906
02:28:21.280 –> 02:28:24.419
Julia Nimchinski: We are transitioning to our next funnel

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02:28:24.940 –> 02:28:33.789
Julia Nimchinski: the shift of AI and its impact on sales. Welcome Mary Shea, former Forrester and Outreach GM. At higher quotient.

908
02:28:34.070 –> 02:28:36.830
Julia Nimchinski: and one of our favorite community leaders

909
02:28:36.950 –> 02:28:40.709
Julia Nimchinski: super excited to have you on the show. How have you been? What’s new.

910
02:28:41.580 –> 02:28:55.579
Mary Shea: Well, it’s great to see you, Julia. We are way overdue for a reconnect, and it’s great to be part of these events as always, and this is a fantastic topic, one that is near and dear to my heart. And what a what a panel we have.

911
02:28:56.880 –> 02:29:04.770
Julia Nimchinski: Before we start off. Let’s just start with your prediction for sales sales and AI for 2025.
912
02:29:05.120 –> 02:29:15.469
Mary Shea: Oh, my goodness, I love it putting me on the spot. Yeah, my my prediction is that when the humans and the machines come together in a meaningful way.

913
02:29:16.180 –> 02:29:24.579
Mary Shea: We are going to deliver faster, better, and more impactful outcomes to everyone who’s concerned. And

914
02:29:24.890 –> 02:29:40.629
Mary Shea: I also harken back to a quote from one of our guests from the last session that we did, Julia, and I can’t remember who it was. This is not for me, but it was one of your fabulous guests, and he said, You know the AI isn’t coming for your job, but the people who can use it.

915
02:29:41.400 –> 02:29:51.559
Mary Shea: We’ll outmaneuver you, and so I think we’re going to get better outcomes, and you want to make sure that you are out maneuvering folks. You’re not getting outmaneuvered yourself.

916
02:29:52.940 –> 02:29:53.700
Julia Nimchinski: Beautiful.

917
02:29:53.820 –> 02:29:55.269
Julia Nimchinski: The stage is yours.

918
02:29:55.540 –> 02:29:56.700
Julia Nimchinski: Let’s get into it.

919
02:29:57.240 –> 02:30:07.579
Mary Shea: Well, thank you so much. I am absolutely thrilled to have such a wonderful group here. And really, what I wanted to cover in today’s session was really looking at
920
02:30:07.880 –> 02:30:11.230
Mary Shea: how generative AI and advanced automation

921
02:30:11.630 –> 02:30:18.439
Mary Shea: embedded into software or standalone can really create better outcomes for buyers and sellers.

922
02:30:18.590 –> 02:30:22.780
Mary Shea: And I know this is a growth panel. But at the end of the day.

923
02:30:22.940 –> 02:30:25.580
Mary Shea: when you have happy engaged

924
02:30:25.700 –> 02:30:31.309
Mary Shea: and dynamic buyers, you’re going to drive more growth for the business. So I like to focus on those types of outcomes.

925
02:30:31.820 –> 02:30:48.630
Mary Shea: So I’ve really looking at really the entire sales cycle. And, you know, starting with top of the funnel mid cycle closing. I also want to touch on analytics and insights and strategy and planning. And so

926
02:30:48.730 –> 02:30:55.459
Mary Shea: AI and AI embedded tools can really help with every aspect of

927
02:30:56.750 –> 02:31:07.989
Mary Shea: of of the cycle. And so one of the things I’d like to start with on the strategy and planning. And I know. Peter, it’s good to see you. It’s good to meet you. Has some thoughts and wanted to weigh in is like.

928
02:31:08.640 –> 02:31:25.120
Mary Shea: I still see a lot of Saas companies really taking a spray and pray approach. And it’s shocking to me that that still happens. So how do you really, you know, help organizations understand how to identify these priority accounts.

929
02:31:26.270 –> 02:31:31.369
Mary Shea: How to understand what to focus on, so that it’s a very targeted.

930
02:31:31.780 –> 02:31:34.620
Mary Shea: personalized approach that’s going to provide

931
02:31:34.750 –> 02:31:36.600
Mary Shea: the results. We’re looking for, Peter.

932
02:31:36.900 –> 02:32:01.669
Peter Mollins: Yeah, I think that’s a great question. I think I go back to kind of your mission statement, if you will, for AI that you started the panel with which is the salespeople that are going to be the most effective. Going forward are those that are partnering with with AI and using it so that they can focus more on the human side of selling. So having more and better interactions with their with their customers, with their prospects, more meaningful

933
02:32:01.670 –> 02:32:07.099
Peter Mollins: interactions. And and that comes from really understanding

934
02:32:07.100 –> 02:32:19.720
Peter Mollins: who to focus on which companies to focus on why you should be focusing on them now, and what to say to them. How are you going to engage them? And I’m sure we’re going to get into a lot more of this. But I think maybe just from a high level.

935
02:32:19.720 –> 02:32:30.379
Peter Mollins: The things that I think about when I think about those kinds of signals that indicate whether or not a customer is a great great prospect to look at. There’s a lot of great 3rd party signals, for sure.

936
02:32:30.490 –> 02:32:38.020
Peter Mollins: So things like, you know, social posts or 10 Ks or other kinds of Internet research. And there’s also just this wealth of

937
02:32:38.020 –> 02:33:06.829
Peter Mollins: 1st party data that you yourself as a company and a go to market team, have, you know, that’s previous deals. That’s previous conversations. Maybe that’s chats that your Sdr team had with your prospect and piecing those things together in order to build a, you know, a full picture of your of your prospects, so you can have a better conversation. That’s really a great place for AI to shine because they can distill all that information down into chunks of information about who to focus on, why, to focus on them, and what to say to them.

938
02:33:08.400 –> 02:33:15.119
Mary Shea: Yeah. So so thinking about the 1st party data, which is always really interesting to me as a former analyst. So

939
02:33:15.570 –> 02:33:16.890
Mary Shea: how do you?

940
02:33:17.303 –> 02:33:19.649
Mary Shea: You know, sort of take some of those

941
02:33:19.980 –> 02:33:26.140
Mary Shea: those data points and data signals and run them through the AI. How does that work? How do you actually do that.

942
02:33:26.650 –> 02:33:40.610
Peter Mollins: Yeah. So the way the way we see it is, there’s there’s a lot of different elements that go along with that 1st party data that’s stored in like your Crm that could be stored within. You know your your dialing platform.

943
02:33:40.610 –> 02:33:57.480
Peter Mollins: and I think a lot of the interesting information comes from. It could be from notes that happened during a sales engagement. But it also is just like the conversations that you’re having the chats that you’re having an Sdr. Calling up and getting someone on the line. And they’re just doing some account research. And they find out that
944
02:33:57.480 –> 02:34:21.190
Peter Mollins: you know that maybe there’s a new expansion that’s happening or a new product launch that’s going on. And so, you know, capturing those conversations, transcribing them inputting them into AI, I don’t want to get into a product pitch, but the idea is, you know, that you can access these various kinds of information, distill them down to what’s really going on within your prospects, and then service that to ae, so they can really do smart outreach.

945
02:34:22.070 –> 02:34:25.155
Mary Shea: Yeah, fantastic. That’s really really helpful.

946
02:34:26.840 –> 02:34:45.920
Mary Shea: you know, I’m I’m I’m curious as to, you know, sort of the the role of the Sdr. And the Ae, and are you seeing some blur there? Now that the technology is more powerful? Are we using? How how are you thinking about those roles. And and is it still, you know, do we still need that level of specialization.

947
02:34:48.010 –> 02:35:12.047
Peter Mollins: So. Yeah, I think I think we do. I think that one of the things that’s really helpful about it is that it does allow account execs to to jump in. Because if you’re automating busy work that’s associated with outbound and allowing them to focus on more meaningful conversations, then, aes, that that hump of of Oh, geez! I’ve got to put this much time into doing outbound. That starts to go away, because now they can focus on, you know, the

948
02:35:12.370 –> 02:35:39.719
Peter Mollins: getting in front of and having more conversations with their prospects, because AI helps to eliminate a lot of the busy work of, you know, finding who to contact, actually helping you to make calls to them, getting them on the phone, understanding what to say to them. So for an ae, it just allows them to do some of their own prospecting just much more efficiently so blurring. I’m not sure I would say that, but I would certainly say that it makes it so. That account execs

949
02:35:39.720 –> 02:35:44.519
Peter Mollins: have that outbounding ability much more at their fingertips.

