Empower your business with product qualified leads for Inventory

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i'm alexa i'm the ceo of focus and today we're going to be diving into how to even start with pqls what is a pql how should i define it how should i operationalize it at my company we find that oftentimes people are very overwhelmed by this and they start with oh no we need a 50 person data science team and we don't have the resources or the technology to do this but really if you take a step back and really think about okay what are customers doing today what have our sales teams noticed and start with this hypothesis we've seen it proven out that it can work for a lot of companies so we've developed a framework based on how experts think about this from dropbox atlassian slack etc and have been onboarding a lot of our initial beta customers using this framework to then think through pqls so we did round one of this pql brainstorm with a small group like this it was great they requested another one so that's what came out of it so how this will work is sandy's going to go through an overview of pql's product qualified leads feel free to interrupt us and then i'll dive into the brainstorm so sandy i'll pass it over to you awesome thanks alexa i'm just gonna share my screen here everyone see it good all right perfect so let's just run through the agenda for today like alexa mentioned we're going to go through an overview of pqls i'm really starting with some basics please interrupt me at any time if you have any questions we're going to run through our step-by-step framework for defining these pqls and i want to call out this segment this section on segmenting pqls 101 which we've uncovered over the last few weeks as being incredibly important to setting yourself up for success when it comes to pqls so we're going to dive into that as well and then we're going to make it all reality with our live workshop with our friends at weight while so tim's going to be running through their examples of how they're starting to think about pqls and we're going to be running that workshop through fig jam and i'm going to pass it over to alexa for that so i'm going to try and be fairly quick with the slides here but again raise your hand if you'd like me to pause and i'm just going to ask alexa to let me know when to pause with a thumbs up so what is a pql like eddie said there it's you know all these acronyms are everywhere they're really easy to talk about in articles but hard to articulate when you're trying to think about your own product so at its simplest pqls are users who have experienced that core value of your product the hard part is figuring out what core value means to you and your product and what signals in the product what aha moments in the product really map to that core value the way that you start to define and set up your pqls this process that we're going to run you through is going to help uncover what those aha moments are what those moments of stickiness are but the pql's at their basic and that's why pqls are so great to use in when you're conducting product led sales because it gives your sales teams your go to market teams a good understanding of not just a potentially great customer or a great user but someone who is really using the product and you can point to specific things that they've done that make them a great user we're going to talk more about mapping pqls to specific goals and customer segments when i get into the segmentation section but i wanted to call it out here as being really important kind of as part of your defining your pqls we're going to run through these five steps for defining that first pql so identify who is a good customer so your icp understand how users get value from your product identify which users are showing buying intent formulating that first hypothesis and then how to test and iterate that hypothesis before we get into okay how do i actually operationalize all of this within my existing go to market workflow so this is like the bird's eye view of how we think about defining your pql pqls are made up of these three categories customer fit product usage so starting with customer fit like i said we're talking about how closely does this user account match your icp so some example signals that you might want to look at are what are the industries that i want to go after what are the geographies that i care about what are the company sizes that really correlate with a good customer fit and what are those specific roles that also indicate a good user then you want to move on to product usage how does the user account interact with your product and again like i said this is going to be very specific to your product so we've got some examples here like dau invite sent but this needs to really correlate to how folks get value in your product how the product grows because invite scent may not be applicable to your product if that's not the primary way it's growing so really have to get very deep into your product and how people are using it to understand that and then buying intent i feel like buying intent is often really forgotten as part of a pql definition but to me it's like the cherry on top of the ice cream sundae because when you have really great customer fit and you've got really high product usage that can be enough for you to say this is a hot lead but if you've got the buying intent and you're not capturing that you may be missing those like incredible opportunities because it's really telling you hey i am ready to buy signals of that are pricing page visits contacted sales clicked on your website they're accelerating how many seats they're adding week over week or they're about to hit a plan limit and i think why it's often forgotten is because a lot of this data is not easily accessible it's usually trapped somewhere whereas customer fit data you might have in your crm product usage data you will often have in your data warehouse sometimes that buying intent data can live maybe in like your marketing tools or somewhere else and it sometimes gets lost but i wanted to highlight it because i think it's super important so how do you actually start to think about each of these categories so starting with customer fit you want to start picking signals and starting to figure out what