Paula Chiocchi: Welcome to the Marketing Influence Podcast. I'm your host, Paula Kiyoki, and today we're diving into a topic that every B2B leader is talking about, but very few are actually executing well, turning data into real business decisions. Joining me today is Kyle Pober from Blend360, a company known for assisting organizations in bridging the gap between data, analytics, and real business impact. Kyle works closely with organizations navigating everything from data strategy to AI-driven decision making. And today we're going to unpack what it really takes to move beyond dashboards and insights and start using data to drive measurable outcomes. Welcome, Kyle.
Kyle Pobur: Thanks for having me, Paula. Glad to be here.
Paula Chiocchi: Yeah, good good to see you. So let's start here. When you think about turning data into real business decisions in B2B, what does that actually look like in practice today?
Kyle Pobur: Yeah, I mean we work with a variety of clients across a bunch of different industries. And when we look at B2B specifically, it's a it's a we we kind of work into two different worlds where they occasionally cross over into each other. But in the B2B side of things, it's really about finding reliable vendors with that are capturing good signals that we can then bring to our end user clients. So here at Blend, we don't own any data, we act as a true source agnostic. Unbiased data partner for our clients, and depending on their custom use case that we're trying to solve for, we'll work with a variety of vendors that are finding all kinds of different triggers across the B2B data landscape. So that could be anything from a new business trigger, someone who's just getting started with their business, a growing business trigger that is indicating that there's growth and an acceleration in what that business is doing, whether that's from a revenue, a headcount, or a location standpoint. And then and then yeah, then it's just really trying to trying to find those correct contacts that are going to give you you know the right decision making power for our for our end user clients and who they're trying to reach. and then as far as insights, it's it's it's it's really an iterative process of using these different sources, what different data points are available, and then driving meaningful results over the course of different marketing campaigns and different marketing efforts. So it's we're fortunate to with a lot of great partners and a lot of great end user clients that are always open to testing new strategies and our our general strategy with many of our clients is to just not get complacent with good results. It's always to leave a a small percentage of test volume available in anything that we do where we can get statistical significant results and ideally find some new strategies that we won't we don't even we didn't even think about at the start that we can then promote as we're
Paula Chiocchi: So that's how do you think that's different from a few years ago? I mean, is it mostly the the signal activity or w what would you say?
Kyle Pobur: yeah, yeah. So Going back just it was it was more cut and dry. This is this is what we have to offer for you. There's the I would say the breadth of information out there has has certainly expanded the way that people can track online activity and intent signals, which is a topic I'm sure y yeah that you guys are are well aware of in in your day to day. But yeah, it's it's the the accuracy and speed of those signals I think are are evolving and changing over time to where you know we can we can bring new ideas that may have not been on the table or you know the conversations that we've had you know between our two organizations the the differences in being able to to to track intent signals and understand where these people are actually coming from with their pain points and what they're trying to solve for that's something that I've seen definitely grow. You know you'll still have your your your standard publicly available information type Sources of you know new business registrations or a phone is coming online, but it's really that that next stage of you know finding intent. And are these people really in the market for what they're doing? Are they at that level of just browsing and trying to find sources out there or are they actively showing buying signals? that's something that we've really been trying to focus on lately.
Paula Chiocchi: It seems like it's moving faster and faster.
Kyle Pobur: Does, it does. Just like just like everything in our world there are there are AI applications that can help the speed of decision making and you know When I talk to people outside of our industry or people who are less familiar with AI, it's I try I try to explain it as hey, it's just a it's another tool for our toolkit. it would be similar if you were going back twenty-five years ago, twenty six years ago at the the turn of the century and you know, you had somebody that was saying, I'm not really sure about this internet thing. I have a pretty good system with with pen and paper and my Rolodex that I call and find find different vendors, you know, things like that. So it's it's just another tool. And
Paula Chiocchi: We can learn.
Kyle Pobur: I think the the biggest difference is the speed and the accuracy, which is which is helping make our jobs a little bit easier. But then you still have to find the the correct sources that that can be trusted. so it's it's a little bit of a a give and take in that situation. But yeah, I was I was just having a conversation actually over the weekend. We're trying to find a a list of alumni members from a a well-known university in the country. And funnily enough, my my father has been in the merchant credit card process.
Paula Chiocchi: Right.
Kyle Pobur: Industry for years. And he had a an initiative early 2000s where he was trying to get in contact with alumni networks and make them the preferred partner. And I said, well, how did you go about finding these alumni lists back in the day? And like, do you do you have any people that I can reach out to to at least start my search? And he said, Yeah, we actually got a physical book mailed to us with everybody's name, and then we had to go through line by line and say, Okay, this is that person's name, this is their most recent recording. business. This is their this is their email if they even had one at that point. And here's a phone number you could call. I was like, well this is great background, but things have changed since 2002 to where we are in 2026. So yeah, it's just it's very interesting seeing the speed of of of how things have changed and yeah I've been here at Blend for five years and even in those five years the the way we go about doing any type of data sourcing or any type of
Paula Chiocchi: Hmm. Yeah. Right, slightly.
Kyle Pobur: insight generation has has certainly changed.
Paula Chiocchi: So did you find your list?
Kyle Pobur: I I'm getting there. I'm getting there. yeah, we we're
Paula Chiocchi: I don't have any ideas for you, sorry.
