The 5 C’s of Product Data with Jason Hein, Global Director B2B eCommerce Association

Transcript

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Jamie Clapper (00:00):
Welcome to Unpacking the Digital Shelf B2B Edition, the podcast for the practitioners navigating complex data, massive catalogs, and the shifting expectations of the modern business buyer. Every month we sit down with the leaders who are moving past the hype and building digital shelves that actually scale. Hi everyone. Jamie Clapper here from the Digital Shelf Institute and I want to welcome you to our first episode of Unpacking the Digital Shelf B2B Edition. To kick off this new series, we are joined by industry veteran Jason Hein, global director at B2B Commerce Association. Jason brings over a decade of merchandising experience from McMaster Car and a foundational role building out Amazon businesses industrial catalog. But more importantly, he brings a fantastic sense of humor and a gift for storytelling that makes complex data actually fun to talk about. Today he's unpacking his 5Cs of product data framework, sharing how to translate heavy technical jargon for everyday buyers and giving us the exact blueprint to build a data model that scales.

(01:11):
Let's unpack it. Welcome to the podcast, Jason. We are so excited to have you here on our first episode of the B2B edition of Unpacking the Digital Shelf.

Jason Hein (01:25):
It is an honor. I don't know that I've ever received an honor as prestigious as being the inaugural host on such a what I am sure will be the leading podcast on e-commerce in the world, perhaps the Galaxy.

Jamie Clapper (01:45):
Oh my goodness.

Jason Hein (01:48):
I set my standards.

Jamie Clapper (01:49):
All I can say is I think this recording is done. This is perfect. We don't need anymore.

Jason Hein (01:54):
We don't need anymore. There you go.

Jamie Clapper (01:56):
Wow.

Jason Hein (01:56):
Jason Heinz says the authoritative podcast of B2B e-commerce in the galaxy.

Jamie Clapper (02:02):
With that kind of praise, I don't even know where to go from there. But no, we truly appreciate you being here on the show with Lauren and myself. I'm sure most of our listeners know you, whether that's through LinkedIn, podcast, in - person events, you're all over when it comes to B2B and just your experience and knowledge within the industry. And that's why we are going to dive right in and give our listeners all the juicy nuggets that they are dying to hear as we think about B2B digital space. Jason, because of your experience working at places like McMaster Car and Amazon Business, you saw firsthand that there was a massive opportunities for companies to do better when it came to bridging the gap with product data. To help organizations bridge that gap, you developed a framework called the 5Cs of product data. To kick us off, what do the Cs stand for and how do you define each of these in one sentence or two for our listeners?

Jason Hein (02:58):
Sure, sure. Yeah. The intent behind the five Cs was really just to help people think about... A lot of people view themselves as the victims of whatever product data they get from their suppliers. And I was just trying to help people think about setting a standard for what good product data should look like because that was the thing that I was spoiled with at McMasterCard in particular. They spent a lot of time and resources over many years optimizing their data to create that experience that they have now. And so the way that I think about it is there's five concepts that B2B companies need to think about and apply to their content in order to create a great experience. And it's a tiered prioritized order. The first C that people need to think about and try to make their data meet this is correct. Obviously, if your product data is wrong, you're going to have a bad day.

(04:04):
That's just how it is. I don't need to go into a lot of detail about that, nor would my lawyer suggest I do that. The next C is the data that you have for your products has to be complete. If you don't have all of the attributes or the photos or a good description that helps each of your customers understand how each two products you sell are different from each other, they're just going to pick whichever one's cheaper and that's not always going to be the right one for them. So making sure that you have images, good descriptions, all the attributes you need for faceted search, the data you need to drive keyword search in your system so that's what you need to help customers build that experience. Third is it has to be consistent. So once you've populated all these fields, you want to make sure that because we live in a world where there's a lot of different terms that are used to describe these products that we sell, want to try to eliminate that variety and pick a term for what we are going to call this.

