WEBVTT 1 00:00:04.200 --> 00:00:08.320 This is the Full Funnel bTB Marketing podcast, brought to 2 00:00:08.359 --> 00:00:10.320 you by full Funnel dot io. 3 00:00:11.240 --> 00:00:15.720 Let's starve well join us of course. 4 00:00:15.800 --> 00:00:18.359 Yeah, thank you so much for having me. I'm psyched 5 00:00:18.359 --> 00:00:20.519 to dive into anything account based. 6 00:00:21.960 --> 00:00:27.039 Absolutely. One thing that I would love to kick off 7 00:00:27.079 --> 00:00:32.079 with is the fact that I have seen you mentioned 8 00:00:32.079 --> 00:00:35.560 on your linked and profile. So when you are leading 9 00:00:35.640 --> 00:00:39.520 the Global Enterprise Demand GAN at Google, you were able 10 00:00:39.560 --> 00:00:42.960 to increase SCL quality score from such a five percent 11 00:00:43.039 --> 00:00:46.520 to age of five percent in under one year by 12 00:00:46.679 --> 00:00:51.399 partner with the SDR team right developing the shared okayrs 13 00:00:51.600 --> 00:00:56.960 and also create this cross functional alignment what I feel 14 00:00:57.560 --> 00:01:00.479 and I feel this is kind of the universe problem 15 00:01:00.560 --> 00:01:06.159 for most bit of B companies, right, but simply don't 16 00:01:06.159 --> 00:01:09.280 have this cross functional alignment. Not just to say that 17 00:01:09.640 --> 00:01:13.120 we often fight for the credit, right, because this is 18 00:01:13.200 --> 00:01:15.760 kind of the typical bit of the setup. Looks like 19 00:01:16.000 --> 00:01:18.879 marketing needs to prove the marketing so are traving, the 20 00:01:18.959 --> 00:01:22.719 sales needs to prove the sales aurce aving to kind 21 00:01:22.719 --> 00:01:25.680 of measure everything by the last click attribution, and that's it. 22 00:01:25.879 --> 00:01:31.480 So well, I would love to dive deeper and understand 23 00:01:32.439 --> 00:01:35.879 The first thing is how do you define SQL quality score? 24 00:01:37.040 --> 00:01:38.040 Yeah, good question. 25 00:01:38.200 --> 00:01:42.959 So SQL quality score, think about your your typical score 26 00:01:43.239 --> 00:01:46.680 is going to be probably driven off of a band 27 00:01:46.879 --> 00:01:50.159 like budget, authority, need timing, and it's going to be 28 00:01:50.239 --> 00:01:54.239 driven by your SDR team. When I was at Google, 29 00:01:54.560 --> 00:01:57.760 you know, granted, this was you know, probably four or 30 00:01:57.760 --> 00:02:00.079 five years ago now, and we were doing this, so 31 00:02:00.159 --> 00:02:02.799 we didn't have all of the fancy AI stuff. 32 00:02:02.560 --> 00:02:03.319 That we have today. 33 00:02:03.760 --> 00:02:09.680 But quality was a combination of marketing data and sales data. 34 00:02:10.080 --> 00:02:14.680 And one of the particular metrics we've looked at obsessively 35 00:02:15.319 --> 00:02:19.280 was the what was called a rejection reason, so other 36 00:02:19.319 --> 00:02:23.039 companies might call it like disqualification reason, but it's a 37 00:02:23.280 --> 00:02:28.000 basically a sub field in salesforce. When an SDR was 38 00:02:28.039 --> 00:02:31.599 on the phone talking to a marketing qualified lead, they 39 00:02:31.680 --> 00:02:36.000 had to market as you know, for six different stages 40 00:02:36.039 --> 00:02:39.960 to either progress it forward or you know, disqualify it. 41 00:02:40.319 --> 00:02:42.680 And so one of those was disqualification, and then we 42 00:02:42.719 --> 00:02:46.479 had a whole sub field of reasons that they had 43 00:02:46.479 --> 00:02:49.879 to select for why they disqualified it. And that data 44 00:02:49.960 --> 00:02:54.439 alone became the most important part of this SQL quality 45 00:02:54.520 --> 00:02:58.960 score because we could see, you know, if marketing registered 46 00:02:59.000 --> 00:03:02.000 a lead as a market and qualified lead, meaning they 47 00:03:02.080 --> 00:03:06.080 met the marketing scoring criteria of you know, ICP fit 48 00:03:06.400 --> 00:03:10.599 or right job title or really right persona, and then 49 00:03:10.639 --> 00:03:14.400 they hit some kind of behavioral engagement score. Marketing thought 50 00:03:14.400 --> 00:03:16.080 it was good, right, And so this is where you 51 00:03:16.120 --> 00:03:19.599 get humbled because marketing said we created the MQL. 52 00:03:19.599 --> 00:03:20.520 Here you go sales. 