Interview: The Rising Importance of First Party Data, with Shelley Bransten, Corporate Vice President, Global Retail & Consumer Goods Industries at Microsoft

Peter (00:00):

Welcome to unpacking the digital shelf where we explore brand manufacturing in the digital age.

Peter (00:16):

Hey everyone, Peter Crosby here from the digital shelf Institute for brands to tell stories and connect with consumers personally. And at scale, the right first party data is increasingly becoming the essential fuel. Shelly Branston took the learnings at top retailers like Smith and Hawkin, William Sonoma, and 15 years leading customer marketing at the gap and is now applying them for the entire in to her role as the corporate vice president, global retail and consumer goods industries at this scrappy little startup called Microsoft. Essentially these days, everyone needs to fail and learn fast in order to take advantage of the fast changing opportunities to connect with consumers and earn their loyalty. Sherry joined Robin me to discuss how she and at Microsoft are focusing not only on helping brands AMAs and manage data, but on the even more vital task of driving valuable action out of it.

Peter (01:12):

So, Shelly, thank you so much for joining us. You've just got such an amazing career. You've spent it you know, the, the first part of it was in retail, you know, some myth and Hawkin William Sonoma, the gap obviously household names, and then you jumped over to the tech side. You led retail and CPG for Salesforce. And of course now you're at Microsoft. Like what caused you to sort of wanna leave the industry and go to a new one, but obviously connected up with, with your old one. What was your, what was the thinking behind that?

Shelley (01:44):

Well, first off, Peter and Rob huge, thanks for the opportunity to be here with you today. I think like everyone in the last 18 months kind of interesting and thought provoking content come to me via podcast has been sort of a sanity outlet and the digital shelf is certainly one of those outlets for me. So thanks for the opportunity to be here. Wow. That is

Peter (02:07):

Great.

Shelley (02:08):

Thank you. Appreciate. Oh yeah, of course. I love the setup and in, in some ways, you know, I don't know that I even think that I left the industry. I still feel like I'm working in retail and CPG, but maybe just a sort of different angle. But same opportunity set. You know, as you said, I I definitely grew up, I mean, my first job was working on the floor at the limited express folding sweaters <laugh> and so I grew up in the retail industry. I've always loved it. I've loved sort of connecting people and products to believe it or not. Even back then data, I was like the biggest geek in a retail company. I built the first database at gap bank and, you know, got to do things that, you know, by this day and age, no one would've ever trusted, you know, a young 20 year old to do because nobody cared about it.

Shelley (03:05):

But I did always gravitate to data as a way to understand consumers and tell stories. And you know, in the, in the thinking that it's sort of better to be lucky than smart, obviously the, the world kind of grew up around me and the technology that's available to brands to use data, to use the cloud, to just understand those basic questions about their consumers has gotten so much better. And so that would, that's part of the reason why I made, I'd say the switch or the evolution from being thinking about it from one specific retailer to then Salesforce, and then Microsoft to think about how we could help brands think about this at a larger scale.

Peter (03:49):

When you think about the, the two worlds that you've been in, you know, the, the, one of the questions is always sort of, was it culture shock when you, when you made the leap over to, to, you know, SAS software, but I'm also wondering about the, the similarities, you know, in cuz in some ways, like you said, you are dealing with sort of the same problem and opportunity set, and I was just wondering sort of how you feel about that, the differences in the similarities.

Shelley (04:17):

Yeah. Well, the first thing I'd say in answering your question is yes, it was culture shock <laugh> <laugh>, you know I could probably do a whole podcast just about what it's like to sort of switch industries at sort of mid-career maybe you know, because we, you grow up in a certain company or in a certain industry and the acronyms, you know, even when you make mistakes, there's sort of a culture of trust around you. Yeah. And then you show up, you know, for me, you know, going from retail to then the, the first jump was really to Salesforce and learning, really learning technology while, you know, kind of being expected to also leader and lead things while I'm learning was, was dramatic. So that was very different. And I would say underneath that culturally retailers for as long as I've been in the industry and before that, and brands are paid to make bets about what's best to be sort of that trusted editor right. Of of what is gonna appeal to the consumer. And in many ways, technology companies are paid to fail fast. And so that was probably the big, the first cultural shift for me was, oh, I'm gonna get paid for doing things more quickly and making more mistakes. And, and that's a huge difference. And I think the worlds are converging yes now, but it's a huge cultural difference between sort of the, the industry, which I grew up in and the industry that I now work

Peter (05:53):

In. That's I, I love that because, you know, that's what we see happening and, and, you know, eCommerce and digital does have a lot to do with that, where that world is just so much faster and it's often ruled over by an algorithm <laugh> and so just you know, to the point of data, like it it's, it's so much, no, it's not more data driven. I know how heavily retail is data driven, but it's in shorter time spirit to, I think, right.

