WEBVTT NOTE This file was generated by Descript 00:00:00.579 --> 00:00:03.849 Mona: So just as a little bit of the, what you're going to see here 00:00:03.849 --> 00:00:08.829 is essentially a summary of all the things that we as practitioners have 00:00:09.119 --> 00:00:13.749 uh, at our um, at Dropbox at one drive and other companies that have. 00:00:14.304 --> 00:00:20.264 Had a strong product led growth motion from free to uh, and also us working 00:00:20.274 --> 00:00:25.305 with, uh, a through uh, that have a strong, free paid, uh, workflow. 00:00:25.424 --> 00:00:29.025 So this is sort of a set of best practices that we've identified. 00:00:29.604 --> 00:00:34.605 So high level before we get into the nuts and bolts of you know, what 00:00:34.605 --> 00:00:38.925 are the key differences between a traditional go-to-market motion versus 00:00:38.925 --> 00:00:40.934 a product led growth marketing motion? 00:00:41.114 --> 00:00:44.035 It's not simply, you uh, success. 00:00:44.064 --> 00:00:44.335 Right. 00:00:44.615 --> 00:00:49.805 Um, In a PLG model, we also need to customize our outreach based on. 00:00:50.295 --> 00:00:54.374 The person we're reaching out to and their usage of our product. 00:00:54.374 --> 00:00:59.215 For instance, if you're reaching out to someone uh, a marketing, a growth motion. 00:00:59.215 --> 00:01:02.335 You have free customers and in a traditional model, you don't, 00:01:02.335 --> 00:01:03.595 it's not as simple as that. 00:01:03.595 --> 00:01:05.095 So I wanted to highlight about. 00:01:05.690 --> 00:01:09.560 Key differences and then we'll deep dive into each of them. 00:01:09.830 --> 00:01:14.560 So the first difference is, you know, in a traditional uh, lead generation 00:01:14.560 --> 00:01:19.410 is happening, uh, through marketing efforts and through uh, prospecting 00:01:19.500 --> 00:01:24.710 and also on a AE prospecting uh, uh, in a PLG uh, we are focusing. 00:01:25.500 --> 00:01:29.430 On those traditional ways to do lead generation as well. 00:01:29.430 --> 00:01:34.530 So it's not uh, in PLG marketing and SDRs are not generating leads. 00:01:34.560 --> 00:01:39.610 It is in addition to Uh, leads are also generated through free trials and free 00:01:40.110 --> 00:01:41.910 Uh, That might be your main product. 00:01:41.940 --> 00:01:45.210 It might be a sidecar product that is predominantly its only 00:01:45.210 --> 00:01:47.500 purpose um, to do lead generation. 00:01:47.920 --> 00:01:52.960 And this particular difference between these two creates an opportunity 00:01:52.960 --> 00:01:57.070 to understand what PQ is are versus what MQL is are, and how we 00:01:57.070 --> 00:01:59.440 combine those two in one unified. 00:02:00.500 --> 00:02:04.760 Um, The second key difference is in a traditional model, we often 00:02:04.760 --> 00:02:08.510 have company information about the prospects that we're reaching out to. 00:02:08.510 --> 00:02:13.480 We are using, you know, we build target account Uh, We have ZoomInfo 00:02:13.480 --> 00:02:17.530 we identify contacts that we want to reach out to in those target accounts. 00:02:18.160 --> 00:02:22.000 By the time we are reaching out to a prospect, whether that's through 00:02:22.000 --> 00:02:25.990 marketing or that's through sales, we have a sense of where that 00:02:25.990 --> 00:02:29.140 human being works in a PLG model. 00:02:29.470 --> 00:02:34.180 In many cases, we don't have that information because users free people 00:02:34.180 --> 00:02:36.280 that are on free trials or free plans. 00:02:36.490 --> 00:02:41.230 They're using personal email addresses and I'm using a personal email 00:02:41.230 --> 00:02:44.769 address, does not let us know that they are using our product or service. 00:02:44.950 --> 00:02:47.559 Um, At Zoom, at Netflix and so on. 00:02:47.739 --> 00:02:48.070 Right. 00:02:48.589 --> 00:02:51.529 Um, and and that's where identity resolution comes into play. 00:02:51.859 --> 00:02:56.029 The third key difference is that in a traditional model, when we are 00:02:56.029 --> 00:03:01.489 reaching out to prospects, we are customizing our messaging based on. 00:03:02.239 --> 00:03:07.070 Enrichment data that we've gotten from ZoomInfo and other providers and intent 00:03:07.099 --> 00:03:13.629 data that we may have gotten uh, our ABM platform, um, who is very familiar with 00:03:13.629 --> 00:03:18.339 your product and is an active user of your product, it's probably a really bad idea 00:03:18.669 --> 00:03:24.189 to tell them the five key things they need to do to get started to use your product. 00:03:24.249 --> 00:03:24.519 Right? 00:03:24.519 --> 00:03:27.549 So product signals become an important part of the company. 00:03:28.944 --> 00:03:33.924 Um, The fourth big difference is in a traditional model, prospects have 00:03:33.924 --> 00:03:39.304 to be educated and informed about the value of the Um, But that, because they 00:03:39.304 --> 00:03:41.074 haven't actually used the product, right. 00:03:41.074 --> 00:03:42.724 They've seen your marketing materials. 00:03:42.724 --> 00:03:44.434 They've maybe gone to your website, maybe. 00:03:45.164 --> 00:03:50.144 In a BLG model users already have a pretty deep understanding of your product 00:03:50.144 --> 00:03:51.704 because they're users of your product. 00:03:51.704 --> 00:03:56.324 So educating them on product is not necessarily a compelling way for an 00:03:56.324 --> 00:03:59.144 SDR to be successful in, in that role. 00:03:59.714 --> 00:04:02.894 And then second, last difference is in a traditional model. 00:04:03.134 --> 00:04:06.014 Our prospects may or may not be technical. 00:04:06.644 --> 00:04:12.824 And in a PLG model, often the users that you are reaching out to have a much higher 00:04:12.824 --> 00:04:17.984 level of knowledge about your product, about the technology, about the space, 00:04:18.314 --> 00:04:24.194 and that really speaks to how SDR teams or teams that are doing outreach to 00:04:24.194 --> 00:04:30.524 users have to be retrained and trained in ways that we've never seen before 00:04:30.584 --> 00:04:32.204 in a traditional go-to-market market. 00:04:33.689 --> 00:04:36.329 And then last thing is in a traditional model. 00:04:36.359 --> 00:04:41.934 Generally speaking deal sizes tend to be um, which basically means customer 00:04:41.934 --> 00:04:45.144 acquisition costs can be higher, right? 00:04:45.174 --> 00:04:52.494 Because if your deal size is 60 K, you can have an AA and an SDR spend 40 hours 00:04:52.494 --> 00:04:54.714 of their time acquiring that customer. 00:04:54.754 --> 00:04:59.104 Um, And so a human led approach ends up being totally fine from 00:04:59.104 --> 00:05:00.604 a unit economic standpoint. 00:05:01.124 --> 00:05:06.104 In a PLG model, often deal sizes tend to start smaller and customer 00:05:06.104 --> 00:05:10.514 acquisition costs therefore have to be much lower, which then 00:05:10.544 --> 00:05:12.854 brings up the need for automation. 00:05:12.884 --> 00:05:18.764 And instead of a human led motion, a technology led motion with a human 00:05:18.794 --> 00:05:24.831 assist Um, uh, essentially how we think um, automation really changes 00:05:24.891 --> 00:05:30.032 and it's all driven by unit And then I will deep dive into these six key 00:05:30.032 --> 00:05:33.482 areas and workflows for each of them. 00:05:33.992 --> 00:05:38.112 discussed was how are we bringing PQS and MQL? 00:05:38.142 --> 00:05:40.482 What are they, how do we bring them together? 00:05:40.542 --> 00:05:43.032 And I think of it as three key steps. 