Save Countersign Looker-on with airSlate SignNow

Add countersignature looker-on

hello thank you guys for joining us you know how we look er at looker my name is Jeff Kurr dot and I'm the VP of data and business technology will also be hearing from been Vivi who's our Director of Finance and Lisa Daniels who has the longest title I think in the company VP of demand generation marketing operation and analytics we've picked a couple different topics and we don't have a lot of time to cover them so we're gonna try to move quickly through these leaving questions for the end and all three of us will be around afterwards if you guys have more questions so VP of data and business technology I'm gonna cover a couple different topics couple of different segments when I talk as we talk give you some background on what's going on with looker at looker talk about some of the growing up challenges that we've seen and then some of the stuff that we've been able to do to overcome those challenges and then I'll leave you with a couple links to find out more so background what what has been going on what does look or look like at looker you'll find a lot of this stuff is similar to you something a little bit different we definitely have out size complexity everyone at looker actually knows how to write look ml in some capacity and so we have a lot of people contributing to our look ml model throughout the day we also have which is not best practice an exceptional number of data warehouses we want to be able to use a lot of the same technologies that our customers do and so we've got kind of one of everything that we try to use in some capacity which ends up leading to a lot more complexity for our ETL pipelines as well as for our modeling layer we also our customer zero and so I don't like to talk about eating our own dog food I like to think about drinking our own champagne and so as customers zero and thinking of ourselves as that it means we're getting that first build right out of our engineering team we actually get one build every night whatever is kind of the latest on the branch and so we get to see a lot of new features first quote we also get to find a lot of the challenges going on with those features in the build also tenure where our longest customer obviously and that that does have an effect in terms of our model and all of the decisions that were made four or five years ago and kind of where the company is today and what we might want to do with it and just the progression of feature sets and capabilities that looker has had as well so growing up right with all that tenure there's a couple things that we've seen can be a challenge legacy look ml we've got a lot of legacy look ml with so many people contributing so many viewpoints the organization's perception of data and what it wants to see evolving changing technologies we talked about some of those databases new ones are coming out all the time and so there's always decisions about what technology to change to and use and then analytics debt where everyone's got a new analysis that they've looked at and some of them are still good and some of them kind of have run their course so we've had to work to overcome these also I'm going to talk about a couple different things and in kind of mindsets that we've adopted that I've helped us to work through some of those challenges and to continue scaling is with the tenure and with those builds and with the complexity we have one of those is thinking of look ml like code look I'm a looper accessible it's very easy to write it's lightly code but I think there's some behaviors that you would see in a typical organization earing organization and have terms of how they treat their code that can be helpful for that temple for us and could be helpful for you to adopt one of those is look ml standards and it's having standards about how that code is written and I've got a little screenshot here of the beginning of ours peut standards page and we have a lot of standards around having comments on our look ml or having descriptions on all the dimensions and measures that we put out or putting timestamps on various things that we set up within our model the second concept within thinking of look ml like code is really around look ml code reviews and that's sharing that knowledge like you're making a decision when you write your look ml and you should be sharing that knowledge and we do this as we do this with almost every commit that comes through is having someone else look at it whose outside of what your part of the organization is or outside of your team to think about does this make sense within the look a male model does it actually work and will it will it translate for the rest of the organization and as you can see here a commit that was recently submitted and approved that I just pulled out and took a screenshot of the last one is the actual model implementation the the model is kind of the base of everything that's going on within looker right you're you're building your looks and your building your dashboards off of that model so we try to be very considerate about when we're going to make a new model or a new Explorer in that model like how is this gonna really work long term within that organization building lots of models and then having lots of looks dependent on them can make it hard to change those models or move away from them in the future and so being a little bit thoughtful and maybe bringing in a few people before making the decision is a tactic that we use and I think works well for us the next kind of concept is around keeping looker relevant and so I'll talk about a couple of things that help us keep lookers looker relevant and one of those is actually more of a behavior a thought pattern than anything and that's making looker that source of truth and this is super easy for us because obviously everyone works at looker and so they want to use looker and so it's automatically that source of truth but it creates this virtuous cycle where for someone that wants someone to want to look at data they want