Product qualified leads for accounting and tax

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we're super excited to bring a framework for product qualified leads to you today we're going to be talking through just kind of our our methodology that we've like i said collected from folks that are already doing this at some of those great plg companies and we're gonna walk you through all of that uh so alexa if you go to the next slide just a quick rundown of our agenda today we're gonna talk about what product qualified leads are actually recently we've heard a couple of different definitions so it will be interesting to see whether we're all aligned on this definition of product qualified leads or if there's any other definitions that you're using within your companies or that you're thinking about we're going to talk about why it's important to think about product qualified leads especially if you are doing plg and thinking about adding sales we're going to go through the pocus product qualified lead framework and then we're actually going to put that into practice and we're going to do a live workshop mel and karishma have raised their hands to be our guinea pigs for this workshop today so i'm super excited to go through their examples and if we have time we'll try and take some more folks through that same workshop and then you know if we don't get if you really want to do and we don't get to you we can always schedule some some more time to do this so back over to you alexa and you can jump through what are product qualified leads cool all right so i'm assuming this term pql isn't new to anyone here but as sandy mentioned there's a lot of definitions for this that we're realizing it's becoming more and more complex so we want to level set think through what this is maybe you debate us and tell us this is wrong before we actually dive into the pro the workshop so how we define pqls is that there are users on your product who have experienced your product's core value so the way that you define instead of pqls within your organization though we see depends on two different things first it can be based on specific goals in your organization so you might have a pql for free trial upgrades or you might have a pql for expansion opportunities you might have a pql for self-serve users that are showing high potential or mapping pqls to customer segments so you might have a pql for smbs that are different than mid-market that are different than enterprise so we're not saying that end of session you're gonna have one pql that you're leaving with you'll have a starting point to start thinking through okay which of these actual users of our product have great potential to get more value out of our product and spend more with our company based on where they are as a customer segment so things like smb mid-market enterprise or their specific goal so pql for expansion versus free trial upgrades anyone else have a different definition of what a pql is or something else that they thought of before coming into this discussion so something that i was curious about and i think this is actually just additive but i think the idea that someone could be could have not yet experienced the product's core value but you know if you can get in front of the right person or to the right team or frame it in the right way that they actually would really be a great fit for the product and get a lot of value and so i'm curious how you think about i know there's like moments of um of moments of wow or like moments where someone really hits your like exact moment where you know they're gonna do well from there but someone who hasn't yet but you know you'd like to get them there it's a great question so how we phrase that is maybe they have really high customer fit but not so much product engagement and that's where in our mind and that's where in our mind the sales assist team can come in saying okay we think that maybe you should be a really good user wiring on the product is there something that you're running into a blocker is there added friction what's going on here we should explore so we can still call that a pql and we'll walk you through how we think about the sec the different sub-segments of pqls being customer fit product engagement and buying intent but it's an excellent point though any other points i know andre asked you unmuted yeah actually that was a great question and i also love the answer and one thing i have um what in your um from your experience are iteration time frames so when you because the reality is at one point you just have to run with the starting definition of stuff right and well in our case it's maybe the number of presentations created or the number of users invited or whatever but what would would you say is a time where you say okay so now we're gonna change those parameters because this is probably something where uh solution can help a little bit but mostly you have to make a decision let's change it up what is a good iteration type for these parameters it's an excellent question and you nailed a point on uh you know pql's aren't excited and forget it you're gonna continuously have to experiment i remember a statistic uh digitalocean when they were starting they started with two pqls and then by the end of the year they had 40. and they were continuously expanding based on new customer segments um new goals that they had for the organization and some of the initial pqls they said you know this doesn't work and so in terms of the length of where you want to experiment you want it to be long enough where you can test some metrics to say is this pql good so maybe one of your goals is you want this to increase conversion you want to have a length long enough that you can say all right i want to test this pql and see if it increases conversion over the next 30 days 60 day 90 days and just i've seen any of those 30 60 90 i think it depends on your organization and how much volume you have of signups to inform how much data