Shelley (06:18):

Absolutely.

Rob (06:20):

I remember back in 2014, when these world, it was the first time that it occurred to me that the worlds were colliding in the way that you're describing. We, there was a webinar with the buyer of the personal care category at Walmart and the, the buyer the role of the buyer at Walmart is changing. We used to be in charge of picking the right consumers at the right price point for our shoppers. And now our role is to get every single product in the category listed. And then what you saw at Walmart over the subsequent, you know, four or five years is just about the entire merchant organization flipped over, you know, and you went from this model where you had folks that were editing and curating to instead folks that are right outta college. And if, and running something similar to the Amazon play where you're just using data to choose, to choose more of what goes on the shelf. And, and that hap I mean, that happened really, really, really fast. So, yeah, I got, imagine that you're seeing this from your seat across the whole industry, flipping over all, all over the place right now, and that's gotta be a, a major disruption in, in every single retailer at this moment.

Shelley (07:30):

Yeah, you're absolutely right, Rob. I mean, in a way it's funny because even though obviously now I'm a technology provider. I, I think the hard, the Mo the thing that I do the most is actually the cultural shift, the mindset shift of more than even the, and, and, and really kind of not actually, but holding executives hands and sponsoring them as they make that shift that you just articulated that Walmart went through of trusting the data leveraging the data and then hiring the right people who understand the data. And that is in many ways more important than the tools that they're gonna use to, to read the data.

Rob (08:13):

Yeah. So let let's stay on culture in big company shifts for a second, Microsoft is one of the couple trillion multitrillion dollar company, crazy market cap businesses in the world. And even growing up in the nineties, in computer science, everyone was terrified of Microsoft in the nineties. And the people have been terrified of Microsoft for decades, cuz it's such an impressive business. Now you, you got in there and said that Satya is super accessible. He's, he's around, he's approachable. Lots of people get FaceTime with him. And that creates a kind of culture that scales to a multi-trillion dollar global company. That's, that's dominant in many ways. And so how is that possible? Like if you, if you're going from the world of retail to the world of Microsoft and, and, and just objectively, you're going to a company that's a lot bigger. And then somehow they've managed to get culture that's healthy and that scales like what, what are they doing well to make that possible? What are your,

Shelley (09:16):

Yeah, I, I mean, I think it's, it is the single reason I know I joined Microsoft is the culture. And, and I'm not unique. And if you talk to leaders within Microsoft who are new, that that is what they'll say. And sometimes, you know, companies write mission statements and they even like paint 'em on the walls or they're in their annual report, but they don't live them. This is probably the most mission driven company that I've been part of. And you know, if you think about to your point where Microsoft used to be, where the mission used to be about a PC on every desk in an, every home. Now the mission statement is about empowering every person and every organization on the planet to achieve more. And so the word computers, not even in there, the word technology's not in, in there.

Shelley (10:07):

And you know, if you, every level of the organization, if you ask them, you know, what's the company's mission statement and, and, and how do we make it real? They could say it. And you know, every meeting that I'm in internal sort of exec staff meeting, it's the first thing Satya talks about. He talks about our mission and our culture and the culture piece is really, and it's well written about in hit, refresh his book, which he wrote sort of in of his transition. But it is really grounded in this idea of confronting your fixed mindset and adopting a growth mindset, which was he initially read as a, as for parenting advice not for sort of, you know, corporate advice. And that's one of the things that it, that I love is that I am not, I mean, you know, I learn something new about hyperscale technology or AI technology every day.

