This episode is in partnership with MrWeb’s Insight in the Mobile Age segment. 

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My guest today is Giles Palmer, Founder and CEO of Brandwatch. 

Founded in 2007, Brandwatch is a global enterprise social intelligence company. It allows users to analyze and utilize conversations from across the social web. Brandwatch employs more than 500 people. 

Giles is also the Chairman of Futrli, an SMB focused software company.

Prior to founding Brandwatch, Giles founded the Runtime Collective, a software company. And, he started his career in accounting. 

Find Giles Online:

Website: https://www.overtheshoulder.com/ 

LinkedIn: https://www.linkedin.com/in/ross-mclean-9766842/ 

Find Jamin Online:

Email: jamin@happymr.com 

LinkedIn: www.linkedin.com/in/jaminbrazil 

Twitter: www.twitter.com/jaminbrazil  

Find Us Online: 

Twitter: www.twitter.com/happymrxp  

LinkedIn: www.linkedin.com/company/happymarketresearch  

Facebook: www.facebook.com/happymrxp  

Website: www.happymr.com  

Music:“Clap Along” by Auditionauti: https://audionautix.com

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[00:00:00]

Jamin: Hi, everybody, I’m Jamin. You’re listening to the Happy Market Research Podcast. My guest today is Giles Palmer, founder and CEO of Brandwatch. Founded in 2007, Brandwatch is a global enterprise social intelligence company. It allows users to analyze and utilize conversations from across the social web. Brandwatch employs more than 500 people. Giles is also the chairman of Futrli, an S&B-focused software company. Prior to founding Brandwatch, Giles founded the Runtime Collective, a software company, and he started his career in accounting. Giles, thank you so much for joining on the Happy Market Research Podcast today.

[00:00:42]

Giles: It’s a pleasure to be here, Jamin.

[00:00:43]

Jamin: Let’s start with a little bit of context. Tell us about your parents and how they informed what you’re doing today.

[00:00:51]

Giles: Yeah, my parents were – well, my dad is no longer alive. My mom is. They were kind of a traditional stay-at-home mom, working dad family. My dad had a very high work ethic. He came from a working-class background. He was kind of a smart guy that did OK at school. And eventually got into quantity surveying. So, kind of geeky surveying. And joined a firm when he was, I think, 27. And retired from the same firm when he was 55. And he worked his way up to senior partner. So, he just kind of grounded out in the same industry for 30-odd years. And just worked really hard. And didn’t put too much pressure on me and my sister to work hard when we were kids. In fact, I often asked him for advice, and he was reluctant to give me any advice. He was kind of like, you’ve just got to figure it out for yourself, Giles. I went to a private school, I went to a good university. I read physics at University, I came out of that. I was bumped up a year at school, so I came out pretty young. And I was trying to figure out what I was going to do with my life. I had no idea about anything at that point. I was kind of 21. So, a little bit like studying law in the US, you can go into British grads often – certainly back then, in the early ’90s, went into accountancy to get a kind of a general business grounding. So, I went and did that. I was really not very good at it. My heart wasn’t in it. And then I kind of joined a few industries, I explored a few different industries. Didn’t really get on with any of those. I was a geeky kid. I was into computer games and programming and stuff. And I kind of got – the Internet really didn’t appear until I was in my late 20s. And when it did, I kind of – me and some friends started up a company, which we call Runtime Collective, building web applications for people. But in terms of my parents’ influence on me, I think if I could take one thing out of that, it would be work ethic. I was kind of a lazy kid. I found schoolwork incredibly boring, but I could do it reasonably well. Especially the sciency stuff. But I always remember my father and my mother basically just kind of giving me a hard time about not working very hard. And now I work really hard. And I think that my dad – and my mom was incredibly organized and around the household. So, together they worked incredibly hard, and they set us up as kids really, really well, and provided an incredibly safe and loving environment. So, I think, I guess one other point on that is, that because I come from a – the family was middle class, we had enough money. I was put through a good school. And it was a very safe environment. I’ve always, I think probably been attracted to risk. maybe more than other people, because I was brought up in such a safe environment, where there was never any sense that there was any jeopardy anywhere. So, I think the work ethic and that kind of loving, safe family environment has kind of given me an appetite for risk, and a conscience to work hard.