to look at it in looker because they want to make sure that it's true and jives with everything else that they everything else data wise in the organization and so their initial reaction is to get that data into looker and that that actual act that step of gating data into looker and making it available kind of forces a thought pattern for people in thinking about how does it fit in with the existing data that's there how does it work in conjunction with the data that and looks that have already been created and that step is super valuable when using your data or looking at it because it can help prevent redundancies in terminology or duplication of looks or concepts that that maybe should be discussed before actually going out and using them and shelling them to people and so making look or that source of truth helps with that and that kind of ends up helping with some of that analytics debt and bloat that you can see the other piece is is actually around cleaning up unused content so we use liquor its API quite frequently and we have a very simple bash script that just hits lookers API and it pulls out all of the reports that haven't been used as within the last 30 days and then sends out an email to the owners of that report letting him know like hey you built this it hasn't been used in 30 days if you still need it let us know if not we're gonna take action on that and some emails come back telling us to remove it or not to remove it or some people ignore it and you know we edit that list and then run the script again to mark those things delete so we don't actually completely delete it at that point because there's always someone who comes back in a panic or was on vacation but by marking it delete it will get deleted later during a future cleanup so the last concept that I'm gonna go into is really around the organization and this is actually at looker really core for all of those other pieces that I've talked about to really hang together well so I look or we have just a core analytics team and they really kind of own and think about looker as their own off of that and in sort of interacting with that core team is really the power users and so these are they have different names and different organizations some organizations don't don't have names for them in my past we've called them data deputies or data stewards but these are people who like to use looker maybe are writing look ml and who really understand their part of the business so someone in sales are really deeply no sales or in marketing who really understands marketing those power users are key for the terminology and the data definitions to actually be accurate and reflect what that part of the organization needs and they help that core our core team scale because there's no way our core team can know everything about every single business unit within the organization having that power user within the business really helps because now those external analysts and business users who maybe don't have the time to learn look MLR are more focused on closing the quarter can just go to that power user who's local and speak the same language and get the answer or get to that look that they'd saved that they maybe need to use in order to accomplish their job and so all of these people and these roles working together with little looker really help make all of those other activities that I talked about function in terms of code reviews in terms of cleanup in terms of those behaviors that are so important so that's real quick just a couple of the ways that we look at looker from my perspective I've got a couple places here that you guys can find out more about some of the best practices that we're applying but also some of the best practices we see from all of our customers what I've described here is the way that works for us and we're different and all of you probably have different scenarios within your own organizations and so what works best for you might be something else and we've tried to bring all those together and distill things and we've actually launched a fairly new site help looker comm which is kind of the porthole that we're portal that we're making for anyone to access more information about how best to use looker within their own company and then we also have some other outlets here that I think are pretty useful discourse is a place for posting it answers your customer success managers that's something very eats it someone very easy to reach out to and then just those new features that you've heard about today there's a booth over there to kind of see what some of them are and how they work a lot of those can impact how you use looker at looker and I think jive with some of the stuff that I've been talking about so far so that's my last slide so I'm gonna introduce our Director of Finance Ben Beebe who's going to talk about how financing accounting looker at looker [Music] thanks Jeff I'm ven bebe I've lookers director of finance joined the company about 50 people or at about 600 people now just first appreciate you all being here I know it's the end of the afternoon and everybody's a little bit tired so look we're strategically placed this Finance & Accounting session in order to wake everyone up with things that are typically very fast-paced and edge-of-your-seat so with that we'll kind of get into what we're going to talk about here today first I'm going to go through a little bit of what my function is like when you don't have a tool or a centralized data platform like looker which is sort of how I came into the company and then from there we'll move into scaling to a reporting environment that grows more with the company as the company grows from there we'll move into sort of nuts and bolts and show you some things that we've actually built in a demo environment but things that are examples that we actually use in our day-to-day reporting in the finance function and then we'll exit out with leveraging looker for business partnering which is really critical to the platform so first finance without looker and this is largely