00:05:43.852 --> 00:05:48.832 First and foremost, you need to take all your free customers and find a way 00:05:48.832 --> 00:05:53.422 to score them so that you can identify which ones of those represent the 00:05:53.422 --> 00:05:55.582 highest opportunity for conversion. 00:05:56.132 --> 00:06:01.172 um, to do that, there are sort of three steps that you can think of The first 00:06:01.172 --> 00:06:08.322 step is identifying in your product what are the key aha moments that signal to 00:06:08.322 --> 00:06:10.762 you as a company that a user of your. 00:06:10.872 --> 00:06:17.337 product is getting value out of using your product So these aha moments really should 00:06:17.337 --> 00:06:20.157 be captured as what we call value metrics. 00:06:20.157 --> 00:06:25.437 Um, At Dropbox, for instance, when a person shared a document 00:06:25.437 --> 00:06:30.117 with another human being that was considered a key aha moment or a 00:06:30.117 --> 00:06:32.007 value metric or a value moment. 00:06:32.337 --> 00:06:39.057 So first figuring out what are the five to ten value moments, and then defining those 00:06:39.057 --> 00:06:46.032 and turning those into metrics becomes important Second is you want to score 00:06:46.092 --> 00:06:49.512 every user based on usage of your product. 00:06:49.692 --> 00:06:54.072 And when we think about usage, we want to think not just about snapshots. 00:06:54.072 --> 00:06:58.272 We want to think about the growth of usage and the consistency of usage. 00:06:58.272 --> 00:07:04.932 So for instance, what I mean by growth is if you are an HR recruitment platform, 00:07:04.932 --> 00:07:09.102 um, where, you know, recruiters can do searches and find candidates. 00:07:09.512 --> 00:07:14.192 Growth of usage would mean that the number of searches they are doing over time is 00:07:14.192 --> 00:07:21.692 increasing and consistency of usage would mean they are doing searches on a regular 00:07:21.692 --> 00:07:24.242 basis, whatever that regular basis is. 00:07:24.752 --> 00:07:29.102 And then the last part of it is quota scoring, which is different from usage 00:07:29.102 --> 00:07:36.152 scoring and quota score is predominantly if you have pay wall gates right? 00:07:36.182 --> 00:07:40.592 Where I can only do five searches as a free user. 00:07:41.122 --> 00:07:45.542 um, in order for me to do a sixth search, I have buy right. 00:07:46.212 --> 00:07:50.772 Being able to see how fast a user is hitting that limit and how 00:07:50.802 --> 00:07:52.782 often that user is hitting that. 00:07:53.257 --> 00:07:57.277 Uh, Becomes a different type of score, which is called a quota score. 00:07:57.277 --> 00:08:01.447 When you combine usage scoring and quota scoring, you are able 00:08:01.447 --> 00:08:03.727 to do PQL scoring effectively. 00:08:03.877 --> 00:08:04.237 All right. 00:08:04.357 --> 00:08:09.457 So now that we've done, our PQL scoring and the output of that basically is 00:08:09.487 --> 00:08:15.217 a huge list of users that have all been scored on, let's say a point 00:08:15.227 --> 00:08:19.807 system, one to um, based on what aha moments they've had with our. 00:08:20.692 --> 00:08:27.302 What is the growth and consistency of usage uh, our product and how often, um, 00:08:27.332 --> 00:08:29.702 how quickly are they hitting quota limits? 00:08:29.912 --> 00:08:34.742 We uh, PQL is identified and you can set a threshold at any level to say, 00:08:34.742 --> 00:08:38.282 okay, anything above this is considered a PQL anything below this is not. 00:08:38.402 --> 00:08:38.522 bit. 00:08:39.032 --> 00:08:41.359 Now the next step is UL's with MQLs. 00:08:41.359 --> 00:08:45.797 Because often we find in a lot Um, right. 00:08:48.447 --> 00:08:50.907 Because at the end of the day, a lead is a lead. 00:08:50.907 --> 00:08:53.007 It doesn't matter how the lead was acquired. 00:08:53.247 --> 00:08:58.047 So the first thing that has to happen here is you need to have good Salesforce 00:08:58.047 --> 00:09:04.857 setup, Uh, so Salesforce's objects, which are contact lead opportunity. 00:09:04.857 --> 00:09:05.187 And so. 00:09:06.027 --> 00:09:10.347 Do not necessarily lend themselves well to companies that have a 00:09:10.347 --> 00:09:12.207 strong product led growth motion. 00:09:12.237 --> 00:09:17.007 So you need to create the concept, either decide that you will 00:09:17.007 --> 00:09:19.257 use the contact object to mean. 00:09:20.802 --> 00:09:28.022 Or we see many uh, use a, create a custom object called user, which they can then 00:09:28.022 --> 00:09:31.202 attach to a contact object or a tenant. 00:09:31.592 --> 00:09:37.653 Similarly, Salesforce has a default object that is called generally be, There 00:09:37.653 --> 00:09:39.573 should only be one account for a company. 00:09:39.873 --> 00:09:42.033 However, in a lot of PLG companies. 00:09:43.248 --> 00:09:49.248 One company can have for a lot of PLG companies, their customers, one 00:09:49.248 --> 00:09:51.408 company can have many different types. 00:09:52.518 --> 00:09:55.848 Across the company that are using our product separately. 00:09:55.968 --> 00:09:56.208 Right? 00:09:56.208 --> 00:09:59.898 So these tenants or service accounts also need to be created 00:09:59.928 --> 00:10:02.298 as custom object in Salesforce. 00:10:02.538 --> 00:10:07.428 So you can have a Salesforce account let's say for Netflix, and then you can 00:10:07.428 --> 00:10:15.258 have 10, 20, 30 service accounts for your product and all the teams at Netflix 00:10:15.258 --> 00:10:17.578 that are using your product independently 00:10:17.898 --> 00:10:21.888 so that's sort of table stakes from a Salesforce setup standpoint. 00:10:22.968 --> 00:10:28.458 The second thing that has to happen here is users have to be matched to contacts. 00:10:28.488 --> 00:10:35.308 And what I mean by that is let's say a person is your product 00:10:35.328 --> 00:10:41.468 and their name uh, Liz at, and they've signed up as liz@gmail.com. 00:10:42.818 --> 00:10:48.588 You also acquired A list of contacts from ZoomInfo, uh, for a company 00:10:48.588 --> 00:10:53.988 Netflix, let's say, and there is a person there or a contact there that 00:10:53.988 --> 00:10:57.498 is called lizGallagher@netflix.com. 00:10:58.698 --> 00:11:04.518 Being able to do identity resolution between lizGallagher@gmail.com and 00:11:04.518 --> 00:11:10.068 LizGallagher@netflix.com and being able to say these are the same human being. 00:11:10.128 --> 00:11:15.093 Therefore, this user is now tied to the Netflix Salesforce account 00:11:15.123 --> 00:11:19.413 in our Salesforce instance is a key part of merging PQLs and MQL. 00:11:20.163 --> 00:11:24.273 And then the last part of this merge is ICP scoring, because as you saw 00:11:24.303 --> 00:11:31.413 in PQL scoring, we had not put any filter on these users based on whether 00:11:31.413 --> 00:11:33.063 they are ideal customers or not. 00:11:33.363 --> 00:11:33.813 And often. 00:11:35.148 --> 00:11:40.518 Doing ICP, sub scoring is not possible until you are able to do identity 00:11:40.518 --> 00:11:43.328 resolution and match a user of your. 00:11:44.103 --> 00:11:49.923 To a company because I CP criteria often exist at the company level. 00:11:50.788 --> 00:11:52.258 is it a mid-market company? 00:11:52.258 --> 00:11:53.938 Are they a technology company? 00:11:53.938 --> 00:11:55.918 Are they based in a certain region? 00:11:55.918 --> 00:11:59.038 And so do they have a certain employee size and whatnot? 00:11:59.068 --> 00:11:59.368 Right. 