you can get to make a good decision about whether this is a good pql or not um but that being said you should always be setting time frames to run these tests which we'll get into so that you can iterate and experiment and improve uh thanks for that what you actually said is like 30 60 90 that you know you have to you have to also see from my point of view the question even started should it be one week two weeks uh a quarter you know it may sound ridiculous that even this um variable variability is in there but we are starting from zero on that and you have these kind of questions so for me like the 30 60 90 is actually an interesting uh perspective on it and we got to see but i think uh a month probably is uh is a good baseline at least thank you so much of course and that's what cyprus did sandy right they started with 30. yeah and then like depending on because some of their pqls then were mapping to like more revenue focused goals and so those revenue focus gold ones were definitely longer because they know that you know the sales cycle is x number of days or x number of weeks so that i think was more in that span of like 90 days or longer i'll breeze through these so we can get to the fun stuff but as everyone knows pqls are the secret weapon of a good plg sales motion without pqls you're trying to figure out which who should i engage what's the best metric and so you end up wasting time and you increase the cac and decrease error but with pqls you have full visibility into user behavior when you should engage and you're able to pick out the best opportunities easily from your self-serve pipeline so we have five steps to set up your pql definition your first one this is before you get to the 40. you're number one before you get too too crazy and fancy first you want to understand what is a good customer your icp so mel this is what we were talking about with customer fit and then you want to think through okay how are users getting value from your product third you want to identify purchase intent signals so did they do something to indicate that they might want to buy or spend more then you'll want to formulate your first pql hypothesis and then finally you'll want to test and iterate this hypothesis in the real world which is that 30 60 90 day theory that we just mentioned so all of this you'll notice does not require a data scientist it does not require a machine learning algorithm we recommend starting simple your sales teams your customer success teams your marketers those are the folks that really understand this deeply because they're engaging with customers then once you have those initial hypotheses you can use data to actually inform and validate and get even more complex predictions and then we have a bonus point where you can use tools there are good tools to help with such as focus gentle plug to help you actually take your pql and operationalize them so get them to the right sales rep at the right time with the right insights so this is the same kind of setting up pql's framework and so what we'll start with is these three different categories for defining pqls like i just said we think about customer fit product usage and buying intent so for customer fit this is how closely does this account match your icp so these are things like industry geography company size and roll product usage so what at the user and account level means indicates that users have been engaged in your product or these metrics such as daily active users the frequency in which they're using your product the recency of which when they've been on your product the time spent on your product free to paid conversion number of insights sent and then finally the last bucket is buying intent or virality signals so was there some sort of account velocity did a lot of people sign up at once did they end up hitting the pricing page and they visited a lot of times meaning that they're probably looking into the next uh tier or upgrade did they click talk to sales as a cta on your website and so there's different questions that you can think through when trying to figure out these three categories so first being customer fit you can ask yourself okay what size company do our best fit customers work for what is the employee account in our threshold for engaging a sales person i won't go through all these right now we can send it to you after as a framework to help you start thinking through like giving you nudges to help through what a good customer fit is same with product usage what actions does a user take if they are activated what actions correlate with high engagement and then finally buying intent so are there any in-product actions that can correlate to buying propensity or any actions on the pricing page so one quick example we'll do is with flock so before we dive into yours we're going to give you a very simplistic example of what a pql could look like for slack for customer fit they might say you know i only want to look at companies that sign up for a product in the tech industry in the us with this company size of 500 to 1500 employees the role being head of us for product usage they might want to look at things such as you know 2 000 plus messages sent is their sweet spot for product usage 100 plus channels created maybe the vp of signed up and invited 35 other people and then buying intent reach a feature limit visited the pricing page hit talk to sales etc so i'll say this is a v1 of a slack pql and then what we do is actually break this up to say okay what is a slack pql for the mid-market team versus the enterprise team or what is a slack pql for someone who's trying to increase conversion as a goal versus someone who's trying to increase irr's goal and so this is kind of the step one of thinking through let's just brain dump everything for slack of what a good customer fit is good product usage and goodbye so finally um after you do the steps one two and three of laying out customer fit product usage and buying intent you're going