[00:04:39]

Jamin: Do you have children?

[00:04:40]

Giles: I do. I have three children. Yeah. Three and a stepdaughter.

[00:04:45]

Jamin: What ages?

[00:04:45]

Giles: Twenty, nineteen. And two – my stepdaughter’s 16 as well, and my youngest daughter’s 16. So, two 16-year-old girls.

[00:04:54]

Jamin: What, out of your parents, your experience, as a child, do you think you like pulled out of that, and then helped install in your children? So, that’s one question. And then I’ll wait. Sorry. That’s one question.

[00:05:12]

Giles: Yeah, one at a time. I think that kind of – I think I’ve taken the work ethic thing, possibly too far with my own children. I think I’ve put them on – put more pressure on them than my own parents did on me. I get particularly impatient with my son, who is very happy doing absolutely nothing other than online gaming.

[00:05:40]

Jamin: I think our sons know each other.

[00:05:42]

Giles: Oh my God. You know, 5:00 in the morning, he wakes you up kind of screaming down the headphones, I’m not – this is just too much. So, but – so I lose my cool, and sorry about that. But, and then I guess the other side is, what you – the habits that you pick up from being parented in a certain way, and providing that kind of environment for your children, I think they’re very strong. So, the second point that I made about providing that kind of safe home environment, we do that. And we have a very kind of low drama household, and we all kind of muck into a degree. We all cook, and the kids clean up after dinner, pretty much all the time. And so, there’s – that side of things, I think is good. And hopefully that will give them a sense of solid footing. But, yeah, I think I do give them a little bit too much of a hard time on their academics. I think I should probably just ease up a little bit there. That’s my own guilt coming through, from when I was a kid and my parents guilted me, too. So, they’ll probably do it to their kids as well, and add into writing down the generations and some of these tragic things, right, that you do what your parents do. You repeat the mistakes that your parents – luckily my parents didn’t make too many.

[00:07:02]

Jamin: How has the – your relationship changed with your kids since COVID-19?

[00:07:08]

Giles: Oh. I think my relationship with my kids has changed less than their relationship with their granny, my mother, who’s on her own, stuck at home. And I’m really proud of the fact that my kids, who historically would never pick up the phone and talk to their granny. I mean they would speak to her if I called her, or she called me, when they’re with me. but now they’re actually calling her. Like a couple times a week they’ll pick up the phone and they’ll talk to granny. They’ve got a really good relationship with her. So, I’m really proud of the way that they’ve jumped on that. Because they see her kind of vulnerability. She’s in her 80s, she’s on her own. She’s lonely. And they care for her. So, that’s been super nice to see. In terms of my relationship with them, well, the oldest is at university, so she’s not living at home. So, that hasn’t really changed too much. And the younger two, well, my 16-year-old daughter, we exercise together, which we never did before. And she’s at least as fit as I am. So, I’m quite impressed that she – because, yeah. So, not too much, is the answer, short answer.

[00:08:30]

Jamin: The amount of time I spend with my children is – has gone up, just absolutely dramatically in this period. And like you kind of said, the mundane things that I normally would do by myself, so just working out. Now I’m doing it in the house, and so, there’s a lot more joint activity around that. Whether it’s my little ones jumping on me, or big ones joining in.

[00:08:53]

Giles: Yeah, it’s better.

[00:08:55]

Jamin: Yeah, it feels like part of the silver lining for me in this process has been the opportunity to be able to kind of connect at a different level. And a slower level with my kids.

[00:09:07]

Giles: Yeah, that’s good, that’s true – it’s true. You can have more conversations because you’re not all rushing around.