00:11:59.648 --> 00:12:05.618 And so I CP scoring is where you are going to take your PQLs and your MQLs 00:12:05.638 --> 00:12:08.278 and then, or your all leads essentially. 00:12:09.481 --> 00:12:13.891 Which of these meet our criteria for our ideal customer profile. 00:12:14.421 --> 00:12:22.071 uh, that further sorts, that big list uh, um, two leads that are now showing 00:12:22.071 --> 00:12:26.931 high usage, hitting their quota limits or approaching quota limits quickly 00:12:26.931 --> 00:12:31.911 and consistently can be matched to an account that we care about. 00:12:32.031 --> 00:12:36.431 And the account is a target account and an ideal customer profile. 00:12:38.316 --> 00:12:41.046 And then the last part of it is segmentation right now 00:12:41.046 --> 00:12:42.336 that you've done all of this. 00:12:42.336 --> 00:12:50.316 And like visually how to imagine this is you have a huge table and every row is 00:12:50.316 --> 00:12:53.496 a user and columns include things like. 00:12:54.711 --> 00:12:56.241 Where does this user work? 00:12:56.331 --> 00:12:58.611 What is their PQL usage score? 00:12:58.641 --> 00:13:00.171 What is their quota score? 00:13:00.171 --> 00:13:05.002 What's their ICP score based on the company that they work uh, Now we 00:13:05.002 --> 00:13:09.862 want to be able to slice and dice this big list based on things like. 00:13:11.272 --> 00:13:15.232 How are some of them engaging with different marketing content, with 00:13:15.232 --> 00:13:21.147 different marketing Um, We wanted to be able to see which of these users are 00:13:21.147 --> 00:13:26.487 ideal for a sales assist motion based on the fact that they tried to pay with 00:13:26.487 --> 00:13:28.417 their credit card, but could not succeed. 00:13:28.842 --> 00:13:34.509 Which of these users um, director and higher level titles uh, suggest 00:13:34.539 --> 00:13:38.889 that they are able to be economic buyers for us, which of these users 00:13:38.889 --> 00:13:43.889 are stuck on certain product features and would most benefit from product 00:13:43.899 --> 00:13:46.109 help or outreach uh, dimension. 00:13:46.259 --> 00:13:46.559 Right. 00:13:46.829 --> 00:13:51.679 And so sophisticated, fine grain segmentation becomes sort of the way. 00:13:52.684 --> 00:13:58.694 Operationalize these amazing that you've been able to identify and unify. 00:13:59.324 --> 00:14:00.884 I see a question from. 00:14:01.699 --> 00:14:02.269 Ash. 00:14:02.899 --> 00:14:05.809 Yes, Ash, I am in an island paradise right now. 00:14:05.809 --> 00:14:09.229 I'll show you actually, I'm sitting right in front of the beach. 00:14:10.564 --> 00:14:15.784 Um, So the question from Ash is most of the time, these data sets sit in 00:14:15.784 --> 00:14:19.174 different systems adopted by different sets of people across the enterprise. 00:14:19.174 --> 00:14:22.024 Are there triggers web hooks, streaming data capabilities? 00:14:22.024 --> 00:14:22.924 That, yes. 00:14:23.104 --> 00:14:23.794 Great question. 00:14:24.904 --> 00:14:28.524 You know, um, there are two ways to connect this data together. 00:14:28.524 --> 00:14:32.604 You're absolutely Uh, The first way to connect this data is you use 00:14:32.604 --> 00:14:37.674 a solution uh, Fivetran and dump all this data into your warehouse. 00:14:37.974 --> 00:14:44.324 And uh, essentially uh, a sort of an intelligence layer on top with your 00:14:44.324 --> 00:14:50.134 data engineering team, and then use a solution um, Hightouch or Uh, To push, 00:14:50.484 --> 00:14:55.614 some subsets of this data into systems of action, like outreach, HubSpot 00:14:55.614 --> 00:14:57.964 Marketo, uh, Salesforce and whatnot. 00:14:58.144 --> 00:14:59.694 Um, That's one way to do. 00:15:00.459 --> 00:15:04.849 Another way to do it is to use something like Falcon, sorry, shameless plug. 00:15:05.029 --> 00:15:07.129 And what we're doing is under the hood. 00:15:07.129 --> 00:15:11.359 The exact same thing that I mentioned, which is we create a snowflake instance, 00:15:11.359 --> 00:15:16.849 use five train and dump all this data in do ID resolution on it, and then 00:15:16.849 --> 00:15:18.929 give you a segmentation experience. 00:15:18.949 --> 00:15:25.729 And then under the hood, use high touch or Censuc to push that data out to the 00:15:25.729 --> 00:15:27.349 systems of action that you care about. 00:15:28.474 --> 00:15:37.789 So those are the two ways that we've seen that so you know, How we recommend 00:15:37.789 --> 00:15:42.409 doing value metrics is you start as get your product marketing and sales 00:15:42.409 --> 00:15:47.589 team in a room and write down your Uh, And, and I, I will get to how you 00:15:47.589 --> 00:15:49.419 get to this from a user perspective. 00:15:49.719 --> 00:15:53.769 You should first write down your hypotheses about what you believe the 00:15:53.769 --> 00:15:57.009 core value of your product is to your end. 00:15:57.009 --> 00:16:02.259 Customers ask yourself three levels of why, because often when I ask people 00:16:02.259 --> 00:16:06.489 this question, the first answer is a feature or capability and pardon 00:16:06.489 --> 00:16:10.089 my language, but no user gives a fuck about a feature or capability. 00:16:10.329 --> 00:16:13.529 They're looking for a uh, that they, that they want. 00:16:13.529 --> 00:16:13.829 Right. 00:16:13.979 --> 00:16:17.749 So as an example at um, the first assumption we made was. 00:16:17.899 --> 00:16:22.569 Um, Dropbox is valuable when uh, create a document. 00:16:23.379 --> 00:16:24.009 No, it's not. 00:16:24.879 --> 00:16:29.259 They, the document only becomes valuable if it is synched to another 00:16:29.259 --> 00:16:32.709 device or if it is shared with another human being, because you are 00:16:32.709 --> 00:16:34.239 trying to collaborate with someone. 00:16:34.509 --> 00:16:39.999 So our first answer for what is a value metric for Dropbox was 00:16:40.029 --> 00:16:42.099 number of documents created. 00:16:42.444 --> 00:16:43.014 Wrong. 00:16:43.194 --> 00:16:47.994 It ended up being number of documents, shared, and number of 00:16:47.994 --> 00:16:50.634 documents, synched across devices. 00:16:51.294 --> 00:16:53.334 So validating. 00:16:53.334 --> 00:16:55.644 So we come up with our list of hypotheses. 00:16:55.704 --> 00:17:03.334 Then we go and dive in our data to see, is there a correlation between our hypotheses 00:17:03.384 --> 00:17:06.084 and actual conversion events for users? 00:17:06.174 --> 00:17:06.534 Right. 00:17:06.804 --> 00:17:11.664 So if I tell you that 90% of your customers that have not converted. 00:17:12.684 --> 00:17:13.944 Created a document. 00:17:14.409 --> 00:17:19.089 And 90% of customers that have converted also created a document. 00:17:19.329 --> 00:17:23.169 Clearly document creation was not the difference that made the difference 00:17:23.169 --> 00:17:24.729 between these two groups, right? 00:17:25.089 --> 00:17:31.359 Versus if I told you the number 90% of users that did not convert, never 00:17:31.359 --> 00:17:37.077 shared a document and did not have more than one uh, using our product versus. 00:17:37.797 --> 00:17:42.897 70% of customers that converted, shared multiple documents 00:17:42.957 --> 00:17:45.569 and uh, two or more devices. 00:17:45.929 --> 00:17:47.489 Now you can see the difference. 00:17:47.519 --> 00:17:48.749 That's making the difference. 00:17:48.929 --> 00:17:52.139 This is where a lot of data scientists will jump on me and 00:17:52.139 --> 00:17:54.059 say, correlation is not causation. 