needs are, and of course, we'll help you identify the right super metrics solution for the problem as well.

So we have another question here. Do Supermetrics have hands-on support to move data to the data warehouse here?
So in terms of hands-on support, there's kind of two ways that we move to this and I think this touches on the previous question. So of course, when you buy a Supermetrics for data warehousing product, you will have access to a customer success manager. That person is your dedicated point of contact, and they'll ensure that you have not only an onboarding provided by either myself or someone on my team here at Supermetrics, but also architectural reviews and ensuring that the solution is actually means tested to ensure that you're actually getting the data that you need and it's accomplishing, it's helping to accomplish the business goals that you have here. So these are all different pieces that could be helpful for ensuring that you can get the data to the warehouse.

That by and large is its own managed service for our, excuse me, that is its own included inside of the purchase of a Supermetrics product. However, by and large, you're going to be responsible for implementing that. You're going to need to build the dashboards, build out the data modeling, so on and so forth. That's what comes standard with the supermetrics pricing and packaging. However, like I mentioned, we do have professional services and consulting that could be added on top of that. If you do have a project in mind where you do need some help, whether it's building dashboards, whether it's data modeling, there are a couple of different ways that we can get involved and we can help. So if you do have any questions about that, whether you're a current customer or whether you are new to the Supermetrics ecosystem, certainly something to bring up with your representative at Supermetrics and something that hopefully we can work on scoping out.

Then we have another question on can you touch more on data models in a data warehouse, scalable and flexible enough to account for the constant change we are seeing in data models being leveraged by key platforms like Google Analytics for and Facebook here?

Yeah, so good question here from this user here. So to discuss more about data models, we kind of have two approaches to managing data models here at Supermetrics. So with our BigQuery products, with our data warehousing products, you either use our, what we call our standard schemas, or you can use custom schemas. Now, when I say schema, schema, just essentially think of it as the list of tables essentially that are going to be built inside your data warehouse.

We build our standard schemas basically based on our usage data from our, at this point, over 70,000 customers over this point, 700,000 users. So we have a pretty good idea of how customers are using these different data sources and what business questions they're actually asking and trying to answer with this data. And so these standard schemas could be really helpful. We are committed to basically maintaining these standard schemas to the maximum extent possible associated with these APIs. These are schemas that you can feel comfortable building on, and by and large will answer most of the business questions that you have.

That said, should you decide that you want something a little bit more custom? This was the case with Tambourine. We actually took a look at their business needs and said, Hey, the standard schemas are good. Maybe they'll solve most of the needs, but actually we could benefit from some more customized solutions. So we actually built out a set of custom schemas for Tambourine to bring in the data exactly the way they needed to help account for the scale.

And with these custom schemas, we can now actually ensure that any metric or dimension changes that are happening in the underlying APIs, we can ensure that these are incorporated into the data model. Now, are there downstream effects? Of course there are. Sometimes these are unavoidable, but to a large extent, it's super metrics. This is one of the value propositions that you get working with a company like us. We try our best in order to ensure that any changes to the upstream APIs are basically, you're not impacted by those, right? So if there's a field name change or if there's a couple of metrics dimensions that have changed their cardinality or something like that, we try to ensure that that is going to be as impact as possible for your different downstream data models. Of course, if you've built something on top of it, there may be instances where that needs to change. However, our product managers are really good at understanding these different API changes and communicating these well in advance or with as much advanced notice as is possible here. So good question from this user here.

Anonymous attendee is asking, would Supermetrics help support in the data warehouse execution like BigQuery?

Yes, in many ways. So we can move the data into BigQuery, we can help with the data modeling piece, and of course we can help with the initial setup as well. Like I mentioned, standard onboarding included with all BigQuery packages. And then of course we do have the custom consulting services as well, professional services that can help stand up something a little bit more complex. So we've got time for just a few more questions here and then we can go ahead and wrap up the session here as well.

So we've got one here where we can talk about how long does the process usually take, getting data into BigQuery, custom modeling, what's the cost and structure for each part?

So this is a question that I'd say is pretty challenging to answer. We've done professional services projects for customers that have lasted approximately two weeks. We've done professional services project that lasts for several months. Typically, it's what we tend to prefer, like I mentioned, is we can do fully managed projects where we kind of help you take the solution end to end and build out this marketing data warehouse and ultimately deliver the value to you and your customers. But we tend to prefer taking on projects where we can essentially teach you how to fish essentially, right? We want to be able to build something that is to your liking in terms of your delivering on your business objectives, but not something that's so overly complex that you're not able to maintain it or necessarily able to leverage it or change it or modify it after we have delivered this solution. So there's a variety of different components that go into it.

I'm not going to talk about cost structure here overall, but what I can speak about is that of course there's going to be a fee for Supermetrics itself, the software, and then of course a fee for professional services. Now, whether that's one time or whether that's ongoing, those are things that we can talk about in a more specific scoping scenario here.

Another question here for current customers, are their fees tied to the demo service?

Excellent question. Very easy answer. The answer is no. All of our products, I believe, yeah, I'm pretty sure all of our products have a 14 day free trial that you can sign up for. I think that's a great note that we can use to end on the webinar.
So like I mentioned, if any of the products that you learned about here today are useful, are interesting to you, you can go directly to supermetrics.com and sign up for a free 14 to trial for any of the products that are available in our lineup here today.

Thank you very much for joining us today on another webinar series where we were fortunate enough to be joined by Christina from Tambourine. Christina, thank you so much for joining us today.

Christina Davis:

My pleasure. Have a good one.

Evan Kaeding :
Thank you for listening