Shelley (11:04):

I am a learn at all. I am not a know at all, but to be surrounded by people that wanna be learn at all and you know, we're not done by any stretch and there's still some people who think they're no it alls and <laugh>, and then he'll say, you know, he'll say like, well, you know, walking into a meeting and calling that person to know it all, isn't the answer either. But it's how you bring people along in this, learn it all. You know, that I think has been, you know, so incredibly inspirational for me. And you know, to your point of sort of what this means for retail and CPG, you know I, my, we look at ourselves and Microsoft, you know, just is public. You know, we missed search <laugh> we missed mobile. We got the cloud, you know, and and I think for many brands, it's a great story of how you have to disrupt yourself, how you have to look at what trends you've missed and then how you accelerate towards the ones you understand now. And that's sort of a narrative that I find is incredibly inspiring when I'm out there talking, cuz it's not, you know, it's not easy to be a CPG brand needs to, or a retailer. And in, and Microsoft as, as a company that we had to disrupt ourselves in order to sort of see where the puck was gonna go.

Rob (12:21):

Yeah. That flexible mindset description that you have there. And it, I remember when, when Satia first came on, board analysts were writing a lot about how, again, like you said, missed search, missed mobile. That was the story. And they missed it in large part because they were a windows company mm-hmm <affirmative> right. And that, and it was from that legacy, you're saying of EV a PC in, in every desktop, a PC in every home and the giving up of what they were to become something new and greater and to do it, you know, to go from being a windows company, to this general empowerment company with a major cloud business, as quickly as they've done it in just a few short years while retaining really great people and bringing new really great people on board, I think is just an absolute achievement. So yeah, I think, I think you're right. There's a lot of parallels to be made if you're a CPG company and you're looking your particular category getting disrupted and you, you're looking at losing share against lots of upstarts and whatnot. Microsoft is a pretty good case study of transforming. And then, I mean the amount of growth that we, that you, that you all have seen in the last decade is unbelievable.

Shelley (13:26):

Yeah. Yeah. And to your point you know obviously sort of the investment to invest in the cloud, we had to disrupt some of our, you know, legacy businesses and that is, was not, and that was controversial. And so, you know, I can imagine it's painful, it's classic CPG of like, you know, kind of, when do you retire some of your cash cows, so you can invest in the future, whether it's D TOC or, you know, all sorts of other kinds, the brands it's, it's very analogous. And it's hard.

Peter (14:00):

Yeah. I've, I've certainly, you know, we're lucky enough to talk to a bunch of brand manufacturer executives who come from digital and come from e-commerce and those are the hardest conversations to have, especially, you know, particularly pre COVID. But, but of course still today where, you know, maybe, maybe 85, 90% of revenue is still coming from in store motions, but in order to get good at this one, you have to overinvest in, in advance of returns, in advance of, of market share. And and so when we think about one of those area of opportunities, that's really where we want to dig in with you because the, a lot of this change is wait, what we have to be retailers too is, do we wanna do that? <Laugh> that seems really hard. And it's a bunch of new marketing and selling and, and it motions that they've never had to think about before and their, their businesses are already complex enough. And so I would, I would love to dig in, you know, as the woman who created the first database app, <laugh> you are, oh my gosh, I like that. But let, let's, let's start with first principles sort of, you know, what is first party data and why does it matter to a brand manufacturer in this sort of digital first omnichannel world? What is, what is, how big a change is that based on where all the places is that you worked retail at and, and worked with brands, we'd love your thoughts.

Shelley (15:36):

I love the question. I love it because, you know, first I, I spent so many years just trying to convince people how important data was and now, like we have all these categories of like zero party data, first party data, second party data, which is the mystery one, and then third party. So you know, first party data is generally seen as the data that a brand or retailer would collect directly from their customer. And so you, we would think about, you know, a loyalty program. So when someone's opted in a homepage traffic, their location, CRM, you know, sort of an L program and you know, the data in the first party data in and of itself is not really, what's interesting. What's interesting is that the consumer has raised their hand and shown some interest in, in that company. And so it's sort of the, I always say it's like the digital breadcrumb of, of an intent and signal.

Shelley (16:34):

And, you know, we gotta remember, I always sort of, when I talk to brands and they're feeling like they're getting, you know, dis intermediated, I gotta remind everyone, you know, 33% of the GDP around the world is retail and what they hold is the demand signal for the world. And that first party data is the sort of underlying signal for the demand signal. And the, the question is, what are you gonna do with it? You know, cuz retailers have had a lot of data for a long time. And so I think that's, what's exciting to me in working with many of the branded manufacturers and the retailers is not just the capture of the data, but actually like what's the, what's the in strategic initiative or ambition of what to do with that data.