that icp definition looks like for your pql and in order to do that you should ask yourself some questions i think most of these questions are pretty obvious like what size company do our best fit customers work for what employee count or thresholds are best for engaging a salesperson but a lot of this will be really looking through your existing data and then also talking to your customer facing teams in order to start answering these questions and starting to identifying like employee count greater than 100 is what we care about for pqls or we care about the fact that we own like we only want to look at latin america because that's a target for our sales team so that's those are the types of things that you want to start to dig into here for product usage again like i said it's all about getting to signals that are going to correlate to aha moments or to that core value in your product so you want to understand what are those moments that a user goes through before they become activated because those are leading indicators of them getting value and then you also want to look beyond getting value so what are indicators that they're becoming sticky in the product and that they're going to be you know long-term users and have really high retention one interesting way that we've kind of gone through this exercise with customers is actually asking them what correlates to a bad user experience so like what is the unhappy path for a user in your product so by looking at the inverse you can kind of get down to okay these are the moments that do correlate to a good user experience and these are the ones that we want to surface to our sales team and then finally with buying intent are there ways a user can indicate that they're interested to buy or buy more what are and this isn't always just exclusively things that happen on your pricing page or hand raisers but it can also be in product actions like one thing that we talked to a recent customer about was they were actually tracking clicks outside of the the user's plan so a user is on a plan there are certain features that are gated but they're visible to the user so seeing how many people go and click in that kind of gated area which indicates interest in perhaps upgrading but wouldn't be something that you would be able to service unless you have that in your product but something really interesting to consider if you don't have it already also reaching out to support so not just reaching out and contacting sales but also reaching out to support and asking questions shows indications that they're deepening their engagement with your product and perhaps are looking to do more which would naturally also mean that they perhaps want more capabilities within the product which could lead to an upgrade i've gone through a lot so i'm gonna take a quick pause and and just see if there are any questions on these three categories we just talked about one question uh regarding an icp do you find it's beneficial to to determine that by segment or like by product sku or just universally have an icp that is a great question and we're going to talk a little bit more about it when i get into the segmenting portion of this but the tldr is it's going to depend on how you want to set up your go to market workflows so i think it may be helpful kind of at the top level to have a broad icp in mind and then you want to be able to segment that down based on what you're trying to achieve through your product led sales motion or through this project with pqls so that may mean segmenting it by your customers like this is my customer segment that i'm going after or it might be like you mentioned by product sku or it could be by product led sales goals like free to paid conversion or see expansion great question though we're going to talk a little bit more about it and i'll add that it's very experimental like within specifically icp you might have different segments by industry or geography or company size you might have an smb mid-market in enterprise and from there you'll have different pqls attached to them as you get more and more mature you'll have more pqls right like your qualified lead for smb in the united states might look very different than enterprise in europe all right so step four in the process is to take all of that kind of digging through and reflecting and turning it into an actual hypothesis for your pql so one of the questions is how do i actually get to creating this hypothesis so we recommend talk to your customer facing teams and find out who they believe are the most highly engaged users or any other patterns that they could tell you that exist within those really engaged users are they from a specific industry do they tend to be a specific role just trying to mine those customer facing teams because they are the front lines and they talk to your customers and users all day if you have product data then you want to be able to work backwards from that product data to kind of start building out these cohorts of users and trying to group them by similarities any patterns that you can uncover in that data and then of course you want to start segmenting them based on those customer fit criteria so company size geography business model and all of that becomes the basis of creating a hypothesis for your pql and we're going to make this real when we talk to tim in a little bit but here's an example of of what this could look like so once you've gone through that exercise and this is an example of slack you may decide that i as the team at slack our ideal customer fit for a specific pql is that they are in the technology industry they are in the united states and the company is between 500 and 1500 employees and we're specifically targeting the head of operations and now all the product usage signals that we've got here as an example makes sense for slack and specifically that vp of op signed up and invited 35 people because what we're trying to do with this pql is really uncover opportunities to expand within expand a free trial account to a paid account and then that's why with the buying intent we want to know when the vp hits talk to sales or the vp has visited that pricing page so what this looks like from a testing perspective is i've got my hypothesis for my pql and i've named it the mid market free to paid