#298: Listener Questions Answered Live from Marketing Analytics Summit!

Summary

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Podcast Summary: The Analytics Power Hour

Episode #298: Listener Questions Answered Live from Marketing Analytics Summit!

Date: May 26, 2026
Hosts: Michael Helbling (President, Stacked Analytics), Moe Kiss (Director of Data for Product, Canva), Tim Wilson (Head of Solutions, Facts and Feelings), Val Kroll (Head of Delivery, Facts and Feelings), and live audience
Location: Marketing Analytics Summit, Santa Barbara

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Episode Overview

The Analytics Power Hour hosts a lively, candid, and unscripted live session at the 2026 Marketing Analytics Summit. The team answers listener and audience questions on analytics career challenges, the realities of partnership vs. order taking, the impacts (and ick factors) of AI, how to rebuild trust in data, and the future of analytics teams. This episode is true to the show’s ethos of “closed topic, open forum,” blending irreverence, insight, and honest discussion.

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Key Discussion Points & Insights

1. Becoming a Partner vs. Order Taker with Stakeholders

[03:00 - 08:52]

• Val Kroll shares her take: “If you don’t want to be perceived as the person in the room taking notes, don’t take the notes,” but also values being useful and accountable.
• Moe Kiss argues for genuine curiosity: “Just be curious, go ask them, get inside their heads and start asking, how can I understand?”
• Tim Wilson urges empathy: “It’s being curious and thinking about the pressures that person has in their role, what are they trying to solve?”
• Insight: True partnership comes from proactive curiosity, showing up prepared (using available info before asking for stakeholder time), accountability, and not just defaulting to output-oriented delivery.

Notable Quote:

“I lay awake at night fretting about the industry being, I want to be a partner, but I behave like an order taker.” – Moe Kiss [05:15]

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2. Attitudes Toward AI: Skepticism and Practical Use

[09:03 - 16:42]

• Audience member Michelle Kiss asks: Are you an AI skeptic or a fan? What have you found it genuinely useful for?
• Tim: “I’m a skeptic, but still excited… using AI to prep for prospect conversations, pulling in public info, generating contextual tables to have richer stakeholder conversations.”
• Val: “Excited and terrified… AI makes coding approachable again and helps with admin, code, and writing better Slack messages… but also fear the big shift.”
• Moe: “I’m a skeptic and a fan. Great for debugging code and clarifying thought, but very wary of so-called ‘insight’ generation and unvalidated anomaly detection.”
• Michael & Moe: Conversation veers into new tools (e.g., PRISM Cowork for GA4), emphasizing transparent, repeatable, and organized analysis workflows.

Notable Quote:

“That’s been the most fun one…show up in conversations with something richer than ‘what are your goals?’” – Tim Wilson [11:09]

“I get very, very nervous when I have vibe coded and yes, it brings results out, but I’ve now seen in the wild how that can go awry.” – Moe Kiss [13:00]

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3. The “Ick” Factor with AI Adoption

[16:59 - 25:55]

• Audience member Jen Koons: Which uses of AI “make you feel icky?”
• Mo: Not a fan of meeting note generators—“drives laziness and not paying attention… not telling me what people were actually thinking about.” Also worries about AI-as-training for junior analysts replacing real human collaborative learning.
• Val: Meeting summaries help with ADHD but worries about unvetted, unmodified AI outputs distributed as-is. “The bit that I get stressed about is analysts are like, yes, I can pump out more stuff… I’m never even going to look at it.”
• Michael: Draws a boundary at anthropomorphizing AI (“AI does not have feelings”), warning against shifting human empathy or feedback mechanisms to machines.

Notable Quotes:

“There is a very, very real part of this job that is human and communication and collaborative creativity.” – Mo Kiss [18:05]

“I get sort of this icky feeling…when relating to AI as if it were a person.” – Michael Helbling [22:05]

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4. Personalization & AI Agents for Stakeholders

[23:55 - 26:04]

• The hosts debate the effectiveness and weirdness of using AI to build out stakeholder “personas” for hyper-tailored communications (via uploading transcripts, etc.).
• Consensus: It's only valuable if it forces actual critical thinking about what makes a stakeholder tick—not as a mindless data dump.

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5. If Analytics Teams Could Deliver Only One Thing…

[26:11 - 28:43]

• Joe Domoleski (listener question): If you lost all tech stack except for one deliverable, what would you provide to prove marketing analytics value?
  • Tim: A tight narrative on an A/B test—"format can be anything, but a compelling results presentation."
  • Mo: A well-told data story, perhaps with slides; narrative and communication matter more than the form factor.
  • Val: Would choose a Marketing Mix Model (MMM), but the delivery/communication is more crucial than the analytics output itself.

Notable Quote:

“You can conduct a great MMM, you can build it, and then you can deliver it horribly—it doesn’t show anything.” – Moe Kiss [28:08]

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6. Rebuilding Trust in Data Post-Issues

[28:58 - 34:46]

• Audience Q: How do you regain trust after prior data issues—especially when both client stakeholders and internal team keep talking about “discrepancies” and “trust”?
• Michael (tongue-in-cheek): "Stop inviting the negative internal person to meetings."
• Mo/Tim/Val: Move away from the “is the data right?” debate.
  • Redirect focus to the key business decisions at hand.
  • Address uncertainty by aligning on “what certainty do we truly need?”
  • Build advocates by having deeper one-on-one conversations, especially with “doubters.”
• Val: Sometimes the best approach is personalized, strategic relationship-building with stakeholders, outside of group meetings.

Notable Quote:

“Once you’re having the discussion about is the data right, you’re losing.” – Moe Kiss [31:10]

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7. The AI Efficiency Push: Tips and Cynicism

[34:46 - 38:11]

• Audience Q: "I’m being asked to show AI-driven efficiency improvements as a marketing analyst—ideas?"
• Mo: Expresses skepticism at the “efficiency is all that matters” framing—more dashboards isn’t always better; friction is sometimes valuable.
• Val: Sometimes you just “suck it up”—report the required efficiency savings for leadership, but focus the team's real work on quality and long-term value.