Peter (17:27):

Yeah. That brings me to a central point of tension that I have when I think about this stuff a lot is you hear people talk about data is the new oil, you know, like as in data, in and of itself is gonna get somebody out of this mess and we just need to collect all the data and then you see folks investing in data lakes and just, you know, it's almost like they're just dumping stuff into the lake with, just to make sure that they've got it all first start, first thing is capture it. Just make sure you capture it, we'll figure out what to do with it later. And like, I, I kind of think that it's, the data itself is maybe overvalued in the sense that data, without a purpose and data, without execution behind it, doesn't, doesn't really matter that much.

Peter (18:14):

And I, but, but you know, people talk about both, both sides of this, you know, one side of it is, well, yeah, but it's better to have it because at some point you're gonna need it and we know it's valuable and it is the signal and it tells you what the future demand is gonna be. And on the other side is, well, it's a lot of work to capture this stuff. Why are we capturing it? If you don't know exactly how we're gonna use it? So how existential is first party data for a branded manufacturer for its own sake versus first party data with a particular end in mind. And, and, and if, if the latter, like what's a really strong and that a branded manufacturer might be looking for for first party data,

Shelley (18:54):

That's a big question. But I, I <laugh>, I would definitely gravitate to the second part of what you raised. I mean, first party data with an intention of how to use it and the outcomes you wanna drive, you know, the versus just standing up a data lake. You know, I would definitely always work with our customers on, you know, what are you gonna do with it? You know, how are you going to understand your consumer better? How are you going to think about, you know, connecting, you know, sort of from intent to fulfillment, how are you gonna think about digitizing your factory? How are you gonna empower sort of your, your, the frontline and your factory or in your distribution? So I would definitely gravitate towards not data for data SA sake, cuz you can spend, you know, I mean most companies today spend 70% of their time just prepping data when we look at it.

Shelley (19:53):

Like, and so yeah, yeah, yeah. That's, that's easy. And then, and these are obviously right, they're very expensive people to have doing the prep. They're hard to recruit and it's not that much fun if you, if you sort of put yourself in, in those shoes, you know, prepping data versus delivering insights and test that the latter part is a lot more fun. What's the, the tooling is now good enough that you don't need to spend that 70%, percent of time prepping data, but that's where most, you know, companies we really work with are we're seeing their they're, they're spending a lot of time and that's just wasteful. Yeah,

Peter (20:31):

That's that's wild. So is it, this, is it the, the 70% that you're seeing is that due to some type of legacy structure of the way that they're setting up data capture or is it because of this philosophy that we need to be capturing the data and therefore they end up with a lot of data prep baggage, or is it some combination of that? It's

Shelley (20:51):

Probably a combination of that, but it Al always goes back to, I think your initial question, which is what's the intent behind the data? Like what are the three or four? Not that you're not gonna know all of them, but what are the three or four use cases that matter most to you? What there it's, you know like with some of our major CPG customers, it's, you know, inventory allocation, demand, forecasting pricing, and promo optimization, like those kinds. And those are all kind of, you know, of course major use cases. If you start with that kind of end in mind, you're gonna move faster. Then if you're just, you know, kind of trying to get all sorts of data from different sources together.

Peter (21:33):

So when you think about the, the shift where brand manufacturers are becoming their own retailers, whether it's on marketplaces or DC channels or social commerce channels with a lot of that being, and, and, and first party data being really for many of them introduced somewhat for the first time, they've always relied on other things. And so how, you know, if you have any use cases, you know, I, I don't expect you to mention names or anything, but we'd love some examples of where you you've seen brands sort of, what is that test and learn strategy? Can you have a test and learn strategy when you're building a data lake, the size of Wisconsin or something, or you know, so, so where, where do people start? What is it that then moves the program along? Like what are those milestones in, in like the first year of an effort like this?

