BILL.COM - Janani Venkataraman. When Fifty Surveys Beat Three Hundred? Synthetic data, the challenge for agencies & getting to better decisions in B2B research.

We talk with Janani Venkataraman about how AI is changing market research, from sample sizes and synthetic augmentation to the skills that still matter when automation accelerates the basics. We also challenge the old divide between UX research and consumer insights and argue for “insight systems” that triangulate signals across the organisation. We coverL

• Janani’s path from software engineering and sales into consumer insights leadership 
• How Bill.com fits into a broader fintech automation ecosystem and why integration matters 
• Hybrid approaches to understand hard to reach audiences
• “Signals versus insights” and why sometimes the numbers matter less than directionality 
• Why UX research and consumer insights are converging as org charts flatten 
• Triangulation across sales, product feedback, market research and UX to build confidence 
• A risk and reversibility framework for deciding what to automate with AI 
• What agencies can do next: redefine speed as faster decisions, not faster decks 

All episodes available at  https://www.insightplatforms.com/podcasts/

Suggestions, thoughts etc to futureviewpod@gmail.com

Transcript

SPEAKER_00 0:08

Anywhere between 50 to 55% of the traditional market research work is going to be eliminated by AI. The word speed and the meaning and the interpretation of the word speed has quite frankly changed in the AI era. One of the main reasons why we do more in-sourcing or we try and keep more in-house is to try and keep up with the speed of things that are happening in the in the organization. 50 high quality, you know, knowing that these are the type of audience that I want to speak to is probably better than getting 300 B plus grade survey responses. So that plus the qual data plus the synthetic augmentation has been a fantastic way to sort of tackle some of that sample problem that I was talking about.

SPEAKER_01 1:04

Welcome to FutureView. And this week I'm delighted to talk to Janani Venkatalman. Now, those few clips at the beginning of the episode give you a flavor of some of the topics that we cover. Key issues such as the supposed dichotomy between UX research and consumer insights. You could reference the narcissism of small differences as insight platforms referred to it. The impact of AI taking over market research processes, the true meaning of speed, and why we should potentially think again about sample sizes. And who better to talk to many of those issues than Gennani? A consumer insights leader with 15 years of experience across media, fintech, and SARS, most recently leading marketing research at Bill, the financial automation platform. She's made quite the journey too, from India to Sydney, where she spent her formative years as a professional, and more recently to San Francisco, where she says the density of innovation is frankly impossible to resist as a researcher, even though she does miss Sydney's coffee. Let's get into it. Shannani, firstly, thanks so much for joining today from the Bay Area. Early for you, and really delighted to have you on the podcast.

SPEAKER_00 2:06

Thank you for having me, Henry. Excited to be here.

SPEAKER_01 2:08

Brilliant. Now you probably know where I'm going to go. And I thought we might start with a traditional icebreaker, and that's something that most people don't necessarily know about you or they might find surprising, you know. So something that sort of isn't not necessarily a deepest, darkest secret, but that isn't, you know, in the public domain.

SPEAKER_00 2:25

Yeah,

From Engineer To Research Leader

SPEAKER_00 2:26

sure. Most people actually assume I've always been in research, but that's not the case. I did actually have two prior lives, as I like to call it, before landing in research. So I started in software engineering straight out of university. I did that for a couple of years, kind of built software myself, and then moved on to the world of resales. So I was actually selling software in that, in that context. So by the time I, you know, kind of landed in research world, I had, like I said, built the software, sold the software. But there was this kind of question that I always carried with me, which was like, but what do what do customers and users actually think? You know, what what do what do they want? And so that sort of question probably pulled me into the world of research. But it's it's great to have that grounding and that background because that engineering brain, I, I, I like to joke, it never really switches off. And that's a perspective that I I seem to bring with me in my role as a researcher, too.

What Bill.com Does In Fintech

SPEAKER_01 3:32

One of the obvious questions is what on earth does bill.com do?

SPEAKER_00 3:36

Yeah. So look, I I try and explain this way to people who don't know anything about Bill. If you are a business owner and you're running your business, you no doubt have to deal with payments, invoices, expenses. Bill.com is basically the software that helps you navigate that chaos. So, in in summary, we bill.com is a financial automation software and it cuts across the landscape of accounts payable, accounts receivable, and spend and expense management. Okay.