where what my role has been before I came to looker which as you can see from the slide all roads really lead to excel within finance Excel is a fantastic tool would never say that it's something that you could fully get rid of in any kind of a setting but when you don't have a centralized data platform Excel really becomes the place where you produce your work where you do all your testing where that then goes to be shared out amongst your business partners and there's a few fundamental problems with that and I think we're all familiar with it so I'll move pretty quickly but it's a manual workflow you're doing repetitive work in there these are things that you produce on an ongoing basis that need to be updated over time and then shared out but it's a it's you're downloading from different systems and then you're compiling those in Excel and sharing from there which brings about kind of urgent control issues you have different people working on the same set of tabs within a workbook but then you're all trying to mash those together at the end of the day to produce something that's usable the distribution of that then becomes static you're sending these files out in via slack and then my business partners are limited to the next version of that file whenever my team gets around to updating it with the most recent results and lastly as your organization grows sort is your reporting environment and so does the complexity of your reporting so I may be producing the same five pieces of work but those have exponentially more complexity to them which takes a lot more time to produce so how do you scale that reporting environment into something that works for you in the long run and this is gonna be a shock to everyone but looker has become our central solution for that problem first of all how is this different from the old world of Excel being the central platform Excel is still a critical tool for us it's something that we do a lot of our testing in we do a little bit of a modeling scenario planning Excel takes data inputs which looker does not you can't simply go into looker and type things in but once we've built things in looker that we find we want to save and preserve over time we then take those models and we recreate them in looker and we take that underlying logic and we save it so we can use that and reuse it over time the other thing that's different about this is this is a live view into the most current data in your systems at all time you remove the element of there being static data the element of having to send out new versions of this over time once I've shared the dashboard or shared the look my business partners can then go in there and access it whenever they need to and then the other part that you get from this is sort of an augmented data element I have these different data sources flowing in there your ERP for us it's NetSuite and NetSuite is now joined up with Salesforce so I can slice and dice my GAAP compliant financials by the infinitely many fields that are available to me in Salesforce because we've joined those two on a unique key and lastly the ability to share out so I no longer have to send these in emails or slacks across the company I can send certain pieces or parts that I would otherwise not want to share certain confidential information but parts of the company but others may need it so for example we may share with our CSM organization the number of invoices of the amount in invoices that are outstanding or past due we don't want them to have full visibility and all of our financial but looker allows us to share just a piece and to leverage this and models like customer health scores and other things so now kind of getting into the meat of what we've actually built the first thing here is our quarterly reporting package and it says quarterly but this really could be monthly annually what have you and this is all of our key metrics across the company so this is really meant for an executive level or management level view and we have things like revenue by type overtime we have operating expense as a percentage of revenue gross cash collections and it doesn't all fit on one slide but there's all the buzzy SAS metrics that you probably all know and love and work with your own organizations things like customer acquisition cost lifetime value all those are built in here as well and all the logic is saved off but one important thing to note here is that you know the power of looker or large power of lookers that you can drill into the bottom level this dashboard is really meant for executive level consumption or management level consumption so we've disabled that feature if people want to come dig into these things more we don't really want them to be coming or asking questions or trying to reconcile debits and credits and getting deep into accounting details so for this particular use case we've actually disabled that drill function and we ask people to come and work with our team and partner and then we can kind of explain things and build in a way that makes sense all right so full transparency this one is a little bit of a self-serving use case I wanted to pick things that would be applicable to the room and people would be familiar with but we also have a large contingent of our sales team attending join so this is a shot across the bow on this one this is travel and entertainment spend monitoring of liquor so as you submit expenses and Expensify that flows over to NetSuite and we have bi person detail of exactly what you spent and what hotel you stayed in and what night so that doesn't mean that we're actually going in and looking at it but sometimes just having it there is is enough so what you're looking at here is each one of these bars in this chart is actually a different person so if I wanted to say okay those purple bars they're a little bit thick and maybe a little bit small you can kind of see it we have Keenan rice here at global head of alliances who's got a kind of a big bar there in the last month and all is aboveboard here Kenan's been doing a lot of travel to