conversion so again thinking as if i'm slack i've given it a description and now i can use this template to like align the rest of my team on how we're going to build out this pql test it in a real world scenario and then turn on that iteration process until we feel like we've gotten it right so with this template you want to give your pql a name you want to give it a description you're going to figure out how you're going to measure success so in this case we want to see if there is some sort of improvement in the pql to sql conversion you want to choose the parameters of your experiment so is it going to be an a b test or is it multivariate i would recommend av like keep it simple are we how are we going to split up the team are we going to do 70 of the team gets the pql and 30 don't is it gonna be a 50 50 split and then how long are we going to run this experiment for so 90 days 60 days 30 days kind of depends on your sales cycle and then you can use this template to document the impact of what did you see what happened when you ran this test positive results negative results inconclusive and use this as a as a tool to kind of align the entire team that's running this experiment so again i'll pause see if there are any questions about this process right take silences we're good once you've done those initial experiments and come up with a few kind of starting hypotheses around your pqls now we want to get into the actual operationalization and if you're here last time you maybe saw a slightly different version of this chart and we're now updating it to reflect what we've recently learned which is that you got to start with segmentation before you get into thinking about routing and assigning pqls and trying to get to you know the insights in action segmentation and prioritization of your pqls is perhaps like the most important thing and then you want to get into assigning your pqls so how are you going to route those pqls to the right people and then how are you going to get them the right insights so that they can take the next next best action which could be either manual or automated or you know orchestrated through some sort of tool so we're gonna specifically just focus on that first piece segment and prioritize because we believe that segmenting pqls is the key to really effectively operationalizing them and when we're talking about segmentation what we're really talking about is defining the who what and how so we want to figure out who we're trying to target what messages we want to share with them and how to communicate that message so it's about defining a pql and also then thinking about that downstream playbook that you're going to apply to convert that pql so based on talking to our customers and some research that we've done within the pls community we've identified these three kind of most popular ways to think about segmenting pqls so sales readiness product led sales goals and customer segments so the first one is sales readiness and this is a pretty simple like two by two matrix where you've got product usage and customer fit and when you go up into the right you've got your sales ready pql because you've got incredibly high product engagement and excellent customer fit these are your like most ready most hot leads and you want to you want to think about them as being kind of the breeding ground for where you put your most expensive resources so sales ready pqls should really be taken on by your account executives and they are the ones that are going to be doing the follow-up and thinking about what playbooks to run so when you think about defining the sales ready pql you're also then thinking about the downstream actioning of that pql so sales ready pql taken by the account executive and then they automatically know what playbooks that they need to run because you've already defined them on the other hand if we look at sales assist ready this is somebody who's got you know good product usage but not great perhaps they are still kind of in that onboarding process they haven't quite gotten to the real value yet but they are an incredibly good customer fit so this is a great place to deploy your sales assist team or whatever you will call that internally product specialist or your onboarding team and they're going to be able to nurture that lead to get deeper into the product perhaps they're unblocking them getting them past some friction points so that they can increase their usage because they are incredibly good fit so if you can get them to hire usage then the sales assist team can pass that lead on to your account executives if we look at the the bottom half of this matrix this is where you you see great usage on one hand with keep warm this is a great place to put your sdr bdr team perhaps this cohort needs more qualification because they haven't quite risen to the level of meeting your customer fit thresholds or your requirements but doesn't mean that they're not good leads in the long term so you kind of want to get them either qualified by your sdr bdr team or you want the product or your marketing channels so your self-serve channels to continue nurturing them until they reach the level of being a sales ready pql and then on the bottom left is people you don't want to touch you don't want your sales team to ever have to reach out to low product usage or low customer fit so these might be users that have signed up for your product but are just kind of hanging out at this point you want the self-serve channels to either nurture them to a point where they are interesting to your sales team or that they churn but you should never put any of your expensive human resources let's say like human touch points on these leads the next way that we like to think about segmenting pqls is by product led sales goals so you can have a number of product led sales goals that i've listed up here in this chart so seed expansion free trial conversion enterprise consolidation startup program partners free to enterprise conversion these are all possibilities of why you are embarking on product led sales one way to think about your pqls is aligning them to these different goals that you might have so in this example i've taken free to enterprise conversion as my example so i want to create a pql specific to that and so my definition for this pql is