[00:09:14]

Jamin: All right, give us a little bit of context about Brandwatch. You started the company in 2007, you have 500 people now, which congratulations on your success. What is Brandwatch doing today?

[00:09:27]

Giles: We are trying to bring together some of the innovations that we’ve been working behind the scenes for the last couple of years, around natural language processing. Which are driven in large part by some of the big advances in deep learning. So, universal translation and some of the open AI projects that have come to light in the last couple hears. And we’re trying to simplify the ability to analyze this enormous amount of social data that we get in our system. Just to take a step back, the data that we collect and store is vast. I think we get something like 700 million posts a day, and we’ve been doing that for 12 years. Something like one and a half trillion pages. So, it’s enormous. And in that archive of basically online conversation, there are interesting insights. It’s just how the hell do you get at them, and how do you sift the signal from the noise? And it’s a really difficult challenge because it’s difficult from an engineering point of view, because it’s such a huge dataset. And it’s difficult from an analysis point of view, because it’s natural language. And the structure is different, depending on the source. So, the structure of a tweet is different from the structure of a forum, that’s different from a structure of a news site or whatever. So, how you bring all of this together into one system, to try to create a – the ability for users to get at the insights hidden in this vast dataset is extremely challenging. But – so we have this kind of always on, small innovations, inside the platform, sort of thing. Adding new data sources, analyzing images. Sometimes significant incremental improvements, sometimes small improvements. And then there are these longer term engineering breakthroughs that are really risky, but if you can create something which really shifts the ability to analyze this data at scale and make it more useful and more – and easy to wrestle with, then you’re doing a massive service to the users. So, that’s – we’re working on trying to productize some of the big kind of engineering programs that we’ve been – and when I say bigger, I don’t mean loads of people. Like it can be three or four people, but they’re kind of big efforts. They’ve gone on for multiple years. so, that’s the first thing. The second thing is – I think about the application that we’ve built, a bit like the first kind of Model T Ford, as a one size fits all. You can use it for lots of different purposes, but it’s one thing. It’s the same color for everybody. And, of course, people use our system for lots of different purposes. So, we’ve been kind of pulling that apart into the kind of jobs to be done framework, or user-based, use-case-based approach, and we’re trying to take what we’ve built and think of it more as a platform on which we can kind of create applications which are more – which are easier to use and more targeted to specific use cases and jobs to be done. So, that’s the second thing. The third is that we’re learning about scaling a company and doing that globally. My leadership team is 10 people, and there’s – I think there’s three in Boston, one in New York, four in the UK, one in France. I’m missing somebody. Maybe five in the UK, one in France. So, we’re a distributed leadership team. We’ve got distributed sales team, distributed marketing team, and so on and so forth. and how we structure that so that it’s efficient but not siloed is something that we’re learning as we go. And actually, this COVID, this COVID crisis has helped us work more cross-functionally, which is really interesting. Because the problem with being siloed is that it takes ages to get stuff done. You start behaving like a big company, which is awful in some ways. But it’s efficient in other ways. So, getting that balance right is really tricky. And then the final thing is something that we announced that we were doing about a year ago, when we acquired Acuity – a company called Acuity, which is an online mobile like survey company. And we’re trying to bring together different datasets to give a bigger picture, a better picture for consumer behavior. And we’re calling it Digital Consumer Intelligence. So, we’re kind of trying to expand what we – the way that we think about ourselves and how we innovate from social media analysis, or social intelligence, to digital consumer intelligence. So, taking different data sources and trying to match them up and make sense of them. Including even things like online transactions or search data, or some – a lot of that stuff is very early stage. But I’m trying to think long term.

