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Greetings, and welcome to Alteryx Third Quarter 2018 Earnings Conference Call. [Operator Instructions] As a reminder, this conference is being recorded. I would now like to turn the conference over to your host, Chris Lal, General Counsel. Please go ahead.
Thank you, operator. Good afternoon, and thank you for joining us today to review Alteryx's Third Quarter 2018 Financial Results. With me on the call today are Dean Stoecker, Chairman and Chief Executive Officer; and Kevin Rubin, Chief Financial Officer. After prepared remarks, we will open up the call to a question-and-answer session.
During this call, we may make statements related to our business that are forward-looking statements under federal securities laws. These statements are not guarantees of future performance but rather are subject to a variety of risks and uncertainties. Our actual results could differ materially from expectations reflected in any forward-looking statement. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our SEC filings available on the SEC's EDGAR system and our website as well as the risks and other important factors discussed in today's earnings release.
Additionally, non-GAAP financial measures will be discussed on this conference call. Please refer to the tables in our earnings release in the Investor Relations portion of our website for a reconciliation of these measures to their most directly comparable GAAP financial measure.
With that I'd like to turn the call over to our Chief Executive Officer, Dean Stoecker. Dean?
Thanks, Chris, and thanks to everyone joining us today to discuss our Q3 results. Alteryx continues to benefit from increasing demand for analytics, particularly advanced analytics. And this trend, coupled with our strong execution, enabled us to deliver a very strong quarter. Q3 highlights included 59% increase in total revenue, a 99% increase in international revenue, continued strong gross margins and $5 million in positive cash flow from operations.
We believe we are changing the conversation around analytics as customers are looking for partners that can support their analytic journeys from beginning to end and from simple to sophisticated. While our customers often start their analytic journeys with simple questions, they are increasingly seeking answers to complex problems and looking to leverage the full range of advanced analytic capabilities of our platform, including data science and machine learning, to help them make better data-driven decisions. Supporting our customers during these analytic journeys is job #1 for Alteryx. Our relentless focus in customer success is reflected in our sustained net revenue retention rate which remains very strong at 131%, the eighth consecutive quarter above 130%. Our customers not only continue to buy more of our platform, they are recommending it to others as evidenced by our recently completed Net Promoter Score survey where Alteryx customers adorned us with the highest NPS in our history. The advocacy from these customers benefits us in many ways.
In Q3, we increased our net new customers by 375 and now count more than 4,300 customers in our growing worldwide community, including 1/4 of the Global 2000. Some of the new customers added in Q3 include 7-Eleven, Cowen and Company, J.Crew, John Hancock, Michael Kors, Textron and Workday. We have focused our R&D investments in making the platform easier to use for data analysts while extending its ability to handle more sophisticated data science outcomes for the trained statisticians, both driving an increasing number in a variety of use cases in most every industry and in most every functional area of an enterprise.
When customers use the Alteryx platform and achieve significant financial benefit, either in improvements in operational efficiency or better insights that lead to top or bottom line improvements, these ROIs drive additional investments in the Alteryx platform. Existing customers that expanded their Alteryx investments in the third quarter include Cisco Systems, CIBC, Grant Thornton, McDonald's, Pacific Life Insurance Company and UBS. For example, Pacific Life, a provider of life insurance, annuities and other investment product and services to individuals, businesses and pension plans account more than half of the largest U.S. companies as clients, created their own analytics culture by empowering users to ask and answer difficult questions. The company's Life Insurance Division recently embarked on a digital transformation effort and sought a platform to be the foundation for both their analytics and reporting needs. The finance department adopted Alteryx to start the cultural shift within their organization. Before Alteryx, the finance team took a more traditional approach for analytics and reporting. After Alteryx, 75% of the analytic projects are done with the Alteryx platform, which has resulted in significant time savings and reduced expenses in only the first 6 months. Manual or Excel-based projects that took over 60 hours can now be completed in just 30 seconds. The team is thrilled to see where the Alteryx platform can take them next.
We also continue to see strong demand outside of North America as evidenced by a near doubling of our international revenues in Q3 and did business in more than 70 countries. We added a diverse set of customers, including AkzoNobel Sourcing in the Netherlands, Anheuser InBev in Belgium, IKB Deutsche Industriebank in Germany, the National Rugby League in France, the Real Estate Development Fund in Saudi Arabia and Oxford University Press in the U.K.