that they sign up for a free trial they fit my ideal customer profile and that they've got significant product usage some example signals would be that this account is not a paid customer yet but they did sign up for a free trial they meet my target industry and they are considered an enterprise customer so that they've got a thousand plus employees and that this specific account has invited 10 plus new users within a certain time span and then i want to create an owner for this and obviously because it's free to enterprise conversion i want to put my account executive on it so you can see kind of by starting with that goal you can make your pql extremely specific to the goal and then you can also be very specific about who owns it and what playbooks to run against it and then the last one is probably one of the easiest ones to do because most go to market teams already do some level of customer segmentation so if the primary way that your go to market workflow is segmented today is by your customer segments or by your industry verticals then this is probably a great place to start for pqls as well one way that i like to think about it is by thinking about how your place pricing plans are segmented so if those are segmented in some way by company size where you've got you know your lowest tier for individuals and smb and then you've got higher tiers for mid market and enterprise then think about making your pqls kind of fit that same segmentation perhaps your product has different behaviors across industries so a tech startup acts differently than a financial services company in your product then you might want to think about segmenting your pqls that way so it's all about figuring out like how these filters fit into your existing go to market and starting at that point because you're going to make your pql process a lot simpler that way all right before i hand it over to alexa we just talked a lot about segmentation so i want to see if there are questions but i also want to say that alexa is going to be running through this live example and she'll be talking a lot about what we just reviewed so let me know what do you think let's do it for any questions before we dive in okay sandy are you going to share your fig jam or do you want me to actually do you mind do you mind sharing your screen and i can walk through it yeah once amazing okay so that was a lot of information on everything from defining your pql so then operationalizing it and so you might be thinking oh my god how am i going to start this for my business it's just me doing it or you know i'm trying to think about it for my organization and i have to get buy-in from everyone everyone will say the way to do it take a step back start with a couple pqls from your goals and what you're trying to accomplish and then we can worry about operationalizing it in the organization yeah we can share it for sure christine asked if the presentation will be shared so what we're gonna do today is talk about that first part of what are our first pqls that we can then iterate and experiment with and then we can also send the presentation to think through more okay how do we tell this is the right pql doing the a b testing getting everyone bought it and et cetera so tim we had an awesome pql brainstorm with him in the weight bile team and he was kind enough to offer his insights and pql genius brain to do kind of a mock workshop with this group so the the process that we're doing today is something we really do start out with with our early beta customers and hopefully this will be helpful for you if you want to run this internally with your company we recommend usually getting folks from the go to market side so sales marketing customer success as well some of the data side analytics all together to start you know hashing out what are these important metrics and then taking it from there so without further ado i'll dive in with tim but i really hope that everyone can unmute ask questions say how would this work for my organization so that we can make this really helpful for you all so you feel like you can leave with the tangible uh and practical skills so first what we're going gonna start out with is segmentation so i think earlier evan asked the question around okay but how do i segment my business there's a lot going on here there's two questions to think about first is one okay who needs who needs pqls and then two what are their goals so we'll start with you'll see here in the fig jam we typically have everyone access the fig jam and start playing around and writing their thoughts but we'll read it for you this time tim so that you only you only have to speak our first thing i'd love to ask you is okay who needs access to pqls at your organization and if you can just rattle out those rules and why they would need pqls yeah absolutely so i think first and foremost we have our sales organization so that's you know the vp sales our aes and you know in certain maybe niche niche cases are account managers themselves and so these are the people that are actually converting some of these high potential users into into a higher you know upgrade class i would also say that i would love access you know being being part of the growth squad and so that would be myself my boss the vp growth and like really across i guess the marketing organization there is there's value there and so that would be jenna who's also on this call you know our life cycle marketing manager as well as our acquisition manager so really really across the growth and sales team we see this as a pretty pretty high impact initiative and have you know frankly frankly put a lot of time and effort into into trying to integrate you know these types of ideas and so i know everyone is pretty excited to get to get their hands into some focus data and so looking forward to to next steps with our personal our profession yeah totally awesome so it sounds like sales team they want access to high potential users so maybe users that have been on the product for a while or have done something in the product to trigger that the sales team should be reaching out growth what's the use case for you you want to see just what's going on with the data maybe some scoring models how would you use how to get access to pqls and the product link sales process so i i see