Kyle Pobur: Yeah, we're we're we're going down a couple different paths. thankfully our our end user client is a an affinity member that's working directly with the the university. So there's a there's a pathway there for us. But yeah, finding some of those those trickier to find, especially professional type contact details for people, you know, with any type of association, it's it's gonna be disparate at at best and you know or or at the worst and at the best, you you'll you should get some decent coverage, but it's all about, you know, updates and refresh schedules and it brings us back to that first that first element of accuracy. Mm-hmm.
Paula Chiocchi: So on the flip side of that, why do you think so many organizations still struggle to bridge the gap between having data and actually using it to make decisions?
Kyle Pobur: yeah, I th I I would break that into two two pieces. So one, there is there's so much out there that it's it's hard to know where to start. So you know, we just brought on a couple of new people here at Blend and we were just even having the discussion that Working in the data landscape is is is difficult and it is something that a lot of people don't know about. I mean, get anyone listening to this podcast is likely going to have some sort of interest and some sort of involvement in the industry. But you know, when you describe to other people outside of our world what we do and how we do it and the the nuances of of what we're trying to find out there, it's a difficult, big, complicated landscape that that not a lot of people know about. So just know.
Paula Chiocchi: I agree with you.
Kyle Pobur: Just knowing where to start is really really the the the area where we see it and it's it's across all organizations, Paul. It's it's it we work with some if they're not Fortune 500, they're very close. And then you talk to some of these data teams, and they're you know, there's there's very little sophistication going on or very little use of third party data going on, which is is shocking, but it also, you know, that that seeing those kind of conversations. Conversations happen, make my job very easy because then you can come in at a base level and be like, okay, I'm coming from an education standpoint, first of all, of this is how this all works. here is how it can be effective for you, and here is a way to dip your toe into this world and kind of join the organizations around you and how they're leveraging that data. So, really knowing where to start, and then the second part that I was going to get to is really on how you distill that information, how you Determine is this going to be a reliable source or not. Unfortunately, a lot of that comes with trial and error. So, you know, thankfully, I have the years of experience of working in these spaces of saying, okay, you know, these are a couple red flags that I'm looking for when I'm reaching out to a new source. And some of that may be something as simple as a lot of the data landscape is you know, there's a lot of crossover, there's a lot of overlap, there's a lot of companies. Companies that we work work with or that are out there in the landscape that act as resellers of their data and then they make their data available to you know organizations such as Blend or other data vendor type agencies. So just having that industry knowledge of what are you looking for? Or hey, I'm seeing these three data sources that all look like great sources of D2B information. They have all of the attributes that we're looking for. these are all kind of looking like the same. same data sets. So being able to recognize like, hey, am I being efficient in my efforts or or am I understanding that, you know, I'm not going to name any specific names, but are are the their data sets out there that part of their business model is to resell their data to other organizations. How can I sniff out those to where it's like, okay, I'm telling the client I'm going out and I'm getting them a multi-source campaign where we can try three different sources, run it over a couple months.
Paula Chiocchi: Yeah.
Kyle Pobur: And say, hey, we're looking to promote two sources and remove one of the sources. If all three of them are coming from the same underlying data set, that's not going to do very much good for me. And it's going to, if if it was somebody other than me trying to join into this world, they might see, well, there's no difference across all these different data sources out there. And they might have those insights that they they think they're getting, but it's really, you know, it's it's all the same underlying data, so there really shouldn't be much difference. You really have to go out. And if you want to test against each other, is find different sources that are pulling their information from from different underlying data sets and then compiling them. So yeah, this it really, you know, just kind of to circle back to the question, it's you know, being able to understand the the data landscape as a whole and then understanding what some of those red flags are or things to keep an eye out for. Maybe red flags isn't best way, when you start doing down the the actual acquisition of the data.
Paula Chiocchi: Well, and think it's I've seen over the years you mentioned close to being a f Fortune five hundred company, where one company will say, we just have one data source and another will have ten You know, it's like and how you get by with just one data source and I'm talking in terms of like prospecting data and outreach data. I don't I don't think that's gonna gonna cut it. So it's it's it's sort of surprising to me when they say, we just thought that we only needed one data source No.
Kyle Pobur: Yeah. Right. Yeah. Or or the other one we see and it's less so on the B to B side. did I run into it? We work with a ton of consumer data, primarily in the United States, but we do have a couple projects where we're, you know, working in Latam or AMIA or or over in the APAC region. But we do see it more on the consumer side where it's like, Okay, great, you guys are getting a laundry list of two hundred data attributes and you are learning more about your customers, but that's only one part of the puzzle and it it Does translate into the B2B world is okay, you know, all this information now about your customer base, but you're not getting access to the rest of the prospect universe out there. And I think that's where some organizations are still early on in their maturity phase of saying, All right, here's all these insights. This is let's break this group of let's just call it 10 million i 10 million customers. You look at the top 10% of your customers, that makes you at 1 million. What are these 1 million people?
Paula Chiocchi: Yeah.