SPEAKER_01 4:12

So so why is it different then? Here's your chance to pitch. I don't know if these are direct competitors, but the types of systems that I might be familiar with, like Zo Xero and QuickBooks and all those types of companies.

SPEAKER_00 4:23

Yeah, like uh QuickBooks Intuit. QuickBooks is actually how we we like to call them, uh, and I mean this in a very friendly and uh joking manner. We are frenemies in some sense because there's there's this whole kind of financial automation ecosystem, if you will. And each of these kind of players or uh you know functional areas are parts of the puzzle almost. So you might be having an accounting software that's on zero or even intuits, and you need an accounts payable software. So it kind of all plugs together. And one of the realizations I've had since moving into this world of fintech is you're never just selling just the accounts payable software or the accounts receivable software. You are selling your piece in that puzzle. So I think there's a the the last kind of almost five years in this world of fintech has really taught me that we are never alone. We are a part in that ecosystem. And we we got to do everything we can to have that ecosystem come in. I lead market research for Bill, where my role is firmly and squarely aligned to what we call the go-to-market function. So the teams that marketing sales and CX, the teams that are arguably closest to the revenue. Why I say that is because these teams are always, so they have their eyes on the price, which is what is the how how do we drive up that revenue? So kind of coming back to your question, in the realm of B2B space, particularly as you start going mid-market, upper mid-market, enterprise, it's never, it's one solution, one software cannot do it all. Like it, there's no, there's no, you know, universal pill, if you will, to solve all the problems. There are there each kind of player is nuanced and niche and and has a real specialization. They go deep, they go, you know, quite quite specialized. And then the piece, the crucial piece, as I mentioned before, is how do you hum, how do you make that system hum and how do you kind of connect the dots is the real kind of differentiator. If you as a as a SaaS provider can offer that and can say, hey, like it's not just about me, but it's about how do I make your system work, then that you those those are the people who are winning, I would argue.

SPEAKER_01 7:11

Right. But yeah, I do see that. And so what type of research do you supervise then? And I mean, what are some of the you know, the key business issues that you're looking at the most often?

SPEAKER_00 7:22

Yeah, there's a lot of work that I do is, I would say, sort of grouped into two broad buckets. The first piece is all about the market and the competitive space. So I talked about accounts favor, accounts receivable, spend and expense management. These are really broad, you know, financial categories. So really trying to understand how the categories moving, shifting, how how the competitive space looks like, you know, what the, what, what's happening from a macroeconomic perspective? Because there's a lot of, you know, when you are in the fintech space, there's a lot of uh macro factors that make or break things. So uh keeping an eye on all of that is one part of the puzzle. And the other one is all about sort of understanding of customers. So there's, you know, customer segmentation and buyer journey, and you know, really trying to understand how do we provide fundamental customer truths that enable marketing sales and CX teams to like really unlock value for them are sort of the two big areas of focus for me.

SPEAKER_01 8:34

In that case, you're researching a lot amongst you know finance directors, you know, financial controllers, that type of thing, I would imagine.

SPEAKER_00 8:43

Indeed, yes. My research audience is, I will admit, pretty difficult to find in the external markets. They are your top financial decision makers, B2B audiences. So it's it's yeah, it's a it's a fun audience to work with, but uh a challenging one to find in the in the panels, research panels for sure.

SPEAKER_01 9:09

Yeah, I I can imagine. And so, I mean, what do you tend to do? Do you do mainly sort of IDIs and that type of thing? I mean, because surveys must be tough to get that audience to complete surveys of any length, I imagine.