00:17:54.089 --> 00:17:55.169 Totally get it. 00:17:55.379 --> 00:17:57.509 That's why you need a human. 00:17:57.659 --> 00:17:59.519 This is a human in the loop solution. 00:17:59.519 --> 00:18:03.059 So you see the best causal inference engine is the human brain. 00:18:03.914 --> 00:18:10.784 Correlation will help you whittle down the hypotheses from 15 to five, right? 00:18:11.174 --> 00:18:14.894 And then you can decide as the human, ah, this causes this, 00:18:14.894 --> 00:18:16.544 therefore this is the value metric. 00:18:17.024 --> 00:18:22.784 I will also say doing that is in parallel to doing qualitative research 00:18:22.994 --> 00:18:29.781 and finding your 10 most engaged users and going uh, on how they use the. 00:18:30.651 --> 00:18:34.761 What aspects of the product they use, what they love about your product? 00:18:35.091 --> 00:18:40.487 My one caution on qualitative research um, and I've seen product managers 00:18:40.787 --> 00:18:42.587 screw this up over and over again. 00:18:42.587 --> 00:18:44.177 I'm a product manager as well. 00:18:44.337 --> 00:18:49.087 Uh, We uh, qualitative research with the questions that we ask. 00:18:49.387 --> 00:18:52.117 And so if you're going to go down the qualitative research. 00:18:53.047 --> 00:18:57.907 I would strongly recommend an observational approach rather than a 00:18:57.907 --> 00:19:03.007 Q and A approach, watch people as they use the product, as opposed to asking 00:19:03.007 --> 00:19:08.607 Um, Because I have not seen a lot of well-crafted questions that don't 00:19:08.607 --> 00:19:10.977 bias the user to give you the answer. 00:19:10.977 --> 00:19:13.317 That is your hypothesis anyway. 00:19:13.347 --> 00:19:13.707 Right? 00:19:13.797 --> 00:19:16.897 So in a perfect world, both of these go together. 00:19:16.897 --> 00:19:20.567 But, uh, I said, on the data side, you want to be analytically. 00:19:21.587 --> 00:19:24.107 Come up with hypotheses, validate them with data. 00:19:24.157 --> 00:19:29.497 Uh, In qualitative research, you also want to be rigorous and bias for 00:19:29.917 --> 00:19:32.685 observational insights versus Cool. 00:19:32.955 --> 00:19:36.675 Now I'm going to get into a fun thing ID resolution. 00:19:36.675 --> 00:19:40.875 So when I was at Amperity, we built ID resolution for consumer companies. 00:19:40.875 --> 00:19:43.735 And so this is a uh, very close to my heart. 00:19:44.125 --> 00:19:48.145 So in a BLG motion, we have to think about identity resolution 00:19:48.145 --> 00:19:49.615 as three distinct problems. 00:19:50.035 --> 00:19:50.395 Problem. 00:19:50.395 --> 00:19:52.225 Number one is user to contact. 00:19:53.245 --> 00:19:56.185 And this is the one that I was just describing earlier, which is. 00:19:57.745 --> 00:20:01.585 You have a million users and half of them are using personal email 00:20:01.585 --> 00:20:03.235 addresses to use your product. 00:20:04.015 --> 00:20:11.065 How do you figure out where, like, which contacts they map to in 00:20:11.065 --> 00:20:13.045 your contact list in Salesforce? 00:20:13.135 --> 00:20:13.525 Right. 00:20:13.765 --> 00:20:17.215 So I have a list of users here and I have a list of contacts here, and 00:20:17.215 --> 00:20:20.815 I want to be able to match these, to figure out, oh, this user here is 00:20:20.815 --> 00:20:23.455 actually the same as this contact here. 00:20:23.455 --> 00:20:24.685 If I don't do that, I'm going to. 00:20:25.445 --> 00:20:32.225 A lot of duplications across these two sets and from a customer stand point that 00:20:32.225 --> 00:20:37.415 is problematic because as a user now I'm potentially getting two emails from you, 00:20:37.415 --> 00:20:42.335 two outreaches from you that are saying different things, but I'm the same person. 00:20:42.335 --> 00:20:46.145 I just happen to use a Gmail address to use your service. 00:20:46.175 --> 00:20:51.955 And then you got my contact information on zoom info with uh, work, add a work email. 00:20:52.505 --> 00:20:57.495 Uh, And now you're bombarding me with, uh, the wrong message on two channels. 00:20:57.495 --> 00:20:57.795 Right? 00:20:58.365 --> 00:21:04.035 So the way to do user to contact um, there's a very literal version 00:21:04.035 --> 00:21:05.535 of it, which is named matching. 00:21:06.315 --> 00:21:06.945 It's easy. 00:21:06.975 --> 00:21:09.555 The pros of this are it's easy to set up. 00:21:09.555 --> 00:21:11.695 Um, Same thing for email matching. 00:21:11.695 --> 00:21:13.975 You can do an exact match on name and email. 00:21:14.605 --> 00:21:21.215 Exact matches will result in a lower match rate um, one, a lot of 00:21:21.215 --> 00:21:24.455 times people don't give you their accurate first name or last name. 00:21:24.725 --> 00:21:27.905 Second, you end up with typos all the time. 00:21:28.535 --> 00:21:31.865 And so you will just get lower match rate than you expect. 00:21:32.315 --> 00:21:38.285 However, the pros of this are easy to set up The matches that you will get 00:21:38.315 --> 00:21:39.665 will be a hundred percent accurate. 00:21:39.785 --> 00:21:40.175 Right? 00:21:41.585 --> 00:21:44.975 Then we have a more sophisticated approach, which is the thing 00:21:44.975 --> 00:21:46.705 that we built at Amperity. 00:21:46.725 --> 00:21:51.755 My previous company, where we do heuristic matching and the heuristic matching. 00:21:51.755 --> 00:21:56.675 What it's doing is it's looking at things like rarity of first name and last name. 00:21:56.675 --> 00:21:59.255 Like Mona Akmal is a rare name in the United. 00:22:00.265 --> 00:22:04.975 Therefore, if you find a MonaAkmal at gmail.com and you find a 00:22:04.975 --> 00:22:10.945 Monaakmal@falcon.ai, the likelihood that these are the same person significantly 00:22:10.945 --> 00:22:15.895 higher than if my name was, I don't know, John Smiths, I don't actually know very 00:22:15.895 --> 00:22:17.545 many John Smiths, but you get my drift. 00:22:17.575 --> 00:22:17.905 Right. 00:22:18.635 --> 00:22:22.655 Um, And other way to do heuristic matching is you look at the 00:22:22.655 --> 00:22:24.635 username in the email address that. 00:22:25.400 --> 00:22:28.970 And you try to see if there is some heuristic of first name, 00:22:28.970 --> 00:22:33.140 last name, because a lot of people have their email addresses, be a 00:22:33.140 --> 00:22:36.710 reflection of some combination of their first name and last name. 00:22:36.950 --> 00:22:38.900 So you can get really, really fancy. 00:22:39.080 --> 00:22:43.490 What that does is it improves your match But it becomes more 00:22:43.490 --> 00:22:46.900 and more complex to set up uh, more and more complex to maintain. 00:22:48.605 --> 00:22:52.655 The second type of identity resolution that we have to think about is user 00:22:52.655 --> 00:22:57.725 to account, which I also alluded to earlier in user to account, you are 00:22:57.725 --> 00:23:05.015 trying to say Monaakmal@gmail.com actually works at Falcon AI, which 00:23:05.015 --> 00:23:07.235 is a Salesforce account that I. 00:23:07.990 --> 00:23:10.480 But that, that is a target account that I want to pursue. 00:23:10.700 --> 00:23:14.660 Um, With several of our customers, we've been able to identify a whole 00:23:14.660 --> 00:23:20.580 bunch uh, personal email addresses of users and map them to accounts 00:23:20.580 --> 00:23:23.010 and help them see, holy shit. 00:23:23.040 --> 00:23:27.830 Like in this account, we have a lot of people using our product already. 00:23:28.065 --> 00:23:33.195 So that completely changes your perception of how much you've penetrated a target 00:23:33.195 --> 00:23:37.695 account and what tactics to take to turn that into an enterprise bank account. 