Kyle Pobur: People look like it's bridging that gap of taking this is the profile of these one million people, go overlay it over across the 250 million people in the United States and go find you more people that look like that. And whether that's from a simple profiling standpoint or a a custom-built model that is you know designed to target on a specific outcome, which is a lot of what we do here at Blend, that's that's where like the disconnect is. It's that's you know the next step of going down that data maturity path and then Then you really get into the the the fun architecture of it all of okay, how do we keep track of all of this? How do we build that internal database? How do we understand everything about our customers, but also everything about the marketplace at the same time? And same thing happens in the B2B world. It's just you're going from, you know, likely over a thousand data attributes on the consumer side to probably, you know, in the two to three hundreds, some some get up into the four hundreds, depending on you know what what interesting little tidbits. tracking about business there's some there's some wild data attributes out there in the B2B world of things that's like well I'm never gonna I'm never gonna touch that but that's very interesting that you know we have the does this place offer happy hours like just or the or does this place require that its employees wear uniforms like for my clients it's not super important but if there was a you know a a Cyntas or someone who is outfitting these companies with these things like that's a that is the
Paula Chiocchi: Right. Right. Hey, we can.
Kyle Pobur: number one most valuable data point to you. So it's it's it's at the end of the day it's it's super interesting being in the world that we're in and the amount of information out there on businesses or or consumers in our country.
Paula Chiocchi: So when you're working with companies, where do you most often see things break down? I mean, why does data stop short of becoming action for these companies? Is it just the volume of the data that needs to be refined or or what would you say from your perspective?
Kyle Pobur: Yeah, I think I can give you kind of two on that one as well is is the size of the data is number one. It's hey, how do we distill this down from this wealth of information that is out there that we would be receiving to something that's actionable and that matters? and a lot of times that it works on is this data attribute important to me? One. And you need to do a little bit of education in our industry on does the name make sense? Like we talked about happy hours or or we're wearing uniforms. So a lot of times we see attributes in the in the consumer world that hey, this for our particular client, like we're just reading the name of an attribute, it doesn't matter to them. Like we don't care if they shop at Target or Walmart. This is a completely unrelated product. But what I'm saying is on the education aspect, it's a lot of times. It doesn't matter really what the attribute is if it is predictive of a certain behavior. So if their ideal customer versus their you know regular or just standard customer in their customer file, if someone is a target shopper and that's an indicator that puts them into that bucket, that's something that we can use. So really kind of educating that there is a ton of data out there, but Predictive power is typically more po important than a descriptive aspect of that data attribute. And it's the same thing with B2B. So, you know, maybe maybe these places that do offer happy hours, maybe that isn't something that you know would even register to my contacts of saying, Yeah, that's important to us, it's not, but it turns out that's predictive of them buying this software or that they are going to. signs of growth, you know, things like that. So it's the education aspect. And then a lot of times it's like the budget and storage part. It's hard to say, hey, here is this investment that over time, there has to be some trust involved, but this investment over time is going to create better outcomes for you. It's having that discussion with people and saying, hey, can we build you out a roadmap where this isn't going to be something that's going to turn around and and flip the ROI on this investment in the next two to three months. But based on this roadmap that we can lay out for you in this data maturity journey and what our plan is for any outbound marketing, this is where we see this going. This is the path that you guys need to go down to to modernize your processes and the way you go about doing your business. But if we can get to that type of conversation where it's like, hey, this isn't going to be a quick fix. This isn't going to be something that is you know all automatically going to work for you tomorrow but if you follow this path on a three to six month game plan with us and put some trust into us we can we can show that this is you know a positive ROI from from that simple equation but also here is what this is also allowing you to enable as a as an organization that is growing
Paula Chiocchi: Do you do you think the potential reticence is based on lack of people to execute or process or technology or all? And I believe you guys probably bring some of that to the table to help them solve.
Kyle Pobur: Yeah, it's a it's a huge benefit for me working with the brilliant people that I'd work with at my organization. So we have a super strong
Paula Chiocchi: Mm.
Kyle Pobur: set of offerings here at Blend. We have brilliant data scientists, data engineers, a BI specialty. So anything working on measurement and reporting to tell that story of, hey, this is how everything's going that we've put into place. That's super important from the back end of saying, hey, this is what we've done. And then obviously we have a growing AI practice here at Blend, but having those subject matter experts within our organization that are leaders across the industry help me tell that story better than better than I ever could. I you know love being part of this team and allowing them to be able to do that. But it's it's really breaking things down and explaining it into easily digestible ways because sometimes we're dealing with CMOs that aren't very data savvy, but they understand the big picture and what is important for the entire organization, especially being part of that C-suite. But there are times where we do get into the technical aspects of what we're doing and you know people's eyes kind of glaze over, and you can see that, you know. In conversations where it's like, you guys are super smart, this is great, this all sounds good, but I I don't really understand it. And that's where our team does a really good job of breaking it down and saying, This is what this means to you, and this is why this should be important to you. And if you go down this path, this is solving for this goal, but also these are the next three things that you can go after as far as an initiative to to modernize your organization. That's that's really where We see potential roadblocks, but it's something that our organization does does pretty well.
Paula Chiocchi: Remember years ago we had a client say, please come to us with duck and bunny slides that we that we can take to upper management.
Kyle Pobur: Yeah. I like the I like the you to use the method of breaking it down to the explain it like I'm five type situation. So
Paula Chiocchi: Yeah.