Geographically, 2 regions that stood out in Q3 were Latin America and Australia. In Latin America, where we just recently began our go-to-market investment in Brazil, our revenue more than doubled. We intend to further invest in programs and people in the region to capitalize on the growth opportunity. During Q3, new customers in Brazil included Aon Brasil, Banco da China Brasil, Saint-Gobain, Puerto Seguro, Universidade Estácio and Unilever Brasil. Another customer that expanded with Alteryx in the third quarter is B3, Brasil, Bolsa, Balcão, the Brazilian stock exchange. B3 is one of the world's largest financial markets in terms of market values and organizes, develops and operates free and open securities markets for both spot and future trades. B3 first adopted Alteryx in 2016 in its clearing risk analysis and statistics department as a way to streamline their risk reporting and analysis. Alteryx reduced the time to create a risk report from approximately 8 hours to under 60 minutes. Alteryx has subsequently been adopted by other departments seeking similar efficiencies. As more teams leverage Alteryx for self-service analytics, the organization is now able to spend less time delivering ad-hoc reports and more time delivering higher order analytics.
On the other side of the globe, we also continued to see strong activity in ANZ. This was first fueled by the acquisition of our reseller in Sydney, Alteryx ANZ, completed earlier this year. Coupled with additional investments in the region, we are seeing our land-and-expand model working well. Notable Q3 transactions in Australia included Commonwealth Bank of Australia, the Department of Finance, Macquarie Bank Australia, Transport for New South Wales, Telstra and the University of Melbourne. The result in these 2 regions are great examples of the global need for analytics and the untapped opportunity in front of us. They also speak to the strength of our go-to-market execution. Leveraging our Alteryx platform internally, our teams are using a metrics-driven approach, identifying key areas of opportunity and devising a strategy through partners and direct resources to enter new markets. We believe we can replicate the success across the globe.
To further refine our go-to-market efforts, we recently created select verticalized sales groups. While a majority of our go-to-market efforts will continue to be geographically based as we continue to get smarter by leveraging Alteryx, we have identified select verticals that we believe would benefit from verticalized sales teams. This year, we made focused investments in both public sector and health care. Our public sector team, which we began building out this time last year, delivered excellent Q3 results. Public entities are facing the same data challenges as their private sector brethren and are being challenged to do more with less.
During Q3, we saw strong land activity across federal, state and local government with wins at the Department of Homeland Security, the Social Security Administration, the U.S. Army, the Ohio Department of Taxation, the State of Delaware and the City of Miami. Our public sector customers are also driving value and expanding their Alteryx footprint. The United States Department of Agriculture, the National Institutes of Health, the Government of the District of Columbia and the U.S. Patent and Trademark Office all expanded with Alteryx in the third quarter. We believe that we are in the early days of our journey as a company and remain focused on investing to drive customer success and to help transform the way our customers do business. We believe that the investments we are making position us to capitalize on our significant market opportunity.
And as we continue to scale the business, we continue to enhance the team. We recently announced the addition of Mark Anderson who brings his experience at Palo Alto Networks and F5 to our Board of Directors. We are pleased to welcome Mark to the Alteryx family, and we believe that his experience scaling organization will make him a valuable adviser to Alteryx.
In closing, I'm very pleased with our consistently strong results. I am proud of and humbled by the excellence that the Alteryx team brings each and every day to deliver success to our customers. We believe we had built a powerful and sustainable business model that will allow us to deliver sustainable growth going forward.
With that, let me turn the call over to Kevin to discuss our Q3 financials and outlook for Q4. Kevin?
Thank you, Dean. As Dean noted, we had a strong Q3. Revenue was $54.2 million, an increase of 59% year-over-year. In addition to the overall strength of the business, we did benefit from slightly higher services revenue during the quarter as well as better-than-usual linearity. International revenue increased 99% year-over-year to $15.7 million and represented 29% of our Q3 revenue. The strong growth across both the U.S. and international markets reflects the investments we have made and expect to continue to make to grow our business globally. In the quarter, we added 375 net new customers, which represents a 62% increase in net new customers over the same period last year. We now have 4,315 total customers, including approximately 26% of the Global 2000.