00:23:37.815 --> 00:23:38.115 Right. 00:23:38.565 --> 00:23:43.035 And in user to account the way we think about it is you have to 00:23:43.035 --> 00:23:44.925 use third party data enrichment. 00:23:44.925 --> 00:23:50.345 So you can use like a vendor like people or um, to enrich your user information. 00:23:51.515 --> 00:23:56.455 And try to get more metadata on your uh, and where they might be working. 00:23:56.965 --> 00:23:59.485 You can also do things like domain matching. 00:23:59.485 --> 00:24:04.195 So for instance, if someone has given you, like, let's say somebody works at, 00:24:04.195 --> 00:24:12.275 um, Adobe, uh, but they've told you their name is Simon@adobe.co.uk and you want 00:24:12.285 --> 00:24:18.255 to, uh, and you have a person that uh, jane@adobe.com and you have an other 00:24:18.255 --> 00:24:23.835 person that works in some other subsidiary of Adobe being able to do domain matching 00:24:23.835 --> 00:24:25.965 in a, again, there's an exact way. 00:24:25.965 --> 00:24:26.865 And there's a heuristic. 00:24:27.690 --> 00:24:33.450 You can use data sets like Crunchbase, for instance, to say adobe.co.uk 00:24:33.480 --> 00:24:39.200 and adobe.com and Adobe uh, whatever XXX are, all one company. 00:24:39.200 --> 00:24:41.030 And that company is Adobe. 00:24:41.060 --> 00:24:43.910 So that's how I'm going to match users to accounts. 00:24:44.300 --> 00:24:46.670 And then we have tenants to accounts, right? 00:24:46.920 --> 00:24:52.020 Uh, This was actually, we're talking to the head of BI at, uh, Mixpanel 00:24:52.385 --> 00:24:57.585 and they were telling us that, you know, they had to go through a massive, uh, 00:24:57.615 --> 00:25:03.705 data cleaning project um, they had so many duplicate Salesforce accounts because 00:25:03.705 --> 00:25:07.365 for every service account they created, they would create a Salesforce account. 00:25:07.785 --> 00:25:13.935 So now, if we have a hundred service accounts with different teams in 00:25:13.935 --> 00:25:19.575 Netflix, we now have essentially a hundred Netflix salesforce accounts 00:25:19.605 --> 00:25:23.205 that represent actually one company, not a hundred different companies. 00:25:23.205 --> 00:25:23.535 Right. 00:25:23.715 --> 00:25:27.915 So that's sort of also where we use similar techniques, heuristic 00:25:27.915 --> 00:25:32.445 versus um, and use third party data sets like Crunchbase to say, no, 00:25:32.445 --> 00:25:34.125 these are actually the same company. 00:25:34.875 --> 00:25:38.205 One other thing that I would caution is if you don't have the 00:25:38.265 --> 00:25:40.785 um, product instrumentation set up. 00:25:40.815 --> 00:25:44.395 So like if you're using amplitude or Mixpanel or Pendo or Google. 00:25:45.495 --> 00:25:50.265 Make sure that your engineering team, when a new service account is created or 00:25:50.265 --> 00:25:56.605 a new tendency is created, that they are able to, uh, put in the name of the uh, 00:25:56.635 --> 00:26:02.695 which will then help you attach that to a, a Salesforce uh, in a much easier way. 00:26:03.145 --> 00:26:07.255 And then second thing is, make sure your Salesforce structure is set up. 00:26:07.405 --> 00:26:13.510 And again, What I recommend is creating a custom object for every service 00:26:13.510 --> 00:26:18.310 account, and then being able to join that service account with a Salesforce 00:26:18.310 --> 00:26:21.370 account using account IDs, either way. 00:26:22.750 --> 00:26:26.080 Now let's move to the fun stuff, which is product signals, right? 00:26:26.080 --> 00:26:30.820 So we are trying to identify who are our best users and who should we be going 00:26:30.820 --> 00:26:33.040 after to convert them from free to paid? 00:26:33.430 --> 00:26:36.490 And so there are three things to consider here. 00:26:36.520 --> 00:26:41.620 One is snapshot data, which is you identify that a customer is 00:26:41.620 --> 00:26:43.870 using key features of your product. 00:26:43.900 --> 00:26:47.440 And this is like, you know, in the maturity arc of PLG, this is 00:26:47.440 --> 00:26:49.240 where companies generally start. 00:26:49.734 --> 00:26:50.384 Um, So. 00:26:51.834 --> 00:26:54.654 A customer, let's say we're back to the recruiting example. 00:26:54.654 --> 00:26:58.644 If a customer, if a user is doing lots of searches, they are 00:26:58.644 --> 00:27:00.594 downloading a lot of resumes. 00:27:00.804 --> 00:27:05.994 They are sharing those resumes with hiring managers as a recruiting platform. 00:27:05.994 --> 00:27:11.074 Those are all key features of the platform that suggest um, 00:27:11.104 --> 00:27:12.664 high engagement and high value. 00:27:12.714 --> 00:27:16.164 Um, You can do snapshots of these to see. 00:27:17.004 --> 00:27:22.914 At any given point, which user has used feature a, B, C, D, E, and 00:27:23.294 --> 00:27:25.554 decide what to do with that based. 00:27:25.644 --> 00:27:27.654 So you can use it in a couple of ways. 00:27:27.654 --> 00:27:29.364 You can use that for segmentation. 00:27:29.394 --> 00:27:33.534 You can use that for personalized outreach, like sending a marketing 00:27:33.564 --> 00:27:37.464 email to someone saying, Hey, I noticed you shared your first document, 00:27:37.494 --> 00:27:41.454 or I noticed that you shared your first resume with a hiring manager. 00:27:41.634 --> 00:27:44.544 Did you know that if you did this. 00:27:46.329 --> 00:27:47.889 Insert new feature. 00:27:47.889 --> 00:27:52.579 Um, You'd be able to save a lot of time and manual effort, right. 00:27:53.359 --> 00:27:59.061 Then we get uh, stuff that's a little bit more sophisticated, which is okay. 00:27:59.061 --> 00:28:03.321 So if I have a snapshot that tells me a user is using this feature, this 00:28:03.321 --> 00:28:07.851 feature, and I have 10 features becomes a lot of information to process. 00:28:07.851 --> 00:28:10.451 So now you can create higher level segments. 00:28:11.496 --> 00:28:16.566 For users that are using these four features, I consider them an 00:28:16.566 --> 00:28:22.326 advanced user for users that are using these three other features. 00:28:22.326 --> 00:28:26.516 I consider them an intermediary user. 00:28:26.796 --> 00:28:32.629 And I'll give you Uh, One of our customers seek out one of their key indicators 00:28:32.629 --> 00:28:35.349 for an advanced or sophisticated user. 00:28:35.479 --> 00:28:39.079 And seek out is a recruitment platform, right? 00:28:39.109 --> 00:28:43.009 It's used by recruiters to find candidates and then share them 00:28:43.009 --> 00:28:44.449 with hiring managers and so on. 00:28:45.079 --> 00:28:52.609 One of their sophistication features is if a recruiter saves a search and uses 00:28:52.609 --> 00:28:58.109 the carrot in their uh, experience, it indicates that they are an advanced user. 00:28:58.319 --> 00:29:03.239 So you can come up with some pretty simple logic to group users as advanced. 00:29:04.019 --> 00:29:11.519 Intermediate novices that helps you make sense of the 15, 20, 30 possible 00:29:11.549 --> 00:29:14.999 things that they can do in your product, because it's very hard to look at 30 00:29:14.999 --> 00:29:16.739 numbers and try to make sense of it. 00:29:16.769 --> 00:29:17.129 Right. 00:29:18.029 --> 00:29:23.729 And then the next thing that this is where, to me, if you are exploring 00:29:23.729 --> 00:29:29.689 your PLG motion as a, as a, uh, new, it's a new experience for you, you're 00:29:29.689 --> 00:29:31.669 probably thinking about snapshots. 