[00:15:28]

Jamin: One of the trends that I identified in 2019 coming out of that year, so I actually had a podcast episode on this particular topic, which dropped in January of 2020, is that companies no longer can leverage solely consumer insights or survey data, primary research. They now have to layer on top of that, usually two or more additional sources, whether it’s internal transactional data, behavioral data, market data, whatever. It feels like the acquisition that you did, enabling, connecting primary data to social data, secondary data, would create a material shortcut because – and one of the biggest pain points, in fact Mary Anderson at Microsoft identified this early on, in 2019 with me, is that it’s really, really hard to do that well. And the trade-off there is it does take time, but the benefit is that you get context with the consumer insight. So, it isn’t just consumers like it or don’t like it, you get a breadth of content that, especially in today’s market, a post-COVID market, that context is massively, massively relevant. You think about travel, or think about online enablement, or whatever.

[00:17:02]

Giles: Yeah. And I agree with all of that. And then that throws me into a line of thought around the use of that data and in particular, how it gets used in decision making with inside organizations, and whether those decisions are kind of strategic, long-term, significant, where you’re looking at big market shifts, or you’re trying to really figure out how you position your product in a kind of a one-two-three-year cycle. Or whether it’s more, what’s happening today? What’s going on online today? What are our competitors doing? What are our consumers doing? And that feedback loop is quicker and it’s more tactical. And we’re seeing – and, of course, there’s other data that informs that tactical feedback loop, such as sales data or performance of online marketing and search. Which is much more measurable and kind of understandable from an ROI perspective. So, all of this stuff kind of the boundaries around the timeline and the decision making, I think, are really important. And also, having the skills inside organizations or if they’re outside organizations, very close by. So, tight partnership with an agency, let’s say, that can turn this around incredibly quickly. That can run quantifiable analysis on data once it’s been produced or gathered. And then quickly say, oh, this is what we found here. or we haven’t found anything here, there’s no signal, and it’s not in this data. Or whatever it is. And we’re seeing that feedback loop, where information, market research, what will be, I think, historically called market research data, that feedback loop being shortened dramatically inside a lot of our customers. Which is why we think about this idea of being consumer fit, and being adaptable. Adaptable to fast-changing consumer habits or behaviors. And a lot of the customers that we have, and we saw it in the GRIT report as well, getting their budgets reduced, but their workloads increase, and the need for faster turnaround and quicker insights increased as well. So, it’s a rally interesting evolution, I think, of the market research industry from qual to quant, from quarterly to daily, from reports to quant data – quantifiable insights. We’ve seen x number of people do this. We think that this is a trend. And all powered by this availability of big data and the ability to work with it, and compute with it. and so, I think it’s an extremely, extremely interesting time in the world of market research. And in some ways, my personal view is it would be great to change the name of the industry, because the name has got a bit of the stodgy old-school thing going on with it. Whereas, actually, what’s happening with market research, and consumer insights, inside some of the most successful companies of the world today, is really quite groundbreaking, and innovative, and very forward-looking. So, it’s – I think we’re seeing a big sea change shift, like big, big shift in the market research kind of industry, shall we say. And I think it’s going to get bigger, but it’s going to change the way that it is [CROSSTALK].

[00:20:53]

Jamin: Yeah, I totally agree. Even impacting down to like the skills of a market researcher, that there’s a whole evolution around that.

[00:21:02]

Giles: Absolutely.

[00:21:03]

Jamin: So, let’s pull back a little bit about the story of Brandwatch. I want to dive into this as an entrepreneur. So, you started the business in 2007. Facebook did it’s IPO, I believe in 2012, and Twitter the following year, in 2013. So, you were like really ahead of the curve in terms of the – people knew that these were big companies before they went IPO obviously. But having said that, 2007’s very, very early in the social network life cycle.

[00:21:30]

Giles: Yeah, it makes me think we should have got a lot further than we have, to be honest.

[00:21:36]

Jamin: Well, there’s that. And then, we hit a recession, directly after you started the business. So, what was the impetus for starting Brandwatch in 2007, and then how did you navigate that course coming out of, or into – going into the recession?