950
02:35:45.230 –> 02:36:04.310
Mary Shea: Awesome. Thank you. Thank you for that, Peter and Laurie. You and I have not met previously. So it’s good to see you here today and thank you for joining the panel. And I know you. Yeah, I know you have some thoughts on on sort of the the strategy and planning and top of the funnel side of things. You know. What are the top

951
02:36:04.700 –> 02:36:20.300
Mary Shea: activities. If you were to sort of list out the top activities that you think we should be using AI on for for the early phases of the process, whether that’s research or, you know, initiating the outbound, as as Peter and I were just talking about.

952
02:36:20.890 –> 02:36:43.200
Lori Harmon: Well, thanks for that question. And definitely for research. I think AI is great for doing company research, because sometimes depending on the stats of your company, or what you’ve looked at in the marketplace. If you’re doing personalized outreach, maybe cold outreach. And if you’re talking about company goals and aligning that that can give you much higher response rate. So that research is very important for AI to do can save a lot of time.

953
02:36:43.540 –> 02:37:00.240
Lori Harmon: One of the things I haven’t seen that’s as good yet with AI is actually writing the personalized email. I mean, it can draft it. But you always need that human review, and maybe editing of that. I haven’t found that to be quite as good. I think other things it’s doing is, you know, we have auto dialer. We’ve had those for a long time.

954
02:37:01.030 –> 02:37:06.740
Lori Harmon: but I’m combining that with pre-recorded voicemails, so you could just make the whole process much faster.

955
02:37:06.970 –> 02:37:22.420
Lori Harmon: and that will really help. And the other thing for AI to do is prioritizing what the next best action is, whether it’s for an Str or for seller, because it can look at the data Peter was talking about. For example, have people engaged? Is there intent data? That’s 3rd party data.

956
02:37:22.420 –> 02:37:40.249
Lori Harmon: and which is the most important thing for you to do next? Is it follow up on Mql. Is it? Call back to this cold prospect, or from a salesperson’s perspective? Should you focus more on a $200,000 deal that needs more work or a million dollar deal that might already be close to being closed. Where should you spend your time

957
02:37:40.530 –> 02:37:43.210
Lori Harmon: and my last large corporate job

958
02:37:43.420 –> 02:37:48.719
Lori Harmon: using tools that had AI embedded, we were able to increase the per rep output

959
02:37:48.840 –> 02:37:59.459
Lori Harmon: 6 or 6 quarters in a row just by having that AI in there, and better prioritization. So in terms of the per rep output, it was, it went up because we were embracing those changes with AI.

960
02:38:00.130 –> 02:38:24.769
Mary Shea: Wow, that’s pretty pretty exciting impact. So thank you for that. You know. If I go back to the personalization piece of it. Because that’s that’s interesting. What I’m seeing is now the AI models, certainly the ones that we’ve built at higher quotient. And we also buy the commercially available ones and embed those into our software workflows. I’m blown away by

961
02:38:24.920 –> 02:38:36.899
Mary Shea: the level of customization that our AI can do. We also have the ability to help the AI get used to our own writing style, and I use Claude a lot which is helping me.

962
02:38:37.100 –> 02:38:47.689
Mary Shea: I want to know how you’re thinking about this personalization piece. As the AI gets more and more robust, you know by the day what are your thoughts? There.

963
02:38:47.980 –> 02:39:05.699
Lori Harmon: That’s a great point. And actually, we’re my current startup that I’ve co-founded. We are using Claude, and we’ve done a lot more programming and a lot more training of the AI model. So I agree that it’s getting much better personalization than you know. Maybe 6 months or a year ago, when we 1st started with Openai, or some more
964
02:39:05.910 –> 02:39:24.400
Lori Harmon: more untrained AI. That was generic. It was not doing as good of a job, but I do see, as we train it more. It’s doing much better on personalization. But it also depends on how many steps are in your sequence, because what I’m starting to see, is like, maybe the 1st 4 or 5 things are really great, and then, if you have, say 14 steps.

965
02:39:24.590 –> 02:39:27.169
Lori Harmon: it starts to be repetitive. So I think there’s more trade.

966
02:39:27.170 –> 02:39:28.390
Mary Shea: Involves. Yeah.

967
02:39:28.690 –> 02:39:36.330
Lori Harmon: Yeah. So I do think it’s it’s possible to get that personalization over time with a lot more training. And of course, as the Lms improve.

968
02:39:37.130 –> 02:40:05.979
Mary Shea: Yeah, I mean what I’m seeing in our technology and others is very exciting. Now, I don’t think that means that you don’t have the human intervention, because I never want to see a world where we’re just stepping back and letting the AI do its thing, even if it’s 10% customization or 0 point 1% customization. The human intervention and the human involvement, I think, is critical to the success of driving the right outcomes that we need, and I get

969
02:40:06.120 –> 02:40:14.780
Mary Shea: it really gets me going when I when I see people not working directly with the AI. It’s supposed to be an agent right? Not a replacement.

970
02:40:15.160 –> 02:40:22.619
Lori Harmon: Yeah, I completely agree with that. And also, this is one of the reasons why some prospects of any company are just getting like

971
02:40:22.940 –> 02:40:46.960
Lori Harmon: tired of maybe too much email or something, because some companies might just be letting the AI do it, they can probably tell. And then they’re just getting an overwhelming number of emails that may or may not be on on point. But that’s where you definitely need the human oversight to make sure it really is on point for what you want to communicate your value proposition to be able to get the attention of a prospect who could benefit from your solution.

972
02:40:47.430 –> 02:40:48.669
Mary Shea: Yeah, absolutely.

973
02:40:48.670 –> 02:40:53.599
Peter Mollins: Just echo. I just echo what Laura, you’re saying like just that! That’s almost a differentiator. Now, isn’t it? To be.

974
02:40:53.600 –> 02:40:54.030
Lori Harmon: You can take.

975
02:40:54.030 –> 02:40:57.780
Peter Mollins: That human aspect to, to your, to your pitch, or your to your sales, outreach.

976
02:40:58.070 –> 02:40:58.660
Lori Harmon: Yes. Agree.

977
02:40:58.660 –> 02:41:03.410
Mary Shea: Yeah, I really can’t emphasize enough how important I think the human

978
02:41:04.200 –> 02:41:11.530
Mary Shea: machine connection is to making the experience great for everyone who’s involved. So yeah, thank you both for that.

979
02:41:11.940 –> 02:41:22.779
Mary Shea: Well, I’d love to just get back up to the strategy side. And I’m thinking, like, okay. So I want to build my total addressable market. You know. Usually that’s a that’s a

980
02:41:22.780 –> 02:41:44.789
Mary Shea: I don’t know. It used to take, you know, 6 weeks, or whatever trying to work with Rev. Ops, and try to get what I wanted to get as as a C-suite leader. Sandy, help me understand how how you can use AI to, you know. Build out your tam and you know, focus on the accounts that you want to focus on over the course of the upcoming year.

981
02:41:46.690 –> 02:41:52.080
Sandy Mangat: Yeah, happy to chime in there. So yeah, you’re right. I think

982
02:41:52.150 –> 02:42:06.899
Sandy Mangat: building your tam used to feel like this data exercise that happened one time by revops team and there was probably like a number of different data sources that Revops looked at manually, built. This list.

983
02:42:06.900 –> 02:42:31.830
Sandy Mangat: talked to some pretty large companies even recently, who still kind of run a pretty manual process to come up with their tam, I think what AI allows you to do is is, look at, where are your highest performing segments today, and then find lookalike audiences to build that initial tam. I think something that I’m seeing a lot of is just how the tam is being

984
02:42:31.830 –> 02:42:48.100
Sandy Mangat: operationalize a little bit differently today because of the ability to have just a lot more signals available to you on what’s actually happening within accounts. And so the the fit of an account changes a little more dynamically rather than

985
02:42:48.100 –> 02:43:12.799
Sandy Mangat: I create a fixed list. And this is it. For the whole year you may see a little more movement, and certain segments are going to start performing better, because something’s happening within the account, and AI alerts you that there’s a hiring surge, or maybe there’s been new funding rounds. But there’s all of these more dynamic signals that you now have access to that. Give you a better sense of

986
02:43:12.800 –> 02:43:22.939
Sandy Mangat: where you might have more fit as a company. And that’s just a more dynamic fluid process. Thanks to AI rather than being this like fixed thing that happens

987
02:43:23.000 –> 02:43:25.250
Sandy Mangat: once, maybe twice, right.

988
02:43:26.030 –> 02:43:41.970
Mary Shea: Yeah, I really like what you’re saying there around the dynamic process. And now, because the lift is so much lighter with the AI, you really can, you know, do continuous business planning. So I remember maybe a year or 2.

989
02:43:42.090 –> 02:44:00.399
Mary Shea: Maybe it was right. After Chat Gpt got commercialized in 2022 an AI expert that I really admire a lot said, you know what if you’re if you’re doing your business planning once a year, you’re one and done. You’re already behind. It’s already outdated. You should be continuing to update.