(05:15):
We can always deal with synonyms through tuning in the search platform, but using the same terminology is very, very helpful in getting customers to that right product. The fourth C is clear. We don't want to apply the kind of high sophisticated technical jargon terms. Helping customers find the right product is going to be optimized if you help the people who know the least about your product know what to call it. If you're leaning heavily into trademark terms, acronyms, abbreviations, you're not being helpful. Guess what? The experts are going to know what you mean if you use a more generic term. The newbies won't know if you're using all the technical jargon. And then the last one is contextualized and this is the one that is getting a lot of attention these days because it's kind of the newest. In fact, this used to be the four Cs until only about two years ago, but contextualize is acknowledging that I've got different customers and when they're searching for something, if a customer in a hospital searches for glove, they want a different kind of product than somebody who's working in a weld shop.

(06:41):
And our ability to match that customer to the right product is oftentimes decided by our ability to contextualize the data that we present to customers in different segments depending on where they're logged in from.

Lauren Livak Gilbert (07:02):
So Jason, I love the framework. I think it's easy to think through, easy to remember, easy for brands to follow. And you talked about correct and complete. If you're not there, don't pass go, don't collect, what is it, $100 or however -

Jason Hein (07:17):
$200. $200, thank you. Inflation. Time.

Lauren Livak Gilbert (07:19):
Inflation, correct. Yeah. So correct and complete.

(07:23):
Number one, you need a product experience management platform, you need to have accurate, you need to know who owns the data. I feel like we're good on those. Consistent and clear. From a B2B perspective, there's a lot of, to your point, technical attributes and product descriptions and just complications when it comes to describing items on the digital shelf. So let's take an example. Let's say you have a piece of 3M safety equipment or like an industrial valve or something. How as a distributor would you think about translating all of that technical detail into something that is clear and consistent? If a B2B listener is trying to be like, "Oof, how do I do this? " What would your suggestion be?

Jason Hein (08:10):
So one of the reasons this is so important to make it consistent and to make it clear is that one of the ways your customers are going to evaluate you as a supplier is how well do you know what this product is? But the ability to recognize all of these terms that are in use in the market for this product and consolidate all of them into one is a signal to your customers that you recognize and understand all of those terms and you know what they mean and they all mean this. The clear factors into the choice of how you're going to choose to normalize it. Are you going to normalize it to the technical term, the jargon term, or are you going to normalize it to something that's plain English? An expert is going to know how to use the terminology that both the other experts and people who are unfamiliar can recognize because they really understand it.

(09:19):
So it's not only important to help the customer, which is obviously the priority, find what they're looking for, but you're also trying to give them a reason to buy it from you because you understand the market, you understand the product, you understand and you can translate it.

(09:41):
There's more of a signal there than just getting your customer to a PDP.

Lauren Livak Gilbert (09:46):
And what about from a buyer perspective? I feel like buyers are a little bit different than they have been in the past and/or are changing. So whether that's they want self-service information versus using digital versus having a conversation, can you talk about how that's kind of changed how you need to think about being clear and consistent on the different channels with the evolution of the buyer?

Jason Hein (10:14):
So I'm going to Ikea flip you here, Lauren.

Lauren Livak Gilbert (10:19):
Woo, I love it.

Jason Hein (10:20):
So what do you mean by buyer?

(10:24):
Remember in B2B, there's different kinds of buyers. So on the one hand, you have the design engineer who's like super technical nerd. He's the person who's leaning into, they're in AutoCAD and they're like downloading CAD files and they speak all the jargon. There's the purchasing agent who all that they get the requisitions and all that happens is their day, she's getting requisitions across her desk all day and it's literally piece of paper with a bunch of part numbers and words she's never seen before. And her job is just to go out, find these products as fast as possible, spend as little as possible, get them in as fast as possible. And then you've got the maintenance person who has to do a little bit of everything. They're doing the requisitioning, they're doing the buying. It's kind of all over.