[00:21:53]

Giles: So, the impetus was, I’ve been – with some friends of mine, we’ve been running a technology services business for five or six years. And I didn’t want to do that anymore. I wanted to build a product company. We started with not very much idea about how to build software, and we’d learned. We made lots of mistakes, and failed a lot, but we knew how to build software. And I wanted to build a product. So, there was this nascent – small project that we’d been working on around building a web crawler. which was never really finished, but I used that as an impetus to create Brandwatch. I spoke to my partner at the time, I said, listen, I want to go off and do this. And he was like, fine. You can buy me out, and off you go. He went off and joined Google, and then later – latterly [INAUDIBLE] So, amazing guy. But I wanted to build a product. So, we took about a year and a half, maybe not quite that long. Maybe a year, to build out the web crawler and then work on a whole bunch of user interface stuff, and build in some very very basic sentiment analysis around the results to be found. And we launched it in August 2007 as a SAS product. And really our focus then was on forums, so special interest forums. The one that springs to mind is a special interest forum in the UK, called Money Saving Expert – Money Savings Expert. Which is a place where people go to talk about their bank accounts and their finances, and they talk about what financial products are good, mortgages, and so on. And so we were crawling that site and analyzing it, and one of the big bangs in the UK basically wanted to launch a new bank account to targeted to younger people. And they wanted to use our system to understand how that was resonating with inside that community. And so that was our first subscriber. And that was in – so we launched in August, 2007, and they subscribed in September, 2007. We didn’t get our second customer until January, 2008, which was a challenging time for sure, when I was trying to raise money as well to keep the lights on. And then what happened was that the agency world in London, and the UK’s always had a very strong digital agency, or creative agency – advertising agency industry. They started realizing that there was this new ability to tap into consumer insights and understanding consumers. And so they started subscribing to our system. They weren’t paying us a lot of money, a couple thousand dollars a month. I mean it’s not a small amount, but you need a few more – you need quite a few of those to keep a team of 10 going. And we started to see some traction with those agencies, and so we really lent in with them, and we kind of built the product with their needs in mind. And then they had all sorts of different needs. So that’s why the product ended up being this very flexible system. In terms of the recession, it didn’t really impact us – well, who knows? Because I don’t have a control roof in non-recession.

[00:25:29]

Jamin: OK, that’s fair.

[00:25:30]

Giles: We did – getting back to the market research. We did pretty well throughout the recession, mainly because it wasn’t an expensive product, it wasn’t a crazy expensive product compared to people-based market research. Not that we were really thinking of ourselves as a market research platform. at that time. But also, even during the recession, the world was going online, so more people were signing up for Twitter and Facebook, and more people were using Google, and more people were buying iPhones, and getting their Smartphone experience and so on. So, it may have been a global recession on a macro level. But online was still growing like crazy. So – and that was our domain. And it’s continued to this day. So, the move for money from offline to online is – that’s the trend of the last 20 years, right? One of them. And that’s our playground. So, in that respect we were somewhat insulated from the recession.

[00:26:44]

Jamin: SAS businesses, they already get a fantastic multiple relative to service businesses, but it’s when you go through a recession you really appreciate having the guaranteed revenue. It’s a very nice model.

[00:27:00]

Giles: Too right. It’s not guaranteed though.

[00:27:01]

Jamin: That’s fair.

[00:27:03]

Giles: But it’s more reliable. I mean that is one thing that I would say, that we’ve been – the predictability of Brandwatch is actually – it’s pretty good. Except for the last 18 months, where we’ve merged with Crimson Hexagon. That’s been a much more unpredictable time. But up until that transaction, it was – we would put out a number at the beginning of the year, and we’d be there or thereabouts at the end of the year. We were always a little bit optimistic. But we would be within a 5 or 10% of where we thought we would be, almost every year, because it’s reasonably easy to look at the waterfall of put money in here, sign up a customer, have them hopefully renew, or have a certain percentage of them renew, have some of them grow, and some of them churn. And it’s not a complex model. And once you’ve done it for a few years, it’s unlikely to change dramatically the next year. And the recession was no different. But then again, it was early. But we didn’t really have that data back then.