990
02:44:00.510 –> 02:44:13.340
Mary Shea: you know, every 6 months what kind of advice would you have for folks that are building out their their sales plan for the year. How? How frequently should you go back and revisit update, change pivot

991
02:44:13.770 –> 02:44:21.349
Mary Shea: without obviously being a distraction for the for for the team, right? We can’t have constant pivots, but but what’s the right cadence?

992
02:44:22.330 –> 02:44:32.262
Sandy Mangat: Yeah, I think. I can just base it off. What I see with our customers. So focus serves a lot of mid market and enterprise.

993
02:44:32.690 –> 02:44:38.879
Sandy Mangat: fast businesses like Asana and Monday, and I would say that they’re more on like a quarterly cadence

994
02:44:38.880 –> 02:45:03.500
Sandy Mangat: so quarterly they’re assessing their playbooks, refreshing them, and trying to figure out kind of where to double down which segments are performing which playbooks are performing, what? What signals are really actually driving conversion, and then you want to double down on those. Maybe you want to turn some of them off. Experiment with new

995
02:45:03.680 –> 02:45:20.540
Sandy Mangat: new playbooks. I would say. The the smaller your company is like, the more frequent you’ll be experimenting with the types of signals and the playbooks. But your your tam may not change dramatically in any of those scenarios, but how you segment that Tam, and how you actually go and

996
02:45:20.570 –> 02:45:41.239
Sandy Mangat: operationalize it by putting it in front of your reps and and giving them, you know, prioritized list of accounts. All of that is what what probably changes a little more frequently. So if you’re an Smb might be often if you’re more of an enterprise with like 300 reps. You don’t want to do that, you? You probably should stick to a quarterly cadence.

997
02:45:42.160 –> 02:45:44.430
Mary Shea: Got it. Got it. I I like that.

998
02:45:44.610 –> 02:45:52.200
Mary Shea: And before we sort of step out of this threat, strategy and planning, I have 2 kind of kind of questions I want to ask.

999
02:45:54.320 –> 02:46:00.600
Mary Shea: One is, you know, are we at a place where
1000
02:46:01.370 –> 02:46:26.870
Mary Shea: we can have customized territories for reps? Now, we haven’t really talked about territory assignment yet. And is there a way that the AI can help us really understand how to set up the right territory based on signals, relationships. Ecosystem partners out. There is anyone kind of is anyone doing that? Currently

1001
02:46:27.780 –> 02:46:29.579
Mary Shea: anyone have any thoughts on that.

1002
02:46:30.650 –> 02:46:36.220
Sandy Mangat: You mean, from like a like matching the rep to their to the ideal book of business for them.

1003
02:46:36.220 –> 02:46:37.550
Mary Shea: Yeah, yeah.

1004
02:46:38.560 –> 02:46:47.970
Sandy Mangat: I haven’t seen that. But that is something that’s really interesting. Being able to optimize. What accounts end up in someone’s book based on like

1005
02:46:48.090 –> 02:46:58.590
Sandy Mangat: some signals that might be relevant to them. I’ve seen some tools where you can send something on behalf of somebody else.

1006
02:46:59.207 –> 02:47:15.070
Sandy Mangat: Because they have the the connection, like mining your 1st party connections, for example. So it’s better that it comes from. You know the CEO who knows the cro at that company versus me, lowly, Sdr.

1007
02:47:16.280 –> 02:47:28.340
Mary Shea: Well, Sdrs are never lowly. They’re the backbone of every business, so they’re some of the most important people on the team. But, but, Haley, it’s so good to see you again. I know it’s been a number of years, and.

1008
02:47:28.340 –> 02:47:28.910
Haley Katsman | Highspot: Hour.

1009
02:47:29.480 –> 02:47:40.810
Mary Shea: One of my favorites. So it’s great to reconnect. I know we’re gonna talk a little bit about training and optimizing reps so that they can go out and have these really high quality conversations.

1010
02:47:41.170 –> 02:47:51.440
Mary Shea: And and I think a lot about personalized training as as well as long as personalized territories. And to me that is just directionally where we have to go. What are your thoughts?

1011
02:47:52.450 –> 02:48:18.120
Haley Katsman | Highspot: Yeah, I I completely agree. I think, that you know, if you look at what most companies are trying to accomplish today, they’re trying to, of course, drive some level of sales productivity to, you know, impact. You know, bottom line. But they’re also looking at. Okay, how do we grow revenue, and have a set of strategic initiatives like acquiring another company going after a new persona, selling a new product, whatever it may be, and

1012
02:48:18.120 –> 02:48:22.839
Haley Katsman | Highspot: that really is just a change management exercise which does require

1013
02:48:23.010 –> 02:48:47.370
Haley Katsman | Highspot: training and coaching to be able to successfully land that change management because the reality is most reps, will just go back to what they’re used to, what they’re comfortable with. What they know. And so if you really want to drive that change across the business, which is, which is a territory thing as much as it is training right? Like going into a new vertical or a new market, that maybe you didn’t historically have success with. But

1014
02:48:47.430 –> 02:49:02.809
Haley Katsman | Highspot: you’ve changed something about your business so that you can acquire a new set of customers getting your teams ready to go after that is is really a change management problem. And I think that what we’re seeing now is that training used to be kind of this

1015
02:49:03.250 –> 02:49:28.560
Haley Katsman | Highspot: siloed programmatic. You know, boot camp onboarding. And then the ongoing, you know, up leveling of skills was kind of an afterthought for most organizations, and I think that where the magic really happens from a change management standpoint is really at the coaching, the frontline manager level, and that being very much connected to what your overall strategy is, but you know a challenge with Frontline managers is.

1016
02:49:28.750 –> 02:49:54.190
Haley Katsman | Highspot: or they can, coaching consistently. Do they have the time to coach? Are they coaching effectively, is it the right feedback that they’re giving? And so I think what we’ve seen to be really successful is, how can we drive consistent feedback and training in the form of coaching based on what has already been successful, or what you have written down. As your strategy should be successful.

1017
02:49:54.350 –> 02:50:23.009
Haley Katsman | Highspot: to get a faster, continuous feedback loop. And then I think what that ends up doing, which again is is relevant to your overall strategy. Since we’re kind of talking, you know, before going into the funnel, what the strategy looks like, that feedback loop that not only the reps are getting, but that the business and the people that are developing the strategy are getting helps. You iterate on your strategy faster. Just like you want to get a fast product feedback loop. You want to get a fast feedback loop on your initiatives. And so I think that

1018
02:50:23.090 –> 02:50:46.019
Haley Katsman | Highspot: it really becomes a competitive advantage. And those that can say, Hey, we thought this was the direction we should go as a business. But what we’re hearing on calls seeing in meetings, seeing with customers is that it’s not working, or we need to pivot. Let’s iterate on the strategy and then reroll that out to the teams. Make sure they’re trained. Make sure they’re getting coached effectively. And so it becomes the scale mechanism, but also a consistency. Mechanism as well.

1019
02:50:47.330 –> 02:50:49.630
Mary Shea: Thank thank you for that. So awesome.

1020
02:50:49.630 –> 02:50:54.530
Peter Mollins: I love. I love that point, Haley cause, and it’s really how AI can jump in and help here, isn’t it? Because now.

1021
02:50:54.530 –> 02:50:54.990
Haley Katsman | Highspot: Right.

1022
02:50:55.256 –> 02:51:01.913
Peter Mollins: All of all of those inputs that you’re talking about. It’s tough. It’s tough to manage all of those inputs. And the analysis of those

1023
02:51:02.740 –> 02:51:07.949
Peter Mollins: determine where the coaching should focus. So you know, AI obviously can do that really effectively.

1024
02:51:08.660 –> 02:51:14.260
Mary Shea: Yeah, terrific. Thank you for that, Peter. So so let’s talk about top of the funnel.

1025
02:51:14.730 –> 02:51:36.070
Mary Shea: you know, I do think that some of the tools that are out there perform extraordinarily well in this phase of the cycle. Stav. Let’s start with you. Say you’ve got a collection of very, very high value prospects. We used to do Abm campaigns back in the day. But now we’ve got a range of different technologies that

1026
02:51:36.170 –> 02:51:45.920
Mary Shea: can allow us to really engage in deep and meaningful ways. What? What are some of the things that you’re seeing, that allow you to stay

1027
02:51:46.070 –> 02:51:50.240
Mary Shea: very connected to these high value prospects and really make a big impact.

1028
02:51:50.960 –> 02:51:54.820
Stav Levi | Alta: Hey, Hi! So I think there is a

1029
02:51:54.940 –> 02:52:11.770
Stav Levi | Alta: we we see, like 2 kinds of companies. There is companies that focus on outbound approach, and companies that try to like put their like out there their value, and and getting those inbound.