to want to formulate these hypotheses so you're going to talk to the customer facing teams and say okay does this make sense can i validate this with you what we see often is we do this exercise with sales teams and then they want to validate with marketing and customer success even growth and from there really robust insights come out saying well marketing thought this and growth had this perspective oh can we bring that all together and have this really robust pql that we didn't know existed before then you can also validate with data you can look at cohorts of your closed one users and say okay why were these good deals what are consistencies in customer fit product usage in by intent so if you said something like i really think that our really good customer fit is from 500 to 1500 employees validate that with data say is that true do companies with that customer fit often convert at a higher rate and then you'll want to look at common patterns like i said in custody size geography business model and we also can give you some fun mad lib style statements to write if you need extra help writing out your hypothesis so some fun things that we do is actually fill in the blanks we believe our best fit customers blank because they put they use the product and average blank days per week month or year for blank use cases their company profile match is blank and they say that the product adds this type of value to their company workflow or workday and all of this is to say get your hypotheses on paper so you're not spinning your wheels a lot of people will come to us and say i'm in charge of creating pqls but i don't know where to start and i don't have a machine learning data scientist that's okay start here before validating with other sales people other marketers other customers success and before actually validating with your data and then finally something we talked about before is segmenting your pqls to then know how to prioritize and so once you understand what a good customer fit and product usage is and how you can actually you're going to want to know how can i actually use this to inform to operationalize to my business so what we recommend is once you have a really good definition of customer fit and a really good definition of product usage to map this on a 4x4 take all of your leads and map this to say okay who has high customer fit and who has high product usage those are the folks that are sales ready pqls those are the ones that your sales team should be running after right now like those are the obvious low hanging fruit wins for you then what if you have really good high customer fit but low product usage this is what mel mentioned before there's probably something wrong with this if they're really a good customer fit are they running into blockers are they not realizing the value are they running into friction do they are they stuck on something you need to get someone in there not to try to upgrade them to the next tier but to say hey what's going on and how can i help down here you might have someone who has really high product usage but low customer fit if you have time i would experiment with that you might uncover a new use case or a new persona that you didn't know before and you might want to dive in and say how are you using this and then finally low customer fit low product usage do not dedicate any time to those put them into your self-serve motion it is not worth the time of an expensive sales rep so lastly on step five of building testing and iterating what you'll want to do is now write out the plan for how you're going to test and experiment with these pqls so first let's say for example your pql name and defer definition is you want to look at a pql for mid-market free trial conversion and this is going to be that they're the company that signed up had 250 plus employees and they completed eight out of ten onboarding success step sorry and then you want to say how will we measure the success okay do we are we really uh do we really care about acv do we care about pql to cql sql conversion do we care about time to revenue and then you're going to want to choose the experiment parameters so how do you want to test this do you want an a b test do you want to do a 50 50 split of testing this pql versus non-pqls and then what's the time parameter is it 30 days 60 days 90 days and then you want to look at the impact analysis after the test so with this pql did these convert at a higher rate than the typical leads did these have higher acv did they have a quicker time to revenue and then here's the bonus point of operationalizing pqls once you're able to identify and prioritize and surface the right pqls to the right sales reps you want to be thoughtful about how you assign pqls so you'll want to assign route the pqls to the right sales person at the right time and deliver insights and recommendations and actions for the how they should take the lead so for example say there is a mid-market free trial conversion you will want to route that to the mid-market ae who's responsible for free trial conversion rather than an enterprise sales rep that is focused on expansion and this is really important because there could be if you just have a ton of plg leads and self-serve users signing up for your product sometimes you might have a round robin or sometimes you might just say everyone all hands on deck just pick the leads you want but it's not the right method it's not going to be as efficient as getting it to the right sales rep at the right time because you'll want to get the ones that are for sales assist to the sales assist team the ones that are enterprise sales ready to the enterprise sales team so you have to be very thoughtful about which lead goes to the right rep and to get it to them at the same time at the right time with the right actions so i want to get to the live workshop so i'm gonna skip this section because i know that took a while any that was a lot we're happy to send the deck after send