Notable Quote:

“Sometimes the signal you send up doesn’t have to be the same as the signal you send down… don’t get caught.” – Val Kroll [37:46]

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8. The Future Shape of Analytics Teams in the Age of AI

[38:48 - 47:39]

• Host & audience Q: How will the structure of analytics teams change as AI becomes more prevalent?
• Mo: Fundamental analyst skills—curiosity and business acumen—should remain core; AI helps with “unsexy” prep and retrospection, not core partnership or context.
• Val: Teams may split into ever-more technical specialists and business-facing generalists; current stage is messy with “shower-thought” docs everywhere.
• Tim: AI may push us to build new “muscles”—especially empathy through improving communication (thanks to “prompting” analogies).
• Michael: Predicts flattening—specialization retracts temporarily as AI enables more full-stack analytics generalists, later renewed need for experts will reemerge.

Notable Quotes:

“There’s value in the friction. Which does not make me an AI skeptic. I just think the efficiency part is really, really tricky.” – Mo Kiss [36:51]

“…departments flatten out a little bit… AI organizations will compress some roles. Then, organizations will realize they need specialization on the fringes and bring it back in.” – Michael Helbling [46:24]

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9. What Analyst Skills Will AI Take Over First?

[47:52 - 49:46]

• Jim Stern’s question: What are the first three skills analysts have mastered that will be taken over by AI?
  • Hosts: Coding routine SQL; Debugging code; Automated QA/validation steps.
  • Consensus: AI will quickly supplement or automate mechanical/data hygiene tasks; higher-order synthesis, critical thinking, and strategic partnership remain much harder to automate.

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Most Memorable Moments & Notable Quotes

The “Order Taker” Rant:

“I lay awake at night fretting about the industry being, I want to be a partner, but I behave like an order taker.” – Moe Kiss [05:15]

AI, Empathy, and Anthropomorphism:

“AI does not have feelings... as humans, we anthropomorphize things really a lot. I think we very easily buy into this idea that I make my AI feel bad if I yell at it or I make it feel good if I tell it does a good job, when in reality it feels nothing.” – Michael Helbling [22:05]

The Messy Transition:

“I am sick of reading very shitty long documents which were based on someone’s shower thought... But I am optimistic that with time it will help us think better about what we do.” – Val Kroll [39:45]

Efficiency Reporting Real Talk:

“Sometimes the signal you send up doesn’t have to be the same as the signal you send down… but don’t get caught.” – Val Kroll [37:59]

On Data Trust:

“Once you’re having the discussion about is the data right, you’re losing.” – Moe Kiss [31:10]

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Key Timestamps

• 03:02 – How to be a partner not an order taker
• 09:03 – AI: Use-cases, skepticism, and optimism
• 16:59 – What uses of AI in analytics make the panel uncomfortable?
• 26:13 – The one deliverable you’d use to prove analytics’ value
• 28:58 – How to rebuild trust with stakeholders after data issues
• 34:46 – Tracking/reporting AI-driven efficiency in a marketing analyst role
• 38:48 – The evolving future of analytics teams with AI as core
• 47:52 – Analyst skills being automated by AI first

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Tone & Style

• Conversational, honest, and often irreverent
• Willing to call “bullshit” on industry trends and challenge each other live
• Practical, experience-based advice—sometimes with snark, always with candor

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Conclusion

This episode is a masterclass in real-world analytics: a blend of industry wisdom, skepticism, wit, and community spirit. Listeners will leave with a better sense of how to tackle being a partner (not just an order taker), adopt AI usefully (while keeping their soul), re-earn trust in data, manage tricky company mandates, and anticipate the evolving structure of analytics teams.

Final Advice:
No matter the question or challenge you're currently solving—keep analyzing.

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Transcript

[Tim Wilson]

Foreign.

[Podcast Host]

Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

[Event Host]

The Marketing Analytics Summit is pleased to present these four amazing podcasters. We have Michael, the self effacing everyman consultant who knows far more than he lets on. We have Mo, who flew all the way in from Sydney, Australia. She's the determined, driven practitioner, the one to stand up and say, well, that's all very well and good, but how do we actually make it work? There's Val, who co founded the consultancy Facts and Feelings, because we have strong feelings about our facts and no facts about our feelings. And there's this guy named Tim. Ladies and gentlemen, take it away.

[Michael Helbling]

Hi everyone. Welcome to the Analytics Power hour. This episode 298 and we are recording live at Marketing Analytics Summit in Beautiful Santa Barbara. 21 years ago, my very first analytics conference was right here in this city at this conference, formerly known as Emetrix. Similar to today, it was a conference full of smart, engaging and passionate people learning together about how to solve the problems they were facing day to day in the analytics industry. A lot's changed. A lot has changed. But persistent through all of that is this beautiful analytics community and the shared learning. This podcast was actually created to encourage and celebrate. So to that end, I have my co hosts with me and we have put out a survey to gather your questions we've been asking you during the conference. And so we have a number of them to get through and we'll do our best to answer them from some of the perspectives we bring to the table in our various roles. So let me introduce them.

[Mo Kiss]

Mo Kiss.

[Val Kroll]

Hi, folks.

[Michael Helbling]

Director of data for product at Canva. And of course, Tim Wilson, head of solutions at Facts and Feelings. Hello. And Val Kroll, head of delivery at Facts and Feelings.

[Tim Wilson]

Hello. Hello.

[Michael Helbling]

And I'm Michael Helbling, the president of Stacked Analytics.

[Mo Kiss]

Okay.

[Michael Helbling]

What a privilege to be here with all of you in person. It's been a wonderful couple of days, so let's just dive right into it. So we've got a question. This one came from James from Nemorus Children's Health. What's the best approach to establishing yourself as a partner and not an order taker? When working with stakeholders,

[Tim Wilson]

Tim is the one who has the most trouble with the uncomfortable pause, so that's why he looks at us.

[Val Kroll]

Oh, I assumed that was your cue. Anyway, we were having a conversation earlier about taking notes over lunch, and if you don't want to be perceived to be the person in the room taking notes, then don't take the Notes. And I think it's really jarred with me because I have this belief, I mean, I always know that I come back to everything that Cassie Kazargoff says about just being useful. And I sometimes find I'm still like, I might be the most senior person in the room. And sometimes I do still take notes. Part of that's my brain, but part of that is also that I want to be useful and the people in the room are normally smarter than I am and I have much more interesting things to say. So I actually kind of struggle a little bit with the question in and of itself. I think one of the things that maybe means that it's okay to still take notes, but is also adding your voice to the conversation. I think, I don't know, I just, again, we were chatting about this at lunch. When you don't contribute to the conversation, when you're not willing to be accountable for what you say, I think that's when you kind of, you're not the partner, so to speak.