(11:32):
So the design engineer is the person who we don't have to worry about when complete, clear, consistent, like that doesn't apply. They're plugged into the matrix. And if they could, they would and they would never speak to another human being and they'd be perfectly happy with that. We don't worry about them, but we do worry about the buyer, the buyer who doesn't know what these things are and who will feel a little bit better about going to a place where as she's looking at this requisition, we're giving her everything that she needs to match up whatever we're showing her on the PDP to that chicken scratch, that Ralph down in the maintenance department scrawled down on this napkin and then took a picture and sent it to her and told her that we need this by 50 PM or the line goes down. And even the maintenance person who's kind of the generalist.

(12:33):
They might be an expert in some things but not about everything. And so if we can make sure that the experience we're able to create is always there to support them on the things that they don't know, but we make it super fast on the stuff that they do know, that's just a win-win.

Jamie Clapper (12:54):
I love that answer, Jason. And I think it is. It's multifaceted when you think about a B2B buyer. What does that mean? What does that entail? Because there are so many layers to it. It can never just be a simple one-to-one. There are all kinds of different ratios, if you will. So I think because that's a nice segue into the Nexi, which was contextualized. And I think on that maybe folks have a little bit harder time understanding, and I know you expanded on it a litle bit when you went through the framework, but if you can expand a little bit more on the importance of it, how that in itself ties to better shopping behaviors, meeting that person at the checkout, getting them there because you are essentially building the data to support what they're looking for, which could be several different ways when you think about it, right?

Jason Hein (13:48):
Yeah. I'm a man of a certain age. I'm not as young as I once was.

Lauren Livak Gilbert (13:56):
21, obviously.

Jason Hein (13:58):
Exactly, exactly. True. One day I'm going to drink alcohol, but I remember a time when I could go into a store and I knew people who worked there and I would go in there, I'm not a closed horse, I'm not a fashion maven, but I got to the point where they knew what I liked and they could actually help me figure it out. It made me feel good to go into that store and know that, listen, I don't have to worry about this. I'm not going to walk out the door looking like somebody who's never dressed themselves without his mother and that felt good. And I think that's what customers still want that, whether it's online or offline and it's getting harder and harder to find offline, but it's getting easier and easier to execute online. And yet particularly in B2B, B2B is one area where we have not historically been able to personalize the experience for B2B buyers, partially because personalization is a bit of a dirty word in this industry.

(15:28):
It gets carried over from B2C where it's all about tuning the experience around one person, the consumer, whereas in B2B, we need to contextualize things by departments or locations. Our units of work are a little bigger here.

(15:46):
So we have to contextualize things for multiple people all working together who share a purpose or share an intent. And actually the wonderful, wonderful thing about B2B is that we have a cheat code. 85, 95% of our customers, literally the first thing they do when they come in the morning, they log in. We know more about more of our customers than... If Amazon knew as much about me as my B2B customers I've worked with, as I knew about them, he would be president. He would know everything about everyone. We'd be living in a big brother world and literally I would log into Amazon and it would already be placing orders for stuff that I was about to run out of.

(16:52):
Why is it that B2B companies have not done this? Why have they not taken advantage of this steroids on marketing and merchandising and sales? I'll give you two reasons. One is they have not segmented their customer base as precisely as they can. If a distributor has done... If they've assigned an SIC code to a client, whoa, that is some advanced thinking, a little 35, 99 machine shop action going on, like, ma, that is super, super high tech. But the one thing that's come out of the B2C space is a lot of these search and merchandising platforms now, they observe the behavior of everybody in these segments and they can roll it all up.

(17:47):
Somebody comes to our website, we can literally assign somebody to watch and observe and take notes on everything that they do. So the fact that different customers now do things differently means we can be much more precise about how we target these segments. We don't have to just slap one SIC code on an entire factory like the Ford plan in Louisville, Kentucky. We know that there's a maintenance department, it's got three people working in it. We know that there's a paint shop, that's got 12 people working in it. We know that there's an electrical shop. We know that there's the office manager and we can actually take that one location for Ford and it might be 18 different customers, all of whom have different buying patterns, all of whom we want to try to account for in our experience. This is the sort of thing that contextualizing your product data, that understanding which of your...