53 00:03:20.960 --> 00:03:23.000 But then when you get a feedback loop from sales 54 00:03:23.000 --> 00:03:27.319 that says, we just qualified this because it's the wrong persona. Now, 55 00:03:27.360 --> 00:03:30.000 all of a sudden, that data got us thinking, well, 56 00:03:30.199 --> 00:03:33.240 what happened because on our side it was the right persona. 57 00:03:33.800 --> 00:03:36.919 And so, you know, small example, but you know, let's 58 00:03:36.960 --> 00:03:40.479 say in this case, they were selling to a purely 59 00:03:40.599 --> 00:03:45.240 it right, nothing engineering, nothing infrastructure, nothing data, just pure 60 00:03:45.280 --> 00:03:49.800 information technology. When we would send them a VP of infrastructure, 61 00:03:50.360 --> 00:03:54.680 our MQL scoring would pick up and see VP technology 62 00:03:54.759 --> 00:03:58.120 title cool MQL. But then sales would come back and say, 63 00:03:58.120 --> 00:04:00.319 we can't sell that person. We can never sell that person. 64 00:04:00.360 --> 00:04:03.199 They don't buy you know, chromebooks in this particular case. 65 00:04:04.680 --> 00:04:08.919 So that that feedback loop was really key. And you 66 00:04:08.960 --> 00:04:11.000 know t L d R there is you know, my team, 67 00:04:11.039 --> 00:04:13.800 and we ended up going through thousands and thousands of 68 00:04:13.919 --> 00:04:16.839 m q ls manually based on all of these rejection 69 00:04:16.959 --> 00:04:20.959 reasons from the sales team and and basically figured out that, 70 00:04:21.199 --> 00:04:23.879 you know, our m QL scores weren't as good as 71 00:04:23.879 --> 00:04:28.000 we thought they were, and we had to, you know, 72 00:04:28.079 --> 00:04:30.319 kind of take one on the chin and say, okay, 73 00:04:30.319 --> 00:04:31.360 we've got to go back to. 74 00:04:31.279 --> 00:04:33.560 The drawing board and fix some stuff on the marketing 75 00:04:33.600 --> 00:04:35.800 side lot. 76 00:04:36.000 --> 00:04:39.279 And was the hindsight what would you say, is the 77 00:04:39.399 --> 00:04:42.879 road treason? Is the road problem of all of this? Right, 78 00:04:42.920 --> 00:04:46.279 because it doesn't start, let's say, when we have the 79 00:04:46.279 --> 00:04:49.399 fight for the for the even for the quality of 80 00:04:49.439 --> 00:04:53.079 that specifically, right would say that? Is it kind of 81 00:04:53.199 --> 00:04:57.800 the default GM playbook or anson else? 82 00:04:59.120 --> 00:05:00.519 Yeah, it's a question. 83 00:05:00.759 --> 00:05:03.920 I think two things come to mind, and one is 84 00:05:04.399 --> 00:05:09.519 the the traditional enterprise marketing and sales, you know, the 85 00:05:09.600 --> 00:05:14.839 GTM motion I think is fundamentally. 86 00:05:15.720 --> 00:05:21.160 Not broken, but flawed. Right. I think the idea of marketing. 87 00:05:21.279 --> 00:05:24.560 Services this first half of the funnel and they work 88 00:05:24.680 --> 00:05:27.480 towards a metric like an MQL, and then they throw 89 00:05:27.519 --> 00:05:29.639 it over the fence to sales and then you know, 90 00:05:29.720 --> 00:05:32.439 sales takes it from there and they own the back 91 00:05:32.480 --> 00:05:34.759 half of the funnel. And then you can even think 92 00:05:34.800 --> 00:05:37.959 about customer success. If we're talking about a software company, 93 00:05:38.480 --> 00:05:43.439 I think that that linear progression is not it's flawed, right, 94 00:05:43.680 --> 00:05:47.279 because it allows the teams to just get focused on 95 00:05:47.319 --> 00:05:50.279 that one thing that they're responsible for and then they 96 00:05:50.319 --> 00:05:53.959 can forget about it. And so in this case, you know, 97 00:05:54.040 --> 00:05:56.199 I was I was guilty of doing that, right, Like 98 00:05:56.279 --> 00:06:00.600 I had to actually humble myself as a market leader 99 00:06:00.680 --> 00:06:02.639 and say what I'm doing is wrong. 