[Mo Kiss]

I mean, I think it's, I agree. I think it's, don't be an order taker. I think to me it is. And there have been a lot of discussions at this conference. A lot of the discussion in the industry writ large around how are we using AI? And there's to be curmudgeonly and a little cranky about it. A lot of it is how do I take more orders? How do I take more orders and produce more output? And that misses, to me, the fundamental part of becoming more of a partner is to step into their shoes, ask the questions, write it down, be genuinely curious. Don't be trying to get to how quickly can I turn this into a ticket? And I think that has been. AI has nothing to do with that. That has been a 20 year problem in our industry. When we sit around and wring our hands and gnash our teeth about why aren't we brought in, we produced the next dashboard like they asked us to, we gave them recommendations. But did you ever actually sit down up front and say, I just, I'm an analyst, I'm curious, I want to understand what you're grappling with. And there have been, even at this conference, there have been some discussions around that. But I, I lay awake at night fretting about the industry being, I want to be a partner, but I behave like an order taker. And it's like, it's simple. Just be curious, go ask them, get inside their heads and start asking them, how can I understand?

[Val Kroll]

I Kind of want to call a bit of bullshit on it though, because I do.

[Mo Kiss]

On brand,

[Val Kroll]

I get what you're saying. And I hadn't really thought about AI driving more of the order taking. I actually feel like my experience might be a little bit different. I think the thing that I just keep observing is data folks don't want to be accountable. I had this conversation at the back of the room earlier. It's like they want to be like, here's the numbers, don't look at me. I don't want to be on the hook for the recommendation that I'm making. And I don't think that's an AI thing. I think that's just a. Maybe it's a 20 year old industry thing.

[Mo Kiss]

I think it is. I mean, I actually agree that it's like we make recommendations and they're not following them. And a lot of times, like, where did this start? Well, we asked them what their business question was. If you're asking them, even if you're saying I'm asking what the business question is, there is a human element of saying, I am your partner, I am in this with you together. So I'm just pushing for moving upstream and not jumping to the data and jumping to the solutioning and jumping to did I produce the polished report that made a recommendation? And then I'm upset. So you gonna fix that?

[Tim Wilson]

Yeah, I'm just kidding. No pressure, I would say, to get started with that. It's not even always about anticipating all of their needs. I think it is being curious and just thinking about the motivations and the stresses that that person has in their role, like what pressures are they facing. And I think that this has come up a couple of times too about how do we de risk the decision that's upcoming for them or where should they spend that next dol and do a little bit of prep work before you walk into that room to show that you're trying to empathize with what they're dealing with. Because their job is hard too. It's very different than yours. But starting from that place, I think is a good way to kind of say, like, we're in this together, we're side by side, not sitting across the table. And I'm just gonna push something on you at some point.

[Val Kroll]

But I think the difference there, right, is you talked about preparation and I think a lot of the curiosity can come without necessarily absorbing stakeholder time. And I worry sometimes that the push is like, oh, well, I haven't got enough conte time poor. So you Know that sort of thing where it's. I actually think there's a lot of personal responsibility that data folks need to take to be like, I have enough information available to me through our various various slack channels, emails, et cetera to have some of that curiosity before even asking the question of a stakeholder.

[Mo Kiss]

100%, 1,000%. I see your 100% and raise it. But I think if you're thinking about it, you should be saying this is my understanding, but here's something that I can't. I listened to our quarterly conference call and, and there was something that doesn't make sense to me. I tried to figure it out. It doesn't. I think you, marketing manager, might help me explain this because I can't square that circle. So yes, showing up in that. Certainly working with analysts who say, well, what are my list of discovery questions? What is your most challenging business problem? If that's the way you show up, they're gonna be like, I don't have

[Val Kroll]

to admit it, 25 year old data scientists lately. Cause that's kind of how they show up sometimes. No age. No age.

[Michael Helbling]

All right, we're going to move to our next question.

[Tim Wilson]

This is the whole episode.

[Michael Helbling]

Sorry, this is coming from a member who's here in the audience. Michelle Kiss.

[Mo Kiss]

Any relation?

[Val Kroll]

None. The same person.

[Audience Member]

Complete coincidence. Yes. M confuses everybody. Okay, so first of all, my question has two parts. There's a pre question that is required for setting the context.

[Val Kroll]

Yep.

[Mo Kiss]

They're related

[Audience Member]

so that we can interpret your answer. Okay, so first of all, the pre question is where do you guys stand on AI? Are you a skeptic or a fan? Okay, so that's the context. Then what things have you personally found that it is useful for and reliable for? Has to be both.

[Tim Wilson]

Okay, I'll go first. So I would say that I'm a skeptic, but that's just kind of like my nature. I don't think there's anything new or special about that. That's just kind of who I am as a person. But I'm excited at the same time and I think I've been inspired by a lot of the conversations we've been having over the past couple days that have gotten my wheels turning about some different things that I can try when I get back to my desk. So I would say more on the skeptic, but still excited, still optimistic. I don't think, you know, world's ending just yet. One of the things that I have been playing around this is recency bias. But just thinking about how do I start at like day 60, day 90 with where I'm at, with an understanding of the business context for a potential prospect conversation. And so we had been doing a lot of process facts and feelings very manually to kind of go through and listen to like most recent earnings calls or pulling out press releases or seeing what was public just to kind of understand what is the context, what is the competitive nature, where are they competing and using AI and building out some gems to build that out for me and so asking it to give me some tables and comparisons of where do they compete on what messages? Is this about pricing? Is this about quality? And so really trying to figure out like, how do I get into that context so that I'm not asking like question number one, what are your goals? It's like, let me start with something because then I'm going to be able to ask much richer, deeper conversations. And so again, recency bias. But that's been the most fun one just to kind of understand outside of an analysis, what's something I can do to show up in those conversations to kind of position myself to be a better partner.

[Val Kroll]

Mine are not sexy. Like, I don't.

[Tim Wilson]

That wasn't sexy. Build me a table of your competitors.

[Val Kroll]

Well, it depends what you think is sexy. Yeah, I mean I do a lot of leadershipy things and those things tend to be quite admin y. I do think the one. Oh, I forgot your first part of the question. I'm excited and terrified. I would say I'm. I don't. Fan feels like a strong word. I do like a lot of the potential and the personal gains and the way that it's changed how I work. But I have this like, level of fear which I think is pretty rational and normal. It's a pretty big shift. And yeah, a lot of the ways I use it are not sexy. But I do think the thing that has been really nice for me personally is feeling like code is not so far away. It's been a couple years since I wrote code very regularly and there are things that now I feel much more comfortable. I'll go do a quick here's the query. I want to write vibe code something. And I also know enough to know if it's not correct, which is important, but it makes it feel more approachable to a skill that's pretty rusty. The rest of the stuff is making my snarky comments in Slack seem less snarky, like meeting summaries.