(18:45):
Maybe I've got different descriptions for products. Maybe I'm going to show different images, maybe I'm actually going to even create different short descriptions on it, or I'm going to prioritize the facets that I show in my left hand now for customers in different segments. These are all things that you can do with the right contextualization of your product data and it really is the ability to finally, finally manifest the dream of product experience management. This is what it was supposed to do. It was supposed to let us tune the experience for different customers. It just never got really executed well because we didn't have HDTV vision into our customers.

Lauren Livak Gilbert (19:33):
But now we do and there's so much opportunity. I love the way that you described that, but I also think it adds a layer of complexity for the people on the B2B side who own this digital experience because it means that they need to engage with cross-functional people inside their organization as well as all of their customers. You're not just targeting one persona, you're targeting 15 potentially and that makes it kind of hard for them to figure out where to start. And also you're, and correct me if I'm wrong here, Jason, but you're probably working with small scrappy teams who are kind of pulling these digital experiences together. So what's your advice for people trying to tackle this and trying to maybe play catch up or make sure their product data is complete and correct and contextualize it? Where do they start and what should they do?

Jason Hein (20:31):
So it all starts with defining what good is because you're exactly right. Everybody is trying to scramble to catch up with the capabilities of the software, but the thing that we're seeing now a lot of... And those of us in the PIM space have been saying this for a very, very long time. Get your product data, get it good, get it good, get it better. And the reality is that most people just have chosen not to. They've gone all Bartleby Scrivener on it and that just gets nobody anywhere. So the first thing that you really want to do is define what good is. Hire somebody. If you do not have people in your organization that have studied library science or taxonomy or data modeling, go out and find a consultant to help you do this.

(21:26):
It is the foundation for everything that you're going to do because if your taxonomy is confusing, if you've got a bunch of multiple categories that mean the same thing and so your selection's getting scattered all over the place, if the attribution design, the attributes that you're enabled within your system to store and use for things like driving keyword search, driving faceted search, building your short descriptions, writing copy on detail pages, bullet points, if you don't have rules and guidelines around that, you're going to have a bad time and you're going to have an even worse time. It's going to be bad enough when you're just launching a website for a long time. I started doing this work in data probably about 13 years ago now and I thought e-commerce is going to be the thing that's going to make people want to pay attention to data like I paid attention and they didn't.

(22:31):
But what I am discovering is the thing that is doing it is AI because like you were saying, all of the things that need to be done, scrappy teams need to do a lot of things very quickly. A lot of them are leaning heavily on AI to catch up, to fill gaps in product data, to write descriptions, to create images, to syndicate and customize data going outbound to channel partners. But ultimately the old phrase garbage in, garbage out totally applies when you're working with humans in a system, but it is orders of magnitude more impactful when AI can create volumes of garbage out because what you have in is a bunch of garbage data. So now suddenly these companies are scrambling to try to figure out how do we make our data good so that we can actually get an ROI on these AI investments that we're making

Lauren Livak Gilbert (23:43):
And Jason, who owns that? Because it can't just be the two people on the digital team. How do these organizations get people behind them to really make this change, which is a fundamental change across the entire business, but it can't be the efforts of two or three people.

Jason Hein (24:03):
Well, it can be if one of those two people is the CEO.

Lauren Livak Gilbert (24:08):
Fair point. That's

Jason Hein (24:10):
Amazing. Well

Lauren Livak Gilbert (24:11):
Played.