100 00:06:02.920 --> 00:06:03.519 I've got to. 101 00:06:03.399 --> 00:06:06.279 Trust my peers on the sales side. I've got to 102 00:06:06.279 --> 00:06:08.560 take their feedback, and then I have to do something 103 00:06:08.600 --> 00:06:11.480 about it. And so at a macro level, I think 104 00:06:11.519 --> 00:06:16.360 that is present. And then at a micro level, especially 105 00:06:16.639 --> 00:06:21.279 in enterprises or more mature go to market teams where 106 00:06:21.399 --> 00:06:25.720 let's just say you're you're dealing with, you know, thousands 107 00:06:25.759 --> 00:06:29.480 of leads on a monthly basis like five ten, you know, 108 00:06:29.519 --> 00:06:31.600 even more, some of these enterprise teams are dealing with 109 00:06:31.680 --> 00:06:36.120 millions per quarter, and so when you have that much volume, 110 00:06:37.040 --> 00:06:39.920 it's easy to just like sit back and hide behind 111 00:06:40.000 --> 00:06:42.639 all the screens and the dashboards and the trends and 112 00:06:42.720 --> 00:06:46.279 the graphs and not actually get into the data. So 113 00:06:46.600 --> 00:06:50.000 one thing I advise all time is whether you're dealing 114 00:06:50.000 --> 00:06:53.560 with a thousand leads or a million. Just go into 115 00:06:53.600 --> 00:06:55.279 the data, like you've got to go in and look 116 00:06:55.319 --> 00:06:57.120 at the lead profile. You have to look at the 117 00:06:57.160 --> 00:07:00.240 sales notes. You have to look at the behavior of 118 00:07:00.279 --> 00:07:05.199 those contacts that they conducted with marketing and the different 119 00:07:05.319 --> 00:07:08.000 activities and that sort of thing. Like you have to 120 00:07:08.360 --> 00:07:12.000 get close to it because that's where the real insights are. 121 00:07:12.120 --> 00:07:15.040 I think, like the ones we're talking about that really matter, 122 00:07:16.000 --> 00:07:18.399 and you'll notice you'll notice the good, the bad, and 123 00:07:18.439 --> 00:07:18.879 the ugly. 124 00:07:19.000 --> 00:07:21.120 If you do that, love it. 125 00:07:21.199 --> 00:07:24.560 And they're saying AI can help with this, I know 126 00:07:24.680 --> 00:07:28.240 that you go and maybe just a quick announcement for everybody, 127 00:07:28.399 --> 00:07:32.600 So we'll hostile as Savans Annual Virtual Full Funnel signment 128 00:07:32.720 --> 00:07:35.399 next week where Still would be one of the speakers 129 00:07:35.439 --> 00:07:39.360 and he'll be talking exactly about the topic of AI 130 00:07:39.639 --> 00:07:45.040 workflows for each stages of your ABM funnel. So we'll 131 00:07:45.079 --> 00:07:47.720 leave it for that part, right, But anyhow, I would 132 00:07:47.720 --> 00:07:51.439 love to ask you, considering your experience of playing with 133 00:07:51.519 --> 00:07:55.160 A either think AI can help with this initial qualification 134 00:07:55.439 --> 00:07:57.199 and score them? 135 00:07:57.480 --> 00:08:02.040 I do, Yeah, And to give everybody a quick sneak peek, 136 00:08:02.879 --> 00:08:05.800 that's one of the workflows we'll talk about just in general, 137 00:08:05.879 --> 00:08:09.959 the idea of using AI for account scoring, leads scoring, 138 00:08:10.319 --> 00:08:15.439 intense scoring, really scoring in general is incredible And if 139 00:08:15.439 --> 00:08:17.279 you think about it, you know, even when I was 140 00:08:17.319 --> 00:08:19.680 at Google, right, one of the biggest companies on the planet, 141 00:08:20.319 --> 00:08:26.000 I was restricted by resources that were technical to build 142 00:08:26.160 --> 00:08:32.000 things like propensity models, you know, dynamics scoring and things 143 00:08:32.039 --> 00:08:36.759 like that, which are based on statistics and machine learning. 144 00:08:37.399 --> 00:08:42.320 And now we all have access to that skill set 145 00:08:42.399 --> 00:08:47.120 at our fingertips, right, especially if you've used even. 146 00:08:46.919 --> 00:08:47.919 Just general prompting. 