Transcript
The following is a comprehensive transcription of the source excerpts presented in a readable format, with citations included throughout:
Happy heat, what is up everyone. I’m Steve, the guy who hosts this podcast. So this is a pretty terrible intro, let’s start that again. I’m Stuart P Turner, this is the Flow State podcast, hello, welcome. If you are listening for the first time, please ignore the start of that intro. If you are joining me again, welcome back. In a dramatic departure from the past few episodes, today I wanted to zoom into some research that we are producing. Me and the team are doing a bit of outreach at the moment. We’re trying to chat to more people to sense check what we’ve found and see how things are going in their roles and within their companies. Today I just wanted to walk through just a few of the trends and I guess observations that have come out of our own research so far. These are forming the basis of a lot of these conversations, which is obviously very interesting, and I am keen to hear more. So if you like how this sounds and you’re like, “Look I’m definitely seeing that or I’m definitely not,” I would love to hear from you as well.
It’s just me again today, so I’m going to keep it a little bit, you know, sort of shorter and sweeter and hopefully leave you with a few things just to consider following this session. What we’re putting together at the moment, and forgive my lack of official branding and formatting here, maybe I’ll make myself a bit smaller, there we go, is putting together a bit of a white paper to summarize this research that we’ve been pulling together. Now this is a mix of interviews with people, obviously AI assisted desktop research, reviewing brands and research businesses that produce stuff that we read regularly. It’s just really combining all that with our own experience to see where the market is at within the specific area of B2B buying behavior and how that’s been changing over the past sort of five or 10 years. So what I wanted to run through today was some interesting things that have cropped up so far.
I’m not going to go through the whole thing, so don’t panic. I’m just going to call out a few of the key points which I think are interesting and kind of chime with my own experience. As I say, I’d just love to hear whether you’re seeing the same things.
Number one: Generational shifts in buying behavior. Now obviously that’s not a new thing. The new thing we’re seeing at the moment though is we’re seeing a generational shift in terms of the age group of people who are now predominantly in decision-making roles within buying groups. We’re also seeing a crazy sort of paradigm shift in the types of technology we’re using, obviously AI being the main one. So at the moment, this is an interesting stat: 71% of APAC buyers are apparently now under 45. These buyers now bring consumer grade expectations to purchasing in B2B. I often speak about this both on this show and with people that I chat to. I came from a B2C background and I migrated over to B2B. Everybody who works in B2B—you, me, everyone else—is ultimately a consumer as well, and/or first. I think the impact that our consumer experience has on how we buy in B2B is really a bit sort of underplayed. The traditional view of B2B is that we’re all emotionless automatons that just use the power of pure logic to make decisions and we all actually get on together and have the same agendas in our buying groups. I think we all know that that is most definitely not the case. Particularly with people of my generation and below now coming into these buying groups, you know, the millennials, the cool guys, that’s changing attitudes and behaviors significantly. So I thought this was particularly interesting, and this is an area that I’m keen to dig into more. As I said, we’re actively working on this, so don’t judge.
Interestingly, probably very unsurprisingly, 68% of people are using generative AI in vendor evaluations. Not surprising at all, very efficient way to review stuff, as I have been talking about over and over again on this series and previous series. The danger here is: Is the data that those AI tools are pulling in accurate?. Are you getting screwed over because AI has scraped someone else’s website better than yours?. These are real concerns if you are trying to sell to people in B2B. You will have seen the wave of, you know, “how do we optimize for language models?” Nobody really knows, the SEO industry is panicking. This is a pretty serious issue because, if you go back to the Gartner research that we use a lot as well, through the majority of the sales journey nobody wants to talk to you or your team. That’s the other real challenge, right?. People like me are doing this research, we’re not speaking to you, we’re finding the information we can find. People increasingly, as you probably already know, are not coming to your website. They’re just going to go to Chat GPT or Plexi and be like, “Can you just research outreach tools, give me the top five best?”. They’re just taking what’s been given to them as read. So this is a real challenge—a real challenge for how you go to market, how you structure all your content. The tools and the channels that you guys are using—I’m not saying throw everything in the bin, obviously—but they need to be seriously reconsidered is what I would say. We’re having a lot of conversations about this at the moment.