Kyle Pobur: Like let's let's start there, then we can go into all the technical aspects of you know, where is the data being stored, how is it interacting, how can we how can we query it and interact with it? and especially with with all the advances with with AI and LLMs these days. It's you know, how can how can I instead of just reacting to what a dashboard is saying, how can I have a conversation with that dashboard and really say, Okay, I'm trying to put together a presentation for my internal team, what it would be The most important four insights that I can pull out of this dashboard rather than you know taking a couple screenshots and saying, hey, these are our main KPIs that these were hit, kind of take it to that next level down and really start to tell a story behind it because that's that's the biggest problem at the end of our day is we know what we do here at Blend creates a ton of value for our organizations that we work with, and that's reflected amongst the you know the logos that we have on our website across the Fortune 500s, but it's really trying to bring in those people. people that that aren't convinced yet and take them from that, hey, I'm curious about this, I know this is something that we need to do. How can we go to that next level of of you know advancing through using this technology and using our services?
Paula Chiocchi: So I think a lot of companies feel like they're they are data driven, okay? And and so what's a scenario where the belief doesn't actually match the reality that you've seen?
Kyle Pobur: Hmm, that is a good question. thankfully I don't run into too much of that with my clients. we do have a pretty robust front door policy of, you know, evaluating who we want to work with. so anyone that does end up, you know, that I end up interacting with is typically bought into that or or has subscribed to that type of that type of thought process of, hey, we're saying we're data dri driven and we need to back it up with some actual actions. but it's those are opportunities again. It's it comes down to to education and you know going back to the example of like, hey we we get 200 data attributes on our our clients how can we use those and it's it's really breaking it down of saying you know where are you you getting the pressures internally within your organization our our model here at Blend is very collaborative and very invested in the success of the people that we work with so it's really breaking down like hey what are your organization's goals for your immediate team not that not the entire group And then what are your personal goals on you know how how do you act successful in your role? And you're working with me. I'm invested in, you know, saying if Paula, if you were if you were my client, it's like Paula, what what are your end-of-year goals? And some of them are tied to you know financial numbers, whether that's revenue or margin or how many new signups that we get there, and then really kind of taking that step back of saying, okay, this is the end goal, this is what should be pasted behind your computer that you look at every single day. For 2026, how can we use data to help you out with that? Is it driving you know, some organizations are all about driving that top of funnel? so it's like, hey, there could be a business that's trying to go out and get all their small and medium businesses to adopt their software. Hey, we're really concerned with getting just our call center to answer the phone or to make their phone ring over the course of the day. And some some organizations that's the metric that they care about, where it's hey, we believe in. our call center and if we can get a business owner on the line with our group, you know, they can they can go into the deep dive of this is why you would benefit from working with us. And you know, so it's some people I work with, it's hey, just make the phone ring. We, you know, conversions we're not gonna hold you to. That's our team's job once we get to there. And then there's other ones where it's like, hey, we'll track the 10 top of funnel metrics, but at the end of the day, when it comes down to it's lifetime value against customer acquisition. Cost that we really care, and it's really focused on that ratio of of what the costs are. but even if somebody does start in that in that way where it's like going back to your original question of, hey, we're data-driven, this is what we're doing, we're open to testing, we're only really focusing on that number. We can really kind of back it up and say, okay, if that is the number that you're focused on, where are people falling off in the customer experience journey? Where are those. those pain points where do you see people enter all their information and then no longer pursue the rest of that conversation with your organization to become a customer and kind of diagnose those things by looking at the the whole picture but understanding that end goal is really where I like to start and then it's either you know build a build a game plan backwards or forwards in order to meet those goals and that's that's that's really where you can start to point to them and say all right everyone's everyone Out there saying, hey, we're a data driven organization, these are the data points that you need to care about in order to get to your finish line.
Paula Chiocchi: So there's clearly a difference between having insights and actually making decisions. What needs to change inside an organization to make that leap, do you think?
Kyle Pobur: Buy in and trust, I think, within the organization. So some of the areas and again I'm really fortunate to deal with with clients that are bought in on on our way of doing work, but every once in a while you'll see something where it's it's it's almost I don't want to use the word selling, but A lot like going down that same storytelling path to where it's someone that I'm working with that isn't the end all be all decision maker, like they have bosses within their organization, whether that's the CEO or the president or anything like that, just taking it that next layer up of saying, All right, here's all of the insights that we can provide from you for these last six months of campaigns. Here's fifteen things that happened. We saw a a bump in performance. In month two, we saw a slight dip in three. here's some issue, or here are some things that may have caused those. These are the different data sources that we've used throughout the campaign. That might end up into a list of 20 insights, and then it's really taking that group of insights and distilling it down to: hey, here's the top three, or here's the top five action items that we can make going forward to improve those insights going. So it's, you know, whether we get a bump in a couple percentage points in our conversion rates or whether we're it could be the flip side, we could be more efficient. Say our conversion rates are going up and we decide, hey, our marketing spend can actually go a little bit lower because we're getting more juice out of what we what we're actually putting into play here. So it it's really allowing our people to be prepared and be successful for those internal conversations that we always can't be a part of. Our team doesn't Great job of prevent presenting information to our key contacts that are that are tied to our work, but it's those next steps, internal conversations of saying, hey, we've been working with Blend, this is all the great stuff we've been doing, and allow them to speak as educated on everything that we are internally in their organization. I think that's really where it goes from insights to action items, because if we can build that trust not only with the people that we're interacting with. On a daily basis, but also their senior leadership within their organization, they're gonna say, Hey, that worked. What's next? What can we do next year? You know.