Shelley (22:29):

Yeah, well I think you, you can test and learn. And we, we are seeing a tremendous amount of innovation you know, across the sort of spectrum of first party data around, you know, sort of the old 360 view of your consumer all the way through to the data use cases around sort of factory of the future and then most digital and IOT connected factories in the world. You know, there's some super public use cases that that I'll share one, you know, we were talking a little bit, but Mars is a great partner of ours. And know they are taking a data first approach to how they think. I mean, you know, most people don't really realize it, but now their business is large. Their pet care business is larger than their chocolate business. Their, they owned Banfield hospitals you know, in the last and during the pandemic, there was so, so many people adopted pets. We, we <laugh>, we looked like Peter, did you okay?

Peter (23:36):

Yes, Lily. She is not in the apartment right now because she would be making herself known <laugh>. Oh, well,

Shelley (23:42):

Good. <Laugh>

Peter (23:44):

She comes in by the end. I'll introduce you.

Shelley (23:46):

<Laugh> good. Good. Well, we're actually, we're just getting ti like super interesting data about actually how Lily and others, like her coming into meetings like this on actually have made, like, that's one of the things that's made work. People feel more connected in their jobs and this

Peter (24:02):

Yeah. Some humanity through pets. Yeah. Yeah.

Shelley (24:06):

But yeah, we're doing so Banfield, they at Mars, Oak Banfield, hospitals they, they, in the last, you know, during the pandemic spun up the tons of virtual, like all of us did a lot of telehealth visits. They did virtual pet visits. And, you know, then had an opportunity when the pet said, you know, like my dog flash who gained a little weight during the pandemic. Oh,

Peter (24:34):

Poort we all <laugh> poor

Shelley (24:35):

Flash. I know, I know it's you got more walks, but a lot more food, but you know, they, they, they did these telehealth visits with us through Banfield, then they could upsell and cross-sell some of their pet food businesses. And then they've been checking back in on, on flash. And so, you know, you could ask the question, you know, in that example, is Mars a healthcare provider? Are they a C P G? Are they a retailer? And in, in a sense you know, I don't know that it really matters, but that's a use case where the data around just like every single one of those interactions, whether it's Lilly or flash is, get making them smarter about the future experience. And then the same would be, you said about their factories, you know, we're, we're we we've instrumented a bunch of their candy factories to understand when machines are overheating, when, where are people in the right place in their factories.

Shelley (25:41):

And there's you know, a really fun story about like every in for bags of skitles, it's incredibly expensive for Mars. If there's too many Skittles, like just too extra in the bag or people get upset, if you don't have, you know, certain number of yellows or reds. And so now all of that data has been instrumented and it's saving them money. It's improving customer satisfaction and it's helping them with future product development, you know, maybe, maybe there's an opportunity for all yellow Skittles. Don't, don't, don't, don't quote me, but I'm just saying, like, whether it's your factory or your commerce experience those kinds of experiences in banded manufacturing are data rich, but grounded in, in use cases.

Peter (26:27):

That's what I really, they love about the, the, the potential of data to serve so many constituencies from the very beginning of product ideation, all the way up to the, the things that you're talking about. And so I would imagine that part of that early thing is figuring out who are the consumers of this data and how do we make sure that whatever we're doing can serve can serve across those, those many use cases, or at least a handful of use cases that are the most important.

Shelley (26:58):

Yeah. And we're seeing just to your point like the sort of you, we hear this word citizen developer, but I know like from my days at gap, you know, you sort of have these ideas of what you wanted to do with data even then, and then you'd get in the it queue and wait <laugh> and go to meetings and advocate and fight. But now the tooling around the data, you know our customers, we have a product power platform, power BI, like where, you know, someone, people, what I love is we're seeing, you know, folks who work on store in stores and factories, you know, even like some, some some of the truckers or one of our CPG customers, like they're creating apps to let them know the most important information that needs. So it's sort of transitioning from being, you know, in this data lake that no one can reach to getting into the hands of the front line who actually really need it to do their job.

Peter (27:54):

What's really interesting about the, the Mars use case. Is it almost makes me go back on my thought that you must have a use case for the data to have, have an excuse to collect it is because Mar Mars is such an interesting company and the pet division in particular is so interesting because like you say, they went from being CPG to now. They they're like a pet company and they go end to end with the pet. They do the hospitals, they have you know, it's like everything having to do with the pet. They, they can help service and that's, that's a meaningful line extension. And another company that we've mentioned on the podcast that has done similar things is king Arthur baking, where they sold flour. That's what they did. You could go to the grocery store and you could buy their flower.