The second, which I’m amazed that I’m still even having these conversations, is prioritizes mobile first engagement. If you are surprised that people are using their mobile devices first, please go and get a job in another industry because mobile first has been on the agenda for too long to even talk about at this point. If you’re not mobile optimized, what are you even doing, come on? And look, expect real-time responsiveness is the more important one. Where everybody at the moment I feel is missing a bit of a trick is—and I was ranting about this yesterday to one of my team—is ticket systems, slow response times from inquiries, everything’s on email. Where are the real time options to be engaged?. I don’t mean just a crap AI chatbot on your website that can answer like two questions. I mean, how are we enabling providing information from people to people rapidly and asynchronously when they need it? That again is another way that you can get your brand in front of people and deliver a really powerful personalized experience for not much effort, to actually, as noted here, to compress buying cycles. Were I running a large corporate B2B, I would be investing a lot of time there in connecting my human team to my human prospects instead of trying to automate that entire process and just delivering an incredibly subpar, poor, badly optimized, clearly AI-driven sort of layer between me and buyers.
Those three things I thought were really interesting here. I’m having conversations about this now, and I’m keen to have more because this is an area that we spend a lot of time focused on. It’s an area that we all experience as B2B buyers or as consumers, and I think there’s, you know, it’s ripe for improvement. It’s easy to get ahead of the game is the positive side of this. Look, it’s super interesting. As noted here, where it’s possible to do so, obviously it isn’t always, younger buyers like myself and the other under 45s are bypassing formal RFPs and crowdsourcing opinions across professional networks and AI tools. Again, the dark web—not the dark web—dark social, sorry, where people are engaging in private groups more regularly, that is another key area to get into, right? That’s where the influence is deployed and where you can have a voice and deliver a positive brand experience.
Moving on, the structural expansion of buying committees. This all sounds quite boring. As I mentioned, this is a draft to be flow stated up properly. This is a terrible title: Stakeholder proliferation and role ambiguity. What that means in normal people words is more and more stakeholders involved in buying groups. I don’t believe this figure like 30 plus internal stakeholders because that’s obviously directly tied to the size of your business. I can believe the external influencers piece. According to, I think this was a mix of some interviews and some AI-driven desktop research, living the dream and exactly supporting those points I was just making. I know, for example, if you’re dealing with a large corporate like 5, 10 thousand plus people, there may well be 30 plus people involved in a decision even peripherally.
Interestingly, where this becomes a challenge and we deal with this day-to-day right is role duplication is massive. You might look at a company, and I was literally doing this last week, and that company might have like 10 marketing managers, and it’s not really clear exactly what the delineation is between their different roles or remits necessarily. The danger for all of them and in a buying group is that they may have crossover in their remits, which is not helpful to us.
Shadow committees. I don’t know how much truth there is in this one or not, but unofficial advisers influence influencing decisions, shadowy back channel communications. It may well be happening, I don’t know how much influence you can really have on that. I think if you’ve done a good job with your brand, that’s not an issue, and you’re very limited in terms of how you can address that.
The other one I found quite good though, don’t like how it’s characterized here, is temporal misalignment. This refers to members across time zones delaying consensus building. If you like me have been trying to sell stuff or market stuff to B2B brands, you will feel the burn of this. This is obviously a bigger challenge now we’re all sort of working more remotely and in a more connected fashion. The combination of these factors—the generational shift and temporal misalignment—dramatically compresses your window of influence whilst also very unnecessarily stretching out the time to a decision or to a sale. These two things combined along with the role ambiguity just make this job a lot harder.