Paula Chiocchi: Yeah. Yeah. So it sounds like what you do is you're also helping your clients evolve from just dashboards and reporting into true decision making systems. That's what one of the things that bring brings to the table.
Kyle Pobur: Exactly, exactly. And and it it goes back to that point I was making earlier where it's like we don't get want people reacting with their data. We almost want people having conversations with their data. And whether that's in a more of a traditional way where, you know, we're having a thirty to sixty minute meeting and a conversation, just going through some dashboards and saying, Hey, this is interesting here or whether it is that next step of AI enablement where like you can actually type into the dashboard and have those conversational style interactions, that's the next step of where where it's really going. But you know, in the meantime, yeah, for
Paula Chiocchi: your your clients open to the AI?
Kyle Pobur: A a lot of a lot of them are. Some of them are are pushing for it quite hard. So it is just something that it's it's another speed play. It's hey, how can instead of clicking through four different tabs and making sure I'm looking at the right things, you know, can I just have have some of those insights automatically pulled from me? Or we have a new offering here at Blend, it's branded as Clarity and it actually interacts with dashboards to where it could be like, Hey, I'm trying to put a together a presentation for Our CMO and our CEO, here's what their general profiles look like, here's their background, here's what they care about, here's how sophisticated they are in the data landscape. And our offering will go in and pull out. Here are the top three things that are on fire right now that we should discuss immediately. Here are three long-term goals, and here are things that we should focus on, but they aren't something that the is is urgent for us this to discuss, but put it on the roadmap. And really trying to automate some of those some of those decision-making things with intelligent applications that that can really just take large amounts of data and distill it down into those actionable points that you were talking about.
Paula Chiocchi: And it makes your job a little bit easier and faster also to be able to incorporate that. And then I'm I'm I'm s really surprised sometimes when I talk to people and they just say that they're just they don't know how to use the AI or they're concerned about using the I because it really is a time saver.
Kyle Pobur: Certainly. Certainly does. It is. It is. Yeah. It's where we s where where I personally have seen it and I'm trying to get my team into the same way of thinking is anything that we're doing that's repeatable that you could pretty much do nah, I don't want to say with your eyes closed because that's that wouldn't be accurate, but something that you do on a monthly basis. Use those systems in but do it in a responsible way that is you know, we we're Uploading client data to anything. So it's more it's more like internal processes of looking at results and numbers where where I really see it. but you yeah, so there is a we we go through training, new trainings that come out every single week of like, hey, best practices, these are the absolute no's, these are the things you should be using it for, and then here's some new skills that you can use. But yeah, it's really it's it's it's a speed play at the end of the day, and it's saying, Okay, I'm looking at a something that used to be in an Excel space. Spreadsheet that is across 14 different tabs that would take a long time to go through. And okay, I'm gonna type out manually this is what these results mean, or we're seeing a bump in performance from adding a Facebook or a meta treatment, and it is significant enough to outweigh the additional cost. Something like that, you would have to go through and click in on the individual tabs, whether it's an Excel or Tableau or Power BI, and really pull that information out. And this is just speeding that up to where it's just like. There's no sensitive data in here, it's all results. How can how can we help you know make decisions faster on this? Or how can we pull insights that might not have been visible just due to the you know the small nuances and differences?
Paula Chiocchi: Efficiencies are amazing.
Kyle Pobur: Yeah. And then yeah, and then at the end of the day, like the simple stuff like, hey, just take all these insights and put into a PowerPoint for me. It's just used to take, you know, o about an hour to put together a polished deck and now it's it's something that takes a fraction of that time. So again, like we talked about at the start, it's if you're it's a new tool and if if you're not using the tools that are available out there, it's it's gonna be easy to get left behind. Yep.
Paula Chiocchi: I was gonna say it's gonna be easy to get left behind. Can y can you share a practical example where AI or advanced analytics actually influenced the real business decision?