Peter (28:37):

And their flower was high quality. And now king Arthur baking.com. They've they've. They used to be king Arthur flower. Now they're king Arthur baking and their website's. So else anything that you might possibly need for baking, some of it's branded by them and a, a lot of it's not. And so they just became like a baking retailer, even though their primary business is manufacturing. And so there's these business line extensions where instead of selling a product to a consumer, some of these businesses are selling whole solutions to consumers that that, you know, these transitions are such a big transitions that as you go into them, having a lot of data to lean on can't help, but be valuable. I mean, I'm just thinking about, I mean, power BI is an exceptional product. I, I love that product. And I could just imagine you're, you're at Mars. You're trying to reason what expansion into hospitals means. And you're a business analyst you've gone to Wharton or something like that. And you can just get access to every bit of behavioral data from a pet owner for the last five years. God, how helpful would that be in making the next steps? Right.

Shelley (29:43):

Completely. You're absolutely right. And that is the, to your point, like, there's, there's a fine line, I think, between, like you, you wanna know sort of where your, how you're making money, what sort of strategically you wanna do with your pet care business, but then you'll also have to be the hard part is you also have to be open enough to, to like with the king Arthur example where it might enter you into a business, you never imagined we, we also work a lot. I'm sure you do too. With AB and Bev, obviously Anheiser Bush and pretty much fears around the world and, you know, classics sort of CPG retail execution opportunity making sure they're getting sort of the right products to the right stores, but in, in Latin, and especially in certain companies in Latin America countries, within Latin America, they have these B Digitas, which are, you know, really family own places where you go have, have a drink.

Shelley (30:41):

And we worked with them sort of data driven strategy there. And actually what we found is it created the, the sort of the trackability and traceability of sell through and promotion and pricing in those bodegas then helped them get credit from banks because they were then more connected to the center of AB InBev. And and so it opened up a whole new, almost like financial arm for this company that was about, you know, more about selling beer than certainly creating microfinancing for some, from, for local selling of, of their products. And so I do think part, part of what's exciting is the connectivity of the data that then opens up new business models. And that, you know, to your point is actually sort of them going from maybe being sort of, we talk about sort of the tra the shift from show from shelf to ship where the whole business model for CPG was built around the shelf, but everything is now gravitating towards or moving towards thinking about ship and, and, and what the financing layer looks like underneath that and how, how data can help that.

Peter (32:00):

I love that use case because it's, there's reasons to do the, the data at the beginning of it. And then as an emergent quality of having the data and working closely with their, with, with their customers, which in this case, instead of, it's not, you know, Rob drinking a beer, it's the B bodes with, by working with bodes, they then all of a sudden they find this emergent business opportunity of, of financing, which it's only gonna make them stickier in the region and accelerate growth, and it helps them. It helps their customers. It helps the end drinkers. I that's just, that's such a really great, that's a really great example of how this actually works all the way up and down the stack. Yeah. That, you know, it's interesting, you said the moving to ship that Benedict Evans over in the UK, you know, he, he likes to talk about how in the past there used to have drug and club and mass and whatever, as the different retail channels that you might work on as a manufacturer.

Peter (32:57):

And now you should be thinking of, well, is it bike? Is it car? Is it, is it shipping container? <Laugh>, it's like, that's the, that's the big shift here. And if you're, if you're actually making that shift, then having the data on really granular buying behavior and buying purchases make, could actually change the way that you're creating product from the beginning packaging product for shipping models, which distribution partners do you lean in on going forward? Yeah, I you're bringing up actually. I'm sorry. I'm rambling a little bit, but it's really fast getting this thought chain that you're, that you're introducing here. Oh,

Shelley (33:31):

<Laugh> well, right back at you, especially as I think about drones coming to deliver things to my house <laugh>

Peter (33:38):

Oh, man, do we have to do that? <Laugh>

Shelley (33:43):

I dunno, that inevitable think

Peter (33:45):

We do. Oh gosh. I, I kind of hope not cuz even if I see one drone in the I'm like, who are you? <Laugh> right. What are you doing here? I agree. And I wonder if that, can I wonder if that can shift? I don't know. I don't look at a car and think, who are you and why are you here? You know? So I don't know. Maybe it can shift over time. Also the buzzing drives me crazy, but that's a different topic altogether with my therapist. So Shelly, as you, as you think about you, you know, you talked earlier at the beginning, how so many of your conversations are not around the tech? They are instead around the shifts that need to happen to support being able to, of, to use what the tech comes up with. And, and so when you think about the biggest challenges that you counsel your brand manufacturer customers on, you know, what are the, are they silos? Are they lack of human resources? Is it you are actually going in and really trying to figure out what are those use cases? Like? What, what are the sort of the, the highest level package of challenges that you would encourage our listeners to really try and work on inside their organization to sort of ready the way for, for doing a program like this?