more insights what questions you have for me before you we dive in maybe from my side because i just have written down i think six months ago what i assume could be a pqa and currently we have an onboarding team which focus on the let's say signups the first 60 days on them and if they don't get to that point that they are pql then i don't let's say care anymore if they after six months uh they get maybe to that usage i also don't look at them let's say anymore would you recommend that or would you say the time frame doesn't matter it's more like that they get usage to a certain amount or to to a certain point so are you saying if they didn't hit this aha moment or product metric let's say it easy um if they don't hit that aha moment in the first 60 days i don't care about that lead anymore i think it depends on your model and how you're defining pqls and how you want to route it so there's a couple different responses i can see if someone is really high customer fit and didn't reach the aha moment what's going on there do you need a sales assist person or is it just you know maybe they aren't a great lead at this point because it's not a high priority for them put them back into self-serve and maybe six months later they'll get to that point where they have high product usage and that's when a sales person should come in so i think it's something that depending on your business there's a lot of different routes that could be taken and you need to experiment to figure that out so maybe you say i'm going to put these into self-serve and then check in on them 90 days later or you're going to say you know what that requires a sales assist human because they have a great logo for us and they fit our icp we need to figure out what's going on here just to build on what alexa said i love that idea i actually think this is like a perfect example of where you could deploy an experiment because you could see like is there any lift in the speed at which users get to that aha moment if you do put a human interaction in that process and actually our friend rob falcone at guru i think did something similar to this where he started offering a white glove onboarding as an experiment as part of the onboarding process and tried to see one how many people opted into that white glove experience and then those that did how did that impact how long it took them to onboard because they were having a similar kind of issue where there was definitely some high fit high customer fit um users on the product but they were taking much longer than expected to actually reach those milestones um so that would be one thing and i i guess one question i have for you is what's do you know what like the natural like frequency of uh your product is like is it a product that you would use on a weekly basis or is it a daily or a monthly good question it depends if you get a lot of candidates you should be in the tour daily if you get let's say on a weekly basis one candidate it's maybe on a weekly basis normal clients should be let's say within the two let's say two two three times a week okay interesting yeah so that that could also be contributing it's just kind of like the nature of like hiring actually probably makes your onboarding and that time to aha probably fluctuate quite a bit but i i'm guessing like your best customers are the ones that are like hiring like crazy all the time but also for us maybe let's say bad customers also from interest because they have maybe a bad job ad which doesn't convert and then we could go in there help them and maybe implement certain tools from us so that they are better and with that the usage gets bigger and then maybe they qualify but then we already had contacts interesting it's uh definitely a tricky problem but thank you for sharing that any other questions before we jump into the workshop nope all right i i'm actually going to share this link with everybody so that you can all come watch as we fig jam so if you go into the chat go into this link i can see that mel and karishma are already in there which is great and we are going to start workshopping mel why don't we start with you and we're going to start with mapping out your pqls so the way that we run this workshop normally is first we want to figure out what are our goals like alexa said thinking about your goals is an important step in defining your pqls because it may determine how you segment those pqls and then eventually route them as well so an expansion pql versus a free trial to free to enterprise upgrade or free trial conversion the resources you deploy and the way that you think about that pql will all be different so mel tell me about your goals and if you could use the stickies over here in purple to just type out some goals and then if they align to any of these kind of buckets we've already identified maybe drag them over if you don't have any that necessarily align completely that's okay and we'll talk through them the pql that i'm most interested in from a customer success standpoint is the seat expansion it looks like in the next early 2022 we're going to be getting a sales hire who's going to be focusing on the free pool of customers and how to pull them over into converted to paid and something that i'm really interested in is we've been around for four years we have a really nice customer base that we're we're very thankful so it's really like working with us and i think there's a lot of untapped potential in the accounts that we have now to expand both within the team like there are a few seats maybe in the product team expanding the product award or then even expanding across departments so seat expansion is i think what i'm interested in working on okay yeah yeah i know i was uh typing and i was like i can't read that i should probably zoom in so this is great because actually the narrower we can make the focus and if we're just going to think about expansion then we're going to have a a lot more granular discussion when it comes to the next