00:29:32.399 --> 00:29:35.669 If you are a little bit more farther along in your journey, you're 00:29:35.669 --> 00:29:39.269 thinking about growth rates, because we don't just care about the fact 00:29:39.269 --> 00:29:41.039 that someone has used a feature. 00:29:41.399 --> 00:29:45.449 We care about how that usage is changing over time. 00:29:45.599 --> 00:29:50.159 Is it getting more entrenched or less entrenched? 00:29:50.219 --> 00:29:50.639 Right. 00:29:50.969 --> 00:29:54.419 And is user sophistication changing over time? 00:29:54.779 --> 00:29:58.499 Are users actually going from being novices to being 00:29:58.499 --> 00:30:00.479 intermediate, to being advanced? 00:30:02.509 --> 00:30:08.939 Is the uh, usage across all users and all tenants within a company 00:30:08.999 --> 00:30:10.679 increasing over time, right? 00:30:10.919 --> 00:30:15.809 Those growth rates require you to have a historical perspective instead 00:30:15.809 --> 00:30:17.999 of just a snapshot point in time. 00:30:17.999 --> 00:30:19.919 This is the world as of today. 00:30:20.309 --> 00:30:25.139 And we think growth rates actually unlock deeper, more relevant insights 00:30:25.139 --> 00:30:27.179 that are less noisy than snapshots. 00:30:27.689 --> 00:30:30.899 And then the third element of product signals to consider 00:30:30.899 --> 00:30:32.519 is consistency of usage. 00:30:32.549 --> 00:30:32.909 Right? 00:30:33.269 --> 00:30:38.099 So if I told you that there was a person that started using your 00:30:38.099 --> 00:30:43.169 product and they used feature a once. 00:30:44.759 --> 00:30:50.069 Then they used it 10 times and then they used it once and then they disappear. 00:30:51.179 --> 00:30:57.179 How does that compare with someone that uses your product consistently four times 00:30:57.269 --> 00:31:02.969 a day, intuitively you can see that these are pretty different profiles and they 00:31:02.969 --> 00:31:04.919 need to be approached very differently. 00:31:04.919 --> 00:31:08.669 And in all at the end of the day, the point of this entire workshop, 00:31:09.464 --> 00:31:13.694 You need to understand your user behavior at a significantly deeper 00:31:13.694 --> 00:31:15.944 level to do personalized outreach. 00:31:16.154 --> 00:31:18.284 Consumer companies are very good at this. 00:31:18.284 --> 00:31:21.644 B2B companies are just starting to figure out what this is, right. 00:31:21.914 --> 00:31:28.184 And so consistency of usage is really how is a given user using your product or a 00:31:28.184 --> 00:31:31.274 key capability in your product over time? 00:31:31.274 --> 00:31:35.144 Is it a flat line is very spiky? 00:31:35.609 --> 00:31:39.089 Was it a one-time they tried it and then they disappeared. 00:31:39.149 --> 00:31:39.419 Right. 00:31:39.449 --> 00:31:44.009 Which actually happens a lot in free trials and ends up with bad 00:31:44.329 --> 00:31:48.659 PQL signals is you'll see someone just, you know, spike and hit their 00:31:48.659 --> 00:31:50.699 limit and use a bunch of things. 00:31:50.699 --> 00:31:53.549 Value moments have been triggered, Uh, They are a PQL. 00:31:53.669 --> 00:31:54.239 No, they're not. 00:31:54.719 --> 00:31:58.529 You want to look for consistency of usage over a period of time 00:31:58.529 --> 00:32:02.389 to understand if they were just testing or they were actually 00:32:08.470 --> 00:32:15.760 To me, this is squarely in the realm of using a tool like amplitude Mixpanel 00:32:15.760 --> 00:32:23.690 Pendo, aggressively and like in a very sophisticated way to figure out where are 00:32:23.710 --> 00:32:29.200 end users dropping off in your product usage experiences and look at it as a 00:32:29.200 --> 00:32:35.260 user Amplitude does a great job of showing you user journeys over time, all events 00:32:35.260 --> 00:32:40.090 that are, that are associated with a user linearly laid out over time, and you can 00:32:40.090 --> 00:32:41.560 see where the drop-offs are happening. 00:32:41.560 --> 00:32:47.530 Um, Often I don't see that um, a top of mind issue for go to market teams, right? 00:32:47.530 --> 00:32:48.520 Go to that. 00:32:48.520 --> 00:32:52.690 To me, is squarely in the realm of product management using a 00:32:52.690 --> 00:32:54.340 product analytics tool like. 00:32:54.845 --> 00:33:00.465 Um, To understand customer behavior and user Uh, So this is where 00:33:00.465 --> 00:33:02.325 we go back to qualitative again. 00:33:02.355 --> 00:33:02.655 Right? 00:33:02.685 --> 00:33:07.985 I think um, interviewing, uh, users that were using your product in a highly 00:33:07.985 --> 00:33:13.265 engaged way and then turned out and stop using stopped using is a great 00:33:13.265 --> 00:33:17.795 way to get signal on where the pain points are, where there's lack of value. 00:33:18.425 --> 00:33:21.875 And you don't need to talk to a hundred to figure out what the patterns are. 00:33:21.875 --> 00:33:28.185 You need to talk to And so I, I would encourage uh, you know, once a quarter, 00:33:28.185 --> 00:33:32.385 at least you should be looking at your churned customer base and having 00:33:32.415 --> 00:33:37.235 deep conversations, give them uh, reward and incentive, obviously to 00:33:37.235 --> 00:33:39.275 spend 30 minutes or 15 minutes with. 00:33:39.405 --> 00:33:42.945 Um, But it is a good way to match what you will find in 00:33:42.945 --> 00:33:45.105 amplitude, which is data at scale. 00:33:45.105 --> 00:33:48.585 And then also qualitative research with people that have recently turned out. 00:33:50.325 --> 00:33:55.455 So what does the SDR do in this new world? 00:33:56.535 --> 00:34:01.245 I really believe the SDR role is going to get completely transformed 00:34:01.275 --> 00:34:03.915 with product led growth first. 00:34:04.645 --> 00:34:09.895 Support becomes a very important part of what you're doing in sales development, 00:34:09.895 --> 00:34:16.375 because in order for customers to become or users to become worthy or 00:34:16.375 --> 00:34:21.415 worthwhile leads for us to pursue, we first have to make them successful. 00:34:21.775 --> 00:34:26.035 And in order to make them successful, we have to support them in the 00:34:26.035 --> 00:34:27.535 places that they are stuck. 00:34:27.935 --> 00:34:29.105 Um, That can be done. 00:34:30.505 --> 00:34:36.635 Through a great product um, automated hyper-personalized set of campaigns to 00:34:36.635 --> 00:34:38.435 onboard customers and get them stuck. 00:34:38.435 --> 00:34:41.765 Um, It can also be done through rewards. 00:34:41.795 --> 00:34:44.825 So we've seen some customers create usage. 00:34:45.800 --> 00:34:50.810 Like congratulate your users and reinforce positive behavior when they use your 00:34:50.810 --> 00:34:55.520 product or a key capability consistently over a period of time, because you're 00:34:55.520 --> 00:34:58.430 trying to help people build habit right? 00:34:58.640 --> 00:34:59.540 Half the time. 00:34:59.540 --> 00:35:02.930 The reason why people don't get value out of your product is not 00:35:02.930 --> 00:35:04.730 because your product is not valuable. 00:35:05.090 --> 00:35:09.890 It's because they have a different workflow and a habit of doing things. 00:35:10.070 --> 00:35:13.370 Even though that way of doing things is really painful. 00:35:13.760 --> 00:35:15.110 They have a way of doing things. 00:35:15.860 --> 00:35:17.570 And they have a habit built around it. 00:35:18.320 --> 00:35:22.400 This has been something that has existed in game development for a long time. 00:35:22.610 --> 00:35:27.500 How do you create positive feedback loops that encourage habit forming? 