[Mo Kiss]

It's not sexy, so I'll say I'm a skeptic. And a fan. So I'll punt on the first question my skepticism comes from I been developing this observation that I feel like we are as analysts we are often saying this is the thing the tool can do. This is my hammer. So now I'm going to go figure out what the nail is and I'm going to tell myself that the nail is going to solve a problem that has nothing to do with the technology. So I have found it to be very useful on debugging code. Kind of piggybacking off of what you said because coming from having written code debugging it, it is very, very useful. I get very very nervous when I have vibe coded and yes it brings results out, but I've now seen in the wild how that can go awry. I actually find that it's very useful just from a thought clarity and I think that hasn't changed. That probably took me three times listening to Jim Stern give various presentations about writing prompts or to take and it was first time I heard it. Now kind of everybody's saying it of some of the keys with writing prompts but prompts I think by writing. So saying this forces me to organize my thoughts and then I'm going to trust but verify what comes back. That is much more on the help me clarify my thinking. Because if you're helping me clarify my thinking just like a human being who I think no, that's wrong. That's bullshit. You can challenge me, but you may be wrong. I am not. Which wasn't the where is it not useful but anytime it's like it's going to basically do glorified anomaly detection and spit out insights. Yeah, I'll go to the mat for a while on that and I'm pretty triggered with various claims to have it do that. Michael, what happens after you finish a GA4 analysis?

[Michael Helbling]

Traditionally I guess I paste screenshots into a doc, rename it final final. You know, do not change final, lose the source query and then wait for somebody to ask can we break this down by campaign?

[Mo Kiss]

Oh, terrifying. Please stop.

[Michael Helbling]

I would love to emotionally and professionally.

[Mo Kiss]

Well that's why ask why AI just released the Prism Claude cowork connector. It brings the whole Prism Brain to your GA4 BigQuery data. Ooh, the whole brain analytics agent. Harness skills, memory engine, the works.

[Michael Helbling]

Wow.

[Mo Kiss]

So cowork doesn't just answer questions, it

[Michael Helbling]

remembers context, uses repeatable skills, keeps analysis organized.

[Mo Kiss]

Exactly. You're picking it up. Your cowork based analyses are accessible in PRISM Organized, traceable, auditable and Ready to use with your other data sets.

[Michael Helbling]

I love that because currently my audit trail is mostly like, oh, I know I had a reason for doing that.

[Mo Kiss]

I can't remember what it is that checks out. The connector also ships with ready to run funnel and cohort skills right out of the box.

[Michael Helbling]

So I can ask for retention by acquisition channel as not immediately enter a fugue state.

[Mo Kiss]

Right. And every analysis becomes a shareable page. Prism Auto generates a dashboard page right? From Cowork.

[Michael Helbling]

Oh, so the answer doesn't die in a chat thread.

[Mo Kiss]

That's right. It lives as a reusable and shareable analysis.

[Michael Helbling]

Well, that's very rude to my old workflow, but fair.

[Mo Kiss]

We'll go to Ask Y AI. That's Ask TheLettery AI and sign up for the wait list.

[Michael Helbling]

Yeah. And use Use code aph and that'll get you pushed to the top of that waitlist.

[Mo Kiss]

Ask Y AI code APH.

[Michael Helbling]

I like it.

[Mo Kiss]

Cowork.

[Michael Helbling]

It's GA4 analysis, but with receipts,

[Mo Kiss]

which was not part of the question.

[Tim Wilson]

We're only on our second question. You can't get triggered yet.

[Val Kroll]

No, I welcome it.

[Mo Kiss]

I was triggered 36 hours ago. I think it's okay. I go through life triggered.

[Michael Helbling]

All right, we have another question from someone here in the audience, so I'll hand it off to Jen Koons.

[Tim Wilson]

It actually follows up with what Tim was just saying, perhaps a bit. Do any of the ways we promote and use AI in the industry make you feel icky? Do you have a threshold of lines that you don't want to cross when it comes to AI.

[Mo Kiss]

Can I add. I'll add a new one to that. I am not a fan of the note takers in meetings and doing the summaries, which I know is so super controversial just inside and because to me, I've watched and I've watched this time and time again how much that drives laziness and not paying attention in the meeting and not editorializing. And it is flat summaries. I've worked with some clients where they have literally said, oh, you weren't at the meeting. We recorded it. Here's the gemiini summary. And it's just not useful because it's not telling me what the human beings in the room are. Were thinking about. Let me throw one other. I'll be quick. Because, Jim, when you did your closing note keynote yesterday, which was very good, not going to be too useful for people who are listening to this, but there was a lot of talk about using AI to ramp up junior analysts and build Lots of different. Use different tools that kind of let them kind of do self study. And I wound up wondering about where do we teach the junior analysts how to actually relate with people and have the creative collaborative process? And so there's something there too that makes me nervous that we're at a conference right now. Like, are we on some logical trajectory where we think we're all just going to sit at home and just have AI do everything that needs to happen? There is a very, very real part of this job that is human and communication and collaborative creativity. And I get very nervous that people are not recognizing the value of that and trying to have AI replace it.

[Val Kroll]

That was really deep. I was going to talk about meeting notes.

[Mo Kiss]

Go for it. Do I need to move away?

[Val Kroll]

No, no. But so for me, I actually find the meeting notes summaries incredibly useful. And the biggest thing for me is like, as someone who self declared, has

[Mo Kiss]

ADHD and struggles with assignment, can you square the meeting notes with the taking notes by hand?

[Val Kroll]

I do. So I do still often also take notes. But the taking notes for me is me. Number one, it helps me pay attention in the meeting. And number two, it helps me retain the information. Like if we're talking about especially something complex, I won't necessarily fully hear it. And then the next day. I sometimes do look at my own notes. I won't necessarily look at the AI notes, but what I find useful is the next steps. There is always like, no. When you are trying to corral like 20 people, you can be like,

[Audience Member]

what

[Val Kroll]

is the big deal? Why is that the big deal?