you know the growth team as a little bit more of of kind of the enabler of the sales team within this this relationship and so so we've done a lot of thinking about you know what types of of of statistics are we are we looking at in terms of identifying high potential users and so so so we're focused on on building these these scoring models so that we can have the sales team just you know focus on a name in front of them an organization and go sell yeah it's a really good point in what we hear a lot like we don't want sales reps digging through data you know the growth squad should be focused on what are the pqls and then how do i surface them so sales reps can say oh this is who i should go after and why and then marketing i'd love to push on that i don't know if jenna's here but is this more to say okay how do i are they more in the creating pql side of how do i embed mqls in there or is it also okay let's use all of these pqos and mqls to inform our marketing outreach i would say it's probably more of the latter and jenna correct me if i'm i'm wrong here instrumental in in kind of developing our thinking around you know what constitutes um a p2l and and you know frankly these the success or failure of some of our pql initiatives will weigh heavily on on jenna's own campaign success so so i'm sure she's no pressure yes no tim is correct that it'll help inform our messaging and marketing efforts i love it cool okay so now i'm hearing a lot of goals around product qualified leads and pls i'm hearing you know you want to convert high potential users so whether that's free trial or self-serve users converting them into paying or maybe going to a higher plan and i'm also carrying more personalized customized data driven marketing campaigns what other goals are top of mind for you top of mind goals so those two are definitely like most important that's basically how can we drive leads for our sales team without spending money really identifying users that we already have and and upselling them in in wait while's case is we we see a ton of potential in expansion across larger enterprise organizations which may or may not be well interconnected and but so identifying these organizations and exactly figuring out that these that they're on our product and and that they are you know frankly interested in it is is a is a fair goal for us i love it we hear enterprise consolidation a lot where there's different pockets of users within an enterprise how do you roll that up or you can think about it as seed expansion or you can think about it as use case expansion francois i know you're also on the call anything we're missing pretty good job sorry i'm at my lunch you don't necessarily want to see all of that but but no i think that's that's really spot on i think that seed expansion is really something we're excited about definitely and uh your goals are yeah nothing to add awesome okay i'll say after the segmentation piece if you had more time what you can do is say all right what are then the pqls for each of those three goals so what defines a good lead for the first goal of converting high potential users to free to page then do that the same for marketing do that the same proceed expansion as well as roles so you can have a different pql for vp of sales and the ones that would be funneled to the marketing team for now we'll do it more high level just because we have 20 minutes but i just wanted to throw out that that's what we mean by segmentation so we'll go down next if we can go down to the pql brainstorm and i'll go over this slide which you've seen before around the three categories for defining pqls we have customer fit product usage and buying customer fit all about the icp how closely does this account our user match it product usage how are they engaging with your product buying intent did they do something to indicate the readiness so we'll go through each of these and you'll see if they need to move over to the right a little sandy so if you what we'll do is usually everyone in the company will give them a three-minute countdown to go through and say what are all the perfect customer fit treats what are the industries the personas and you usually will see some consistency of some inconsistencies across marketing sales customer success growth etc which then starts to drive the discussion but for now we'll do the sticky note writing for you tim but we'd love to hear what are some things that come top in mind for you for what a good customer fit is whether that's industry geography persona role etc so currently wait while is most active in north america so in terms of geography we're definitely focusing on the united states and canada right now hopefully that won't be the case for for too much longer and we can expand internationally but i think in in for this exercise that works in terms of the size of enterprise organizations that we've focused on we've generally like to look at companies that have greater than at least 500 employees but you know really ideally a thousand and this usually constitutes revenue of uh at least 50 million dollars if not a hundred million dollars like we really feel that is our sweet spot in terms of the types of of roles that we're looking at within the within organizations we've looked at you know iit tech managers you know store general managers in the case of you know some of our big box clients let me think what else is on our mind we are industry agnostic for the most part and so so that is you know somewhat uh somewhat of an advantage for us in terms of targeting but not super helpful for this activity what i will say is that we are least competitive within the restaurant sphere and so potentially that's like a negative customer fit yeah exactly but i feel like we've we've covered a lot of what what i've spoken about francois janna anything else come to come to mind in terms of customer fit i really like the idea of a negative customer feed actually i think that's really good inside and i don't have much to either i just wanted to kind of double click on that idea that you know customer feed is not only positive very good here also like negative i love that i love this 100 those are the ones that you if you remember on the bottom left like funnel them into self-serve like they'll figure something out and they'll have