set up our new Japanese office so the partner network really needed some more attention so there's been a lot of travel over there but let's say Keenan's in hot water and I wanted to know what Keenan was spending this on so I clicked there and I can go one step further and this is also probably a little bit small but I can see by category what Keenan has actually been spending his money on and then this is where you get really sick which is you can go actually down to the next level and I can see every memo that accounting is entered here so I know which karaoke bar Keenan was at for that third carrot for that third party entertainment well this is kind of just a fun one and it demonstrates the power of the drill where in the prior use case you really want that disabled the next one that I'll talk to here is the sales funnel model so this is one of the critical models across looker this is one that was built in conjunction with sales operations with marketing ops with finance and the sales team so what you see here the green bars represent liquor sales cycle so this says if you become an opportunity within our CRM you become a customer in roughly one to six months if you're going to become a customer at all and then on the right there where you see the two different colored shades of pink this is the actual conversion percentage of any given cohort so the lighter colored pink bars represent forecast periods those are immature cohorts as you move back towards the the darker color bars those are more mature or six-month-old cohorts where we can say we know for certainty that those are now six months old and roughly any given cohort of opportunities converts 27 to 30 percent of those into customers over time and this has been a really critical model for us to build in looker if this were to be in Excel this becomes a really difficult thing to get buy-in from man who I'm now trying to give them a budget to produce a certain amount of leads from this we're done working with sales ops and sales to try to figure out the sales hiring plan and when we need to have reps on board and then also the timing that is required to have those leads on board to feed that six-month sales cycle so there's all these different timing elements that are constantly in flux and if anything changes in that model then we need to adjust our model as well and this is one I think really demonstrates my last slide here which is that all of everything that I've kind of just shown you is leveraging looker for business partnering and it's kind of a warm and fuzzies point but it's also really practical from the standpoint of it's much easier to get buy-in from your partners when you're all working off the same source and they're allowed to to bring their subject matter expertise and have Lisa who can say this is how we should look at weeds and sales ops that says here's what a close one deal looks like and when it happens and then my team can then go and use that to model in the same place that we've all been working so finance is really a service organization and that's where we really leverage liquor to support the other areas of business and we try to stay out of their way in terms of them reaching their goals we just try to provide strategic insight and overall direction with the company so leverage liquor for business partnering we've got some great partners that I'm fortunate to work with and with that I'd love to introduce one of those partners right now who is Lisa Daniels VP of Marketing [Applause] hi everybody thanks for coming out thank you Ben yes that's my title I take full responsibility for it I am the vice president of demand generation marketing operations and analytics at Locker I made that title up myself but all of those things really do work well together so I'm very proud to be responsible for them so for today I think I pushed the wrong button today I have a couple of topics for you I'm talking about how we use looker at looker for marketing I'm going to show you how we have flexed and grown with the business in order to better support it I will also show you the data flow we have of marketing and sales data I think you're really gonna like that one and then I have two examples for you marketing operations data and tracking regional achievement for our European marketing team like Jeff and Ben mentioned flexing adapting and adjusting to the business has been extremely important for marketing and marketing analytics efforts it seems like it has been constant change play every six to nine months we're making some sort of material change to adapt when we started back in 2013 we spent the majority of our time focusing on setting up the systems for the proper tagging of data tracking of leads making sure were just prepared to build something bigger over the years we had to adjust we had to adjust to the business back in 2016 it became very clear to the team the marketing team that we needed to plan and forecast our marketing efforts much better so we adopted first touch attribution and that gave us some very strong but short-term metrics that allowed us to quickly fill the sales funnel since then we've adapted we've added influencer attribution the touch attribution and constantly figuring out how to do better web tracking for better optimization and effectiveness across the sales and marketing funnels and of course an important aspect to any analytics is bringing that data together let me show you how we have it a looker for marketing in order to get an insightful understanding of the marketing and sales funnel you really need to see as much data as you can here's how we do it today so we have a good bit of Mar Tech web testing web tracking some JavaScript onlooker comm and that is capturing activity tagging some activity and then we have lead capture through form fills of course we have a chat app we have third-party api's and of course the constant uploads of CSV files all of that goes into our marketing automation platform Marketo from there we do in a data append to that data to add a little bit more so we can have shorter