Before moving on, I would like to remind everyone that, unless otherwise stated, I will be discussing non-GAAP results. Please refer to our press release for the full reconciliation of GAAP to non-GAAP results. Our gross margin was 90.5% in the third quarter, an improvement of 470 basis points from the third quarter of 2017 and in line with the second quarter of 2018. Although we were able to maintain gross margins above 90% again this quarter, we continue to expect increased investments in our support and professional services organization to put some downward on margin. Total operating expenses were $44.5 million compared to $28.4 million in Q3 2017. We continue to invest in programs to drive an awareness and adoption of our platform and expand our teams globally. This includes go-to-market investments and additional quota-carrying salespeople as well as marketing and other supporting personnel to accelerate our global expansion. Operating profit was $4.6 million, which equates to an operating margin of 8%. This represents a significant improvement compared to $885,000 or 3% margin in the third quarter of 2017. Net income was $5.3 million or $0.08 per share. This is based on 65.5 million fully diluted weighted average shares outstanding.
Turning now to the GAAP balance sheet. As of September 30, we had cash, cash equivalents, short-term and long-term investments of $414 million compared to $194 million as of December 31, 2017. We generated positive cash flow from operations of $5.3 million in the third quarter, bringing year-to-date total to $11.7 million in operating cash flow and $5.7 million in free cash flow. Finally, we ended the quarter with 756 employees, up from 674 at the end of the second quarter of 2018 and 515 employees at the end of the third quarter of 2017.
Before we turn to guidance, I'd like to update you on our adoption of ASC 606. As we discussed with you last quarter, we will no longer qualify as an emerging growth company after December 31, 2018, and we'll adopt the ASC 606 when we file our Form 10-K in early 2019. We continue to evaluate the potential impact of ASC 606 on our financial statements and have not yet reached a final determination. We expect to share more information with you when we have reached a final determination on what the expected impact may be. Consequently, the guidance we are providing you today is under ASC 605.
Turning to guidance, as Dean noted, we continued to see strength across our business. We are raising our revenue guidance for the full year 2018. We expect to continue to accelerate investments to support growth as we build a company for long-term growth and scale. For the fourth quarter of 2018, we expect GAAP revenue in the range of $56.5 million to $57.5 million, representing year-over-year growth of approximately 46% to 49%. We expect our non-GAAP operating loss to be in the range of $1 million to $2 million and non-GAAP net loss per share, basic and diluted, of $0.02 to $0.03. This assumes 61.5 million non-GAAP weighted average shares outstanding, basic and diluted.
For the full year 2018, we now expect GAAP revenue in the range of $200.5 million to $201.5 million, representing year-over-year growth of approximately 52% to 53%. We now expect our non-GAAP operating loss to be in the range of $2 million to $3 million and non-GAAP net loss per share, basic and diluted, of $0.03 to $0.05. This assumes 61.9 million non-GAAP weighted average shares outstanding on a fully diluted basis. To close, we delivered solid Q3 results. As Dean noted at the beginning of the call, Alteryx continues to benefit from both industry tailwinds and strong execution. We intend to continue to invest to ensure that we capitalize on the global opportunity for next-generation analytics.
And with that, we'll open up the call for questions. Operator?
[Operator Instructions] Our first question comes from the line of Brent Bracelin from KeyBanc Capital Markets.
This is Clarke Jeffries on for Brent. Growth accelerated to 59%. I was just wondering if you could go to the primary drivers of that, whether that was rising contribution from the add-on products, Promote and Connect, or just generally larger lands, new customers that drove that upside.
Well, I think there's a number of factors that are driving the growth. First and foremost, the market continues to open up for self-service data science and analytics. It's happening around the globe. It's evidenced by the fact that we're investing in lots of the areas to capitalize on that opportunity. The second thing is that it's execution in all regions. We've been investing heavily this last year in quite a few markets from Australia to EMEA, London, Paris, Munich, we opened up Japan, Singapore. And all of our regions continued to execute in Q3 really strongly. I think that points to the fact that our sales model, our playbooks are refined to support both the land and expand. And of course, we continue to fine-tune this, and we're getting smarter each and every day to make sure that we can continue this kind of growth in future quarters. I think we also just benefited from some basic metrics, better linearity throughout the quarter. So that was kind of rush at the end, and we've got some benefits for activity that happened early in the quarter. We can't promise that in Q4, just based upon the way the numbers are rolling out. We did see higher services revenue. Without seeing a huge improvement or growth in a number of adoptions for Connect to Promote, we did have to support the onboarding of lots of larger expands across the enterprise, which led to higher services numbers. And we just -- we think we can continue this in the future.