Jason Hein (24:12):
It's amazing how quickly the C-suite getting involved can fix things. It needs to be the business. I'm really glad you brought that point up, Lauren, because there is a fear I think amongst sales and revenue operations where because e-commerce is perceived as a threat to the traditional sales model, they're going to be like, "I'm going to ignore it and it's going to go away." And that just results in customers who want to self-serve, who want to research on their own time, who want to learn about these products a little bit. Even customers, a lot of customers actually just want to come and learn a little bit before they pick up the phone and talk to your salespeople. Why? Because they don't want to sound like idiots. Nobody wants to get on a phone call with somebody who is perceived as a leading expert in this product and just be like, "I don't know anything.

(25:15):
Just you can do it all for me. " Nobody wants to be that person and especially younger people. Younger people have grown up in a world where they can research any topic they want 24 hours a day so they can at least be conversational about it before going into it. How many of us now go into a doctor with some ideas on what it might be? And that's perfectly normal, right? Our customers want to do the same thing with our salespeople. So where would you rather your customers go to learn about your products? Would you rather they go to Wikipedia where they then scroll down to the sources and they find your competitor listed because your competitor is very savvy and is updating the Wikipedia article on it to drive traffic marketing strategy or do you want them to go to your website? If you are the expert in this product, why don't you have content about it?

(26:22):
It's really no different from are you sending your salespeople to onsite visits without giving them any training on the product? Are you not going to send them to your supplier's training center for a week to learn about it before you have them sell? If that doesn't make sense for your offline, it doesn't make sense for your online. But ultimately to answer your question, it's a sales problem. It is sales outbound, whether it's offline or online, your ability to do it well ultimately falls on the business. And I mean, if you want it to go well, it really needs to be something that sales cares about.

Jamie Clapper (27:03):
Absolutely. I feel like this is one of those louder for those in the back moments as I call them, Jason, where what you're saying is reminding me of so many past experiences that I've had in the B2B space and just the need to educate and get folks on board to understand why it is important, why even if they are not going to purchase online, the value is still there to have the data, to have the content, to have all the information, to educate the consumer so that when they do go make the purchase, hopefully it is your product and it is through one of the channels that you have. So 100% kudos, I'm giving snaps for that comment and that whole conversation that we just had.

Jason Hein (27:47):
I'm sorry if that was triggering for you and brought back bad memories, Jane. This is a safe space.

Jamie Clapper (27:51):
Okay. I'm strong. I'm strong, Jason. This is a data safe

Jason Hein (27:53):
Space. Data safe space.

Jamie Clapper (27:55):
Absolutely. This is a circle of trust

(27:58):
Along with all of our listeners. Great segue though. Obviously product data and foundational and we've talked about all the things that really pull into building that. But you have been known to say when we talk about AI, as you've said that throwing AI at a messy e-commerce site is like pumping Nitro into the tank and jetting down the freeway with no control, which I had to read exactly as you had said it to me prior because it's such a great saying and it is so accurate and I think it's something that the listeners will take with them. For a distributor looking at a wave of new AI platforms right now, what is the single biggest challenge of utilizing AI before fixing the data foundations that you've spent a ton of time talking about today?

Jason Hein (28:46):
Yeah. To extend that analogy, putting the nitro in the gas tank will not help you if you don't have any tires on your wheels. Your tires are what actually transfer your momentum in your direction. They are the thing that keep you grounded in reality and AI it's a Formula One car without tires. It is a rocket without fins. It is a boat without a rudder. Under enough pressure, all three of those things become bombs and they will damage your customer experience, they will damage your customer's impressions of you. And if you are doing that purely to please a board, then somebody needs to do a better job explaining how AI really works to your board.

(29:48):
I know that most people don't have the ability to change that, but it is something that hopefully somebody will put up on a LinkedIn quote somewhere so people can get that message out from some kind of A funny looking Muppet, who cares about B2B. But it really is about the foundation. Product data has never been more foundational than it is in a world of AI because it represents the reality. These are the things that we sell full stop. They are a picture of the things that we sell. They are a 70 character description of what we sell. It's the length, it's the material, it's the finish, it's the brand. It's the color. It's the maximum operational speed. It's a load capacity. It's a minimum of maximum temperature. It's a flow rate at 20 feet of head.