Kyle Pobur: Hmm, there's plenty of them out there and I think of a good one to share with you here. I would say most recently we just did a QBR with a large B2B mailer. They do small and business small and medium business mailings to get people to sign up for their software service and just did recently did like i've said a a big q brand where you know normally we're showing anywhere from seven to ten total views across a a performance of of of an area and or or performance of a campaign and for q br's we produce anywhere between like twenty to thirty different views because there's different breakdowns On geos, there's different breakdowns on industries, there's different breakdowns on hey, who is actually responding and who is this the offers resonating to. so what we were able to do is once we had the the raw data, just the numbers on these are the industries, these are the states that converted, it was able to easily put together a a breakdown of by region of the country what are the top industries that are responding to these offers and what that led to. To is hey, here is a breakdown of in the southeast, these are the top 10 industries that performed. And then we could also look at it from the flip side of like we mailed a ton of these industries and we didn't hardly get any type of response out of it. Now, if you compare that to the Pacific Northwest, there are you know a couple of the industries, business professional services, I think were popping across the board, but there are some different ones that were showing up in the top 10 and the bottom 10 as far as response. Rate above a certain threshold, we were able to take that and say, hey, this is our next recommendation for hey, if it's in these states, we eliminate these SIC codes. If it's in these states, these are the ones that we want to include. So it created an actionable efficiency play, analyzing a bunch of results. That, I mean, when we look at the the industry codes and some of the DMA values or the the the zip code values that we then map to those regions, we're talking In over 5,000, over 10,000, sometimes over 12,000 rows of results that we used to have to manually look through. Now we can take those and say, hey, here's a clean PDF with no sensitive information included into it. How can we make this actionable to increase the efficiency of our campaigns? And at the end of the day, like my job isn't to maximize the amount of mail that someone is sending. My job is to get you the results at the end. Most efficient way possible. So if I can say, hey, we're mailing 100,000 pieces a week and we can bring that down to 50,000 by making these tweaks and still see the same results, that's a cost savings of half for an effort from my clients while getting those same results. And hey, then if you want to maintain that same level of marketing spend, you increase that and you should expect the same the same level of end results at the end the day. yeah. Like I said, I I'm I'm very fortunate to work with some clients that are open to testing and you know, they they don't like getting complacent, same as same as me. So yeah, they you know, we could we're very very dynamic in our our planning and that that just speaks to myself and the team that is underneath me that I'm I'm I'm getting more familiar with these these different projects that we can make dynamic decisions. So it's like, Hey, this source isn't working or we need to change our order selection criteria to focus on These areas, these geographic areas, these SIC codes, and so forth. And or or could be something as simple as like, hey, we're seeing these titles are all of a sudden responding at a higher rate. Let's go find those people rather than just the highest ranking person at that business that could be a better target. So it's all it's all a very iterative process. It's you know, I I d I would never consider myself a scientist, but I've always been very drawn to science growing up in my entire life. of hey there you should always be testing a new hypothesis. You should always be learning something new. And if you're not then you know you get stagnant and you'll eventually get left in the past, no matter how good your your strategy is. it just goes back to the point I just landed on is if you aren't learning and you aren't trying to explore something new out there, you you just end up getting into that stagnant place. So just to answer your question, if you're if you're not doing those things out in your daily life, if you're not trying to you know, do continuing education efforts of taking new trainings, learning new skills, learning new tools and how to use them, you'll just get left to the past.
Kyle Pobur: Thanks for having me, Paula. Glad to be here.
Paula Chiocchi: Yeah, good good to see you. So let's start here. When you think about turning data into real business decisions in B2B, what does that actually look like in practice today?
Kyle Pobur: Yeah, I mean we work with a variety of clients across a bunch of different industries. And when we look at B2B specifically, it's a it's a we we kind of work into two different worlds where they occasionally cross over into each other. But in the B2B side of things, it's really about finding reliable vendors with that are capturing good signals that we can then bring to our end user clients. So here at Blend, we don't own any data, we act as a true source agnostic. Unbiased data partner for our clients, and depending on their custom use case that we're trying to solve for, we'll work with a variety of vendors that are finding all kinds of different triggers across the B2B data landscape. So that could be anything from a new business trigger, someone who's just getting started with their business, a growing business trigger that is indicating that there's growth and an acceleration in what that business is doing, whether that's from a revenue, a headcount, or a location standpoint. And then and then yeah, then it's just really trying to trying to find those correct contacts that are going to give you you know the right decision making power for our for our end user clients and who they're trying to reach. and then as far as insights, it's it's it's it's really an iterative process of using these different sources, what different data points are available, and then driving meaningful results over the course of different marketing campaigns and different marketing efforts. So it's we're fortunate to with a lot of great partners and a lot of great end user clients that are always open to testing new strategies and our our general strategy with many of our clients is to just not get complacent with good results. It's always to leave a a small percentage of test volume available in anything that we do where we can get statistical significant results and ideally find some new strategies that we won't we don't even we didn't even think about at the start that we can then promote as we're
Paula Chiocchi: So that's how do you think that's different from a few years ago? I mean, is it mostly the the signal activity or w what would you say?
Kyle Pobur: yeah, yeah. So Going back just it was it was more cut and dry. This is this is what we have to offer for you. There's the I would say the breadth of information out there has has certainly expanded the way that people can track online activity and intent signals, which is a topic I'm sure y yeah that you guys are are well aware of in in your day to day. But yeah, it's it's the the accuracy and speed of those signals I think are are evolving and changing over time to where you know we can we can bring new ideas that may have not been on the table or you know the conversations that we've had you know between our two organizations the the differences in being able to to to track intent signals and understand where these people are actually coming from with their pain points and what they're trying to solve for that's something that I've seen definitely grow. You know you'll still have your your your standard publicly available information type Sources of you know new business registrations or a phone is coming online, but it's really that that next stage of you know finding intent. And are these people really in the market for what they're doing? Are they at that level of just browsing and trying to find sources out there or are they actively showing buying signals? that's something that we've really been trying to focus on lately.
Paula Chiocchi: It seems like it's moving faster and faster.
Kyle Pobur: Does, it does. Just like just like everything in our world there are there are AI applications that can help the speed of decision making and you know When I talk to people outside of our industry or people who are less familiar with AI, it's I try I try to explain it as hey, it's just a it's another tool for our toolkit. it would be similar if you were going back twenty-five years ago, twenty six years ago at the the turn of the century and you know, you had somebody that was saying, I'm not really sure about this internet thing. I have a pretty good system with with pen and paper and my Rolodex that I call and find find different vendors, you know, things like that. So it's it's just another tool. And
Paula Chiocchi: We can learn.