Great. And then, yes, that was my follow-up question of -- to put a finer point on the professional services side, is there a way for you to help us understand that in terms of what maybe gross margin was targeted for the guidance for the third quarter and kind of how that came into expectations? Or what percent of revenue was professional services in the quarter? Anything in that regard would help.
Yes. No, of course. I mean, so professional services for the quarter is still very low. I mean, if you look at our model, it's been less than 5% of our revenue is attributed for services. We did see just a slight tick-up in services for the quarter. It's not changing the -- materially changing the distribution of revenue. We actually saw a lot of strength in software revenue that more than offset necessarily the proportion. But as Dean mentioned, as we get into these larger enterprise-level deployments, we do tend to do more services with those engagements to make sure that they are successful. And we did see just a slight uptick in services in that regard.
Our next question comes from the line of Derrick Wood with Cowen and Company.
Well done on another big acceleration quarter on revenue. One metric that really stuck out was the large jump in new customers. Kevin, I think you said 62% year-over-year, up 40% quarter-on-quarter, big uptick from past trends. Can you flesh out what drove this and maybe comment on whether the characteristics of the new customer activity is changing in any way, maybe around geo, size or use case?
No, it's -- I think we've said in past quarters that we're highly focused on going after where the tranche of analysts, the Citizen Data Scientists that we're creating and the PhD live, they tend to be in larger enterprises. And so it's not just getting the larger number of land, it's actually landing better performing cohorts as evidenced by the fact that we had the eighth straight quarter of north of 130% in retention. So we use -- I think we've said this before, too, we utilize Alteryx in our own insights teams. We prosecute our own data to understand what markets to go after, what customers to secure, implement our playbooks to make sure that we get, not just the land, but the -- we get them into our go-forward expansion as quickly as possible.
Dean, anything you'd call out in terms of what drove such a big uptick in new customer generation?
Well, I think as an organization, we are highly customer-centric, probably more than most enterprise software companies. We -- in the comments, we noted that we had received our highest Net Promoter Score ever. We've been tracking it for a number of years, and we're starting to get from NPS, which is really a matter of whether or not these customers would refer us. We're starting to get lots of referrals and we're seeing it in all theaters. We're seeing it in all industries, and we continue to identify a whole variety of new use cases. So I would -- and I think I've pointed to the 3 KPIs that matter most to me is net revenue retention, NPS from customers and NPS from employees. Even though as an organization, we have 4 strategic imperatives this year and 150 KPIs, I worry about those 3 things. And I would say that our growth in net new customers is largely due to high NPS numbers.
Great. And as my second question, we're hearing strengthening activity on the advanced and predictive analytics side. Are you guys seeing the same thing? And are deal sizes different when you're working with data science-oriented projects? And I guess if the mix is increasing, does that drive overall deal sizes higher?
So the first part of the question, yes, we definitely see early adoption -- earlier adoption of advanced analytics use cases inside the platform. We think that in Q3, we launched 2018.3. It had a number of really cool key features that data scientists love, so we're bridging that gap between the Citizen Data Scientists who wants to build the -- to build analytic pipelines in a code 3 environment and the code-friendly side of Alteryx that allows the PhD statisticians to leverage things like Python with Jupyter Notebooks, so they can easily share Python code inside of Alteryx Designer without having to do any work. And so we see it at our conference, in the attendance that show up at advanced analytics sessions, the predictive modeling sessions. We see it in the responses we get in top of funnel. And the community that we've got, we see a growing number of PhDs that are sharing and collaborating inside the community.
Our next question comes from the line of Michael Turits from Raymond James.
Well, it's hard to say you really needed to be doing anything better. Nevertheless, does -- is there any sense that you need to increase penetration by lowering price points? Obviously, the -- so Tableau successful with that strategy recently.