[Tim Wilson]

Tim just fell off the stage. For those of you listening, that was

[Mo Kiss]

a Red Fox impersonation. For anyone who.

[Val Kroll]

It's like, this person's gonna follow up with this. By then this person's gonna follow up with it.

[Mo Kiss]

And you can, because it's actually fucking terrible at that. It just goes through and says, this is what it was. And you need the human editorial person saying, what are the real next steps? That's where it's seen it when he tries to go through a discussion.

[Val Kroll]

Back to the question though, of what makes me feel ick. What makes me feel ick is things being shared that have not been properly vetted and QA'd. And meeting notes fall into that category just as much as analysis or a write up does. That's the bit that I get stressed about, is that analysts are like, yes, I can pump out more stuff. I'm going to automate this report. I'll send it out. I'm never Even going to look at it. And I'm like, ooh, that feels uncomfortable.

[Mo Kiss]

But isn't the meeting notes is asking people to do a lot? Because they're like, I got to get the meeting notes out promptly. And it takes an enormous amount of diligence to say, I am truly going to read through these and modify and write my own little summary and write. So it's like you can paint the picture, but watching what actually happens with the people I've worked with, all of a sudden I'm like, oh, this was just barfed out. And I'm sure they scanned through it and I'm sure they told themselves, yeah, that seems about right.

[Val Kroll]

To be fair, I just take the. Like, here are the action items. I don't send the whole summary. Is that worse or better?

[Mo Kiss]

Well, if you take them and you say, yeah, that looks about right. I think that's a problem because I think there is much more. Often there is. The person who's sending those out should have a responsibility to say, these are the things that really need to happen. There's a level of prioritization and wording and body language and. And adding net new stuff that. I know that Joe said that Joe was gonna do this, but the reality is, I know in our organization that Joe is gonna have Mary work with him on this. And putting that sort. There is something in that maybe I'll get off that.

[Val Kroll]

There's a lot of nods, so I'm interested to hear more. Tim, Normally I don't have this kind of live feedback that suggests maybe you're

[Mo Kiss]

right, that they're agreeing with me.

[Michael Helbling]

I think I'd go in a little different direction, which is I get sort of this icky feeling or there's a threshold I don't want to cross with AI in terms of relating to it. And what I mean by that is some of the LLM big companies put out research where they kind of take what the LLM is doing and equate it to emotion and telling you that basically if you behave around your AI a certain way, it may actually impact its performance. And that's sort of what they're doing. And I think it's a really dangerous thing. And I think it's also tricky to talk about because I don't want to advocate for being mean to your AI or whatever, but as humans, we anthropomorphize things really a lot. And so I think we very easily buy into this idea that I make my AI feel bad if I yell at it or I make it Feel good if I tell it does a good job, when in reality it feels nothing. And most importantly, is human to human interactions. I modify them a great deal based on what I know of that person and the empathy and the intuition I'm getting from that conversation. So if someone's struggling, I modify empathy and modifying.

[Tim Wilson]

Tim's gonna need a definition.

[Mo Kiss]

No idea what you're talking about.

[Michael Helbling]

Hang in there, hang in there. And so you adjust to that person. Like if you're giving feedback, for instance. Whereas if I'm giving feedback to an AI, I wanna be as direct and succinct as possible without having to kind of couch it in a phraseology or terminology that keeps it secure in its own quote, unquote emotions which are not real. And so for me, that's sort of a weird line that I think I'd love for us to avoid as we approach AGI.

[Val Kroll]

Can I ask a crowd question?

[Mo Kiss]

Only if you can figure out how to get a mic to them so that.

[Val Kroll]

No, I was going to make you count the hands. A rough estimate. Well, if you each take one and we take the average of each of your guesstimates, maybe we'll have a decent score. Okay. Creating an agent based on your stakeholder one, stakeholder two, stakeholder three, so that you can tailor your comms and, like, have a Persona built out for each of like. I want to know how we feel about the ick factor of like, is that icky or is it thoughtful? Because anyway, I can finish my thoughts afterwards.

[Mo Kiss]

Sam Bert, would you like to raise your hand to that question? There was a whole session on that,

[Michael Helbling]

so here's my take on that, because I learned about that yesterday in a session, and I actually really quite liked it because I look at AI as a tool to take information and position it in the best possible way for the audience that you're presenting it to. Much like you would present maybe a slide deck to one person and a narrative to another based on how they consume or prefer data or information to be consumed.

[Mo Kiss]

Two notes.

[Val Kroll]

Just to be clear, I'm not picking on Sam. That was very Persona based. I'm talking about someone in your team creates one that's like, this is Mo. This is how she receives information. Very personalized. I think that's a bit different.

[Mo Kiss]

And I think there's two aspects, I think Sam's and yours. One, I think if it actually forces the person who's creating it, this is where we. To actually really think about. Like, to create it, you have to think about, what do I need to what do I know about mo? What have I seen about Mo? Can I ask Mo something? So that's like.

[Val Kroll]

But do you think that they would. Or do you think they would just be like, I'm going to upload 50,000 conversations, a bunch of zoom transcripts, whatever, of interactions with this person, and you tell me what you think they're going to like?

[Mo Kiss]

I think that's going to be less effective.

[Val Kroll]

Yeah, it probably would be.

[Mo Kiss]

And then the second part of that, I completely lost what my other thought was. So on brand.

[Michael Helbling]

Well, a lot of these questions are tailored around a bunch of information I uploaded about you. 3. No, I'm just kidding.

[Tim Wilson]

Yikes.

[Michael Helbling]

Okay, we have another question coming from someone who's unfortunately not here, Joe Domoleski. If you were stripped of your fancy tech stack and could only provide one specific deliverable to a stakeholder to prove the value of marketing analytics, what would it be? Live dashboard, a PDF report, a slide deck, a meeting with actionable insights, et cetera, what would you do?

[Tim Wilson]

So one specific deliverable to prove the value of marketing analytics. Just to clarify, that's what it says.

[Mo Kiss]

Yeah.

[Tim Wilson]

Okay. The results of an A B test. So format can be anything. I mean, well, he said deliverable. Okay, so I would do a presentation, tight narrative results of an A B test. That would be my. That's my answer. No explanation.

[Mo Kiss]

I think might not be a prove, but might be to convince or define. I would probably go to some sort of compelling story that I was comfortable. I might have pulled data from various sources. I might have run an A B test, but I would actually tell a really strong narrative. And maybe with slides, maybe not. I think that would actually be more convincing. Like, why does it's a deliverable?