1030
02:52:11.790 –> 02:52:24.519
Stav Levi | Alta: And I think that the approach is really different, like with the outbound. I think that the most important thing is to find the most relevant prospect like to find the most relevant Icp

1031
02:52:24.520 –> 02:52:44.020
Stav Levi | Alta: to select the best audience to approach, because if we find people with high intent, and we know that they have the right buying signal. And I think today with AI, there is a lot of data that we can use in order to

1032
02:52:44.020 –> 02:53:02.069
Stav Levi | Alta: to select the right audience. So I think if we go back to what you said in the beginning, about like 1st tier data. So it’s also like around the Crm. And who is the right persona who is the right industry with the right company, size.

1033
02:53:02.070 –> 02:53:18.220
Stav Levi | Alta: etc. But also from 3rd party data. So who is who have the buying intent? And if we approach the right audience, it will get a much higher chance for us to convert them

1034
02:53:18.220 –> 02:53:26.690
Stav Levi | Alta: later on the funnel, and also even to not just paying customer, but also customer with a with a high lifetime value.

1035
02:53:27.103 –> 02:53:41.239
Stav Levi | Alta: And so this is like in terms of of the outbound, because in the end of the day the the time of the salespeople is really expensive, and we wanna make sure that they going to the right meeting.

1036
02:53:41.390 –> 02:53:58.529
Stav Levi | Alta: And and I think in in terms of qualification. So in terms of qualification. It’s it’s also a big part of the inbound, but also for for the outbound. I think today it’s really like a repetitive task and easy to replace kind of

1037
02:53:58.680 –> 02:54:25.579
Stav Levi | Alta: the qualification and meeting attainment part. So I think this is where AI will definitely going to take the lead in all of the area of qualification meeting attainment like the top of the funnel after having the right lead to. I don’t know SQL

1038
02:54:25.820 –> 02:54:32.199
Stav Levi | Alta: like in those steps. It’s for sure going to be like a big thing.

1039
02:54:32.770 –> 02:54:36.380
Mary Shea: Yeah, yeah, I I think that’s you know.

1040
02:54:36.790 –> 02:55:00.013
Mary Shea: it’s such a balance between. You know, sort of the numbers game versus the, you know quantity versus quality. And I think we’re what I love about. What we’re talking about now is we’re really leaning into the quality. So I predicted in 2,015 or 2017. Julia will keep me honest. That salespeople would work 3 or 4 days a week.
1041
02:55:00.920 –> 02:55:22.379
Mary Shea: Where are we with that prediction is that, are you? Seeing that? And I predicted that they’d make more money than they’ve ever made before. So are these types of technologies and what you just described. Really, when you really understand the intent, and you can go after that person who’s ready for you? Are we who are in sales going to be able to work 3 or 4 days a week. What’s your what’s your take, or anyone’s? Take.

1042
02:55:22.540 –> 02:55:41.530
Stav Levi | Alta: I I think what you said in the beginning, that human like AI, won’t replace human but human use. AI, like we all do. So I think this is also like the the 10 x that AI help can help sales team with. So like

1043
02:55:41.530 –> 02:56:04.499
Stav Levi | Alta: definitely succeed to close more deals and have more like qualified like to make their time really high quality. Talking to the right customers, understand what what is the like, even how to negotiate with them are so much easier.

1044
02:56:05.262 –> 02:56:08.439
Stav Levi | Alta: I think this is like a can help them.

1045
02:56:08.700 –> 02:56:13.559
Stav Levi | Alta: not just work less hard, but much more effective.

1046
02:56:14.010 –> 02:56:15.000
Mary Shea: Yeah. Awesome.

1047
02:56:15.250 –> 02:56:16.090
Sandy Mangat: I just raised.

1048
02:56:16.090 –> 02:56:16.469
Lori Harmon: Got it.

1049
02:56:16.470 –> 02:56:31.029
Sandy Mangat: Data on that point. Oh, sorry I was. Gonna say, yeah. So we’ve run the numbers and our at least within our customer base and focuses an AI prospecting product. So

1050
02:56:31.040 –> 02:56:56.215
Sandy Mangat: reps that are using focus are generating in, I think, across, like our enterprise customers, about 3 x more pipeline, and that’s also about like 1.8 x or 2 x in some of our customers revenue. So we have seen the proof of if salesperson plus AI does the job. We’ve seen better outcomes

1051
02:56:56.650 –> 02:57:07.070
Sandy Mangat: and it’s about 10 plus hours that they’re saving per week, not having to sift through a bunch of data to figure out which accounts to focus on because they’ve got this very prescriptive list.

1052
02:57:07.070 –> 02:57:14.790
Sandy Mangat: And then they’ve got the research out of the box as well, and so a rep I like literally heard from reps, say.

1053
02:57:14.830 –> 02:57:39.230
Sandy Mangat: this has made it so much easier to prospect, but I think the end result isn’t going to be less days prospecting. It’s just, you know, you you spend maybe less of your day prospecting. You’re still gonna probably spend the same amount of days per week. But you’re gonna the ultimate result is more high quality pipeline. And then, yeah, maybe maybe your quota will go up.

1054
02:57:39.470 –> 02:57:39.910
Lori Harmon: Yes.

1055
02:57:39.910 –> 02:57:41.010
Mary Shea: Spoken

1056
02:57:41.990 –> 02:57:50.069
Mary Shea: booking like a true business leader versus a rep. So you’re you’re just saying I’m gonna get more pipeline, and I’m gonna get more deals and and.

1057
02:57:50.070 –> 02:57:55.920
Sandy Mangat: But it’s gonna be enjoyable for the rep to do it like it’s not gonna be a slog to get that pipeline.

1058
02:57:56.200 –> 02:58:04.079
Mary Shea: There you go. Okay, you’ve given us a little bit of a bone there. So thank you for that. And, Lori, I think you wanted to weigh in, or did you have anything to amplify? There.

1059
02:58:04.080 –> 02:58:22.179
Lori Harmon: Yeah, I was just saying that, I think, when, if generally, if companies see that, yes, they will be more productive. But if they got to working less time. Then they would see a quota increase which would offset the less time. But I definitely agree, as, like with my productivity example, you can absolutely produce more with AI. But then they’ll

1060
02:58:22.290 –> 02:58:24.979
Lori Harmon: raise your quota, so you may not get to the 4 days a week.

1061
02:58:26.210 –> 02:58:28.250
Mary Shea: Darn it! I know.

1062
02:58:28.250 –> 02:58:28.879
Mary Shea: Anyway, it’s a.

1063
02:58:28.880 –> 02:58:36.119
Sandy Mangat: Actually, I actually hope one of the the outcomes is also that reps, get to spend time on

1064
02:58:36.180 –> 02:58:59.289
Sandy Mangat: things that are you know, also really valuable that they probably don’t get around to today because they’re slogging away. So more relationship building, showing up in person, going to events, doing the things that AI can’t replicate. So I think that really is the the future that we’re heading towards. It’s AI is going to take care of the drudgery and reps. Get to do

1065
02:58:59.290 –> 02:59:11.859
Sandy Mangat: the stuff that AI just simply can’t, which is, you know, hand delivering a note and a box of chocolates to your prospects in person that delight that only a human can do.

1066
02:59:11.860 –> 02:59:28.990
Peter Mollins: I totally agree that that’s gonna be exactly right getting rid of that drudgery. So you can focus on the human side. And I and one thing I would just add to that is, it’s also from the other perspective, as well like from the buyer’s perspective. You know, you think about what their experience is for for connecting and interacting with the salesperson.

1067
02:59:28.990 –> 02:59:51.000
Peter Mollins: you know, if they if they have on the other end of the line a salesperson who understands their problems, who can empathize with them more effectively, has more time in between calls to strategize with them and to help guide them. You know, that’s going to be a better, much better buyer experience. So eliminating the drudgery totally agree, Sandy, about the idea of you know, that helps you do more prospecting, and, I would just add, also creates a much better buyer. Experience.

1068
02:59:52.270 –> 03:00:09.440
Mary Shea: Yeah, really great points. I mean, what we’re saying is that it creates a better experience for both the buyer and seller. And so I think we could debate offline whether or not we’re going to have that 3 or 4 day week. I’m still standing by my prediction. But what you’re saying, what I’m hearing from the experts here is that

1069
03:00:09.600 –> 03:00:26.839
Mary Shea: the job is going to be more rewarding. You’re going to be doing the higher value tasks that you should be doing, which should be very motivating and rewarding for folks who went into this field for a certain reason, and I can already see that happening now, which I think is is wonderful.