a valid use case but focus more on the ones that you know will be the big dollar signs any questions so far from the rest of the group does this feel like something you could do internally so far okay i'm getting some headphones cool all right we can go into product usage then so same deal tim would love to hear what are the actions in the product that you say okay this is a good user or actions that correlate with i think this user is going to be highly engaged or specific features absolutely so i think first and foremost is we have a series of probably about nine or ten of our features that we consider our onboarding checklist and so so i would i would throw those all in i know in in isaac and ice brainstorm the other day those were included so so i'd start there and so basically is if a customer completes all you know nine ten of these we we see a high correlation with with longer term commitment we also look at you know really really focusing on like the first 30 of activity and i think it's probably similar with with a lot of y'all's products and so you know how many users are our accounts adding within the first 30 days how many times are each of these users logging in to wait while within the first 30 days and we feel we've we've done some of the analysis on this and we really feel like these characteristics are really highly predictive the speed to a certain threshold of of users in weight while's case we've found that accounts with two or more users are are probably probably 50 or 60 times more likely to to be committed long term to our product versus you know even just one and so there's a huge inflection point in that graph there we've also found that you know substantial customer activity within an account especially within the first seven days is is highly predictive of of long-term success and so we we like to focus on those and then we see focus as a tool to kind of bubble up these these users who may have slipped through the cracks and really get them in front of our sales team and the last thing that i'll note here is we also have part of our product is is is a messaging service for for for our customers to reach out to their to their guests of their business and so we have quotas for for these messages and so when when an account is you know within a certain threshold of of this quota they they are ripe for for upgrade just because in terms of use case and they they've found a use case for for our product and have been using it and so so that we like to lean in there these are awesome and i'm curious how did you come up with these hypotheses was it looking through data or was it talking to your sales team or is it just a gut feeling so there's a little bit of both there there was definitely a as with any like great exploration i think there's there's an initial brainstorming phase and so that involved you know talking to to the sales reps really getting getting a sense for what they thought customers cared about and especially talking to to some of our customer success managers that that was was highly highly useful and just bubbling up like what actually matters to real people i think you know especially in in my my field analytics you often lose the little touch with the customer when you're digging through reams of data and so so getting that sort of first-hand albeit anecdotal experience who was really helpful in just orienting ourselves around around a series of these of these features but at the end of the day it's it's it has to do with this kind of testing we've ran some some some quantitative models to see see which which of these features are our most predictive and and had a huge huge list of features originally included and so we've been able to to pair those down to to just a couple now that we think are that we think are highly important and so so i think overall it's it's a little mix of both there's always some qualitative foundation to our ideas and we found it highly useful to actually talk to talk to the people who are talking to customers but at the end of the day it has to be proved out in the data and it was interesting in that some of the some of the features that were called out from our aes and and and csms weren't necessarily the most predictive of long-term success so there was a bit of a of a false flag in terms of you know what they were hearing about customers who self-selected for this service and and that not actually matching up with with what we saw in the commitment data love it that's perfect and it's exactly it's a little bit of a leading question because what we find is that people try to run straight into the data first but if you talk to aes and cs and even some customers oftentimes yes some signals will be wrong but more so than not some of it's intuitive and what we found like we were talking to going pretty deep with slack on how they did this and they talked to a bunch of customers and people that are customer facing about what they think the signals are and then they ran a six-month like data science project to figure out those signals and yes one or two were off but the majority were exactly the same so instead of trying to dive right into the data it's really good to be hypothesis and what your sales cycle is and look at your past 100 closed one opportunities and run that correlation you don't need a full data science team you can keep it very simple so i just want to pause for a second and call out like the reason we start with these sticky notes is to be very hypothesis-driven and start with kind of what are we hearing from the customers and then validate with data really important than doing the other way around because then your wheels will just keep going and you'll find too many correlations and it'll take six months to get to something you could have gotten to in six days any questions so far okay so we'll do we'll do the last one on buying intent so these are any actions in the product that correlate to buying propensity maybe it's an action on a pricing page maybe it's a user if they raise their hand so you can request a demo from our sales team on our website is excuse me i'm getting a call i'm sorry about that so request a demo from the sales team we have you know a button on our website that we find is is pretty useful we also have we track visits to our pricing page and