[Tim Wilson]

That's your constraint.

[Val Kroll]

But see, okay, this is the difference. When I heard deliverable, I was like, it can be anything. And it sounds like you've both interpreted that in like agency consulting land. Very different to me because I would have said if I could pick anything, it would be an mmm. Like I can talk about that for weeks and months. I mean, at some point it becomes stale, but it would probably be an mmm. Like, I'd love to go through an experimentation tool, but I feel like that maybe is not in the spirit of the question.

[Mo Kiss]

I don't know what. Yeah. What is a deliverable? We could get.

[Michael Helbling]

Yeah, that was not the question.

[Tim Wilson]

Taking notes. What is the deliverable?

[Mo Kiss]

Deliverable is. So now I found sound like consultant, but I mean, on an MMM being super compelling and I'm sitting like 15ft from Jim Ditolio. So. And having heard him talk like, you can. You can conduct a great. Mmm. You can build it and then you can deliver it horribly and doesn't show anything, or you can communicate it really, really effectively. So it is kind of what is the deliverable and how effectively is it created and delivered?

[Val Kroll]

Or we could just combine all three and then we'd, like, be winning. You didn't answer yourself.

[Michael Helbling]

I'm going to ask the next question from a member of our audience, Bryce Preslika.

[Audience Member]

All right, so I have a client that had some major data issues previously. Once we've gone onto the project, we. We've cleaned things up. We're in a much better state now. The problem is one of the clients, pocs, continues to act as though we have unreliable data. And even worse, we have a member of our team that continues to use words like discrepancy and lack of trust and keeps using words that don't really help us accurately convey that it's reliable now. So I've sent memos, I've tried to coach them to not use certain words. But with both an internal team and a client that don't trust data, that is in a much better spot now, how would you go about trying to

[Tim Wilson]

regain trust in the role of the client you're supporting? Are they on the business side or are they in an analytics role?

[Audience Member]

They're on the business side.

[Mo Kiss]

Well, now you have to provide an answer because the.

[Tim Wilson]

Yeah, it was just clarity.

[Mo Kiss]

Mr. Wilson.

[Michael Helbling]

Here. I'll kick us off. There's no time for hash measures. Jeez, we're in a world of AI now and we need to move quick. First thing first, stop inviting the internal person to the meetings so they can't screw you up. Second, take charge of those meetings and tell the client that you know what you're talking about and it's time to make decisions and get out the pot. What are they afraid of? No, I don't know if I could pull that one off, but, you know, start to form the communication and put it into the positive realm so you get past that moment.

[Mo Kiss]

I mean, this is going to sound easier than it is in practice, but to me, one of the things that AI has not helped, we have struggled with for 25 years is businesses that are looking for certainty and precision when there is actually they are operating under conditions of uncertainty. And Jen Koontz's presentation yesterday, it's like people think that, oh, the data was never complete, the data was never perfect. It's really no better, no worse. We can always point to stuff that's not working. So that. So to me, once you're having the discussion about is the data right, you're losing if you're using discrepancy, if it's, you know what, hey, can we reset? Can we really nail down the one or the two or the three biggest decisions you're trying to make? Don't worry about the data, forget about the data. Yeah, it's going to be involved at some point. I think a lot of times what happens if you can really get them saying, what I really want to know is, is meta delivering results? And they're like, can't you keep crunching the data from meta? But I know there are gaps. Instead you may say, really let me talk to you about what a geo lift test is. So it kind of goes back to that order taker versus partner and it's tough. They're still working with you so they have some level of trust. But I think we wind up fighting the fighting on the wrong ground. We're on the ground of like, no, but the data is good enough. Well, no, but this and go. I know it's an asterisk and don't use this word. It's like instead of like we so quickly lose sight of. It's such a tangible thing to point to that the data has a problem and we forget to say what do we really want to know? And try to elevate that conversation. Often I think it's like that's actually not the right data set for it anyway. We keep chasing the wrong data set. To most answer that,

[Tim Wilson]

I like that a lot. And the one point that you mentioned that I just expand upon is I think the really rooting yourself in like what level of certainty is really required to answer this question. How much time do I have to turn this around? Is it something you need tomorrow? Do I have a couple months before you're going to make this call? And really just trying to line up the various different methodologies that are at your disposal to bring that to bear, to bring the right evidence to that question. You know, Paula's presentation, like merging those different sources of evidence to, to really kind of paint that picture, I think can shake people loose from focusing on what percentage of people are opting out from whatever cookie banners and things like that. So I think just not trying to play on that move the battle, I guess not play on that turf and kind of say, hey, let's just focus on like Tim was saying, those top questions that you're really grappling with. And let's think about how much business risk would we really be introducing if it wouldn't be perfect. So like, let's think about the various ways we can kind of go about it. And sometimes just injecting a little creativity, if you will, into the methodologies, into that conversation can kind of get them excited about some different ways. Maybe it's a user test, maybe it's, you know, it's not going to be something we're going to look for in a table as an example.

[Mo Kiss]

So be sure to record the meeting and send them the meeting summary.

[Tim Wilson]

Oh, for fuck's sake.

[Val Kroll]

I actually stayed very quiet on that one because this is an area I think Tim generally is normally right in.

[Mo Kiss]

Wait, why is everybody shaking their head now?

[Val Kroll]

But I do sometimes and I'm obviously in house, so it's quite different. I do find if I have a stakeholder like that, I will almost always have one on one time with them. And I'll normally have some questions around, like what would have to be true for us to use this data source or are there other data sources that we could use to supplement the information? So you'd be comfortable enough making a decision with what we have, like kind of trying to tackle it almost one on one. Because especially as soon as you get in a meeting with a bunch of people and everyone's like, oh, well, this data's wrong, so, you know, we're all stuck here. And then everyone whinges about it for the rest of the meeting like it stops being productive. And so I would almost be trying to like really partner with that, like the biggest doubter of the group especially, and build up that relationship and really work with them on some of the methods that Tim and Val are talking about here so that then they can also become your advocate, hopefully over time.

[Michael Helbling]

Excellent. All right, here's another question we got. Everyone at my company is being tasked with showing specific efficiency improvements they've delivered using AI. I'm an analyst who supports marketing. What are some ideas you have that I could do for that? Don't make me do the Hollywood Squares one again.

[Mo Kiss]

I just feel like this. I'm starting to feel like we've beaten this particular horse a lot.