Well, we play, obviously, in 2 very different markets than Tableau, and I think they had to do it because there's been tremendous amount of price compression in their space. Our primary competition is SaaS, and we're actually probably relatively cheap, maybe even too cheap compared to SaaS. So we don't think that there's a pricing scenario where we drop prices. That said, if we get to that point of 40% or 50% penetration of the 30 million disenfranchised analysts around the world or the 1 million who want to who aren't productive at being able to deploy machine learning algorithms without a tremendous amount of work, at that point, there might be some reason to move downstream to get verticalized solutions that are more inexpensively priced. But today, we're in the -- even in our lowest penetrated accounts, we're in the low single digits in penetration rates. So we don't see a scenario where there's a drastic price change either way.
Yes. And Michael, I think just maybe -- and Michael, just to add to that, I think we've talked previously. We do have flexible pricing arrangements, especially in our larger enterprise accounts that do allow us to take advantage of pricing when you get to hundreds and thousands of users. So pricing has not really been an issue for us from an adoption and penetration perspective.
And then second question is regarding spending trends. You've done what you said. I mean, you said you were going to invest in international. Now you're telling us international is growing, and it's showing in numbers, which is great. But there's a very large increase in OpEx this year from a growth rate perspective. So where are we in that cycle? As we go into '19, should -- was '18 the peak in terms that growth rate and in terms of that spending cycle? Just even directionally, how should we think about spending into '19?
Yes. So I mean we obviously haven't guided to '19. So it's hard to comment in that respect. But what we have said and I think what we -- I mean, to your point, we see a tremendous opportunity globally to take advantage of the land grab. And I think as long as those conditions and dynamics remain the same, we will continue to invest and spend to take advantage of what's in front of us. I think we've demonstrated that we've been able to do that in a pretty prudent way, and I would expect that to continue in that respect.
Our next question comes from the line of Jack Andrews from Needham & Company.
I was wondering if we could drill down a bit more on the significance of the 2018.3 release, in particular, because I was wondering, for example, if support for Python is really going to open up Alteryx to perhaps a new group of users. And I was -- the follow-up question to that would be any further comments on the significance of visualytics? I mean, I'm just wondering if the capability of visualytics, for example, may allow analysts to perhaps skip the need to export outputs to a BI tool, in particular.
Sure, great question. So yes, we did roll out 2018.3. We are investing very nicely into the R&D team because we serve 2 audiences that makes it difficult to know what to do next. I think you've seen the Magic Quadrant. We're the only publicly traded company on the leader side of the data science and machine learning quadrant. And so by serving both the Citizen Data Scientists for the analysts living in complex VLOOKUPs in Excel, along with the PhD statistician, we continually have to make the platform easier to consume for the non-coder, while we advance the cost for higher-order analytic outcomes for the trained statisticians. So most of the 2018.3 release was around those kinds of initiatives. So we rolled out additional data support for Oracle and SAP HANA. We added Spark support for Azure HD or Azure HDInsight, I guess, they refer to it as. We rolled out the pipeline tool, because we, one, want more sharing between the quants and the citizen scientists. The citizen scientists are still getting up to speed with building algorithmic processes and understanding those capabilities. And so with Python, for example, we've expanded the capabilities of the platform in an almost infinite way because you can instantly execute Python code in the Alteryx Designer. And so we already see a nice traction of PhDs and other people who are not PhDs but write Python code to be able to execute these workflows, create new tools in Alteryx. And that's really the difference between a platform and a point solution that you typically find in the marketplace. Regarding -- I'd say the other thing that's really important for everybody, whether it's the scientists or the PhDs is, we implemented things like caching in workflows so that if you're prosecuting 1 trillion rows of data, you don't have -- you can cache your workflow up to that point and not have to rerun those capabilities through again, saving just tremendous amounts of time and workloads at executed memory. Regarding visualytics, we actually think we've isolated one of those things that happens in the tech world. I think the threat to all these vendors is that eventually somebody wakes up and realizes that they don't need to visualize anything anymore. They need to deploy machine learning algorithms. And so we have taken the notions of interactive visualization away from any endpoint dashboarding, recognizing that people still love Tableau. They still love Qlik. They still love Power BI, even our interactive dashboarding capabilities. But we're seeing more of them execute inline visualytics because they want to see and understand their data at the beginning of their journey, in the middle of their journey. When they build an algorithmic process, they want to actually see a lift chart, for example, if you're building a regression aggression model. So by the time they get to the end, machine learning can take over. So if we really believe that data science will lead the world, visualization has to change.
Our next question comes from the line of Tyler Radke from Citi.