1070
03:00:26.840 –> 03:00:31.389
Stav Levi | Alta: My prediction is, the quota is going to be higher, and the

1071
03:00:31.920 –> 03:00:36.287
Stav Levi | Alta: and from the sales people is going to be higher as well. With that.

1072
03:00:36.920 –> 03:01:04.890
Mary Shea: Well, you know what some things never change, do you? I’ve never. I’ve never seen a quota that went down from year to year in my 30 years of being in in commercial roles. But anyway well, one of the things I want to go back to Sandy to for a minute is because we talked a lot about sort of signals, and allowing, you know, reps to know there’s been been a funding, or there’s been, you know, new hiring, or there’s been, you know, M. And A. All these kinds of things can

1073
03:01:04.890 –> 03:01:14.543
Mary Shea: be really meaningful to a salesperson. So how? How does how does your solution, or, or, more broadly, you know, solutions in in your category?

1074
03:01:15.810 –> 03:01:24.240
Mary Shea: let the rep know what’s going on and and what are some of the top signals that you know the solution will alert them to.

1075
03:01:26.070 –> 03:01:33.520
Sandy Mangat: Yeah, I think I think broadly, we’re seeing a lot of tools that are

1076
03:01:33.820 –> 03:01:42.879
Sandy Mangat: kind of aggregating signals. Focus is certainly one of them. So that can be internal data. So your product usage marketing.

1077
03:01:42.880 –> 03:01:43.230
Mary Shea: Right.

1078
03:01:43.656 –> 03:02:09.699
Sandy Mangat: Crm, call recordings. Or it’s external data 10 k podcasts, funding round announcements, job posting, like, what are they hiring for? I think the the magic really happens when not only do you kind of bring those 2 together and synthesize them in context, but you really figure out what is relevant to you. I think one trap that
1079
03:02:10.053 –> 03:02:21.020
Sandy Mangat: I’ve seen salespeople fall into is you’re just kind of chasing the signals. So it’s like something happened, and I and I respond, and I say, Hey, I saw this happened.

1080
03:02:21.020 –> 03:02:31.689
Sandy Mangat: and then, without any sort of transition, let me tell you about my solution and kind of, you know. So at focus, one of our kind of like core philosophies is that you shouldn’t do this like

1081
03:02:31.750 –> 03:02:45.339
Sandy Mangat: signal dumping on reps. It’s really about helping them form a hypothesis. The signal is either giving you a a sense of prioritization that this account is surging for some reason.

1082
03:02:45.340 –> 03:03:10.280
Sandy Mangat: or it’s giving you a clue into what their priorities might be. So if, for example, in Focus’s context, if an account is hiring, Rev. Ops generally means they’re thinking about their go to market tech stack. Maybe they’re thinking about revamping could be a good opportunity to talk to them about focus. And so that is what we call relevance, like, what is the relevant signal here?

1083
03:03:10.280 –> 03:03:25.669
Sandy Mangat: It’s not just any signal, and it’s not usually just one signal. It’s a series of things that you have to synthesize and come up with a hypothesis, and that is, and then that helps you craft, really compelling messaging.

1084
03:03:25.690 –> 03:03:54.479
Sandy Mangat: So that’s a great example of a signal that we’ve seen. Work really well is hiring signals from the kind of external standpoint product updates is another one. But it’s going to be unique to your business. What are the clues that you look for? And the best way to figure that out is, look at your recent pipeline and recent close one. What were the were there anything? Was there anything about those accounts that you could.

1085
03:03:54.670 –> 03:03:58.849
Sandy Mangat: you know, extrapolate, and and then publicly scrape like?

1086
03:03:58.990 –> 03:04:28.660
Sandy Mangat: And we found out. That’s how we discovered this hiring signal. We looked at it. And it’s like, okay. A lot of these accounts were hiring. They were growing their sales team they had recently hired a Bdr manager. They hired revops. That’s a great signal, and that just happens to be one that you can easily access. Using AI, you can scrape, you know, job postings and figure that out. So I would recommend people go and do a little bit of that analysis and think about it. To figure out what signals are going to be right for you, because

1087
03:04:28.690 –> 03:04:32.560
Sandy Mangat: what’s right for us is is not going to be right for another company.

1088
03:04:32.830 –> 03:04:45.377
Mary Shea: Right? Right? So I like your. I like your statement, you know. Just don’t blindly signal chase. Right? Be strategic about it. Be thoughtful. Be targeted and and use that information.

1089
03:04:46.230 –> 03:04:51.830
Mary Shea: to really improve the engagement that you have with that potential customer

1090
03:04:52.280 –> 03:04:58.059
Mary Shea: looks like we have a question, oh, go go for it. I want to take a question for the audience. But you jump in real quick, Pete. Peter.

1091
03:04:58.060 –> 03:05:14.310
Peter Mollins: Oh, sure! And and I was actually gonna throw it to to Haley. As a matter of fact, because I as as Sandy was talking. I was thinking, how those signals and some of that. The the identification of like, what you want to say is a great input into sales training sales coaching.

1092
03:05:14.420 –> 03:05:37.129
Peter Mollins: because now you’re identifying, you know, what are, what are some of those pains that someone’s take? Someone’s experiencing a prospects experiencing. And so you want to make sure that your your sales team is is equipped with the right kinds of talk tracks and objection, handling all that. And they’re they’ve practiced on, maybe on AI training bots, or whatever, in order to be ready for those. But I thought Haley might have the take on that.

1093
03:05:38.020 –> 03:05:46.369
Haley Katsman | Highspot: Yeah, absolutely. I mean, I think when you combine those signals with the training. But also, more importantly, it’s really interesting because we’re.

1094
03:05:46.400 –> 03:06:11.019
Haley Katsman | Highspot: I think the best. AI has the best data. And when you look at some of the 1st party data that you have where a lot of companies are writing down things that you mentioned. Peter, like, you know, what’s our persona based messaging? What’s the battle card? What are the FAQ questions, you know. Discovery questions that you should be answering. Most of those are written down in kind of, you know, product marketing or sales training type materials that then usually go into, you know.

1095
03:06:11.020 –> 03:06:23.269
Haley Katsman | Highspot: some Cms, some sharepoint type thing. And it’s just really a total waste of valuable information that’s not being plugged into how you you know, equip and train and coach your reps.

1096
03:06:23.660 –> 03:06:48.600
Haley Katsman | Highspot: So I think, bringing together those signals and then bringing together your strategy. That’s been written down in things like a compete battle card, or like a, you know, even a product brochure, or whatever that is. That’s then where you can have the AI automatically generate the training itself and the feedback that you get on the training and so you don’t have to have, you know, a whole fleet of people that are trying to

1097
03:06:48.720 –> 03:07:12.659
Haley Katsman | Highspot: piece together all the different pieces. You can just automatically do that. And then, of course, the real magic is unlocked. When you can tie. Then that information to the right scenario based on deals that have been won in a similar scenario, so that you can say, not only is this the messaging that you should use. But this is the messaging that you should use in this scenario that has been successful with other reps like you in this type of deal.

1098
03:07:13.420 –> 03:07:35.759
Mary Shea: Yeah, awesome. Thank you. Haley and Peter, appreciate that. Well, I want to take this question from the audience, and I know we have a lot of smart people who are participating today. And the question is, can AI agents leverage ad tech principles such as A B testing look alike and more in the sales outreach world. Anyone want to take that one on.

1099
03:07:36.090 –> 03:08:01.150
Stav Levi | Alta: I think I can take it as a huge part of our product is around the A B testing and the messaging. So I think that, like I really connect to what Sandy said, that in the end of the day it’s a creating hypothesis from it, and I think there in my previous job. I led the growth organization@monday.com. And we use to do a lot of A B testing.

1100
03:08:01.230 –> 03:08:17.049
Stav Levi | Alta: And the thing with AB testing is, you need to be consistent about like you learn something, and then you need to build on top of this learning like a new hypothesis and keep learning and improving, and I think AI and agents

1101
03:08:17.050 –> 03:08:40.269
Stav Levi | Alta: can do it in a great way, because there is a great consistency. The forming of the hypothesis. Let’s say, hypothesis is not something that it’s leaking. And the thing is, for example, to try

1102
03:08:40.270 –> 03:08:52.779
Stav Levi | Alta: different hypothesis of messaging and see what’s work for which audience, right with a lot of different variances and find the best messaging for a specific audience.

1103
03:08:53.455 –> 03:09:00.939
Stav Levi | Alta: So it’s a, it’s, it’s definitely it’s definitely a thing. And and I really believe in it personally.

1104
03:09:01.620 –> 03:09:03.830
Mary Shea: Awesome, awesome. Thank you for that stuff.