[Audience Member]

What?

[Val Kroll]

You mean AI or efficiencies? Well,

[Mo Kiss]

yeah, I mean the AI piece and the. I guess my qualm with the efficiencies and I totally recognize the person asking the question, they don't have control over that. That's being pushed down. And it's an organizational challenge. But, but efficiency is like Producing more with the same or producing more with less or producing the same with less, whatever. And it's like, more what? And moving down the path of saying, well, we're producing more dashboards faster, we're responding to requests faster, and everybody feels resource constrained. And like, we can't hire, we can't double our headcount, so AI is going to help us keep it fixed. And I think this is where I'm feeling like I'm beating a dead horse. And I am the dead horse. I don't know that that has this idea that if we. It's volume. Volume is the issue, that we just need to generate more. But that volume of whatever we're producing is going to someone like, there's. There's value in the friction. Which does not make me an AI skeptic. I just think the efficiency part is really, really tricky. I think I've seen that in articles that. That's happening across. A lot of companies are saying, we're just trying to make this as a. Actually, Jim talking on. I think day one was showing like, this is just a chase for headcount reduction and something's not right there.

[Michael Helbling]

So you managed to answer that without giving this poor person any tips on

[Mo Kiss]

how to do their job.

[Tim Wilson]

They should have been at Marketing Analytics.

[Michael Helbling]

Yeah, that's right. There's a lot of tips there.

[Val Kroll]

Can I jump in though? Helbs, I learned this recently and I'm still kind of reconciling it. So I'm going to obviously tell a bunch of people the exact advice I got and we can all try it out. Report back to me. I got some advice recently. Essentially, sometimes you just suck it up and you do it. And a lot of conversations around AI at the moment are about productivity gains, not quality gains. And I find that just. It gives me the ick. However, there comes a point where sometimes you're in a position and you just suck it up and you do it and you go, oh my God, my team saved five hours a week. Here's all the dot points. Send it up to leadership. Move on with your life. And then you get your team together and say, okay, let's have a conversation about how we improve the quality of our work. That's what matters here. So sometimes the signal you send up doesn't have to be the same as the signal you send down, but shoot, you better be smart about how you do it and don't get caught.

[Mo Kiss]

But you're setting yourself up to actually send a better signal up down the road, right? So I Love that from doing both.

[Val Kroll]

Like obviously Tim, that was the grand plan.

[Mo Kiss]

Check the box. But then separately say but here's the real value we got and yeah, yeah.

[Michael Helbling]

And we've been battling a problem like this since forever. I mean there used to be a time in our industry when we thought if we collected every single piece of data we would somehow magically know more and sort of this redux of a single similar way of thinking. And so we have to kind of manage through it effectively. All right, we've got another question and this one comes from also someone in the audience, Sam Burge.

[Mo Kiss]

I think Michael surprised himself and didn't realize he should have already been on the move.

[Tim Wilson]

So we'll fix that in post.

[Val Kroll]

How do you think analytics teams will look differently from today with AI in the future?

[Mo Kiss]

I mean, I hope that on the one hand they are still smart, curious, business thinking, technical, kind of have a broad set of skills. So I don't think the teams will necessarily look a whole lot different how they work. They're obviously going to get, I guess efficiencies and changing ways of working and become more prepared. But that's a tough one. Why did I jump in and answer that? I don't think I have a good. Let's just buying time for one of you guys to say something smart.

[Val Kroll]

I don't know. Okay, this is my guesstimate and this is what I'm observing within my own team. It seems like folks are kind of splitting a little bit. There are the folks that are going much more technical. I would almost to some degree even a little bit more specialized. And then there are the folks that it feels like the chasm between the technical and more the business facing generalist is getting a little bit wider. I don't necessarily see that as a bad thing. I think when folks have a particular strength in one direction, we should encourage that and that's a great thing. I do think it makes it harder for teams and how they work together and and all of that sort of stuff. I had a massive rant a little earlier today because I am sick of reading very shitty long documents which were based on someone's shower. Thought that never should have left the shower, but now is in a 4,000 word document and being flung around our organization. And then someone else comes in and writes 50 comments on it. And then and someone's like over to you now, Mo. And I'm like, what do you want me to do with this? I am now doing all of the thinking work of having to read it and it's pretty watery garbage having to respond. Also trying to figure out what you want me to do with this because it was a shower thought bubble and what my hope of where we get to is that it actually helps us think more critically, not less. I think we're in the shitty stage right now. I'm optimistic. I'm going to bitch about the shitty stage we're in right now because it is shit reading all these documents. But I am optimistic that with time it will help us think better about what we do. I don't know. You're creating a hypothesis instead of having to tap your coworker. You can sense check. Have I got all the key components that I need to have a really strong hypothesis for this experiment? Are you going to.

[Mo Kiss]

No. I think even knowledge management, the ability because AI is helping so much with unstructured data, the culling through what has happened in the past, but it feels like it is an elevated role for what a great analyst should be doing five years ago is still the same, which is being deeply embedded in the business context and the business needs. It's been historically very hard to get the historical what have we done? So I think some of those, the. The preparation, the pulling this together using the tools. But I don't think it should change from what I think great analysts are doing, which is still having a deep connection to the business. It's a shifting kind of tool set.

[Val Kroll]

Can I just also add, Sam, one of the things I actually loved about your presentation was the idea also that we can change the format very quickly to. To suit different types of people. Like, I am an audio person. I would absolutely listen to a podcast on business metrics.

[Mo Kiss]

What I just remembered the second point I was going to make back on that question.

[Val Kroll]

All right, fine. Was this like from 10 minutes ago or.

[Mo Kiss]

It was, but it was on that it was asking. Trying to figure out the best way that somebody would how to respond to them. So when you said the audio person, how often do people actually know themselves? So for all the listeners or anyone who wasn't in Sam's session, it was the there's the marketing person who says I just want to have my cup of coffee and listen to the podcast. And there's a little trigger in me that thinks sometimes we seldom really know ourselves. So differentiating between somebody who thinks that's what they would want and someone who actually that would be effective. And that had rang true from what we've dealt with for 100 years. We've had people saying I just need a dashboard that Does X. And we deliver them the exact dashboard and they're like, this isn't helpful. Where's this other thing? So there's this other layer that I think analysts historically have needed to follow. And the same thing, opening up all these different formats is great, but what somebody says would work and I think you have to deliver it to them. But figuring out like, does that actually work? And giving them the opening, if they said, oh, I thought that would be really cool, cool, and you tweaked and tuned the tone and the content and everything. But giving them the out to say, you know what, that actually didn't work. I don't know that I wouldn't have known it wasn't going to work until I actually tried it for a while, which we have not done in the industry very well forever. We get in sort of a whiny mode of saying we've given them all these dashboards and they're not using them and it becomes this adversarial thing because. And then if we ask them, don't you want these dashboards? Well, they ask for them, of course they're going to say, yeah, yeah, yeah, this is really useful. We haven't figured out how to say no, that mechanism didn't work and it's okay. And we need to have the trust and we need to try something different so we can go back. And that was my other point.