[00:28:09]

Jamin: Let’s shift gears. I want to talk about the role of diversity in consumer insights. So, as a professional market researcher, sample frame, or the definition of who is it you want to talk to, is really important, and you want it to look like census or whatever, so that it’s representative of the people that you are trying to represent. And so we all know what that looks like as consumer insights professionals, or even as aspiring consumer insights professionals. I want to pull back a little bit, and talk about the role of diversity as it relates specifically to the research team. Do you think there needs to be some consideration when doing the analytics as it relates with the actual people doing the work of the insight, as opposed to just the sample frame?

[00:29:03]

Giles: I think that the two are related. If you’ve got a small sample size and you’re asking direct questions, then the skill of the market researcher to not over conclude, because it’s a small sample, and because it’s prompted, and run the process in an as unbiased a way as possible, is absolutely critical. And obviously, if you get two market researchers, they’re going to do it in a slightly different way, so maybe doing it multiple times to have kind of a sort of double-blind type of approach, is good as well. With the sort of data that we’re looking at, it’s massive in scale, so you have fewer biases because of the sample size. And most of what we do is trying to pull insights out of unprompted data. So, there isn’t the kind of leading question idea. so, where that leads to is a need for skill in using the products to get to the insights. So there’s a high level of – or is a different type of skill. Rather than the skillset of running a panel, or setting up a research questionnaire, it’s more the skill of interrogating the data, which is different. And then the second skill, which is important is quant type skills. So, trying to understand statistics. Because the data sizes are huge. So, how can we – how much confidence can we have in what we’re saying often is a statistical analysis. And then it comes to the actual individuals doing the work. And most of our research team are kind of under 35. So the average age is probably 30. And these are young people who are very competent and confident using these sorts of kind of programs and web applications to do this kind of analysis. We find that they pick it up really quickly, like with less training, and they natively get into it, reasonably quickly. It’s almost like watching people use a search engine. And then in terms of the diversity of the group – so I would say that it would be helpful actually if we had some older people in our research team. We’ve struggled to find them, to be honest. Because this is such a new – it’s a reasonably new thing. Anybody who’s a bit older would have to learn it anyway. Anyway, so I haven’t really thought enough about that. But in terms of the actual diversity of the team, we’re also seeing, in our research team, in terms of gender, probably more women than men, which is interesting. And then the one thing that I think is the same for all of the people that we think are the best researchers that we have, is a mindset, and that mindset is a curious mindset. They want to understand things. They want to dig around. They want to go on an exploration, on a journey. So, in terms of – yeah, I don’t want diversity in that respect. I want everybody to have a curious mindset. In terms of male/female, I don’t think that we see too much difference between the two. I would say we’ve probably got a few more women. Demographic diversity, again, we’re quite diverse. It’s really an age thing. I think for us, it’s a young team, and when I look at some of our customers who are doing the most innovative work, their teams are even younger than ours. They pull people out of university, or they take people off the university, and they train them how to use a programming language like R. And then train them how to use our platform. And they give them sales data, and they say, right. Tell us what’s going on inside all of this sort of stuff. And some of them have got very big internal teams kind of crunching this data on a daily basis. So, it’s interesting. I’m not sure – yeah, I’m not sure I’ve got too much insight into researcher diversity. I think it’s more skillsets. And these skillsets are reasonably new, as I was saying.

[00:33:45]

Jamin: Yeah, and when you think about leveling up, if you’re sort of in the boomer framework, which I am, when you think about leveling up your skills, I started my career statistics, and then SPSS, of course, was basically the – it was by far and away, the dominant platform for market research statistics in analytics. And then, just because I was a hacker, I was able to pick up SQL, but I haven’t actually learned R, and I do do some Python. Do you have – is there a specific language you think that researchers should self-teach? I mean there’s a hundred different self-teach platforms out there now.