1105
03:09:04.060 –> 03:09:32.909
Mary Shea: Well, I want to talk a little bit about mid cycle, and I know when we think about Gen. AI and automation. We tend to think a lot about the planning and the prospecting right? Because there’s such a strong use case, particularly on the top of the funnel. But there’s some really wonderful mid-cycle activities as well. And and Haley, I want to turn to you on on sort of the call recording and trying to, you know, correlate those recordings to outcomes and the learnings. I’ll be really honest with you.

1106
03:09:32.950 –> 03:09:34.520
Mary Shea: I love the concept.

1107
03:09:35.080 –> 03:09:42.860
Mary Shea: And when I was co-CEO of a Revenue Enablement company. I wanted to listen to all the calls, and I just couldn’t. I mean.

1108
03:09:43.020 –> 03:09:43.340
Haley Katsman | Highspot: I just.

1109
03:09:43.340 –> 03:09:44.560
Mary Shea: Didn’t have the time.

1110
03:09:44.560 –> 03:09:46.240
Haley Katsman | Highspot: You don’t have 10,000 h.

1111
03:09:46.715 –> 03:09:51.409
Mary Shea: Didn’t have 10,000 h, and then I try to like speed up the call, and you know people would be talking really fast.

1112
03:09:51.410 –> 03:09:52.050
Haley Katsman | Highspot: Yes, but.

1113
03:09:52.370 –> 03:10:16.620
Mary Shea: It wasn’t practical for me, but I I wanted, because that’s like where the rubber meets the road. Right? You’re gonna know. Do I have a good sales team. Am I gonna hit my numbers? Do I need to change pricing? What are the objections? You know? What’s the vibe from? You know the the customer base, and you know I can’t go higher and do voice of the customer. It’s gonna be dated by the time I get my report so

1114
03:10:16.620 –> 03:10:28.590
Mary Shea: practically, how can a CEO or chief revenue officer really benefit and get those insights from what I think is the most important conversations that are probably happening at their company.

1115
03:10:29.260 –> 03:10:42.329
Haley Katsman | Highspot: Yeah. Well, I think what you said you want is actually just hit the nail on the head. You said I wanna see the objections. I want to see how they’re handling, pricing. I want to see all these like specific topics, if you will. And I think that

1116
03:10:42.460 –> 03:11:11.600
Haley Katsman | Highspot: you know it’s it’s actually kind of interesting. Because, like, where enablement space kind of started out was really like, how do you help manage content now? It’s evolved into training. And how do we help coach with call recording and really recording calls? You run into the same issue that you run into with getting, you know, thousands and thousands of marketing content. How do I find what I need at the right time without having to go? And, you know, look at a thousand different items. Otherwise it’s like, Yeah, I hope you have 10,000 h to sort through all those calls. So I think that it has to be framed around.

1117
03:11:11.930 –> 03:11:38.039
Haley Katsman | Highspot: Why are you listening to the calls in service? Of what right? And if you don’t have that container, then it makes it very hard to figure out what calls you should go and listen to. And so that’s why kind of what I was talking about at the beginning. You have to have somewhere where you’re writing down your strategy, and maybe your strategy is, we are going to increase our pricing, and we want to understand how we are landing. The value of premium pricing right for a particular product, as as you mentioned.

1118
03:11:38.410 –> 03:11:43.230
Haley Katsman | Highspot: That can be a whole initiative across your organization. You have to understand

1119
03:11:43.400 –> 03:11:48.160
Haley Katsman | Highspot: what’s the new pricing. How are we going to sell it? What are the talking points?

1120
03:11:48.310 –> 03:12:05.810
Haley Katsman | Highspot: All of that information? Well, you have to be able to in a system be able to codify that initiative as a topic that then cuts through the noise of all of the tens of thousands of recordings and says, Here’s all of the calls for this product, where pricing was discussed.

1121
03:12:06.190 –> 03:12:06.740
Mary Shea: Okay.

1122
03:12:06.740 –> 03:12:24.609
Haley Katsman | Highspot: And it just, and it quickly gets you to that list of calls, and then not only says that, but and tell me the ones where the deals were lost, where there were no decision, where they were, one where they progressed. So that you’re because really why you’re looking at calls is, you want to see what are the best practices, so that I can amplify those across the team, or

1123
03:12:24.610 –> 03:12:49.360
Haley Katsman | Highspot: what are the bottom performers doing that? I need to coach to right. It’s like good and bad, and and then I need to take action from that. So if you can’t quickly get down to this product defend pricing who did it well in close deals versus who didn’t and lost deals. Then there’s no use in recording the calls, otherwise they’re just sitting in a system somewhere. And so I think that’s where AI can play a really big role of a quickly getting you to exactly what you’re looking for, but be connecting it to action.

1124
03:12:49.360 –> 03:13:05.659
Haley Katsman | Highspot: actual data, so that you’re not trying to figure out. Oh, did they do? Well? Did it move the deal forward? The AI is telling you. Yes, it moved the deal forward, and here’s the call, and here’s literally 2 min and 35 seconds where they talked about pricing. And like that should take one second. And it really transforms. Then

1125
03:13:05.860 –> 03:13:20.090
Haley Katsman | Highspot: how leaders get involved in calls like what I’ve seen is it used to just be frontline managers listening to call recordings. Now I see Cros listening to call recordings because they can quickly go from strategy all the way to an actual call where it’s happening.

1126
03:13:20.820 –> 03:13:26.819
Mary Shea: Yeah, I think it’s I think you’re right. And I think it’s really crucial for Cro. Cmo.

1127
03:13:27.070 –> 03:13:33.299
Mary Shea: chief product officer and CEO even more so than that frontline manager, because.

1128
03:13:33.300 –> 03:13:34.210
Haley Katsman | Highspot: Totally.

1129
03:13:34.210 –> 03:13:46.307
Mary Shea: Decisions that those individuals make are going to be much more profound right in terms of swaying the business in one direction or another. And so what you described was what I wanted, and I never actually got it. So

1130
03:13:47.060 –> 03:13:47.780
Mary Shea: thank you for that.

1131
03:13:49.960 –> 03:13:51.400
Mary Shea: Thank you for that.

1132
03:13:51.743 –> 03:14:03.090
Peter Mollins: One of the things I would say about Sdrs. Is there? There are. There’s no one else in the company that speaks to as many prospects, and knows as much about what’s happening.

1133
03:14:03.090 –> 03:14:04.149
Mary Shea: Right, right.

1134
03:14:04.150 –> 03:14:27.049
Peter Mollins: Market as possible, so getting recordings on their prospecting calls, and hearing the objections at the very top of the funnel, I think, is also critical. And if I could just add one other point to to Haley’s Point, there also another way to action it that AI is fantastic at is being able to take the objections that you’re hearing on these calls and turn them into training bots like, you can actually have a

1135
03:14:27.050 –> 03:14:29.460
Peter Mollins: micro micro learning. Yeah, exactly.

1136
03:14:29.460 –> 03:14:39.259
Peter Mollins: Yeah, for all intents and purposes, feels like a real conversation with a human that an Sdr. Or whomever can practice on to overcome those objections. So another great outlet for AI.

1137
03:14:39.840 –> 03:14:56.449
Mary Shea: Yeah, I agree. I agree with you on the on the Sdrs. And one of the things I would do, you know, is simply take some of these Sdrs out to coffee, you know. Have a 15 min conversation, and I do like to go old school a little bit. I like the blend of the both. And you can learn a ton so great, great point.

1138
03:14:57.170 –> 03:15:13.150
Mary Shea: So thinking about, you know, sort of larger enterprise types of sales. And we all know the research how the buying committees are expanding, decisions are taking longer. It’s more complicated managing some of these sales process.

1139
03:15:13.740 –> 03:15:14.970
Mary Shea: How do you

1140
03:15:15.130 –> 03:15:22.220
Mary Shea: use Gen. AI to help with account research and understanding sort of where an account is from

1141
03:15:22.420 –> 03:15:30.180
Mary Shea: an account. Health perspective. I don’t know Sandy, or anyone want to weigh in. Weigh in on that at that perspective.

1142
03:15:31.560 –> 03:15:39.180
Sandy Mangat: Yeah, I think the account planning piece is so important. And it’s 1 of those things that reps

1143
03:15:39.460 –> 03:16:02.990
Sandy Mangat: probably only do on their most high value accounts regularly. So building that really robust account plan, whether you are using a framework like spiced or medic, or something else, and enforcing that I’ve heard from frontline managers is a difficult task, because it is time consuming unless you have something helping you out.

1144
03:16:02.990 –> 03:16:24.400
Sandy Mangat: So I think that’s where AI helps a lot is having an evergreen account plan that not a manual task you have to continuously do throughout the sales cycle. It’s just continue to get updated as new things happen within the account. So it’s, you know, when you’re at top of funnel, it’s probably more reliant on external data as you get deeper into the funnel.