[Val Kroll]

Maybe we should let Val get a word in edgewise.

[Tim Wilson]

Well, back to, like, how do we think teams will change? I think that there's going to be some new muscles that are built. I think one of the things that we had been talking about this conference is how this has given us a lot of energy and excitement. And I think that there's been some creativity injected, which I think is just fun. Even if we're just talking about little things that we do on the side, that's not ready for prad. But it's just kind of like stretching us in some new ways, which I really appreciate. The other thing that we were also talking with you about, Sam, you and I were chatting about, is the, the communication skills and how that's going to be improving. Because I think. How often have you been in a conversation, even over the past couple days, perhaps, where people are just ragging on their stakeholders like, oh, they're so dumb. They just don't get it right. And it's like when you're prompting and you're talking about something you need the AI to do for you and it totally misses the boat and you're like, oh, geez, I totally forgot to give you this piece of context. Of course you didn't understand what I was trying to say. Think about your poor stakeholder, that you were not giving that context for how many years. The video of we were talking about this, the dad with his son and daughter about making the peanut butter and jelly sandwich, about like, take the bread out of the bag and he's like trying to. And he like ends up like putting the knife through the whole loaf of bread, whatever, because he's just trying to follow the directions. But I think it can help us build a little empathy for our stakeholders because, like, they're. They're not wired the same way we are. They don't have the same background that we do. And so I think it's one of the byproducts is that it will help us become better communicators and hopefully have a little bit more empathy for people who don't make all the immediate connections that our brains do just because we're nerds.

[Michael Helbling]

I think organizationally, I think we'll see departments flatten out a little bit. Like over the last 15 years, we've become super specialized in a lot of different disciplines because analytics is actually multidisciplinary. And I think AI will push that back together a little in a lot of organizations. And then those organizations over time will start to realize there's still a need for some of that specialization on the fringes, and they'll find ways to bring it back in. But I think we'll all express experience, some compression where an analyst will go back to being able to code something and also write a data pipeline and also go access the data lake and also building a dashboard and a great visualization. And those were all things that like analytics people were attempting to do 15, 17 years ago. And then we realized we needed specialization, we needed a data engineer, we needed an analytics engineer, we need a data visualization expert. And I think we'll begin to flatten those out with AI organizations.

[Val Kroll]

That does worry me a bit, though.

[Michael Helbling]

I don't say it's good or bad. I just think that's what will happen.

[Val Kroll]

I feel like sometimes folks are over indexing on the generalization at the moment and thinking that, I don't know, a product manager can do a data scientist job. And I mean, some product managers are doing engineering jobs also. Interesting choices. So I think we're over indexing on the fact that folks can generalize. And I heard there's a, like, outcomes

[Michael Helbling]

follow expertise, even with AI. Okay. We have time for one more question, and it's our last question and we have someone who is an audience member who's going to ask it, and it's Jim Stern.

[Event Host]

My question is, what are the first three skills that analysts have mastered that are going to be successfully taken over by artificial intelligence?

[Tim Wilson]

I wish we could get a question about AI. No shade, Jim. Just looking at Michael. First three skills. Say it again. First three skills. Sorry, I was being an asshole. First three skills.

[Michael Helbling]

What are Tim's favorite skills? So probably like the. What is it? The ink to data ratio. So that probably going to be the first thing AI takes over.

[Tim Wilson]

Data pixel ratio.

[Michael Helbling]

Data pixel ratio. I was paying attention, Tim. SQL. I don't write SQL anymore.

[Mo Kiss]

I am so much more on the debugging. SQL debugging. R. I think debugging coming over really, really quickly. I think a second one would be qa' ing or validating or vetting the results of an analysis. Having that the thing that you're supposed to go to another analyst or try to come at it a separate way. I'm not sure what the third one is. I think it's a lot of things that are going to be supplemental that we should be doing, like not doing the QA but giving me the list of, you know, check my logic, check the things that I. So maybe it's not what the junior analyst is doing. I think it's what the junior analyst ideally is working with another junior analyst or senior analyst to look over and review. There's not a whole lot that I see whole hog handing the keys over on. I came up with three, but I don't know if they fit the criteria.

[Michael Helbling]

We'll have to go from there, but that's going to be. We have to wrap up. And I want to say first a huge thank you to Jim Stern for organizing the Marketing Analytics Summit

[Mo Kiss]

25 years.

[Michael Helbling]

And save your applause because also to all of you for being here and bringing your energy and your questions, your insights and experiences in a world changing daily. With AI, it's the people, the community and human connection in our industry. It feels all that much more special and crucial in these changing times. And obviously there's a huge audience also listening and we'd love to hear from you, too. And you can reach us at our LinkedIn page or the measures like chat group or by [e-mail]. please leave comments, ratings and reviews on whatever platform you use to listen. We do read all of them and Tim brings them up in meetings and

[Val Kroll]

it makes us set. KPIs.

[Michael Helbling]

Yeah, it's terrible. I can't wait till AI replaces that.

[Mo Kiss]

All right.

[Michael Helbling]

And I know that I speak for all of my co hosts, Val, Tim Mo, when I say, no matter the question or challenge you're currently solving, keep analyzing.

[Podcast Host]

Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions on Twitter @analyticshour, on the web at analyticshour IO, our LinkedIn group and the Measure Chat Slack Group. Music for the podcast by Josh Crowhurst

[Mo Kiss]

those smart guys want to fit in, so they made up a term called analytics. Analytics don't work.

[Podcast Host]

Do the analytics say go for it no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition, ladies and gentlemen,

[Event Host]

that is so much fun. Thank you so much for adding the spark to the end of the Marketing Analytics Summit.

[Mo Kiss]

You guys are awesome. Rock flag. And AI gives me the ick.