[00:34:31]

Giles: When we started our research teams, we have one in the UK, that was run by somebody who came out of Nielsen. She was – she’s now a head of product. She’s kind of, I would say, more of a qualitative market researcher. Comes from a social science background. And then the guy that was running the team in New York, was more geeky, came from a CS background. And they developed two different methodologies which were both popular with different customers. So, the guys in New Your put together a Python library, and they were – they tried to do a lot of stuff using programming and trying to interrogate the data using algorithms and Python scripts. And R. And in the UK, it was very much using social data with – as a new spin on more traditional kind of market research report writing sort of approach. And I think the two can blend really well together. So, we encourage people to learn Python, and to kind of take – learn how to run experiments, and journals – Python journals, and so on. But also there’s still a lot of stuff that’s baked into our platform that you can just use the platform to pull out the insights, and then put them into a report. At the end of the day, it really is driven by what the customer needs are. And if the customer wants fast turnaround, and wants to understand lots of different – I’ll take an example. We have a customer who’s a Smartphone manufacturer. And analyzing Smartphone data around their products online is really challenging, because they get an enormous volume. So, doing that by hand is like forget it – so you have to create rules and categories and machine learning algorithms to try to kind of crush this data into more bite-size chunks that are then – you can look at from a quant point of view. And say this is changing more than this, and this – and that’s when these kind of big data approaches are really, really useful. Versus something where you’re looking at maybe a smaller product or a smaller audience, and actually having a human being interpret the data rather than looking at it from a quant perspective is a better approach. So, I think the two different types of approach are really important. And we encourage our people to be proficient at both. But some will actually lean one way or the other.

[00:37:45]

Jamin: Sure. I mean I think that’s a really interesting framework. The third leg of the stool for me is how qualitative may be able to humanize the researchy reports, but do you see any appetite there amongst your customer base? I’m thinking about things – yeah, like video open ends, or traditional qual, or what have you.

[00:38:13]

Giles: OK. So, the format of the report, and the way that it can be easier to access. I totally agree with that. I think that’s a huge skillset. In fact, we built a whole product around trying to do that. It’s called Vizia, and it’s about visualizing these – well, first of all, it takes in data from different places, and then it’s a visualization-building tool. But nevertheless, a tool can’t do it all. It’s really about how you can, as a human being, create a compelling, interesting, authentic story message that lands with the audience that you’re trying to get to. And, of course, the longer you take on that, the more you craft it, the more you script it, the more you put into the video, the better it is. But then you might not have time to do that, because the demand is like, I need this today. So, again, it’s one of these really, really interesting areas where market research has been PDS forever, and it doesn’t need to be. it can be a video. But then, how do you make it such that it’s searchable? Or how do you structure the video in a way that it gets the right messages to the right people, so not everybody has to listen to the whole thing? I mean, very tough questions to answer. I don’t think there’s a simple answer to any of that. But having that toolkit at your disposal, as a market researcher, is critical.

[00:39:47]

Jamin: All right. So, my next three questions are going to be more of a format of rapid fire. So, are you ready?

[00:39:54]

Giles: Yeah.

[00:39:57]

Jamin: First question. How will the market research space be different in the next five years?

[00:40:02]

Giles: More quant. Faster, easier to use, cheaper tools. Five times bigger as an industry.

[00:40:09]

Jamin: What is the biggest issue facing today’s market researchers?

[00:40:13]

Giles: Complexity of data, amount of data, the time it takes to do stuff. And the cost.

[00:40:21]

Jamin: Last question. What are three characteristics of an all-star employee?

[00:40:27]

Giles: Oh. They’re all behavioral. They’re all about attitude. So, willingness to learn, ability to collaborate, and work ethic.

[00:40:45]

Jamin: That’s perfect. Thanks for handling those three big questions so well. It was remarkable. I do really want to dive into your 5x bigger point that you made about how the market research space will be different in the next five years. The space overall has been relatively flat at an aggregate level according to SRM. Or in line with whatever market. Why so optimistic?