1145
03:16:24.400 –> 03:16:44.386
Sandy Mangat: It gets more reliant on that internal data. And then it becomes really useful for reps when they’re doing meeting prep, or they need to build a business case. Building any sort of collateral to kind of drive that that forward. So that’s that’s really where I’ve seen it be helpful?

1146
03:16:44.880 –> 03:17:07.569
Sandy Mangat: using that type of account plan to have deeper conversations. So that reps show up to the call, knowing a lot more context. And so they’re not wasting time on, you know. Maybe, like the the shallow questions they can like really go deep pretty quickly, and have more meaningful conversations and probably uncover things that maybe they wouldn’t have otherwise.

1147
03:17:08.280 –> 03:17:10.260
Mary Shea: Awesome. Yeah, thank you for that.

1148
03:17:10.370 –> 03:17:36.679
Mary Shea: So so let’s start to think about closing the deal, because that’s what we all want to do to drive growth right in the end, and of the tools that are available out there, ones that I’ve worked at companies that have those tools. And as a user of some of the others. What I really loved as a business leader was some of these revops tools that would really let you know the health of every single account in your pipeline, and I wasn’t necessarily relying on

1149
03:17:36.680 –> 03:17:50.769
Mary Shea: the manager or the rep, or the manager’s manager to tell me if it was coming in. I was just looking at the behavioral activity that I could see on my dashboard, and I’d know whether that deal was at risk, and I could jump in and course correct, or if it was tracking really nicely.
1150
03:17:53.270 –> 03:17:54.290
Mary Shea: You know.

1151
03:17:54.500 –> 03:18:03.579
Mary Shea: Haley, let’s go back to you like. Let’s take this not now down to the rep level, away from the C-suite to you know, how can I, as a rep.

1152
03:18:05.200 –> 03:18:13.859
Mary Shea: tighten up and refine and hone my closing skills, learning from some of the other reps that may be closing more than I am. How can

1153
03:18:14.320 –> 03:18:19.049
Mary Shea: can different solutions that are out there really help everyone become that top closer.

1154
03:18:19.830 –> 03:18:26.119
Haley Katsman | Highspot: Yeah, well, I mean, I think it goes into a lot of what we’ve talked about. And I think that one of the hardest things.

1155
03:18:26.882 –> 03:18:28.539
Haley Katsman | Highspot: Is, you know.

1156
03:18:28.600 –> 03:18:36.320
Haley Katsman | Highspot: reps are constantly looking for what their peer top performers are doing, and then the challenge that they have.

1157
03:18:36.320 –> 03:19:00.549
Haley Katsman | Highspot: and then that is oh, well, it might not be my segment. It might not be my region, my territory, the product that I’m selling the vertical, whatever it may be. So they have to kind of do this translation right from, okay, what is the top performer doing to how does that fit into my deals? My book of business and the conversations that I’m having with my buyers, and I think that that’s where.

1158
03:19:00.550 –> 03:19:05.769
Haley Katsman | Highspot: instead of it being this, you know, kind of long loop of.

1159
03:19:06.230 –> 03:19:19.039
Haley Katsman | Highspot: hey? Let’s see what the top performers are doing. Let’s embed it back into the enablement strategy. Let’s roll out a training at the next go and then let’s up level the teams, I mean, that’s like that can be a quarterly or an annual cycle, right? And if you can short circuit that

1160
03:19:19.340 –> 03:19:27.220
Haley Katsman | Highspot: to help the reps, see what the top performers are doing that are either similar to them, or

1161
03:19:27.250 –> 03:19:44.460
Haley Katsman | Highspot: then help them translate that into the conversations that are upcoming with their deals and their buyers, then that really helps them not have to rely on a manager not have to figure it out themselves. But it’s really short circuiting that best practice feedback loop.

1162
03:19:44.460 –> 03:20:03.950
Haley Katsman | Highspot: And I think you know, if anything, it’s very empowering for them, right? Because it’s giving them the confidence I mean so much of selling is about confidence right? And giving them the confidence that if one of my peers did this successfully, and I’m in the late stage negotiation process, and I’m up against our top 2 competitors. Here’s how a peer.

1163
03:20:04.060 –> 03:20:28.740
Haley Katsman | Highspot: you know, differentiated and was able to, you know, defend the business case and close the deal. They’re going to be a lot more confident going into that conversation and well equipped with those best practices. And so I think a you’re gonna see more at bats like, just because they’re more confident that they’re actually going after it and taking maybe the scarier move to sell the newest product that you build as opposed to just going with kind of the status quo

1164
03:20:29.148 –> 03:20:38.640
Haley Katsman | Highspot: but also, you’re gonna see higher conversion rates because you’re just quickly amplifying all those best practices of of your top performers, and that could be anything from

1165
03:20:38.780 –> 03:20:57.510
Haley Katsman | Highspot: the plays. They’re running the content that they’re using the digital sales rooms that they’re creating for engagement, how they’re showing up with messaging on a call. It could be any of those activities. But ultimately I think it’s empowering them instead of making them dependent on the business, and showing them what the best practices are.

1166
03:20:58.070 –> 03:21:05.379
Mary Shea: Yeah, absolutely. Thank you. Thank you for that. Any any other use cases on on the closing side that anyone would like to share.

1167
03:21:06.440 –> 03:21:09.499
Lori Harmon: Well, I’ll just make a comment there, if you don’t mind, Mary. So you know there.

1168
03:21:09.500 –> 03:21:09.870
Mary Shea: Love it.

1169
03:21:09.870 –> 03:21:20.799
Lori Harmon: I completely agree with what Haley said, but, as I mentioned earlier, there are definitely tools out there. So some reps, you know, you’re going to gravitate towards the big deal. I’ve got to get this close, and yes, you probably do.

1170
03:21:20.800 –> 03:21:21.460
Mary Shea: You do.

1171
03:21:21.460 –> 03:21:27.179
Lori Harmon: But they may not have the nuances without AI to say, well, that deal is probably going to close anyway, so you could go.

1172
03:21:27.180 –> 03:21:27.770
Mary Shea: Right.

1173
03:21:27.770 –> 03:21:45.809
Lori Harmon: Smaller deal that needs more work, and AI can aggregate the information in your Crm. All the recordings, all the notes, the things that Peter was talking about earlier and actually suggest the next best action for the rep to take to get something over the line. So I think that’s just additional functionality that AI can provide.

1174
03:21:46.130 –> 03:22:13.469
Mary Shea: I really love that because you know, I think of, you know, back when I started my career was when when you’re sort of doing cold, calling cold outreach! Who would you call? Well, the people that you wanted to talk to? Not necessarily, you know. So you’re not necessarily spending your time on the right types of activities. And I love that that concept where this deal is potentially going to close anyway with or without you. But this one really needs your intervention, and we we probably

1175
03:22:13.720 –> 03:22:34.309
Mary Shea: are always spending the time on that one that’s going to close, anyway, because it feels really good, right when you’re in that final stage. They really like you. They like the product. You’re you know. You’re probably breaking bread together. What have you? And to have the AI say, Hey, you got this one, you you don’t have these 3 focus on those like. That’s a big winner in my mind.

1176
03:22:35.010 –> 03:22:48.959
Haley Katsman | Highspot: I actually think it applies to coaching in the exact same way, like, I remember way long ago, when I was leading our Sdr. Team. I would see that a lot of the managers would gravitate towards coaching on the hardest accounts.

1177
03:22:48.960 –> 03:23:13.919
Haley Katsman | Highspot: because that’s where the reps were like. I don’t know what to do. I need help like I, someone help me in these scenarios, and I’m like, if you spend an hour of your time coaching on that account versus an hour on one of our top tier in our Icp best accounts. The roi of your coaching is far greater than on the ones that they’re asking you to help on. And so many managers are dependent on figuring out where to coach, based on what their rep is bringing to them.

1178
03:23:13.920 –> 03:23:21.869
Haley Katsman | Highspot: And if you have the ability to decide as a manager what you’re going to coach on, based on what has the highest probability to close

1179
03:23:22.150 –> 03:23:39.090
Haley Katsman | Highspot: that moves the needle so much more in the same way that you’re talking about what deals with rep focuses on. And if both things are true, reps are focusing on the deals that matter, and managers are coaching on the deals. That matter. Then that’s where you really get the, you know, an increase in win rates and close rates.

1180
03:23:39.090 –> 03:23:42.619
Mary Shea: Yeah, I mean, managers fundamentally, aren’t that dissimilar from reps at the end of the day.

1181
03:23:42.946 –> 03:23:43.273
Haley Katsman | Highspot: Is.