147 00:08:48.000 --> 00:08:52.919 Right, Like my first introduction to call it like statistical 148 00:08:52.960 --> 00:08:54.200 analysis was was. 149 00:08:54.200 --> 00:08:57.080 Just with chat GPT. Right. I don't really know that much. 150 00:08:57.320 --> 00:08:59.960 I'm fairly technical as a marketer, but I could go 151 00:09:00.080 --> 00:09:04.799 back and forth with chat GPT and basically build you know, 152 00:09:04.919 --> 00:09:07.960 a machine learning expert, right, and then started to push 153 00:09:08.080 --> 00:09:10.799 further and say, okay, well, what kind of machine learning 154 00:09:10.840 --> 00:09:12.480 methods could you use? 155 00:09:12.519 --> 00:09:13.320 Which ones are. 156 00:09:13.200 --> 00:09:17.279 Best for things like lead scoring or finding trends within data? 157 00:09:17.559 --> 00:09:19.360 And then you can go and you know, build that 158 00:09:19.440 --> 00:09:21.639 base of prompts and put that off to the side, 159 00:09:22.440 --> 00:09:25.360 eventually evolve that into more of like an agentic use 160 00:09:25.519 --> 00:09:28.679 of AI, where you can feed data in more real 161 00:09:28.759 --> 00:09:31.279 time and dynamically and then you can get an output 162 00:09:31.799 --> 00:09:32.919 at that point in time. 163 00:09:33.600 --> 00:09:35.919 And so it's incredible because I don't. 164 00:09:35.679 --> 00:09:39.480 Know too many off the shelf software programs today that 165 00:09:39.600 --> 00:09:44.799 have that level of customization and can deliver lead scoring 166 00:09:45.080 --> 00:09:49.279 at that at that scale and that level of personalization, right, 167 00:09:49.320 --> 00:09:52.799 And so I think that's that's the really the most 168 00:09:52.840 --> 00:09:56.120 amazing thing to me is we can we can We're 169 00:09:56.159 --> 00:10:00.000 pretty close to being able to build our own ABM 170 00:10:00.080 --> 00:10:04.360 tech stacks that are completely unique to our team, our product, 171 00:10:04.480 --> 00:10:08.720 our market, our motion, and we can, like you know, 172 00:10:08.799 --> 00:10:11.480 execute a lot of the steps that we use traditional 173 00:10:11.519 --> 00:10:14.000 software for we can now do with AI. 174 00:10:15.759 --> 00:10:19.200 Yeah, that makes perfect sense. I totally agree. And just 175 00:10:19.240 --> 00:10:23.519 to wrap it up, so one of the solutions what 176 00:10:23.639 --> 00:10:25.559 I have heard from you and just correct me if 177 00:10:25.559 --> 00:10:31.360 I'm wrong, kind of to make the first step towards 178 00:10:31.399 --> 00:10:35.240 alignment with sales, even if we play let's say the 179 00:10:35.360 --> 00:10:40.360 MCL playbook and we can't do anything, is just manually 180 00:10:41.559 --> 00:10:46.639 qualifying all like, really do the market and lead qualification 181 00:10:46.799 --> 00:10:50.879 before transferring innocent to sales. Right, So this is the 182 00:10:50.919 --> 00:10:53.919 first thing and the second one, and you can do it, 183 00:10:54.080 --> 00:10:56.399 say yeah, you can do it manually whatever you prefer, 184 00:10:56.799 --> 00:10:59.639 but also what I feel is really important and I 185 00:10:59.639 --> 00:11:04.000 would like to get your take on this. When we 186 00:11:04.080 --> 00:11:07.279 do this qualification, we just need to think about that 187 00:11:07.320 --> 00:11:10.720 this is just an individual contact, right, and this content 188 00:11:10.840 --> 00:11:15.919 might have completely different journey compared to the account. Right, 189 00:11:16.360 --> 00:11:19.360 So if we want to make sense of that engagement, 190 00:11:19.480 --> 00:11:23.799 we need to pair the signal, right, or the engagement 191 00:11:23.840 --> 00:11:27.600 that we captured it with that content was the historical 192 00:11:27.679 --> 00:11:30.480 interactions with the account, all the signals, so all the 193 00:11:30.519 --> 00:11:34.840 information that is available about this account, account research, et cetera, right, 194 00:11:35.360 --> 00:11:40.200 engagements with other Buying Committee members and then together with sales, 195 00:11:40.799 --> 00:11:44.440 make sense of that engagement. Right, what could it mean 196 00:11:44.600 --> 00:11:49.120 in terms of like account activities and strategic initiatives they 197 00:11:49.159 --> 00:11:53.960 have and what could be our best next action that 198 00:11:54.080 --> 00:11:58.480 would add value for this specific individual? Right? What do 199 00:11:58.519 --> 00:12:02.120 you think about this? I'm just complicated to my chance. 