SPEAKER_00 9:22

Yeah, we we have an internal joke about, you know, can we afford with a B plus grade sample for this particular, you know, question or business objective? But it's, you know, I guess the it to be really upfront, that is a

Rethinking Samples With Synthetic Data

SPEAKER_00 9:42

challenge. So we've started, I mean, my approach in the last couple of months has been to really rethink the whole notion of a traditional quantwork. To give you a concrete example, one of the recent studies I did was to do start with a qual, you know, face, a couple of a handful of IDIs, pretty expensive one-on-one interviews because we're talking to like chief financial officers, like really, really high-end audience. So five to seven interviews, and then actually design a quant exercise, get a very limited number of responses. I I, if I recall correctly, it was less than 50 responses, but you know, uh sourcing that from a B2B niche, B2B sample panel, and then using that to and using synthetic approach to actually augment that sample and you know, getting a base size of 150 responses using that synthetic, you know, brain, and then sort of putting these three sort of data sets together, if you will, to come at a come at a you know overarching understanding. It has been uh a bit of a learning curve, I will say, in terms of changing approaches, changing the ways of working, but I do believe 50 high quality, you know, knowing that these are the the type of audience that I want to speak to, uh, is probably better than getting 300 B plus grade survey responses. So that plus the qual data plus the synthetic augmentation has been a fantastic way to sort of tackle some of that sample problem that I was talking about.

SPEAKER_01 11:33

Yeah, it's super interesting. And we we could go really deep into this question, but maybe just briefly. But how did you validate the synthetic augment?

SPEAKER_00 11:44

Yeah, look, it's again, that's been a journey that I've been on, Henry. We've there's a number of different sort of experiments that I've done. I think I'm at it at that stage where I have stopped sort of questioning validation and really started asking the question around, is it the appropriate use case for it? Because if if we start with the question, is this comparable to the human data or is this the like the the the equivalent of the human data? I think I think we've already like lost the argument because it in in most of the instances it's not. But then I would kind of ask the uh ask the question back, like is the hu human data the the you know a pristine truth? Like what's to say that the source of truth that we supposedly call the source of truth is the absolute truth. My gosh, there was a lot of truths in one sentence. But but that I I think kind of anchoring our interrogation and questioning around is this the right use case for synthetic data? If yes, how do we provide effective guardrails around the synthetic solution creation so as to make sure that our outcome is within that permissible guardrails? So, you know, I think you you probably are starting to guess where I'm going with this. It's really when you're working in the sort of in the space of what does the majority think? I think synthetic is a is a really good alternative to you know traditional sample sourcing. But when you start having questions that kind of anchor on the fringes or the edge cases, that's where I would I would say that synthetic is probably not going to cut it for for that particular use case. And you need to kind of pivot to your traditional approaches.

SPEAKER_01 13:52

Yes, that does make a lot of sense. It's also an interesting point around trying to grade synthetic data against the human data and going back to the idea of the source of truth, keeping stated survey data as that source of truth. So would you consider doing things like, for instance, taking the synthetic data and then trying to match those against outcomes of some type?

SPEAKER_00 14:22

So let me give you an example use case that has actually worked really well for us. You know, we we do a lot of creative messaging and creative feedback, good sort of evaluation work. We've created a synthetic archetype that is reflective of a typical accounts payable decision maker. Now that's a pretty broad definition of who an accounts payable decision maker is, right? There are some fundamental truths to who that or what that archetype wants or Belvin wants, but does it cover the like the whole spectrum of everything that they need or don't need? Probably not. But it like I said, it's you're working in the in the majority space here. I like to call the output from synthetic as a signal rather than an insight. You know, I reserve the word insight for a human research execution, but it's it's really a signal. So the signal then says, hey, this particular execution, execution A would resonate with 80% of the AP users, execution B would resonate with 50%. What I'm anchoring on is the directionality of that synthetic response that says A is actually going to resonate better than B. So I think I think that's the interpretation game or interpretation lens that I bring. And I keep telling my sort of stakeholders and the partners that I work with that let's treat this as a signal, let's treat this as a direction, directional kind of guidance. And let's forget the numbers that are being spit out by the solution. Let's simply go with the direction. And does that feel right? You know, it actually actually does. So a lot of that type of work that requires iterative feedback and probably requires us to narrow down, say, 15 options into just like two or three, that's those are perfect use cases for a synthetic solution.

SPEAKER_01 16:33

Yeah, thank you. I think that's a really, really helpful and cogent explanation of it. I like that signals versus insights.

SPEAKER_00 16:42

Yeah. I mean, training the synthetic brain, I I will say is is a process that actually takes time. So one of the arguments I see people make for synthetic solutioning is the speed, right? Like I really, I really have pause when I hear the word speed because it is not as simple as clicking a button and spitting out 300 survey responses. And I do also want to sort of caveat that by saying it might probably be the case in a B2C or a or a or a gen pop type of a audience profile. But in my world, in my context of B2B, financial decision makers, that training piece as is is sometimes the the longest phase of my project. So I I don't think that's actually a a winning argument, at least in my universe.