part of this which is moving down into the signal brainstorm so this is the framework that alexa just reviewed so we're going to go through each of these and start answering some of these questions so mel when you think about your best fit customers and especially the ones that are let's say already starting to naturally expand what are some of their attributes like what are the employee counts that you're looking at for those expansion opportunities are there any particular industries or personas and any geographies that we want to specifically target for this so some of these things i think we're still learning and still figuring out but for the geography piece we did have a data scientist already run the data and they have segmented into tier one tier two tier three like geographical areas and so folks in tier one are much more likely to buy and have been successful so tier one which has like whatever eight countries within it is something that we think about industry we tend to sell to other software companies but that's also um i wouldn't actually that doesn't seem as useful as knowing whether or not someone will expand the company size to be honest i don't really know my guess would be 50 to 150 and then roll something that i have been working on is product teams who are using whimsical and so within there i uh when like a head of product or product manager joins that is that is someone who i've worked with a lot and i think and they have been successful okay is this is this sort of what you're looking for okay exactly so tier one those eight countries that you define as part of tier one tech um specifically product teams and that head of product or pm role is really important to you and that 50 to 150 company size it's perfect i'm curious if you also for the expansion to other departments are you picking up now on okay there's a lot of people that are on the product team but oh interesting they also just invited someone from the marketing team so there are other specific departments that when they pop up you get excited yeah i think starting with product it goes um laterally to marketing and engineering we're seeing two which is really nice and so either of those departments i think are great wins and i and i feel very confident that i can help them get value when they're using one school awesome so it sounds like if someone starts product and then they secondary department being marketing or engineering that's a good trigger for you yep cool awesome any other thoughts on customer fit or should we move on to product usage uh let's do product usage awesome so again the questions here are let's think about um like you just said someone invites a user from a different department that sounds like it's an obvious product usage trigger for you for expansion are there any others like any other features within the product that tell you this account is ready to expand um and are there any other features that are like lagging indicators of that kind of expansion and stickiness so something that we're doing right now and i'm wondering if we could find a way to get like one step further upstream is that we're monitoring seat count change both increase and decrease and so someone increases their seat count by over 20 percent we're sent this is pretty manual right now but we're sending an email their way and saying we noticed that you added some seats we're so excited to see that we'd like to offer if anyone would like some training as they're getting onboarded we're here to help is anything else on your mind and so we're sort of building on um we're sort of building on momentum that we're already seeing so like it's a little i wish we could get some piece of information that was right before they added the seats maybe do you have a concept of uh like an admin in your product and like or someone who manages billing and when does that come in like when does someone become an admin yeah is the admin usually the person who is the first like or do they buy the the first license within the company or is this like someone who comes in once they reach a certain amount of seats and then it necessitates an admin first user is the admin and they can promote others too okay i asked that because like i'm curious whether you know promoting more users to admin could be one of those kind of more upstream signals of this increase that you're talking about i like that that makes that makes sense to me and then thinking about the seats what's your pricing model like in terms of seats seats our free plan is based on usage so you can come in at any seat count you want and then you have a set number of items that you can create once you want to get unlimited items that when that is when you switch to paid and then it's based on per seat we have an editor role that's our paid seat they can do everything we have a viewer role which is a free roll we also have guests which is actually an interesting product usage lever maybe to monitor why do you think the guest is interesting to monitor i think it's someone who so guests are free versus inviting as someone an editor to your workspace and so i think people are starting to use that when maybe you're on the product team and you're like you know i really want to get something done with the marketing team and so i'm going to invite them as a guest so they're starting it's their first opportunity to dip their toes in the water and i'd like to make sure the guests want to stick around and do more interesting okay uh so i it was interesting what you mentioned about the pricing model so the free plan has those unlimited seats but you've restricted certain features in that plan and then when you upgrade you're now paying for additional seats and you now have unlimited access to some more advanced features what are some of those more advanced features i'm curious and are there any indicators when or is there a correlation between that seat expansion and some of those more advanced features so unfortunately actually the