200 00:12:02.200 --> 00:12:04.440 Oh you need just to say, hey, guys, you got 201 00:12:04.440 --> 00:12:07.639 to buy and commit to member engaged, go get them? 202 00:12:08.080 --> 00:12:09.200 Yeah no, no, this. 203 00:12:09.279 --> 00:12:13.440 Is fantastic because you've actually just set the stage really 204 00:12:13.480 --> 00:12:16.320 well for what the talk next week will be about. 205 00:12:16.440 --> 00:12:19.600 And so I know the title is you know AI 206 00:12:19.720 --> 00:12:21.200 workflows for ABM. 207 00:12:21.759 --> 00:12:24.200 But really what we're going to go through in that. 208 00:12:24.120 --> 00:12:29.879 Session is my philosophy on ABM and how it's evolved. 209 00:12:30.240 --> 00:12:32.240 And you now hear contact. 210 00:12:31.759 --> 00:12:36.039 Based marketing right, So there we actually need to sort 211 00:12:36.039 --> 00:12:41.840 of reinvent what the demand creation and capture process looks 212 00:12:41.919 --> 00:12:46.000 like and map that to account based funnels and contact 213 00:12:46.000 --> 00:12:48.840 based funnels. And when we do that, we can then 214 00:12:49.000 --> 00:12:52.519 look at the intersection of signals and data and activity 215 00:12:52.919 --> 00:12:56.480 between accounts and contacts, and I think that's really where 216 00:12:56.519 --> 00:12:59.120 the where the magic is. And so so one, if 217 00:12:59.200 --> 00:13:01.960 you if you have the infrastructure and the architecture to 218 00:13:02.039 --> 00:13:05.679 collect that data, and you do are able to collect signals, 219 00:13:06.080 --> 00:13:09.399 you now have the sort of m VP for AI. 220 00:13:09.840 --> 00:13:11.799 In my mind, you need to have a good. 221 00:13:12.120 --> 00:13:14.559 Base of data that you're that you're building any of 222 00:13:14.600 --> 00:13:17.360 these AI workflows off of. So that's number one, which 223 00:13:17.399 --> 00:13:21.679 is fantastic. Number two is you now have extremely unique 224 00:13:21.759 --> 00:13:26.559 data across third party, first party owned operated sources that 225 00:13:26.600 --> 00:13:29.919 you can use for personalization, which is which is also 226 00:13:30.000 --> 00:13:33.200 exciting because we've all gotten those messages that are like 227 00:13:33.679 --> 00:13:37.279 half personalized, like they they scraped something from our most 228 00:13:37.320 --> 00:13:39.720 recent LinkedIn post. And then slapped it in an email 229 00:13:39.960 --> 00:13:42.159 and blasted it out to us. That's not personalization. 230 00:13:42.879 --> 00:13:44.320 We need. We need we need. 231 00:13:44.200 --> 00:13:46.960 Dozens of signals at a contact level, so we can 232 00:13:46.960 --> 00:13:49.559 build a profile of who that person is, what their 233 00:13:49.600 --> 00:13:51.879 pain points are, and what they care about, and then 234 00:13:51.919 --> 00:13:55.080 come up with a completely unique message that you know, 235 00:13:55.159 --> 00:13:58.200 almost gives them that aha moment at that point in 236 00:13:58.240 --> 00:14:01.279 time when we deliver something to them. And with those 237 00:14:01.320 --> 00:14:05.120 two things in place, then the third is that next 238 00:14:05.200 --> 00:14:08.840 best action piece, which is something I remember I heard 239 00:14:08.879 --> 00:14:12.279 that term like six or seven years ago. I was 240 00:14:12.320 --> 00:14:17.759 talking to a machine learning engineer. It's fairly common in software. 241 00:14:17.840 --> 00:14:17.960 Right. 242 00:14:18.000 --> 00:14:22.440 You think about you buy a hat on Amazon, it's 243 00:14:22.480 --> 00:14:25.879 then going to show you a product for hat cleaners 244 00:14:26.039 --> 00:14:29.639 or hat storage or hat hangars. Right, that's just next 245 00:14:29.639 --> 00:14:32.799 best action, Right, it's taking your existing behavior and trying 246 00:14:32.840 --> 00:14:35.720 to predict what you'll do or buy or need next 247 00:14:36.120 --> 00:14:38.159 based on that historical behavior. 