Kind of a follow-up along the similar lines of questions on the data science. I was just wondering if you could talk about your opportunities today, maybe the mix of customers that are kind of the non-technical crowd, advanced business user versus the data scientists. And maybe how that mix has been changing?
Well, I think remember the proportional disparity between the 2 groups is huge. There's 30 million citizen scientists or soon-to-be citizen scientists, and there's only about 1 million, 1.5 million PhD statisticians around the world, and their needs are very, very different. They all have the need to get the data ready for an analytic outcome. That means they've got to have a complete set of capabilities from regular expressions to Fuzzy Matching to all the capabilities we have in the 250 tools inside of our platform. But most importantly, the scientist wants to be able to continue to work on the hardest problems in the organization. They want to write code in the process. We refer to them often as the astronauts because there's typically only 2 people who get shot to the moon, and these are the people and organizations who are solving the hardest problems. And so they want a tremendous amount of flexibility in the kinds of algorithmic processes and machine learning models that they build. The -- they're either working in R or Python, and they're coming out of school with those skills. They're not coming out of schools with SaaS skills or certainly not asking to go work for companies that are using the last great analytics company software. They're trying to stay towards modern platforms for modern enterprises. The citizen scientist is the one who's drowning in Excel and worrying about complex VLOOKUPs, trying to do data reduction to get big databases into 1 million row limit for processing in Excel. And I think that there's probably more fiction written inside of Excel than inside of Microsoft Word, and so we're addressing both of these personas with a single platform with sharing mechanisms to build that bridge so that companies can actually get digital transformation success.
Great. A lot of good one-liners in there. A question for Kevin as a follow-up on -- just looking into Q4. Obviously you've had a really strong quarter a year ago with calculated billings, the metric that everyone looks at, growing north of 60%. I'm just curious how you would be encouraging us to look at that setup heading into Q4, just anything to call out in terms of seasonality. I mean, I know you mentioned there's a little bit more favorable seasonality from a rev rec perspective in Q3, but just any thoughts on bookings or billings basis heading into Q4.
Yes. Thanks, Tyler. I mean, we don't guide to calculated billings. We obviously understand the importance to you guys. What I would just say is go back to Q4 guidance and look at that relative to growth rates and how we think about the year.
Our next question comes from the line of Brad Sills from Bank of America.
This is actually Jacqueline Cheong on for Brad. Can you talk a little bit about how the new offerings, Connect and Promote, are doing? And any color on product traction and adoption by new or existing customers would helpful.
Thanks for the question. So we actually are doing pretty well with both products. As we have said from the beginning that it's probably going to take us 4, 6, 8 quarters before we can really get our hands around what's happening. There's obviously been a lot of development work in getting a unified look and feel of the 2 products relative to Designer and Server. We have seen some early successes. I think we came into the acquisitions with the hope that large enterprises would buy the entire platform. And of course, we have seen that. We weren't certain whether or not people would land with Connect to make sure that they had the right kinds of cataloging and asset management before they deployed lots of Designers. We have seen some standalone lands with Connect. We've seen people land with Promote by itself without buying Designers, and part of that is because these personas that I was just referring to with Tyler's question is that sometimes the analyst just wants to solve a problem inside of Designer that the astronaut, the PhD statistician trying to play algorithmic processes, whether it was built inside of Designer or not. So we're still gathering a lot of cohort data on this. We're encouraged by the early results, and our hope it would be that as companies begin to deploy large tranches of Designers and Servers across the enterprise that more of our customers will embrace the end-to-end platform.
Got it. And then for a follow-up, you guys have one of the best net revenue retention rates. Can you double-click on how that trend is holding for new customers? And how are or how do you think the new offerings, Connect and Promote, will affect that?
I don't know yet. Again, we will need -- in order to do anything around net retention on product specific for Connect and Promote, we'll need at least another 4, maybe 6, maybe even 8 quarters. It took us 12 quarters when we went to our land-and-expand model with Designer to actually have a good optics on what was happening. Because we don't land that often with either Connect or Promote, it may take us a lot longer. So I don't know how it will impact it. What we do know is as long as we maintain an NPS the way we've maintained it, as long as you provide a community where people can learn and gather information and share insights and get up to speed quickly, I wouldn't expect there to be a tremendous difference in NPS or net retention.