forms on the website all of that goes into Salesforce our CRM and that is the point we combined the sales and marketing data from there it all goes into the databases I think Jeff talked about those and we then add in user data so that we can send it back to our marketing platform and do customer marketing and then finally finally finally finally it goes into looker our instance of looker and we have one marketing analytics Explorer that the entire marketing team at looker can access and the majority of the folks do have their individual dashboards it's kind of a challenge what is your dashboard anybody in the company can look at the information it may not have a ton of meeting for them but they're welcome to come and talk to us okay so let me get to the actual use cases I have two for you today marketing operations data and tracking goals for our European team our marketing operations team looks at a couple of types of data the first one of course is that marketing data pipeline as slide we just looked at they need to make sure it's working correctly number two they also look at what they're doing through tickets that get created for every project they work on so before I dive into this first topic let me give you some context so you a little bit better idea what I'm talking about marketing operations has the very important job of making sure leads are routing to the correct salesperson in a reasonable time they also oh and then we also use lead scoring in order to better prioritize leads for follow-up by the salespeople okay so that's some context for you let's look at this first chart so this chart is showing leads that have received a demographic score and a demographic score is a score based on information we have about the company the person works at size location industry of that type of thing and what you can see in this beautiful chart nice and simple chart answering one question so we had an issue the week of July 2nd it went from the high 90s of lead scoring to the low 70s what does that mean who cares well fortunately our marketing operations team found out about it because it's a very important issue they found out about it because they have an alert set on the data and looker and what this says to me is we are sending 30% fewer leads to the sales team that means we are potentially developing 30% less pipeline for future revenue not a good thing to have happen fortunately the team dug in and they found that the vendor the data a Penton vendor was not adding country code so we didn't know where to route a lead fortunately they did find a a workaround within the next week which was really good because it took our service five weeks to fix could you imagine the emails I would be getting if I had five weeks of 30% less pipeline being developed it would been more it would have been more than been coming to my desk so I was very happy they found it and fixed it the second thing I wanted to show you is the tickets and how our marketing operation team uses the tracking of those tickets so tickets get created for every project they create here is a dashboard that they use to look at those tickets by week by future date all kinds of things looking closer what you can see is what have they been working on a team that's rapid-fire working on things need to needs to pause and say what have we done and in this case we also have the ability to say what are we going to be working on because those tickets have an expected completion date this allows our head of marketing operations to say ok I have more projects than I have people maybe I need to get a contractor or there's a project being requested for a skill set we do not have so she can go be prepared to get a contractor or consultants ok now let me show you what we've been doing with our European team for tracking to regional goals so tracking achievement is important for any marketing team it shows you and the other teams that you are supporting the business and you have the sales team covered it also because marketing data has such a long lifetime you have the opportunity to have some early warning systems set up you're gonna hit the goal or maybe you're gonna miss the goal and you need to go talk to your finance team but this is a table that the Santa Cruz global demand generation teams the data itself is a sample but this is the actual table they use it's pretty straightforward it's not the most beautiful thing you've probably seen to here on the stage today but it is highly meaningful to the team what you have is the forecast here's what the marketing team says they are going to get for each of these segments and regions they've worked on this with the guidance of Ben's team finance team putting forth the final company goal I also have here the actuals what are we actually achieving for these different line items and then of course some KPI conversion rates so they can very quickly see how things are working or not working and then this year tada tada we have tracking for our European team the team's been growing quickly we've been getting them working with the data very quickly they've absorbed it fantastically so it's just great to see this so cleanly but the reality is we need far more detailed forecasting planning and tracking for the European markets so here's the top of a dashboard that our head of European marketing has this is again the top of a dashboard that goes then into business development goals sales goals it's all in one dashboard that the management team in Europe looks at and what you're seeing here is very similar to the other dashboard achievement a goal for a certain time period but you're seeing right behind me very regional specific information UK Germany France and this helps the head of European marketing do two things she can reassure those salespeople in the far-flung countries of Europe that marketing has their back and number two she can help guide the team and Santa Cruz that's helping her execute a lot of these things she can very clearly direct them with data on where she wants them to focus for the future [Applause] you