UX Research Meets Consumer Insights

SPEAKER_01 17:44

Yes, totally take that aboard. I started us going down this rabbit hole, and now I've got to pull us back out of the synthetic data rabbit hole, back onto what we were actually going to talk about, which was traditional consumer insight and UX research. When we first met and we were introduced, we were talking a bit about how historically they differed.

SPEAKER_00 18:04

Yeah.

SPEAKER_01 18:04

But that actually, in some ways, they've started to come together, or they are more similar than they are different. Is that right, what I've just said, or would you clarify that in any way?

SPEAKER_00 18:16

Yeah, Henry, that's uh that's a topic I feel quite strongly about because I've had the wonderful opportunity to sort of live and work in both worlds. And I do sort of make the argument that this is historically about sort of org chart planning or uh you know organizational structure rather than rather than the difference in discipline. Right. So if you think about consumer insights, market insights, it's always kind of rolled up into marketing or brand. UX or design research or product research is always kind of rolled up to product or design. So it's really sort of two different stakeholders, stakeholder groups, different types of questions, different budgets. You know, it's probably like consumer insights and market research answering who are the customers and will they buy this? And then UX and product research probably answering questions around can they actually use this? But you when you sort of strip away all those different questions, different stakeholder groups, different org charts, I think fundamentally we're we're both in the pursuit of the same end goal. You know, it's about how do you how do you take that customer truth or you know, customer understanding to sort of enable these stakeholder groups to make better decision making. Um so I think with that grounding in terms of, hey, we're both after the same North Star, I I actually think there's there's a lot of kind of org flattening that's happening. Organizations are getting leaner, yeah, you know, teams are shrinking. So by choice or by force, UX and market research in many organizations is actually coming together. And I think, I mean, I've I've had also the fortune of sort of bringing these two teams together in a couple of different roles now. There's a real opportunity for us to take a business question or a business challenge and sort of analyze it from different altitudes and bring all of that together to create such a rich way of understanding that problem space and understanding the user. Um and I think that's that's where the real unlock is. I mean, for teams that can do this quite effectively and and do this well, um it's uh it's a phenomenal way to approach research team integration.

SPEAKER_01 20:56

And and how easy is that? Because I have heard what have been described to me as some misconceptions around the nature of the types of people. There's this sort of perception that on the consumer insides people, they're very good at telling stories and articulating things, um, but they can't really add up. You know, and maybe a bit blunt. Whereas, you know, on the UX side of things, you've got a bunch of engineers who are really good at understanding that, but they can't communicate anything. That is a perception uh that some organizations would have. And so how have you found it in terms of either ease or challenges of integrating the two sides?

SPEAKER_00 21:36

Yeah, I, you know, I think um we we haven't mentioned the AI uh word just yet, but again, you know, by by choice or by force, there's a lot that has now kind of forced upon us as a profession, as a as a you know, as a role. It's it's it does have it does. require insights people to and I say this with a lot of like respect for my colleagues and friends in this in this industry. I think it's time we stopped being precious. It's time we let go of our individual egos. I am a market researcher. I'm a consumer insights professional. I'm a UX researcher. I I actually don't think that's the that's that should be our mantra anymore. We quite frankly AI enables us to bleed around the edges of any role or any function that you are part of if you are a UX researcher you're running a usability study the the participant is giving you feedback what's to stop you from using Claude using your design system mocking up a prototype and getting real-time feedback I mean is that is that the role of a researcher or is that the role of a designer right or who's to say that isn't the role of both right similarly on the on the consumer insight side like what's to stop you from kind of putting together a strategy document right there's there's I there's I think the real opportunity unlock and the cost of making a bold bet bet quite frankly with AI has has never been you know more easy or more uh you know like low cost. So I think the challenge partly is in the minds of people and I think that that there has to be that unlock that hey you know you pick your flavor of the data that that's available out in the market Henry but anywhere between 50 to 55% of the traditional market research work is going to be eliminated by AI right like now that's not that's not a peripheral issue. That's an existential issue right so if you strip away that what what's remaining is is is something that we need to be really really critical and like we need to take a very strong look at what's the value. And I think the value of bringing that 3000 feet you know view and the ground floor view and sort of packaging that together is is phenomenal. And the teams who kind of let go of that preciousness around the title and the job nature are the ones who I'm using AI to kind of accelerate that are are the ones who are going to win.