248 00:14:38.440 --> 00:14:39.600 So we can now do that. 249 00:14:39.679 --> 00:14:43.960 We can now do that dynamically based on the scoring 250 00:14:44.039 --> 00:14:44.440 that we. 251 00:14:44.360 --> 00:14:46.480 Can evolve when it comes to an account and. 252 00:14:46.519 --> 00:14:50.399 Contact level, and then again bringing those two data sources 253 00:14:50.440 --> 00:14:53.799 together so we can get a view into what is 254 00:14:53.879 --> 00:14:57.440 generally happening in this account, and then who within the 255 00:14:57.519 --> 00:15:01.159 account is most likely driving that decision, and then we 256 00:15:01.200 --> 00:15:04.159 can act accordingly and leverage all of the data that 257 00:15:04.200 --> 00:15:07.720 we have from prior customers and prospects to say, Okay, 258 00:15:07.759 --> 00:15:10.039 this is the next best thing we should do, right. 259 00:15:10.039 --> 00:15:10.799 And that's when you. 260 00:15:10.759 --> 00:15:15.519 Get tactically into the effectiveness of AI. Right and thinking 261 00:15:15.559 --> 00:15:19.000 about a recommendation engine that your marketing team is now 262 00:15:19.080 --> 00:15:23.720 powered by. I just get pretty fired up thinking about that. 263 00:15:23.720 --> 00:15:25.720 That's pretty cool, right, and we can you can build 264 00:15:25.759 --> 00:15:28.440 that now exclusively for teams. 265 00:15:29.279 --> 00:15:32.039 Love it? Thank you? By the way, guys, Yeah, welcome 266 00:15:32.080 --> 00:15:35.000 to ask us any questions. As always, we'll do this 267 00:15:35.039 --> 00:15:38.240 whole final life. We're going to cover all of them, 268 00:15:38.399 --> 00:15:40.720 so feel free to drop them in the chat and 269 00:15:40.799 --> 00:15:44.440 I will pick them up. And what I would love 270 00:15:44.519 --> 00:15:52.000 to move next is to ask you about the process 271 00:15:52.039 --> 00:15:56.559 you used to create this share OKAYRS with SDRs, right 272 00:15:57.399 --> 00:15:59.559 of course, I mean this is the first step to 273 00:15:59.600 --> 00:16:05.240 the cross functional alignment. But I assume that you need 274 00:16:05.279 --> 00:16:08.360 to do a lot of change change management internally, right 275 00:16:09.639 --> 00:16:13.799 of course. Typically you have different incentives, different processes, and again, 276 00:16:13.879 --> 00:16:17.399 as we poke, you can even have this credit conflict, 277 00:16:17.840 --> 00:16:22.720 so can you walk us through the entire process and 278 00:16:23.080 --> 00:16:24.639 just explain how did you do this? 279 00:16:25.799 --> 00:16:26.080 Sure? 280 00:16:26.159 --> 00:16:31.039 Sure, Yeah, it's a disclaimer. Is easier said than done. 281 00:16:31.399 --> 00:16:34.600 Uh I would. I think of it in two components, right, 282 00:16:34.679 --> 00:16:37.720 there is the change management piece that you mentioned. That's 283 00:16:37.799 --> 00:16:41.799 like the people side, and then the other side is process. 284 00:16:42.360 --> 00:16:46.279 And I think a lot of go to market organizations 285 00:16:46.360 --> 00:16:49.679 they jump right into process. So i'll share that first, Right, 286 00:16:49.720 --> 00:16:52.720 that's your typical Okay, we're going to use HubSpot for 287 00:16:52.799 --> 00:16:55.240 lead scoring. We're going to have a demographic score, we're 288 00:16:55.240 --> 00:16:58.519 going to have a behavioral score, and oh, you know 289 00:16:58.679 --> 00:17:01.519 sales has these s LA's and they're going to run 290 00:17:01.519 --> 00:17:04.640 a band script or use spin methodology, and you know, 291 00:17:05.119 --> 00:17:08.279 all these teams they have all of these wonderful processes 292 00:17:08.319 --> 00:17:11.200 in place, but then they don't have the people aligned 293 00:17:11.480 --> 00:17:12.400 and that will break. 