00:35:27.650 --> 00:35:31.070 Right when I was at Zulily, we talked about this a lot. 00:35:31.430 --> 00:35:38.730 How do we make Zulily, a daily habit for a young uh, who was our ideal customer? 00:35:40.425 --> 00:35:45.765 The second aspect of the SDR role that I think is, is very interesting, is to 00:35:45.765 --> 00:35:48.105 move into a sales assist role, right? 00:35:48.345 --> 00:35:53.535 Which is I'm a user, I'm a developer and a lot of BLG companies do tend 00:35:53.535 --> 00:35:55.755 to bias towards a technical audience. 00:35:55.785 --> 00:35:59.055 These are people that are very sophisticated users of your product. 00:36:00.375 --> 00:36:04.845 They often have no idea how procurement works, how privacy and compliance 00:36:04.845 --> 00:36:08.595 works, how to build business justifications for purchasing software, 00:36:08.865 --> 00:36:13.395 or like how to even purchase things online in a professional context. 00:36:13.665 --> 00:36:22.045 So an SDRs role, uh, evolves, to support that user in logistics of making 00:36:22.045 --> 00:36:24.145 purchases, like going through a purchase. 00:36:25.455 --> 00:36:29.655 Helping them navigate the procurement process because they're unfamiliar 00:36:29.655 --> 00:36:34.335 with it and helping them build a business justification that will 00:36:34.335 --> 00:36:39.885 be relevant for their stakeholders internally, to make the buying decision. 00:36:40.035 --> 00:36:40.395 Right. 00:36:41.025 --> 00:36:45.615 And that's generally stuff that, that SDRs have not done in the past. 00:36:46.035 --> 00:36:51.575 And then the last part is I believe SDRs in this world can actually do a lot of. 00:36:52.495 --> 00:36:56.035 Gary quota and close deals or move them to AEs. 00:36:56.065 --> 00:36:59.575 So for smaller accounts, and we see that, you know, we were talking to 00:36:59.575 --> 00:37:05.725 someone at Twilio and their SDR team is a quota carrying team because for 00:37:05.725 --> 00:37:09.415 a lot of smaller accounts, you're trying to go from free to paid. 00:37:09.625 --> 00:37:12.265 You don't need an AE to be involved for that. 00:37:12.265 --> 00:37:16.195 An SDR can drive all the way to close in that scenario. 00:37:16.549 --> 00:37:16.969 training. 00:37:16.969 --> 00:37:22.369 So if, if the SDR role is evolving, then what training should 00:37:22.369 --> 00:37:25.009 support that transition and that. 00:37:26.189 --> 00:37:31.289 First is product training and in product training, you have to 00:37:31.289 --> 00:37:33.449 really, really go deeper, right? 00:37:33.659 --> 00:37:35.099 It's not as much. 00:37:35.489 --> 00:37:35.849 Yes. 00:37:35.879 --> 00:37:40.199 It's definitely still about being able to communicate value succinctly 00:37:40.199 --> 00:37:45.809 being able to discover pain points and whatnot equally important now is having 00:37:45.839 --> 00:37:51.419 a pretty deep knowledge of the product and being able to answer level one. 00:37:51.524 --> 00:37:55.744 Um, Questions from a technical depth standpoint, right? 00:37:55.924 --> 00:37:59.344 That's a gap in training with SDR teams right now that I 00:37:59.344 --> 00:38:00.574 think needs to be addressed. 00:38:00.874 --> 00:38:06.754 Second, have a deeper knowledge of your competitive landscape um, 00:38:06.994 --> 00:38:12.574 again, your users in this case are very knowledgeable about the space. 00:38:12.874 --> 00:38:16.834 They probably know your product better than you do because they actually use 00:38:16.834 --> 00:38:19.114 it and it was built for them not, you. 00:38:20.239 --> 00:38:26.179 So to have a knowledgeable conversation that is high value for this type uh, 00:38:26.199 --> 00:38:29.959 engagement, you need to understand the competitive landscape, your 00:38:29.959 --> 00:38:34.779 differentiators, uh, uh, a good technical understanding of your product 00:38:34.779 --> 00:38:40.359 and where it fits in and be able to do uh, level um, conversation around 00:38:40.359 --> 00:38:45.019 pricing again, with smaller deal cycles, uh, talking to a more technical. 00:38:45.994 --> 00:38:52.894 They don't want to have a large production talk to an AE and get, 00:38:53.414 --> 00:38:55.244 uh, custom pricing built out. 00:38:55.274 --> 00:38:58.094 They want something that is a little bit more lightweight, 00:38:58.124 --> 00:38:59.324 a little bit more transparent. 00:38:59.394 --> 00:39:04.044 Um, So having all that information at your fingertips becomes really important. 00:39:04.344 --> 00:39:08.484 And then the last part of it is procurement, which is helping the, the 00:39:08.604 --> 00:39:13.744 person that you are reaching out to the MQL or PQL that you're reaching out. 00:39:14.664 --> 00:39:19.314 Helping them quickly understand how you can help them with privacy compliance, 00:39:19.314 --> 00:39:23.844 business justification creation, and what a good procurement process looks like 00:39:23.994 --> 00:39:26.214 so that they know what they need to do. 00:39:26.454 --> 00:39:29.034 They love your product, they want to purchase it. 00:39:29.064 --> 00:39:34.134 They just don't know how to in their company and your job is to really 00:39:34.134 --> 00:39:39.954 help them understand how procurement works and turn them into a champion 00:39:40.014 --> 00:39:44.644 that is going to go make things happen and then the last thing is around 00:39:44.644 --> 00:39:48.724 automation, which we talked about just a reminder, Why is automation important? 00:39:48.724 --> 00:39:56.134 Because PLG companies generally tend to start with lower deal sizes, which 00:39:56.134 --> 00:40:01.684 means customer acquisition cannot be heavyweight human led and costly because 00:40:01.684 --> 00:40:03.784 the unit economics on that are not great. 00:40:04.024 --> 00:40:04.324 And so. 00:40:05.059 --> 00:40:09.619 Whenever, generally we want to go for cheaper customer acquisition, uh, an 00:40:09.619 --> 00:40:11.959 effective path to do that is automation. 00:40:12.079 --> 00:40:12.379 Right? 00:40:12.709 --> 00:40:15.859 And so what are the types of things that we can automate? 00:40:16.379 --> 00:40:17.579 Uh, And how do we automate. 00:40:18.284 --> 00:40:23.834 First and foremost, you need to have a lot of attributes associated with the contacts 00:40:23.864 --> 00:40:29.724 that you are going to reach out Um, Those attributes need to include things like 00:40:29.724 --> 00:40:31.694 what product features are they using? 00:40:32.214 --> 00:40:35.004 What value moments have they had already? 00:40:35.004 --> 00:40:37.314 What is the growth rate of their usage? 00:40:37.524 --> 00:40:40.224 What is the consistency of their usage? 00:40:40.554 --> 00:40:42.174 Are they sophisticated? 00:40:42.294 --> 00:40:42.714 Advanced? 00:40:42.919 --> 00:40:47.549 Uh, Intermediate or beginner, are they stuck Um, Have they 00:40:47.549 --> 00:40:50.939 actually tried to purchase, have they tried to contact sales? 00:40:50.939 --> 00:40:54.479 Have they had a support interaction with your company? 00:40:54.689 --> 00:40:59.279 So being able to get all of those attributes, you need to almost think 00:40:59.279 --> 00:41:04.559 that contact object in Salesforce is a hundred X richer in terms of 00:41:04.559 --> 00:41:06.959 attributes than it's ever been before. 