Shelley (34:56):

Yeah, well you know, certainly a big use case that's top of mind for all the CPGs and retailers that we're talking to right now is sustainability mm-hmm <affirmative> and sort of the, the, which is fundamentally a, a data question, right. Of track and trace and record. So that's a top use case that we're really privileged to work with a large, a lot of the largest brands around the world to how they, how they understand that. Then another sort of, these are like sort of the, the greatest hits, but the another one is you know, obviously around personalization and personalization around your consumer and personalization around thinking about price for, for specific consumer sets. And then, you know, I would say the use case around direct to consumer, like we talked about is a very top, top question, top opportunity that we're talking to our brand manufacturers about. And I don't think we, I don't think they're done on retail execution either. No. So, I mean, there's a lot of, there's still a lot of surface area. There's a lot of clip where out there there's a lot of sort of tribal knowledge that hasn't been that hasn't leveraged best in class technology and it's just costing these companies a ton of money. So those are some of the top use cases we're really we're really focused on.

Peter (36:35):

So it sounds like these, these customers are coming to you. Pretty sure they want to do something here. It's it really is more sort of how does our organization need to evolve to be able to, to pull this off and, and what are the structures we need in place? The the understandings, the sort of silo breakages that may need to happen to, to make all of this work

Shelley (36:58):

That's right. That's right. And maybe, you know, sort of orthogonal to this discussion, but certainly top of mind to all the companies I'm talking to is sort of this hybrid work paradox that we're all navigating through. There's not a, a CPG that I talked to. That's not trying to sort of, I don't, I just someone say, I don't know when we're gonna get to a new normal, and I don't know that we'll even know it when we get there. Mm-Hmm <affirmative> but you know, the way we're gonna collaborate the way we're gonna plan the way we're gonna merchandise, the way we're gonna understand our data, that's all shifted. And in that we're all working about average, about two hours more per day. We're seeing that just in, you know, sort of our telemetry we're seeing, you know, 200% increase in chat messages over the weekends and ramping wow.

Shelley (37:52):

Work related chat. Yeah. Yeah. So the days are longer, the breaks are fewer. The weekends are now work time. And I guess that that's the disheartening part of it. <Laugh> yeah. The heartening part of it is that that both on the retail and the CPG side, the, the folks I'm talking to are very curious about the data. They wanna do something about the data and they recognize that in these businesses, our, you know, creative businesses, it's not sustainable for us to be working this way. So how do you know then there's questions back to us of like, you know, how do you force breaks? How do you monitor that? How do you guide people about the most productive work in the most productive times? We, we recently saw that, you know, teams that spend at least three hours together, uninterrupted. So three consecutive hours together, every single month are the most productive teams when you tie it back to sort of financial metrics. So, you know, we're learning about us sort of how we're gonna work. And I think how that work actually, how we learn to work will, will really help you know, sort of the performance in retail and CPG,

Peter (39:06):

Peter, are you willing to commit to three hours in a row with me? <Laugh>, I'm barely willing to commit to three minutes with you, Shelly. That's also disheartening message. <Laugh> no, we we've spent way more than that together. <Laugh> who knows. I do look forward to getting back in the studio with you. That'll be fun. Well, that's, I mean, it, a, that is disheartening to hear tho those statistics. It is heartened to hear that companies are coming to you to help them solve that, not solve that problem illuminate the problem at the very least through data. And then you can make different decisions and that they, they have concern about that. I mean, you know, everyone's writing about the great resignation these days, but I feel like that's part of it is, is the option that has happened caused a lot of reevaluation. And then also at the same time caused a ton of pressures on people, both personally and professionally that still, we've not we've not really reckoned with

Shelley (40:10):

Agreed. And the data is so murky. I mean, you know, we've got sort of 70% of people, people saying they want to maintain the flex flexibility and then 67% saying that they want more sh you know, kind of shared collaboration time. And so you've got this, this hybrid paradox, as you're saying, and it's not clear sort of, and, and, and it very much, I think does vary by where you are, what stage you are in your career. Mm-Hmm <affirmative>. But you know, certainly like, you know, people who can have a good setup at home and you know, are further along in their career. Maybe this has been somewhat advantageous to 'em people who are early in career and really want that collaboration and might have like little kids really running around and in a smaller space, it's been incredibly challenging. Stressful. Yeah.