294 00:17:12.720 --> 00:17:13.599 And then in. 295 00:17:13.559 --> 00:17:15.759 Other cases, which I think is better, is you get 296 00:17:15.759 --> 00:17:18.200 the people aligned and they don't have the process yet, 297 00:17:18.279 --> 00:17:20.240 and that's okay. We got to get the people aligned 298 00:17:20.240 --> 00:17:23.519 first and then we can build process around that. And 299 00:17:23.559 --> 00:17:26.000 so for me, like one of the let's see, this 300 00:17:26.119 --> 00:17:26.920 probably took me. 301 00:17:28.000 --> 00:17:28.680 This took me like. 302 00:17:28.640 --> 00:17:33.240 Eight months to be honest, of getting an enterprise sales 303 00:17:33.240 --> 00:17:36.480 and marketing team aligned, but a couple of the key 304 00:17:36.519 --> 00:17:42.160 components that were really critical, where I had autonomy and 305 00:17:43.039 --> 00:17:49.039 ownership of the entire demand generation process, the budget, the activities, 306 00:17:49.279 --> 00:17:55.160 the resources, the channels, you know, the website like those things, right, 307 00:17:55.200 --> 00:17:57.759 So I do think that is important. You don't, as 308 00:17:57.799 --> 00:18:00.880 a marketing leader need to own it, but you at 309 00:18:00.960 --> 00:18:03.759 least need to get alignment with your peers so that 310 00:18:03.960 --> 00:18:07.920 everything that demand generation touches or is adjacent to. 311 00:18:08.440 --> 00:18:10.880 Is complete, completely aligned. Right. 312 00:18:11.079 --> 00:18:14.000 Without that, you're going to run into problems where you 313 00:18:14.079 --> 00:18:17.480 might have the best LinkedIn campaign on the planet, but 314 00:18:17.519 --> 00:18:21.039 your landing pages are terrible because your because your web 315 00:18:21.039 --> 00:18:23.839 team isn't bought in and they're just giving you like random, 316 00:18:23.960 --> 00:18:25.400 random stuff that doesn't quite fit. 317 00:18:25.680 --> 00:18:27.839 So you've got to get that alignment first. 318 00:18:28.920 --> 00:18:32.400 The other piece was the person I worked with on 319 00:18:32.440 --> 00:18:37.200 the sales side was basically a mirrored version. 320 00:18:36.920 --> 00:18:39.759 Of me, but on the on sales. So so she led. 321 00:18:39.599 --> 00:18:45.200 The SDR team and her ownership was really on you know, 322 00:18:45.319 --> 00:18:50.599 think of like SQL too, like stage two opportunity. And 323 00:18:50.640 --> 00:18:52.960 then depending on the deal side, there was an account 324 00:18:53.000 --> 00:18:56.079 executive or you know, maybe a senior sponsor or executive 325 00:18:56.079 --> 00:18:59.559 sponsor or something like that involved. But her role was 326 00:18:59.640 --> 00:19:03.519 on that core piece of SQL to opportunity creation and 327 00:19:03.559 --> 00:19:07.400 then progression into an actual qualified deal. And so we 328 00:19:07.400 --> 00:19:13.680 were both completely incentivized to work together because I essentially 329 00:19:13.720 --> 00:19:17.559 owned the steps prior to SQL and she owned all 330 00:19:17.559 --> 00:19:18.920 the steps after SQL. 331 00:19:19.400 --> 00:19:23.759 So when we got together, you know, one we aligned 332 00:19:23.880 --> 00:19:24.559 on like. 333 00:19:24.519 --> 00:19:27.960 Overall principles like okay, marketing is going to be a 334 00:19:28.880 --> 00:19:32.559 you know, a generator of revenue. We're going to generate meetings, 335 00:19:32.599 --> 00:19:34.880 We're going to generate opportunities. We're going to connect that 336 00:19:35.000 --> 00:19:37.559 to revenue. We're going to be data driven in how 337 00:19:37.599 --> 00:19:41.960 we analyze the effectiveness of marketing campaigns and channels and tactics, 338 00:19:42.440 --> 00:19:43.119 that sort of thing. 339 00:19:43.160 --> 00:19:45.000 And so we came together at that level. 340 00:19:46.119 --> 00:19:49.559 But then the other piece was we started by saying, 341 00:19:49.599 --> 00:19:53.319 what is the single most important metric that we should 342 00:19:53.440 --> 00:19:57.880 both be accountable for? And when I say accountable, I 343 00:19:57.920 --> 00:20:00.759 mean going on to a bi weekly pyce line review 344 00:20:01.119 --> 00:20:04.720 with the GM of Product, the VP of Sales, the 345 00:20:04.799 --> 00:20:08.319 head of marketing, and like going into the fire and 346 00:20:08.799 --> 00:20:13.079 together owning why sqls. 347 00:20:12.680 --> 00:20:13.359 Were what they were. 348 00:20:13.400 --> 00:20:16.519 So that was the metric we picked that was the determinator, 349 00:20:16.880 --> 00:20:19.279 so I was held accountable to SQLS, so was she. 350 00:20:19.759 --> 00:20:21.920 And then the next level was what I talked about 351 00:20:21.960 --> 00:20:25.359 with the