1182
03:23:43.893 –> 03:23:48.290
Mary Shea: We were all the same. Yeah, I love it. What a what a wonderful.

1183
03:23:48.290 –> 03:23:48.700
Sandy Mangat: But.

1184
03:23:48.700 –> 03:23:50.210
Mary Shea: Yeah, jump in. Please do.

1185
03:23:50.210 –> 03:23:57.640
Sandy Mangat: Okay, I love Lori and Haley’s Point. And what I’m hearing, is it? It all comes back down to

1186
03:23:57.700 –> 03:24:27.569
Sandy Mangat: account selection and like focus, no matter what stage of the cycle you’re in, it just seems like if you can pick the right accounts and pick the right focus for your team. That is probably 80% of the battle, and it like both. I loved both of your points on that. And I think that’s the thing I would want to just continue to to stress to leaders and reps. It’s really, you know, spending the time on using AI and data to figure out that account selection piece.

1187
03:24:27.650 –> 03:24:39.480
Sandy Mangat: whether you’re mid funnel, late stage or early, and quite frankly, I haven’t even thought about it from that closing perspective. So I really love that you highlighted that it’s so important.

1188
03:24:39.600 –> 03:24:43.489
Mary Shea: Yeah, I like it, too, because, like human nature, like.

1189
03:24:43.550 –> 03:25:04.200
Mary Shea: we focus on the things that make us feel good, not necessarily what we need to do, which is why all the books that you read about time management? It’s like, start with the most important thing you have to do at the beginning of the day right? But we don’t. Usually I’ll be like, well, 1st I’ll have a coffee, and then I’m going to call, you know someone who I know is going to want to talk to me, and that’s not necessarily what we should be doing, but
1190
03:25:04.200 –> 03:25:16.860
Mary Shea: great, great, great comments. Well, I want to do sort of a power wrap up here, keeping it really short. And going across the group. If if I’m a c-suite commercial exec

1191
03:25:17.040 –> 03:25:18.309
Mary Shea: or I’m a rep.

1192
03:25:18.720 –> 03:25:20.289
Mary Shea: What are the top?

1193
03:25:20.500 –> 03:25:29.760
Mary Shea: You know one top thing that you would recommend for for me as a C-suite exec or a rep, as it relates to really bringing

1194
03:25:29.900 –> 03:25:37.320
Mary Shea: generative AI and advanced automation and new technologies into your process. And, Haley, you’re you’re front and center. So I’m going to go with you first.st

1195
03:25:37.620 –> 03:26:03.659
Haley Katsman | Highspot: Yeah, I mean, you’ve heard me talk a lot about it today. I would say that so many businesses are dependent on their frontline seller, frontline managers to execute their strategy as a business. And so I would say the more that you can leverage AI to. Give your managers back time, make coaching consistent, and scale your go to market initiatives consistently. That would be where I’d make a big investment is on on coaching.

1196
03:26:04.230 –> 03:26:05.860
Mary Shea: Awesome! Lori.

1197
03:26:06.540 –> 03:26:17.930
Lori Harmon: Well, I would say, one is, when you look for AI solutions, try to get a platform that can work across different functions versus stitching a bunch of things together, and when you’re rolling it out, don’t just say, you know.

1198
03:26:18.190 –> 03:26:35.590
Lori Harmon: throw it over the fence and do one training and be done with it. It takes people time to absorb it, and so I would say, do the incremental fashion. So people are adopting a little bit at a time and getting more comfortable because there’s still some resistance from Sellers on AI. So just help them embrace it so they can benefit from it.

1199
03:26:36.360 –> 03:26:46.510
Mary Shea: You could buy the best technology in the world with the best buttons. And if people aren’t going to press those buttons, you’re not going to get the results. So that’s such an important point, Laurie Stav. Why don’t you weigh in.

1200
03:26:46.730 –> 03:27:08.940
Stav Levi | Alta: I think that if I was need to give one advice, so it’s a think our. Why, I think principles from ad tech and marketing were like thinking return of investment. And, like Ross, or like metrics like this, is not the things that we are right away. Things in our revenue and sales.

1201
03:27:08.950 –> 03:27:31.810
Stav Levi | Alta: places and salaries are the investments in many of the cases. So think about the places that repetitive job and things that you don’t have to pay a full salary for can be replaced, and keep the time of your sales team to the important tasks.

1202
03:27:32.550 –> 03:27:34.769
Mary Shea: Love it, love it. Thank you for that, Peter.

1203
03:27:35.160 –> 03:27:57.349
Peter Mollins: Yeah, do a little different flavor. But a similar kind of theme is what Staff was saying, which is the more that you can have your sellers and your top performers establishing real human connections with your prospects, the more that they’re going to be long term great customers for you, and the best way to do that is to automate the busy work that’s clouding up their day.

1204
03:27:57.350 –> 03:28:17.300
Peter Mollins: And the more that, and that’s what AI is particularly good at is doing that research identifying the people that you want to target. It’s great at helping you to connect with more of the right people. It’s great at helping you to coach and to train and to be effective. So that when you have those conversations you’re as effective as possible, so more human is what I would suggest.

1205
03:28:17.420 –> 03:28:23.890
Mary Shea: I love that. That’s what we started out with. So thank you for amplifying that. And, Sandy, bring us home.

1206
03:28:24.810 –> 03:28:52.180
Sandy Mangat: Yeah, I mean, I echo everything y’all have said, especially Peter, and stop your points. Resonate a lot. Just so. I don’t repeat the exact same thing. I think 1 1 exercise I would recommend. All leaders go through is just mapping out all of the jobs to be done by your sellers. And that has been the most effective way to really figure out where AI fits. And I use kind of like 4 criteria

1207
03:28:52.180 –> 03:29:10.249
Sandy Mangat: research, orchestration, personalization or analytics like, I think those are 4 of the buckets where AI is most impactful. So rate those jobs to be done. Do they fit into that category? And then you can easily figure out where to remove the the drudgery and the busy work.

1208
03:29:10.810 –> 03:29:31.889
Mary Shea: Awesome. Thank you for that. Well, Julia, I think this is a wrap. I’m going to pass the baton to you, but I 1st have to say thank you to this amazing panel, and I will be reaching out to some of you. I would like actually to get some pitches on your solutions. I’m absolutely fascinated, and love everything that I’ve heard. So thank you all very, very much.

1209
03:29:32.090 –> 03:29:33.250
Haley Katsman | Highspot: Thank you. Mary.

1210
03:29:33.420 –> 03:29:34.130
Lori Harmon: Thank you.

1211
03:29:34.130 –> 03:29:35.439
Sandy Mangat: Thank you. Mary.

1212
03:29:35.930 –> 03:29:36.370
Mary Shea: Okay. Thank.

1213
03:29:36.370 –> 03:29:55.410
Julia Nimchinski: That’s a phenomenal panel. Thank you so much, Mary. Before you go. Where can people find your latest research? For years I was just waiting and waiting and waiting for all of your work in Sales tech back in the days at Forrester and then outreach. And now I feel like I’m missing out. So.

1214
03:29:55.660 –> 03:30:11.399
Mary Shea: You know I’m missing out on it as well. I’ve been very, very busy helping a new company go to market and build its American presence. It’s called higher quotient. We’re focused on the future of work and talent, and I’m finding that my interest.

1215
03:30:11.840 –> 03:30:25.789
Mary Shea: you know, revenue is near and dear to my heart. But now I’m really, really focused on the ways people work. So I’m trying to get more time to do my research. And I’m missing it as well, and thank you for reminding me. It’ll be coming soon.

1216
03:30:26.140 –> 03:30:30.260
Julia Nimchinski: Thank you. Fingers crossed. And last question, how can our people support you?

1217
03:30:31.460 –> 03:30:53.550
Mary Shea: Yeah. So people can reach out to me on Linkedin. I’d love to connect. I’d love to have discourse. I love to do things like these types of sessions. And I want to learn about the latest and greatest technologies, many of which we talked about here today. So if you’ve got one. Let me know. I’m always going to be open minded and open ears.

1218
03:30:54.360 –> 03:30:56.739
Julia Nimchinski: Thank you so much. Here’s 1. Thank you. Kevin white.

1219
03:30:56.740 –> 03:30:57.340
Mary Shea: Right.

Table of contents

The Shift of AI and Its Impact on Sales
Predictions for AI in Sales for 2025
AI-Driven Sales Strategy and Account Targeting
First-Party Data and AI in Sales Outreach
AI in Personalization and Sales Engagement
AI in Sales Territory and Training Optimization
AI’s Role in Sales Efficiency and Productivity
AI-Driven Insights for Sales Coaching and Decision Making
AI in Sales Closing and Pipeline Monitoring
AI for Sales Leadership and Strategic Focus