(30:49):
These are the things that customers look for. And if you deploy AI on those in a speculative way, like, "Oh yeah, we'll just have AI fill the gaps." It'll go out into, "Yeah, we'll go, we'll use Claude. We'll have it find all the product data." In the world of B2B in particular, that is a terrible idea. If you're just doing it, dumping it into Claude, you cannot rely on the data that lives natively in these LLMs to populate your product data because one, it's going to lie. They're programmed to give you what you're asking for even if they have no idea where it came from. You have to make sure that the sources we're using to populate these foundational elements of the experience we're trying to create, the product attributes, the descriptions, the images are coming from places that we can trust. Because if the information's wrong and somebody gets hurt, which is a very common thing in B2B, then you get sued, you try to get your supplier to indemnify you, but they say, "Well, you didn't get this data from us.

(32:07):
I don't know. Where'd you get it from?" Claude? Okay. Go have them sue Anthropic.

(32:17):
There are not enough people talking about the issue of product liability and product data. The sources that you use, and there are some. There are sources out there that not only will pull data, they use AI to find the data, pull it in, but they'll also flag it with where we got it, when we got it, and how we know it's authoritative. So that's the thing that I think I want to make sure I've answered your question. The problem with AI is AI is just a thrust forward, but there's no guidance, no direction, and almost never breaks.

Jamie Clapper (33:05):
Absolutely. 100%. And you answered it. I think there is this understanding that AI will fix problems and just automatically do it. And I think there's an assumption that it'll just take all the problems away. And I think for our listeners, it's really good to express again the value of product data, the five Cs as you have it, to be able to understand how they build that foundation to better suit themselves when they are building out their digital shelf, their agentic shelf for their B2B world, if you will. And I think there is a value in talking about AI because obviously one, it's a hot topic. Two, I think everyone has their thoughts around it. I know in previous roles I've been told go do AI with no boundaries, with no details. And I think there is that assumption that it'll fix things when there are things that AI can't fix, they can support, but you have to make sure that you're doing it in an order that makes sense for your business.

(34:10):
So 100% answered the question and I think gave a lot of visuals to our listeners as well. I know I have a few words. As I think about AI, you are a storyteller for sure, Jason. We 100% have appreciated having you on the podcast today. I know we could probably talk on and on and on just because you are so knowledgeable and you have a ton of experience, but I think you've given our listeners a lot of value to act on when they think about their business and what steps they can take towards being supported within the digital space.

Jason Hein (34:47):
Yeah. I think if there's one takeaway I would tell people to take away from today's conversation, it is to keep about AI. It's to always remember that AI is very, very good at giving you outputs. It is not very good at evaluating those outputs. That is your job.

Lauren Livak Gilbert (35:09):
And will continue to be our jobs. As people have fear around

Jason Hein (35:12):
What

Lauren Livak Gilbert (35:13):
This looks like, that is where we will continue to play a very key and critical role. And I hope that that is underlined and bolded for people as they hear this. We will always have a place in this. It will help you do your job better. And my takeaway is to follow Jason on LinkedIn because he has a lot of great content and a lot of great thoughts.

Jason Hein (35:34):
Oh, thank you. Thank you very much.

Jamie Clapper (35:37):
Absolutely. Well, again, thanks, Jason. We wholly appreciated having you on our first episode of the B2B podcast.

Jason Hein (35:46):
Oh, thank you so much. This was so much fun. I will very much look forward to the second time I am on the BB podcast.

Jamie Clapper (35:53):
Absolutely. You got it. Thanks for tuning in to Unpacking the Digital Shelf B2B Edition. To keep learning from the best in B2B, subscribe now and join our community of leaders by becoming a member of the Digital Shelf Institute. Thanks for listening and we'll see you next time.