quality metric, was we first looked at SQLS 352 00:20:25.400 --> 00:20:27.359 going up or down, what was the delta, what was 353 00:20:27.400 --> 00:20:29.759 the change, and then why and then we used all 354 00:20:29.799 --> 00:20:33.480 the data to figure that out. So so that's one 355 00:20:33.759 --> 00:20:36.920 is like align on the like the ownership and the 356 00:20:36.920 --> 00:20:40.599 accountability of like your team and your area. Then choose 357 00:20:40.599 --> 00:20:45.319 a metric, become jointly accountable for that metric, and then 358 00:20:45.480 --> 00:20:48.279 you know the third thing is I actually like Google 359 00:20:48.400 --> 00:20:52.599 used the like the Smart Goal system. 360 00:20:52.079 --> 00:20:53.400 Which I probably forget it now. 361 00:20:53.400 --> 00:21:00.319 It's like specific, measurable, attributable, I think, et cetera. 362 00:21:00.319 --> 00:21:01.079 So you can look it up. 363 00:21:01.119 --> 00:21:04.200 But smart system was the like criteria we use to 364 00:21:04.240 --> 00:21:08.960 create the okayrs and then OKRs themselves. It's objectives and 365 00:21:09.039 --> 00:21:11.559 key results, so you had to write those in a 366 00:21:11.599 --> 00:21:15.759 way that were pretty descriptive. Like basically it was like 367 00:21:16.160 --> 00:21:20.039 I'm going to or my team is going to do X, 368 00:21:20.640 --> 00:21:24.480 and it's going to produce Y by this timeframe. So 369 00:21:24.480 --> 00:21:26.680 that's where you use that smart system to write these 370 00:21:26.720 --> 00:21:31.079 goals that will will live you know, throughout the year 371 00:21:31.119 --> 00:21:34.559 if you're doing like quarterly planning, so then all of 372 00:21:34.599 --> 00:21:37.559 those things come together, right and when you have bi weekly, monthly, 373 00:21:37.640 --> 00:21:41.240 quarterly pipeline reviews, this is very clear what you're reporting 374 00:21:41.279 --> 00:21:43.759 on and what you're accountable to. 375 00:21:45.000 --> 00:21:49.799 A lot of I guess how much? So uh, what 376 00:21:50.000 --> 00:21:52.799 I would love to where I would love to pre seeds, 377 00:21:52.960 --> 00:21:55.319 and what I would love to touch Next is the 378 00:21:55.400 --> 00:22:00.240 account precression right kind the parts I have described from 379 00:22:00.279 --> 00:22:03.440 the beginning. I know you've been working a lot on 380 00:22:03.519 --> 00:22:07.480 this and on the framework that you have established out. 381 00:22:07.559 --> 00:22:11.759 Try to pull it in hopefully it will be visible 382 00:22:12.440 --> 00:22:15.759 for everybody, but guys, just in case you have a 383 00:22:15.839 --> 00:22:18.960 button two zoom in. Obviously, if you're watching this from 384 00:22:18.960 --> 00:22:21.319 the smart one, it won't be very convenient, but if 385 00:22:21.359 --> 00:22:24.839 you sit with your laptop you can try to zoom 386 00:22:24.839 --> 00:22:31.000 in and see the framework itself. I also Steve published 387 00:22:31.000 --> 00:22:33.960 it on LinkedIn, so you can download it from his 388 00:22:34.079 --> 00:22:37.160 LinkedIn profile. So I would love to ask you a 389 00:22:37.200 --> 00:22:42.119 couple of questions around this, right so, as we spoke 390 00:22:42.240 --> 00:22:46.519 with you one day. Account progressional account velocity is one 391 00:22:46.519 --> 00:22:50.079 of the most important metrics to track ABM efficiency for 392 00:22:50.119 --> 00:22:53.440 the long sales cycles. For a simple reason, you have 393 00:22:53.680 --> 00:22:57.960 a delayed revenue outcome. You can't just expect if your 394 00:22:58.000 --> 00:23:01.880 Selle cycle ankss eighteen months, you can't expect revenue from 395 00:23:01.880 --> 00:23:03.920 a single program that you are run in for the 396 00:23:03.960 --> 00:23:08.400 first month of months number two. Right, But you need 397 00:23:08.400 --> 00:23:12.000 some leading indicators in place that kind of tell you 398 00:23:12.119 --> 00:23:13.759 that we are doing the right things. 399 00:23:13.960 --> 00:23:14.160 Right. 400 00:23:15.559 --> 00:23:20.599 This is why the account velocity helps because it kind 401 00:23:20.599 --> 00:23:24.279