[00:41:19]

Giles: I think it might be a – the main concern – issue as well – I think that there’s certain things that go on right – today, that are market research, that are not called market research. And they’re not captured. So, I think it might be that the way they’re measured today, is not the way that it needs to be measured going forwards. But I’m optimistic because I remember Bill Gurley [ph]. He’s a famous VC who invested in Uber. I was listening to him at a conference once, and he was saying that Uber 5x’d the San Francisco taxi market. And they 5x’d the San Francisco taxi market, not because five times more people were trying to travel, but it’s because they made it cheaper, faster, and easier to use. So, rather than walk out into the street and think, where’s a taxi, and not find one, you use your Uber app, and it comes to you. And then you don’t have to tip and all of that annoying stuff, but kind of gets in the way a little bit. But they were also cheaper. And that stuck with me. And this was like, I don’t know how many years ago. Seven years ago, five years ago, something like that? Where you make something faster, cheaper, and easier to use, the usage will go up. And that’s what’s going on with market research. It’s no longer find just – find a focus group, get them in a room, spend a whole day figuring out what they feel about something. Write a report, charge a lot of money for it, and then deliver it three months after it’s commissioned. Those days are – that doesn’t happen very much anymore. So, where you can make it cheaper, faster, and easier to use, I think we’ll see usage going up. And what we are seeing on the client side, is that companies that are able to adapt more quickly to the changing consumer landscape, are more successful. So, if you’re a company, and you want to be successful, if you’re not baking consumer insights into almost every decision that you’re making inside your organization, then you’re likely to be out innovated by somebody who is. You are not adapting as quickly as your competition. So, if that were possible, the reason why that hasn’t happened up till now is because this is not possible. It’s too slow, it’s too expensive, it’s too hard to do. But if we could make it faster, easier, and cheaper, then it will happen more. So, I mean 5x could be a massive understatement, because I think it’s no longer market research. It’s decision management. And if you’re not baking consumer insights into decision management inside your organization, you’re probably not going to win.

[00:44:15]

Jamin: Last question. What is your personal motto?

[00:44:18]

Giles: I don’t – I want to get one. Did you ever see the film, the “Fantastic Mr. Fox”? Which is Wes Anderson’s adaptation of a Roald Dahl novel?

[00:44:37]

Jamin: No, I have not, but now I’m going to watch it.

[00:44:38]

Giles: You must. It’s absolutely fantastic, and I don’t know how old your kids are, but even if they’re teenagers, they’ll still love it. It’s utterly brilliant. Anyway, there’s this badger in that film that does this thing, where he makes a noise, and he goes who-who, and then he clicks his fingers or something, and then the fox goes, what the hell is that? He goes, that’s just my personal thing. It’s my personal motto. It’s like, it’s a bit weird. But I don’t have a personal motto. The cultural kind of foundation upon which Brandwatch is built, and it was done – from the very beginning. In fact, it was a cultural foundation on which Runtime Collective was built. It’s probably baked into the name, Runtime Collective, hippie sort of name. And that is the Golden Rule, is treat other people as you would like them to treat you. And that underpins kind of how I think about behavior, culture, all those sorts of things that we do inside this company. And what we do with our customers and so on and so forth. So, I guess, if I live by the Golden Rule, probably more than I do right now, then I think that would be a good thing. So, I guess the Golden Rule is my guiding principle.

[00:45:53]

Jamin: My guest today has been Giles Palmer, founder and CEO of Brandwatch. Thank you, Giles, for joining me on the Happy Market Research Podcast today.

[00:46:02]

Giles: It’s a pleasure, Jamin. Thank you so much for having me.

[00:46:04]

Jamin: Everyone else, if you found value in this episode, which I know I did, especially the 5x – reverse 5x’ing the market research base, super interesting stuff. Tweet, screen capture or tweet, share on social, tag me, I will send you a shirt. Have a great rest of your day.