Kyle Pobur: I think the the biggest difference is the speed and the accuracy, which is which is helping make our jobs a little bit easier. But then you still have to find the the correct sources that that can be trusted. so it's it's a little bit of a a give and take in that situation. But yeah, I was I was just having a conversation actually over the weekend. We're trying to find a a list of alumni members from a a well-known university in the country. And funnily enough, my my father has been in the merchant credit card process.
Paula Chiocchi: Right.
Kyle Pobur: Industry for years. And he had a an initiative early 2000s where he was trying to get in contact with alumni networks and make them the preferred partner. And I said, well, how did you go about finding these alumni lists back in the day? And like, do you do you have any people that I can reach out to to at least start my search? And he said, Yeah, we actually got a physical book mailed to us with everybody's name, and then we had to go through line by line and say, Okay, this is that person's name, this is their most recent recording. business. This is their this is their email if they even had one at that point. And here's a phone number you could call. I was like, well this is great background, but things have changed since 2002 to where we are in 2026. So yeah, it's just it's very interesting seeing the speed of of of how things have changed and yeah I've been here at Blend for five years and even in those five years the the way we go about doing any type of data sourcing or any type of
Paula Chiocchi: Hmm. Yeah. Right, slightly.
Kyle Pobur: insight generation has has certainly changed.
Paula Chiocchi: So did you find your list?
Kyle Pobur: I I'm getting there. I'm getting there. yeah, we we're
Paula Chiocchi: I don't have any ideas for you, sorry.
Kyle Pobur: Yeah, we're we're we're going down a couple different paths. thankfully our our end user client is a an affinity member that's working directly with the the university. So there's a there's a pathway there for us. But yeah, finding some of those those trickier to find, especially professional type contact details for people, you know, with any type of association, it's it's gonna be disparate at at best and you know or or at the worst and at the best, you you'll you should get some decent coverage, but it's all about, you know, updates and refresh schedules and it brings us back to that first that first element of accuracy. Mm-hmm.
Paula Chiocchi: So on the flip side of that, why do you think so many organizations still struggle to bridge the gap between having data and actually using it to make decisions?
Kyle Pobur: yeah, I th I I would break that into two two pieces. So one, there is there's so much out there that it's it's hard to know where to start. So you know, we just brought on a couple of new people here at Blend and we were just even having the discussion that Working in the data landscape is is is difficult and it is something that a lot of people don't know about. I mean, get anyone listening to this podcast is likely going to have some sort of interest and some sort of involvement in the industry. But you know, when you describe to other people outside of our world what we do and how we do it and the the nuances of of what we're trying to find out there, it's a difficult, big, complicated landscape that that not a lot of people know about. So just know.
Paula Chiocchi: I agree with you.
Kyle Pobur: Just knowing where to start is really really the the the area where we see it and it's it's across all organizations, Paul. It's it's it we work with some if they're not Fortune 500, they're very close. And then you talk to some of these data teams, and they're you know, there's there's very little sophistication going on or very little use of third party data going on, which is is shocking, but it also, you know, that that seeing those kind of conversations. Conversations happen, make my job very easy because then you can come in at a base level and be like, okay, I'm coming from an education standpoint, first of all, of this is how this all works. here is how it can be effective for you, and here is a way to dip your toe into this world and kind of join the organizations around you and how they're leveraging that data. So, really knowing where to start, and then the second part that I was going to get to is really on how you distill that information, how you Determine is this going to be a reliable source or not. Unfortunately, a lot of that comes with trial and error. So, you know, thankfully, I have the years of experience of working in these spaces of saying, okay, you know, these are a couple red flags that I'm looking for when I'm reaching out to a new source. And some of that may be something as simple as a lot of the data landscape is you know, there's a lot of crossover, there's a lot of overlap, there's a lot of companies. Companies that we work work with or that are out there in the landscape that act as resellers of their data and then they make their data available to you know organizations such as Blend or other data vendor type agencies. So just having that industry knowledge of what are you looking for? Or hey, I'm seeing these three data sources that all look like great sources of D2B information. They have all of the attributes that we're looking for. these are all kind of looking like the same. same data sets. So being able to recognize like, hey, am I being efficient in my efforts or or am I understanding that, you know, I'm not going to name any specific names, but are are the their data sets out there that part of their business model is to resell their data to other organizations. How can I sniff out those to where it's like, okay, I'm telling the client I'm going out and I'm getting them a multi-source campaign where we can try three different sources, run it over a couple months.
Paula Chiocchi: Yeah.
Kyle Pobur: And say, hey, we're looking to promote two sources and remove one of the sources. If all three of them are coming from the same underlying data set, that's not going to do very much good for me. And it's going to, if if it was somebody other than me trying to join into this world, they might see, well, there's no difference across all these different data sources out there. And they might have those insights that they they think they're getting, but it's really, you know, it's it's all the same underlying data, so there really shouldn't be much difference. You really have to go out. And if you want to test against each other, is find different sources that are pulling their information from from different underlying data sets and then compiling them. So yeah, this it really, you know, just kind of to circle back to the question, it's you know, being able to understand the the data landscape as a whole and then understanding what some of those red flags are or things to keep an eye out for. Maybe red flags isn't best way, when you start doing down the the actual acquisition of the data.