Yes. And I guess I would point you to commentary we made on the last conference call where we did talk about how newer cohorts have been performing a bit better than some of the older cohorts. And as you think forward, I would expect that contribution from new products would be an accelerator to net retention. So hopefully, we'll see that play out.
Our next question comes from the line of Ittai Kidron from Oppenheimer.
A lot of my questions have been asked. I do want to, though, Dean, kind of ask you again about Tableau and specifically, platforming. Clearly, they were very bullish on that yesterday, and they've already seem to be moving very quickly from a feature enhancement standpoint. They've talked about 40 new features there. I know their product is not anywhere close to what you're doing. But still, are you seeing -- what are you seeing from Tableau customers? Are you still seeing very good attach rates with those type of business activity? And is there anything that makes you concerned when you look at competitive landscape here?
No, not at all. We -- as I said, sequentially, we've grown faster in Q3 than Q2. We believe that what Tableau has done is great. It's necessary for them. It's a lock-in strategy since they're really writing to ease in spreadsheets. I would do the exact same thing that they're doing. Remember, our focus is on higher-order analytic processes, and we have very different strategies in the market we serve. While there's a similar personas in the enterprises, they're doing different kinds of things. We're focused on the entire continuum of analytics, not just the descriptive and diagnostic side of analytics, which is probably the lowest value in the value chain for analytic processing. So we haven't seen any change in this. We actually see this as a tailwind, as we said last quarter, that we expect that as they educate people on the value of data prep or visualization that people will understand as they march up the continuum to algorithmic processing and predictive modeling and cognitive sciences that they'll move to a true end-to-end platform for their work. At the same time, I'll just iterate that we have a great partnership. If anyone of you had gone to the Tableau TCC in New Orleans, we have the biggest booth. We have the most activity. We were mentioned on stage. We continue to engage with their salespeople in the field, and so we will enjoy that partnership, and we're excited about what they're doing. It's helping everybody.
Got it. Maybe I can follow up on that. If you can help us think about the breadth of adoption of visualytics with your customer base, how broad is it? And how often is it the sole or main visualization layer that customers use with your solution?
Well, what I see in the marketplace, I've been on the road around the world the last 35 days or something like that, and I met with dozens of customers in every industry in many, many countries. And the most common implementation actually is multiple dashboarding capabilities. We -- it's uncanny how many large enterprises I go into where there's a tranche of Power BI, a tranche of Tableau, in some cases 3 with Qlik. There's usually 2. Our customers use our inline visualytics for a different purpose. Those organizations don't offer that capability, so we did start to track our -- using telemetry, our adoption of inline visualytics. I don't know if we'll report on it. I haven't seen it myself yet. But we're not building visualytics to replace dashboard. That's not our intent. If we wanted to go fight in the bloody ocean at the top of the stack, we would have done it a long time ago. We are in a space that is -- where there's little to no competition and a much larger TAM. The TAM is a $30 billion space, and we continue to enjoy a market that is uncrowded.
Our next question comes from the line of Rishi Jaluria from D.A. Davidson.
This is actually Hannah on for Rishi. Nice to hear about such strong international growth you had this quarter. I was wondering if you could talk about if there is [ this ] in adoption of your products abroad versus domestically? For example, is there a disparity in advanced analytics use cases across geographies?
That's a good question. We watch the data very closely in our trial experiences. Trials, a 14-day trial is a preferred way for people to experiment, to do a POC, get to some outcome right away. There really isn't much of a difference in the land motion. Whether you're a scientist in the pharma space or you're an analysts in the FinServ space, your experience is the same. You're just trying to get to an analytic outcome. You download the trial. You build an analytic pipeline, a workflow and you save a tremendous amount of time if not driving top and bottom line performance. The expansion after that initial batch, and it's still roughly $10,000 land, a couple of designers fees picked up a little bit this quarter just because of -- I think what happens is when people leave their employer and they go to a new employer, they automatically buy Alteryx. They don't have to think about going through that trial process. But the path that people go through is, irrespective if it's a large enterprise, it has a key data officer or the -- an analyst doing it in a self-service way on a credit card in a departmental area, the expansion playbooks tend to change. If there's a chief data officer in the mix, we start the top down selling motion right away because somebody cares about operational efficiencies and getting to data science outcomes sooner. And the other companies will just continue to limp their way to greatness by adding additional seats each and every month or each and every quarter. So the land is the same. The expansion tends to take a different path depending on the persona. The countries, pretty much the same. I've been in Emirates and Saudi Arabia and Singapore and Tokyo. And everywhere I go, it's pretty much the same story: people swimming in VLOOKUPs or scientists who are unproductive because they're not able to deploy machine learning models.