SPEAKER_01 24:35

Got it can we put a pin in that for a moment because I would love to go back to this question of what's the 50 to 55% that's going to disappear and where is the value just so I can understand like a little bit better. So whether in build.com or within similar organizations where you have both UX and consumer insight who owns some of the pieces of it so imagine you know you've got things like marketing because you said historically they've gone after the CMO that's within sort of consumer insight and then you've got usability labs and UXR type of work going on. And then when you've got for instance like those little widgets that sort of pop up and go, what was your experience like? Now that's not the correct phrase I'm sure but you know the ones the ones like the little mini surveys who who would be doing that that's the UXR team.

SPEAKER_00 25:24

Yeah yeah it could be done by the the product or UX or the product researchers it could also be done by product marketing or even the product managers. So there's a there's a whole host of teams that's you know sort of talking to the customer. I mean it's I I I feel like there's insight generation is happening in pretty much every single part of the organization. It's I think the the value that probably researchers the traditional researchers now can bring is sort of stitching or connecting those dots together and kind of making sense of that.

SPEAKER_01 26:05

Yeah that's where I was sort of beginning to think about it and maybe it it might be a nice segue into the next kind of question. But as you say you know you've got the UX research you've got product marketing people imagine you've got the sales teams must be a huge source of insight because potentially because they're constantly talking to customers and finding out what they think about the market and why they want to buy your product or not and yeah and is it at that point now I'm not necessarily talking about build.com specifically but yeah gently do you think where it's all coming together and yeah.

SPEAKER_00 26:40

I I mean that's certainly my philosophy to research and something that I've really you know I've started being quite vocal about my my approach. I think each one of those sources they have their own inherent biases they they have their inherent flaws right the sales team to to pick on them for just a second they are talking to people who are pretty pretty pretty close to the bottom of the funnel so there's a a strong intent there they're probably like already decided in terms of their purchasing so there's there's certain kind of angle orientation that comes with that data but nevertheless pretty valuable data source to mine. You know the market research data is probably like I said sitting at that really really high altitude so you're probably missing on some of that deeper you know middle of the funnel bottom of the funnel kind of orientation and then the product researchers have got such a deep lens on the ins and outs of using the product so there's probably you know orientation missing from an outside ins perspective. So the my my philosophy has been to always approach problems from a like an insight system perspective right it's never one study like it's never a quant study that answers all the all the questions or solves the entire problem it's like how can you triangulate and when you triangulate are maybe three out of the four data sources pointing towards the same direction right that that gives me confidence that hey this this direction that three out of the four data sources are pointing towards probably the right one because yes there's one that's pointing in a completely different direction but by by way of majority and by you know by by relying on the everything else that we have that's probably a better direction for us. So I think that there's real there's a lot of value and benefit in first of all insights people being mindful that hey there are insights data sources that are available throughout the organization go find them go go figure out what they are and and also be mindful of what biases they come with and what what their contextual limitations are and then figure out how do you kind of stitch them together and connect the dots to provide that directional guidance to your stakeholder.

SPEAKER_01 29:29

Yeah and and that again will will I think be sort of a segue onto potentially how we sort of future proof the industry as a whole but it reminds me Steve Phillips at Zappi, I think, put it really well because he said that one of the challenges of the consumer insight people is that they tend to be quite siloed in their thinking and they can they may call it rigor. I think it was a phrase you and I touched on before they're very purist but they may not be actually that pragmatic but he said on the positive side they've actually got the skills because they are used to taking multiple data sources they take qual and quant and this and that and desk research and all the rest of it. They're actually used to synthesizing it in a way in which many departments aren't but let's go to the negative picture first.

What AI Replaces And What Stays

SPEAKER_01 30:16

So what's the 55% or so of the market research sector or process or whatever it might be that's basically just going to be taken up by AI?