free plan and our first paid tier is not super feature differentiated it's more the amount of content you're allowed to create so if you can picture it's a digital whiteboarding tool so if you can picture you're allowed to make 1 000 items in your flowchart or your mind map or your wireframe and so you can do that in free or paid but you can only create 1 000 items in that free plan and so i'm um that's something that actually might change in 2022 because not having those feature differentiated at that free to pay is an interesting choice but does that make sense yeah that does and actually that makes me think of um you andreas because uh of pitch do you guys have a similar model where you're trying to incentivize kind of like more people using pitch versus trying to limit that or is your pricing model a little different well right now um we we do not incentivize it um but we are also like in the midst of discussions of revising uh how we wanna wanna do that in 2022 so um what i'm hearing here resonates really with with the desired discussions going on um in our company cool all right i i feel like we have enough here for product usage let's go to the last uh brainstorm and that's on buying intent so i think this one's pretty self-explanatory um actions that correlate to buying propensity um are there like we just discussed it sounds like there aren't a ton of those feature-based paywalls at the moment um because there isn't that uh differentiation but there sounds like there are paywalls in terms of amount of content um i'm curious like how much hand raising do you get about upgrades um or about c expansion like do people reach out to ask questions about can i add more users i would like to learn more about your more advanced plan it's really low to the point of i think that one of the things that a value prop we bring for whimsical is it's incredibly easy to use and a part of that means that low there's lower like support interaction but i also think there's something where we don't quite have enough if not moments of friction moments of positive like engagement or like a roadblock just to like make a point of contact with yes allowing someone to raise their hand or tell them they should and so i'd say it's pretty low at the moment and we've had a very product focused product led growth strategy up till now but i really think we could like pour gasoline on it if we found those correct moments to add in either the right tech touch or a human andre is 100 seconds what what mel is just saying i'm sometimes even surprised when i reach out to people like was something pretty generic so hey if there's ever anything we can do talk to me and then they're like super happy that that we reached out and and i'm i'm i'm very thankful for that but it's like how how did it even happen that they didn't actively reach out before and ask a support question or interact with a bot on the website so completely seconded what what mel is saying there which is a nice low hanging fruit so for the record i'm not complaining i'm just saying this is like what yeah well i can say andreas and pitch what got me was the when i had a download when i wanted to download a pitch i wanted to get rid of the pitch logo and so then i had to upgrade and then i wanted to talk to sales so maybe for your case it could be every time someone downloads a report it could probably be a positive indicator oh yeah we you mean when you export the pdf right yeah yeah oh yeah absolutely this is like one of the one of the touch points uh that we have and this is like this is what i say was such a basic thing hey i seen your workspace has exported a couple of pdfs look at our pro plan i'm very happy to take you through that and it's like how well you you did then but so many people don't and they are kind of just waiting for somebody to reach out to them which is which is nice i guess um so completely seconded what mel said and i love that the journey was like that for you that's a nice little thing with the pitch logo mel i have another question for you so um are there any like points of friction when it comes to adding new users so if i wanted to invite like my whole team at pocus do you know of any friction points that exist in that process i'm sure there are some and i am not totally sure what they would be there might we would it be helpful if i walked through the example an example of one i know we solved already or not right yeah i i ask because sometimes it's helpful to also think of like the negative signals for each of these and so i'm curious like if there are any negative signals on expansion all right i'm going to give you two examples first one that we solved second one that i think might be something the one that we solved was we used to have um a concept of personal workspaces in whimsical where only you worked by yourself and then if you wanted to collaborate with other people you'd have to create a shared workspace and that was the place that allowed you to work with your teammates but as we were looking at things as i was answering support questions people whimsical is a very social app actually and so or social in the capacity that you want to share content and i um sorry one moment my cat is visiting hey bibo you gotta go thank god i'm not the only one who's uh that's happening too yeah she's actually an awesome cat but she probably shouldn't be on this call the we realized that what we really wanted and people who were most successful were inviting their teammates so we actually removed the possibility removed the friction of personal workspaces because you can actually use a shared workspace by by yourself if you want and so now we're no longer getting calls or like emails from people that say hey i'm in this workspace but i can't invite anyone and then we don't have to say like oh switch to a teamwork space and do all of that so we fixed that one that's not a problem a current signal that we're seeing is sometimes people invite guests instead of inviting an