Okay, that's useful. And then a second question, was there any vertical this quarter you saw increased adoption? I know you talked about supply chain and corporate finance last quarter, but any verticals that were most surprising to you?
Well, we called out the public sector. We continue to invest in state -- federal, state and local, and we've seen some great lands and great expands in that market. I think that governments around the world, because it's not just happening here in the States, but governments around the world are trying to be better stewards of taxpayer dollars. They're trying to do this with -- they're trying to attract the next-generation data worker who is willing to go put their souls into government work. And so I would say that it was more of a surprise, the kinds of land that we had, and I expect to see significant expands in the future. But I didn't call it out specifically around health care, but we stood up the health care vertical. It's a small team at this point. Health care is a messy space for lots of reasons. There's the payer side and the provider side, and there's tons of EMRs that have lots of complex data. There's disparate files everywhere. There's tons of use cases. And so we stood it up. Hopefully, in the next 2 or 3 quarters, there'll be reports and some pretty interesting facts and figures about health care. But as of today, those are the only 2 verticals that we have put tons of resources around and we'll continue to invest in, in the future.
Our next question comes from the line of Mark Murphy from JP Morgan.
This is Pinjalim on behalf of Mark. Dean, I wanted to talk to you about artificial intelligence. I mean, we're hearing in the field about a lot of companies who are applying machine learning and AI to the analytic process itself, not only just on the cleaning, prepping and modeling part, but also on applying the AI/ML to the model selection, on what I think people are calling autoML and whatnot. How do you envision the AI's role in Alteryx's product road map? And how do you get there through M&A or organic development?
Well, there's -- even advanced data scientists has a continuum around it. Our goal has been to amplify human intelligence while we begin to roll out artificial intelligence. And I think it covers off a range of things, things like assisted modeling, where the -- if you throw a black-box approach or an auto modeling approach to a citizen scientist, you're not really gaining anything until they actually understand what model drives performance, what datasets drive performance, what feature engineering needs to be developed in order to have a model come out. Second, you need to make sure that the person who is looking at the R-squared or the k-means centroid or whatever the model is trying to perform against, they actually understand what it means and how to decision off of it. So our goal is to make sure that these citizen scientists understand the benefits and the risks of advanced modeling. At the same time, we believe that the scientist is trying to get rid of the heavy workload that keeps them from getting to the moon. They're working around the edge cases. They're working at the really hardest things, and the last thing they want to do is build the next-best action model for Salesforce. And as a result, auto modeling comes to mind where it can be a black-boxed because the scientist knows what's happening. There are dozens and dozens and dozens of players in the analytics space. We see a lot of them. Most of them aren't standalone. They're going to have to be part of a platform if they're going to see success. That doesn't mean they won't have good outcomes because some of them will have to get acquired or will have to go away or will have to continue to raise money to stay in existence. So we're focused on it. I think that with our balance sheet, we're very careful about build and buy decisions, and we're careful about what we would invest in. We kind of -- we understand the road map on what we want. How we get there is still in play.
Got it. And Kevin, on the dollar-based net retention number, I mean obviously, very solid at 131%. But is there any way to understand the expansion versus the gross retention number, how the gross retention number has been holding up in the last few quarters and in this quarter as well?
Yes, I mean we don't explicitly talk to gross retention, largely because we think that the net number speaks for itself. I would just comment that we continue to see strong expansion activity amongst our customer base, and we haven't really seen dynamics shift in that respect for the 8 quarters that we've been north of 130%.
Ladies and gentlemen, we have reached the end of the question-and-answer session. And I would like to turn the call back to Dean Stoecker for closing remarks.
Thanks, operator. Thanks again to everyone for joining us today. We continue on our journey to make Alteryx synonymous with analytics across the enterprise. As we head into Q4 and 2019, we remain focused on building a business that we believe will have continued strong revenue growth and long-term sustainable profitability. We look forward to updating you on our progress in the future. Thank you.
This concludes today's conference. You may disconnect your lines at this time. Thank you for your participation.