SPEAKER_00 30:28

Yeah look I think there's a whole kind of range of activities that researchers have to do in anywhere from kind of scoping the project to ultimately delivering insights and you know there are now promises of AI kind of replacing every single step if not the entire you know process. So I I for me personally I sort of look at it in this kind of framework of risk and reversibility right so if you think of that as a you know a two by two diagram and on one axis you've got the risk and then the other one is reversibility where there's low risk and high reversibility I I'm actually kind of AIing away AI away that that stuff right like let's just go for it I I start using you know agents or I start I setting up I start setting up automated kind of workflows. And quite frankly you know if it doesn't work it I do keep an eye on some you know these things. If it doesn't work then I can always kind of pick and change and and modify that but at that that space is like the easiest or the low-hanging fruit for me. And then obviously on the other end there's the high risk and low reversibility that's where you know I still think that's that's my value you know as a researcher that's the real the the judgment piece, the human understanding piece, the organizational context piece that I uniquely bring as the researcher and an insights profession. So I think it's for I would I would sort of invite each researcher to kind of make their own or or to plot out their own map in terms of this framework if this is helpful to you to figure out hey like what are the some of the low-hanging fruits that I should quite frankly be you know shifting to AI because I I I don't think it's like researcher or AI. It's like how do you become better with AI? So like I said the cost of the cost of making bets or experimenting has probably never been lower.

SPEAKER_01 32:51

Yeah thank you I think that is a really really useful framework to to apply to that. And now this is potentially a slightly controversial question within that context but with AI and possibly an implication of more insourcing because it sounds like you're doing more insourcing as you take your bets with with AI is there a future for agencies where do they sit within the equation yeah so Henry I started my research career in agency land.

SPEAKER_00 33:26

So I I say this with a lot of like love and appreciation and admiration for my agency friends. I think there's the you know like the you you you mentioned the word purity and I I would almost always equate the wonderful agency partners that I work with with that term purity because they bring that methodological expertise, the rigor, you know they really sweat over the details so that I mean as a client side or a brand brand side buyer, I don't have to so I I hundred percent think there's a there's a role and a place and a unique value that the agencies bring. But a question in my mind and I I would love to actually throw this back to all the wonderful agencies who are probably listening the the word speed and the the meaning and the interpretation of the word speed has quite frankly changed in the AI era. You know one of the main reasons why we do more in-sourcing or we try and keep more in-house is to try and keep up with the speed of things that are happening in the in the organization. So problem or like a caveat that we always have when working with agency partners is speed. Like it does take a a little longer than doing everything internally. So I am curious how the agencies are actually evolving and kind of helping brand buyers like myself to fundamentally you know redefine the word speed because that that is going to be a challenge and the expectation of teams I mean I know our product teams and our marketing teams are sort of running million miles an hour.

SPEAKER_01 35:18

So I I would love to hear agencies uh take on this because that is if they can figure that out then I do think that they they 100% bring that value and expertise which is which is indispensable quite frankly yes it's a great question maybe when we put this up we'll put it or as a question on LinkedIn but it does strike me having worked actually almost exclusively on the agency side in various guises that maybe the traditional agency conception of speed is how quickly can I deliver a project whereas the client conception of speed is how quickly can I take a decision.

SPEAKER_00 35:57

Right.

SPEAKER_01 35:58

Yeah that's a that's a brilliant way to frame it yes and I think potentially those are different things and maybe that's a really good challenge to agencies to think how can I get the client or point to make a decision quicker, not just to deliver a project.

SPEAKER_00 36:13

Yeah and I, you know like on that on that sort of same win, that purity versus pragmatism point also comes into play because I think the agencies do anchor quite heavily on purity uh maybe maybe they start to balance a little bit more pragmatism and get comfortable with messy messy outputs. You know it's something that product researchers are quite comfortable with you know they take the they take the product and the design stakeholders on that journey. So lots of lots to I guess think about from an agency land perspective.

Agencies, Mentors, Media And Closing

SPEAKER_01 36:50

Now um Janani I am conscious of time and so if it's all right with you I might just jump on to a wrap up so it's a chance for some shout outs I assume they're all going to be positive rather than negative but who have been some of your biggest mentors and as you mentioned them could you potentially just provide a quick summary of of what they taught you that might be useful for other people to take on board as well yeah I like far too many wonderful managers to to name but I will probably kind of group them into the kind of broad buckets of insights kind of work that I've done.