Peter (41:09):

Yes. Yeah. We really are right in this moment where everything is up for grabs on some level, how do we, as a communicate with each other, how do we determine what's true and what's not true as a business? What are your channels that are gonna be growing versus not growing on, on one month, six months, five years? What are the business opportunities that you're not seeing, you know, like AB in Bev, helping the, the BOGO bodes in Latin America to finance themselves or Mars moving into full service, pet care hospitals, there's just, there's so much kind of churn and disruption and uncertainty everywhere. It does for me to get back for the topic here, folks that have good data and have good data analysts and understand that they've gotta have a flexible mind to go forward. They at least have a little bit of light in the darkness in, in, in this moment. So, I mean, I, I there's, there's so much that you, that you've said in this conversation so far, that for me is, is very thought. It's just thought provoking mean this there's this incredible time that we're in right now. And so much of it is trying to figure out what the right path is to go forward.

Shelley (42:21):

So, yeah, you're right. And you know, someone once said to me in this time, you don't need a map. You need a compass cause you know, who knows where we're going. But I do think <affirmative> back to bringing us back, like the data is what guides the compass. And so, you know, I, one of my favorite things to do, just even when I do sort of Microsoft, like executive briefings, not to put people on the spot, but just sort of say like the fir opening thing is like, who owns the data in your, a company and what's your data strategy. And, you know, I mean, it, it's not, as it's not given all like to all the talk about datas the oil and data's the hair and you know, all the stuff you would think, well, you have a merchandise strategy because you spend a lot of money on that and you have a real estate strategy. But so like just asking that question and you know,

Peter (43:17):

I just pictured people like looking at each other, hoping somebody else will answer that question. And, oh, my gosh, makes me sweat was thinking about the favorite part of that is that the answer for gap is still Shelly <laugh> no, no, no, no. They've not. You're not even doing, you're not doing the tech support ticket, helping <laugh> oh, call someone, call Shelly <laugh>. Well, Shelly, thank you so much for, for joining us. You know, this is a, it's a big topic to handle in 35 minutes or so, but the it's, it's great to get a sense of kind of what are the forces driving the need for, for understanding, gathering, understanding, and then activating this asset at brand manufacturers. And it's so great to connect up with somebody who's been, you know, on both sides of the aisle. It, it definitely gives a different and more acute perspective. And we really appreciate you joining us.

Shelley (44:19):

Well, it's my absolute privilege. I think the, the, the best way we can sort of have good strategies is by what you guys have created, which is having conversations about where people are and where they wanna go. It is it's not clear for anyone. And so I'm super grateful to podcasts like this one that help all of us navigate you know, where it is we wanna go with, with, with one of our most precious assets, which is our data.

Peter (44:50):

Yeah. I'll never or forget putting together. We put together a gathering of, of some brand manufacturer executives and they had lunch together and then spent a lot of the day. And, and then when they were sort of reporting back to us about their experience, one of them said, you know, it was so great to come here today. I found my people, you know, it's a very low only thing this I think for, for the folks that, that we look to to help and, and educate. And so bringing together an ecosystem around these problems and, and challenges and connecting people up with others is I think in some ways, the most important work of this moment where people tend to be more and more separated. So how do you make those connections? So you coming here is, is another example of that and we're, we're grateful.

Shelley (45:37):

Oh, my absolute pleasure. Thanks for having me.

Peter (45:43):

Thanks again to Shelly for sharing her brain with us. It's a great conversation to close out our 20, 21 podcast year, Rob and I are going to take the week off between Christmas and new year's to rest recharge and give your ears a rest as well. We are grateful for the time you have given us this year to share these conversations with you and thanks as always for being part of our community.