editor even on the free plan and i think they're just confused in the ux and we are doing some improvements there but is that helpful for thinking about the buying intent okay yeah like that seems like a perfect example of like you know obviously you want to fix that in a more self-serve way but in the interim like could this be where this could be an entry point for you to have that kind of like expansion conversation with a human touch point potentially or just like even more of a low touch approach of setting an email and being able to like proactively reach out with the solve and maybe even proactively saying hey we're fixing this could be an interesting something to trial i know we're running out of time so i want to now move on to kind of the last step and we won't actually go through and let's like write down all of these definitions together but i wanted to walk through how we think about this so once you go through that pql brainstorm and luckily for us we focused on that very narrow use case of c expansion we want to then start like naming and defining different pqls to test and so i would say we want to take some of those signals that we defined as customer fit product usage and buying intent and then map them to maybe let's say two or three pqls that we want to test and then the next step is like to figure out does this data exist where does that data live so that you could start to like maybe analyze this information and see just manually like okay if i say that these three things are true from customer fit product usage and buying intent what users currently fall into that cohort of pqr and what what can i learn about that like what what patterns exist there what have we done to positively impact their experience what have we done maybe wrong like what are some missteps that we took with those particular users and maybe even then apply this to like historical information so take this pql and apply it to opportunities that have already expanded have shown like you know the positive outcome of the expansion within an organization do those map well like is the conversion really high there and then maybe that's when you start to think about okay how am i going to operationalize this as part of you know my customer success team sales team whatever team is going to be working on this thank you so much mel for running through this example amazing and i just want to leave with like tangible notes if we can go back up to the screen around next step sandy just so we know what would happen from here so mel that you you're not leaving being like okay now what do i do um what we recommend is you map out some of these pqls you say okay seed expansion let's put a hypothesis down i think for seat expansion within the same department i want to look at when the vp of product is on the product is engaged and they're on the tech industry and they've over 500 uh employees in their company and c expansion has increased by 20 and then you're going to map another pql for your other your other goal of saying i want to know when they expand to other industries or other departments sorry so maybe that pql you're going to define it as vp of products engaged invited 10 people in marketing and invited five people in engineering or whatever those metrics are and what you'll do is you'll take this data and you'll validate it you'll pull out your data around usage and customer fit even if it's manual like even if you're just like using excel asking someone to just gut check and you're going to say okay is this real is this what's happening and you want to continue experimenting with these pqls and once you get it to a point where you're confident like this could be good enough then is where you operationalize it so then you want to plug it into a system to say how can i now have a slack alert every single time someone signs up when they're a vp of product in the tech industry and see the expansion is greater than 20 so how can i alert sales teams so that they know when this is happening and then they can take the right action so it's exactly what andreas was saying how can a sales rep know every single time someone wants to export a pdf file okay because at that moment that's likely the the inflection point where sales should engage and so you want to be very proactive to whether it's an automatic sales reach out or if you want to start a marketing cadence to just say every time this happens send an email that's when you want to start to operationalize and so i didn't want to leave you hanging thinking like okay now i have this data now what if you want to keep chatting about it we can like talk to you also how pocus comes in to operationalize this and experiment but we're also always here to answer questions like we love kind of getting into the weeds and the data to try to understand really everything we can about pqls one thing to quickly share the alexa before i hop off is like the way we test this stuff in a minor scale at vidyard is by just building out outreach lists based off of like the pql criteria before we operationalize so we have like a ton of like different csvs literally that like our sales team is working through and like hacking through just to see what signals would actually be useful and then when we hit like a list where we find that the success rate is fairly high that's the point at which we know okay these signals are pretty useful and we should go in and operationalize and automate that so it could be like an easy way mail for you to test early and like validate what signals might be more effective in the early days cool yeah thank you everybody for um using my stuff as an example and these are all good ideas all right amazing thank you guys so much this was so awesome thank you mel for sharing your example again sorry karishma we didn't get to yours but i it sounds like everyone's keen to maybe do another one of these if you have any more questions you're all in the community so please slack alexa or i with your questions