00:41:07.199 --> 00:41:09.019 And it's live, it's able to show. 00:41:10.259 --> 00:41:14.639 Usage and trends over time, not just a snapshot of where they are. 00:41:14.669 --> 00:41:19.349 Snapshots are great at things like this contact's name is Mona because 00:41:19.349 --> 00:41:23.939 it's not going to change over time or their title is blah because that's also 00:41:23.939 --> 00:41:25.649 not going to change a lot over time. 00:41:25.679 --> 00:41:26.009 Right. 00:41:26.339 --> 00:41:30.599 But when we're thinking about live attributes, like usage, like 00:41:30.599 --> 00:41:35.549 sophistication, Those are things that do and should change over time. 00:41:35.549 --> 00:41:38.759 So you need to think about those as live attributes, that 00:41:38.759 --> 00:41:40.109 show you trends over time. 00:41:40.109 --> 00:41:41.069 Not just a snapshot. 00:41:41.549 --> 00:41:49.349 Once we have this very rich contact row in Salesforce, we can then do sophisticated 00:41:49.349 --> 00:41:54.189 segmentation and sophisticated segmentation is key to automation, Um, 00:41:54.219 --> 00:41:56.829 Because what do we lose when we automate? 00:41:56.829 --> 00:42:00.219 Generally we lose that human touch, that personalization. 00:42:00.669 --> 00:42:00.849 Wow. 00:42:01.839 --> 00:42:06.429 If you have enough data and you create enough refined segments, 00:42:06.429 --> 00:42:08.409 you don't have to lose out on that. 00:42:08.409 --> 00:42:10.539 Very personalized human touch. 00:42:10.539 --> 00:42:15.289 You are just using technology to imitate the human touch instead uh, you know, 00:42:15.289 --> 00:42:17.209 manually doing that work yourself. 00:42:17.239 --> 00:42:17.599 Right. 00:42:17.779 --> 00:42:22.219 And so fine grain segmentation based on these attributes that we've talked 00:42:22.219 --> 00:42:23.959 about becomes really important. 00:42:24.199 --> 00:42:26.509 And then the ability to sync these segments. 00:42:27.319 --> 00:42:31.969 Two systems of action, like outreach, HubSpot Salesforce Marketo becomes 00:42:31.969 --> 00:42:34.909 really important because that's where you're going to do something with it. 00:42:34.999 --> 00:42:35.359 Right. 00:42:35.659 --> 00:42:39.829 And then last is even automate the outreach, right? 00:42:41.904 --> 00:42:47.124 A hundred times the outreach sequences that you have right now, and auto-enrolled 00:42:47.124 --> 00:42:49.344 segments into those sequences. 00:42:49.344 --> 00:42:53.994 And those can be outreach sequences, Marketo sequences, HubSpot sequences, 00:42:53.994 --> 00:42:57.504 whatever marketing automation platform you use, whatever sales automation, 00:42:57.534 --> 00:42:58.764 enablement platform you use. 00:42:59.719 --> 00:43:04.369 Being able to push those segments and then just auto-enroll them. 00:43:04.369 --> 00:43:07.759 So that the first outreach doesn't even have to be a person. 00:43:07.789 --> 00:43:15.019 It can just be a highly personalized email that is being sent to the right user at 00:43:15.019 --> 00:43:17.719 the right time with the right message. 00:43:18.019 --> 00:43:20.119 Except no human was involved. 00:43:20.149 --> 00:43:22.039 We just set it up and then. 00:43:22.774 --> 00:43:24.964 We let the system just run this right? 00:43:25.324 --> 00:43:29.104 Only when people respond back, should a human get involved. 00:43:29.104 --> 00:43:36.004 That is going to save you thousands of hours in terms of human time, 00:43:36.094 --> 00:43:39.754 which then dramatically brings down your customer acquisition 00:43:39.754 --> 00:43:44.554 costs to take your free customers and turn them into paid customers. 00:43:44.864 --> 00:43:52.134 And, obviously you, uh, last point on this is in order to create that many sequences 00:43:52.134 --> 00:43:58.704 you need to hire and have bandwidth within your marketing organization, 00:43:58.744 --> 00:44:00.994 um, to be able to pull that off, right. 00:44:02.794 --> 00:44:03.274 Attendee: Mona. 00:44:03.304 --> 00:44:04.294 Thank you so much. 00:44:04.384 --> 00:44:08.164 It's a pleasure to make your acquaintance, uh, Samuel and uh, exchanging 00:44:08.164 --> 00:44:10.294 ideas for the last 10 days or so. 00:44:10.294 --> 00:44:13.324 And I have been thinking quite a bit about this transition. 00:44:13.324 --> 00:44:17.284 Since many SDRs are trained to do the exact opposite of the kinds of 00:44:17.284 --> 00:44:19.834 things you've suggested and the. 00:44:20.424 --> 00:44:25.584 Mona: Person who will lead this feigning does not exist inside of a company. 00:44:25.584 --> 00:44:29.634 If you kind of step the highest level, you've got a lot of growth marketers. 00:44:29.664 --> 00:44:34.434 You've got an emphasis on digital automation platform to do onboarding. 00:44:34.764 --> 00:44:38.664 You've got PQL platforms, but you really don't have somebody that sits 00:44:38.664 --> 00:44:43.524 in the middle there that says, how do we engage our SDRs to level them 00:44:43.524 --> 00:44:45.744 up, to listen, to guide and to scout. 00:44:47.269 --> 00:44:49.429 I am so aligned with you, Chris. 00:44:49.459 --> 00:44:53.599 And you know, the fun thing is that creates an opportunity. 00:44:53.599 --> 00:44:59.929 I think there's a big business to be built in leveling up and retooling 00:45:00.289 --> 00:45:05.449 and retraining SDRs at all these PLG companies so that they can stay 00:45:05.449 --> 00:45:08.359 relevant in this new, um, new age. 00:45:08.599 --> 00:45:09.019 Right. 00:45:09.589 --> 00:45:10.429 And you're totally right. 00:45:10.429 --> 00:45:10.729 It doesn't. 00:45:12.419 --> 00:45:16.259 And so I think it's important for everybody in the community to start 00:45:16.259 --> 00:45:22.909 defining the skills and abilities, uh, and to understand at what point you're 00:45:22.909 --> 00:45:27.019 going to create a cross-functional team that pulls from say support. 00:45:27.464 --> 00:45:31.484 Yeah, uh, that pulls a bit from marketing that pulls from customer 00:45:31.484 --> 00:45:38.864 success to try and reinvent the role and move away from essentially forcing 00:45:39.104 --> 00:45:44.264 an outbound motion into a product, a understanding type of arrangement. 00:45:44.684 --> 00:45:45.164 Totally. 00:45:45.224 --> 00:45:45.974 That's brilliant. 00:45:46.064 --> 00:45:49.754 I love that suggestion and yeah, I think the support angle is 00:45:49.754 --> 00:45:51.494 really, really fascinating because. 00:45:52.269 --> 00:45:56.619 Often, I would say about half the skills that we're talking about 00:45:56.619 --> 00:46:01.599 with respect to, um, the new role that an SDR may have to play. 00:46:01.719 --> 00:46:05.209 Actually, half of those skills live in support uh, and like 00:46:05.209 --> 00:46:07.419 one third um, in marketing land. 00:46:07.419 --> 00:46:10.509 And then the rest is sort of more traditional SDR. 00:46:10.509 --> 00:46:11.709 They do need to be combined. 00:46:12.669 --> 00:46:16.719 I think it would be great to host another workshop to try and further 00:46:16.719 --> 00:46:18.189 articulate some of these skills. 00:46:18.219 --> 00:46:18.909 Thank you so much. 00:46:19.359 --> 00:46:19.809 Of course. 00:46:19.809 --> 00:46:20.589 Absolutely. 00:46:20.589 --> 00:46:24.639 And I would love to co-host that with you cause you're spending a 00:46:24.639 --> 00:46:26.079 bunch of time thinking about it. 00:46:26.079 --> 00:46:27.009 That would be super fun. 00:46:27.369 --> 00:46:28.089 I'd Perfect. 00:46:28.089 --> 00:46:29.319 well, thank you guys. 00:46:29.409 --> 00:46:32.939 Aloha from Um, Hope you all have a kick-ass week. 00:46:32.939 --> 00:46:36.499 Make it Uh, Thank you so much for joining and spending time with us. 00:46:37.669 --> 00:46:38.029 Cheers.