SPEAKER_00 37:29

So I as I mentioned I started in the agency land and I think that the rigor of quant work that was instilled into me every report being reviewed 50 times before it kind of goes out the door like sweating over the details looking at the data like pouring over the data basically all of that I really kind of I must say that grounding has really shaped the the researcher that I am today. So shout out to all the wonderful managers that I've had in the agency land in the in the world of news media I spent quite a long time in the world of news media and I think that's a fascinating space as a researcher. So I love working with the marketing teams in uh in the world of news media just the innovation the thinking the the the commitment to kind of bringing that that news to to the world really inspired me and kind of shaped me as a person. And obviously more recently this name is not going to be you know a surprise or like new to you but Mark Ritson, Professor Mark Ritson has been really I would say quite inspirational for me because I think he does have that angle of uh like to use borrow a phrase that he uses he talks about bothism it's never this or that it's this and that. So I think he has a very like no nonsense approach towards marketing and insights which uh which I really really like so a lot a lot of uh lot of heroes and you know most most often than not they are actually closer to you than than you think yes I think that that's a good maxim for life as a whole now we touched on this when we first spoke but what are some of the things that you most enjoy now you're living in the US you're in the Bay Area but also what are some of the things you actually miss about living in Australia because when we first and you said you're you're from India sort of in some ways Australia kind of feels a bit like home. Yeah yeah I mean I uh Sydney was where I sort of spent my formative years as as a researcher as as a woman you know as a young mother sort of raising my kids so I I truly miss the fact that you could walk to any street corner in Sydney sit down have that beautiful just just amazing cup of coffee Starbucks and Pete's don't quite cut it the same way for me like any any American can fight me on this like that coffee culture and the spirit of that coffee culture is something that I I think uh Australia is truly unique in that so I I truly miss that about uh living in Sydney.

SPEAKER_01 40:27

And then and then how about the US?

SPEAKER_00 40:29

So we've uh you know we've um said we're not enormous fans of slightly over milky or watered down coffee however what are the really what are the things you enjoy about living in the US yeah I think uh so as I mentioned I live in the Bay Area and it's it's just fascinating the the the amount of innovation I mean this is the Silicon Valley is Silicon Valley for uh you know not just in terms of the geographical kind of structure it's also the the people it attracts the organizations and the companies that call it home there's just so much happening which is really fascinating for me as a as a researcher every day it's like something changing in one of the organizations that kind of has a ripple effect on the entire globe to to to be honest. So I think the ability to meet and have conversations with so many people who are quite frankly shaping all of these innovation is is really valuable and I quite I quite enjoy that. Now just a final question is there any particular media you'd recommend so I mean you could be books movies TV shows even podcasts yeah music if you want I love podcasts I mean I'm a huge podcast fan I keep listening to so many different podcasts you know again on the marketing realm one of my favorite podcasts is actually Uncensored CMO by John Evans. I I really love that I find the conversations the people that he brings on really interesting right now Henry I'm actually rereading this book called First 90 days by Michael Watkins. I so that book was recommended to me when I started a new job and it's also equally applicable when there are organized you know sort of organizational changes at play. That's sort of the situation that I'm in right now and I'm really finding it quite fascinating to reread that. So anybody who's either in that cusp of change or maybe navigating some organizational changes I would highly recommend that book.

unknown 42:47

Okay.

SPEAKER_01 42:48

Thank you so much, Janani I know you've probably got to get to your next meeting but I really really appreciate you taking the time it's been super interesting and really thought provoking in a good way.

SPEAKER_00 42:59

Thank you so much. It was great love lovely to have this conversation with you lovely to talk to Janani.

SPEAKER_01 43:07

Now I didn't really mean to push down the whole synthetic data rabbit hole. It wasn't part of the plan but I thought Gennani's perspective was really insightful as somebody who's actually been using it internally. I also like the idea or metaphor around considering different data sets as giving perspectives from different altitudes depending on how you want to look at the data. More to come with another great interview in my humble opinion anyway coming up next, Charlie Butler, the CEO of Bounce is a really good one I think. Thank you again to Gennani, to Insight Platforms for their continued support and to you for listening. See you next time