and that's also you know a signal that's highly correlated with with overall intent it's think buying intent you could also qualify some of the some of the product usage features i talked about particularly some of the the quota quota percentages you know reaching reaching high amount of of the amount of the messages they can send or the visits they can book in a single month yeah i think that's probably like at a high level what we're focused on right now i would maybe add upgrade requests from exactly inside the product let's say maybe that's kind of a sign of when it happens definitely love it yeah and this one buying intent we usually see has the fewest but they're good they're powerful but fewer than the hypotheses from others i'll also say that wait while was probably like our most data driven and like knew their data more than most that we work with so it is okay if you do this with your team and you're just standing there saying wait i think we're good at this industry but i think it's this industry or this geography or this product feature that's the point of the brainstorm like get multiple people in a room like throw the ideas on paper and then validate anything to add tim i think i think you got it all i feel great that we were the most some of the most data-driven clients that you've had that means that francois and i and jenna are doing our jobs well so sandy can vouch for that i actually had a question so one of the ones that i'm always curious about is the visits to the pricing page because i've gotten some questions about this one it's like how do you think about the threshold for when to start taking visits to the pricing page seriously is it just one visit is enough or is it multiple how do you think about that so it's a good question we for this particular stat i don't think that i've i've dug into it on a very very granular quantitative level i would say though and i think this is what you're alluding to is on its own it's not quite as powerful as a signal as as some of the other buying intent requests that we have on the sheet but i think in in in concert with the rest of them especially with good product usage and and good icp it can be a powerful signal that the customer is is at least open to the idea of of paying for something so so yeah it's it's not perfect but i think it definitely has a place in in the hierarchy whether it's it's higher or lower than some of the other features i think can be debated and i would debate probably placing it lower importance on it but but absolutely um worth including and at least exploring yeah i think you hit the nail on the head there with the you know when it's in concert with other things that are ex like extremely correlated and very indicative of a good customer like you know if you're targeting that store manager and the store manager specifically is the one looking at the pricing page then you know it's it's really relevant so it's less about number of visits but how the visits relate back to those other signals that you pick if only there was a product out there that could compile all these signals for us for us it's been a bit challenging also to find the perfect kind of pricing kind of visit data we've also seen this clear bit and zoom info and um our buyer profiles can be so diverse that you know sometimes it's a saw manager sometimes it's a you know it's a head of variation sometimes you know so we we've had a bit of a challenge with that so that's why it's also lower on the list but i would be curious with you know anyone who who has had experience doing it very very well you know anyone who had great success with tracking you know visitors on the website and using it as a powerful signal i would actually be curious to learn from them anyone have a good take on that maybe we can we can pose that to the broader community it's a great question great question i know we have three minutes so i want to i want to wrap up what we would do next which you can do internally or like what we're going to do with the wait while team what we have done is we're going to now take this data and validate those options so we're gonna say all right is this accurate like i said run those really quick correlations to understand does two users per account actually equal xyz and then what we'll do is say okay let's look at the segments we've identified let's start simple let's say for an ae which which signals which combination of customer fit product usage and by intent should be funneled to an ae versus a sales leader versus a marketer and you can do the same for sales goals which equals something for free to paid conversion and then which for enterprise expansion and from there you'll want to build those calculations which is just like a weighted sum of these signals and you'll say okay surface these leads to the sales rep at this time and give them the insights around this and then over time you'll want to experiment you'll say hmm i don't think that looks right and then edit the signals a bit and you'll continue to iterate and experiment as you learn more about your customer base and as you continue to go into new markets so i know wait while one of theirs is maybe international expansion then we're going to have to create a new pql for enterprise expansion or sorry uh international expansion and so this is meant to be the starting point as a kickoff to go to all those other operationalizing processes but if you want to do this with your team sandy and i are happy to send you the fig jam or send you or help co-facilitate it with you all and if you want to learn more about how we're building this in progress happy to do that for you we're available you can just slack us in the community slack channel we're happy to be helpful and we always love doing these because we learn from them as we go but i do want to give a really huge thank you to tim francois and jenna you are incredible you know pql so well so can we just get off me and give a little bit of a round of applause for them for stepping up and doing the the scary pql brainstorm yes i got a dm about how to join the slack channel go to you know what just email me your email address and i'll add you and you don't have to go through the whole process i will write my email address special special permission for everyone here thanks so much everyone