As noted here, these diverging agendas force marketers to identify and address competing interests simultaneously. That’s not a new thing, but what I find interesting is that a lot of marketers and sales people I talk to still face the same challenges. We have very very broad “motherhoody messaging” up at the very top that does not address competing interests or even different agendas effectively. It just tells you very very basic stuff. Then down at the bottom end you got sales people just plugging away trying to get these deals progressed and qualify people and move things forward. They don’t necessarily even know all of these people or the broader buying group. The traditional challenges I think have just become worse.
Crossborder hierarchy mapping—I’m not going to talk too much about because I don’t think that’s particularly interesting, I think that’s just a given. But what I did want to talk about was these technological and data limitations because this is where things are getting pretty tasty and getting even worse. Here we have fragmented MarTech ecosystems. What I found surprising was that apparently APAC organizations average 50 plus disconnected applications for customer engagement. That causes problems you’re all very familiar with, I’m sure: data silos, identity resolution failures, delays to insights if you even get any insights at all. That all slows down your time to market, time to sale. It’s just dragging horribly on your ability to go to market quickly and effectively. I would love to speak to people to see if this 50 plus applications figure is true because I would say anecdotally from the kind of companies I deal with it easily could be. I find it fascinating how people just buy stuff and expect it to just work together and then you just build this huge pile of technology that just doesn’t work together. All it does is create loads more manual admin. You need more people to manage the tools; your road maps to integrate things just drift endlessly into the future. Everyone sort of forgets that we should actually be using these things to make our lives easier. Big fans of less is more at Flow State, that’s how we like to live.
HubSpot analysis of Singaporean firms reveals 62% of buying intent data never reaches sales teams. I can believe that from my conversations with salespeople because I know that they always end up having to dig up their own information and do most of the job themselves, so that does not surprise me at all.
Finally, the last one I wanted to chat through was AI adoption gaps in stakeholder analysis specifically. Another interesting stat: 68% of buyers are using AI tools, but only 39% of APAC marketers leverage AI for buyer profiling. I think that is because marketers probably know that a lot of the data is not very good. It’s very easy to extract what appears to be a large very useful set of data very quickly. What is very hard to do is actually pan for the flakes of gold in the huge bucket of dirt that you have just captured. My main concern is that the current obsession with AI and its efficiency continues to make everybody focus on the tools and the process, and then the tools in the process become the job.
These issues are very top of mind. For instance, data quality (AI just pulls in whatever it pulls in, very little vetting or QA of the information) and privacy regulations are massive concerns. Also, skills shortages. Marketing and sales have become so fragmented and you have to be across so many different things. Your job’s essentially become like an engineer of various different tools, and you’re constantly having to learn how to do the same things in different ways.
The result, as noted, is a growing detection gap where AI’s enhanced allegedly research outpaces marketers’ identification capabilities. I would actually characterize that the other way round. I would say that marketers’ identification capabilities are completely fine and so are salespeople’s. I think that AI enhanced research is actually letting everyone down here because it makes it look like we’re doing better work when we are in fact not. We’re doing the same work allegedly in a faster time but the actual output is nowhere near the same kind of quality.
I wanted to just give a rapid fire flavor of where our current research is at. As I mentioned, this is a draft, so this is an in-progress document. What I would love is, as I mentioned, let’s chat. Are you seeing these things, what do you think about this? My colleagues and I are reaching out to a few people at the moment, but if you’d like to chat to us, just give us a shout. We’re going to be publishing a version of this white paper, which will summarize this research at some point later this year. We’ll do this when we’re happy that we’ve got a decent body of interviews under our belt and we’ve got a sort of good handle on what we’ve produced and we’ve analyzed it correctly. I hope you found that interesting. Obviously there’s an AI layer here still, there always is now. Let me know if you’d like to chat. You can talk to me, you can talk to my colleague Ross, you can ping us at the Flow State socials. I look forward to hearing from you, and we’ll keep you posted as we keep this research train rolling. I’ll speak to you next time.