Paula Chiocchi: Well, and think it's I've seen over the years you mentioned close to being a f Fortune five hundred company, where one company will say, we just have one data source and another will have ten You know, it's like and how you get by with just one data source and I'm talking in terms of like prospecting data and outreach data. I don't I don't think that's gonna gonna cut it. So it's it's it's sort of surprising to me when they say, we just thought that we only needed one data source No.
Kyle Pobur: Yeah. Right. Yeah. Or or the other one we see and it's less so on the B to B side. did I run into it? We work with a ton of consumer data, primarily in the United States, but we do have a couple projects where we're, you know, working in Latam or AMIA or or over in the APAC region. But we do see it more on the consumer side where it's like, Okay, great, you guys are getting a laundry list of two hundred data attributes and you are learning more about your customers, but that's only one part of the puzzle and it it Does translate into the B2B world is okay, you know, all this information now about your customer base, but you're not getting access to the rest of the prospect universe out there. And I think that's where some organizations are still early on in their maturity phase of saying, All right, here's all these insights. This is let's break this group of let's just call it 10 million i 10 million customers. You look at the top 10% of your customers, that makes you at 1 million. What are these 1 million people?
Paula Chiocchi: Yeah.
Kyle Pobur: People look like it's bridging that gap of taking this is the profile of these one million people, go overlay it over across the 250 million people in the United States and go find you more people that look like that. And whether that's from a simple profiling standpoint or a a custom-built model that is you know designed to target on a specific outcome, which is a lot of what we do here at Blend, that's that's where like the disconnect is. It's that's you know the next step of going down that data maturity path and then Then you really get into the the the fun architecture of it all of okay, how do we keep track of all of this? How do we build that internal database? How do we understand everything about our customers, but also everything about the marketplace at the same time? And same thing happens in the B2B world. It's just you're going from, you know, likely over a thousand data attributes on the consumer side to probably, you know, in the two to three hundreds, some some get up into the four hundreds, depending on you know what what interesting little tidbits. tracking about business there's some there's some wild data attributes out there in the B2B world of things that's like well I'm never gonna I'm never gonna touch that but that's very interesting that you know we have the does this place offer happy hours like just or the or does this place require that its employees wear uniforms like for my clients it's not super important but if there was a you know a a Cyntas or someone who is outfitting these companies with these things like that's a that is the
Paula Chiocchi: Right. Right. Hey, we can.
Kyle Pobur: number one most valuable data point to you. So it's it's it's at the end of the day it's it's super interesting being in the world that we're in and the amount of information out there on businesses or or consumers in our country.
Paula Chiocchi: So when you're working with companies, where do you most often see things break down? I mean, why does data stop short of becoming action for these companies? Is it just the volume of the data that needs to be refined or or what would you say from your perspective?
Kyle Pobur: Yeah, I think I can give you kind of two on that one as well is is the size of the data is number one. It's hey, how do we distill this down from this wealth of information that is out there that we would be receiving to something that's actionable and that matters? and a lot of times that it works on is this data attribute important to me? One. And you need to do a little bit of education in our industry on does the name make sense? Like we talked about happy hours or or we're wearing uniforms. So a lot of times we see attributes in the in the consumer world that hey, this for our particular client, like we're just reading the name of an attribute, it doesn't matter to them. Like we don't care if they shop at Target or Walmart. This is a completely unrelated product. But what I'm saying is on the education aspect, it's a lot of times. It doesn't matter really what the attribute is if it is predictive of a certain behavior. So if their ideal customer versus their you know regular or just standard customer in their customer file, if someone is a target shopper and that's an indicator that puts them into that bucket, that's something that we can use. So really kind of educating that there is a ton of data out there, but Predictive power is typically more po important than a descriptive aspect of that data attribute. And it's the same thing with B2B. So, you know, maybe maybe these places that do offer happy hours, maybe that isn't something that you know would even register to my contacts of saying, Yeah, that's important to us, it's not, but it turns out that's predictive of them buying this software or that they are going to. signs of growth, you know, things like that. So it's the education aspect. And then a lot of times it's like the budget and storage part. It's hard to say, hey, here is this investment that over time, there has to be some trust involved, but this investment over time is going to create better outcomes for you. It's having that discussion with people and saying, hey, can we build you out a roadmap where this isn't going to be something that's going to turn around and and flip the ROI on this investment in the next two to three months. But based on this roadmap that we can lay out for you in this data maturity journey and what our plan is for any outbound marketing, this is where we see this going. This is the path that you guys need to go down to to modernize your processes and the way you go about doing your business. But if we can get to that type of conversation where it's like, hey, this isn't going to be a quick fix. This isn't going to be something that is you know all automatically going to work for you tomorrow but if you follow this path on a three to six month game plan with us and put some trust into us we can we can show that this is you know a positive ROI from from that simple equation but also here is what this is also allowing you to enable as a as an organization that is growing
Paula Chiocchi: Do you do you think the potential reticence is based on lack of people to execute or process or technology or all? And I believe you guys probably bring some of that to the table to help them solve.
Kyle Pobur: Yeah, it's a it's a huge benefit for me working with the brilliant people that I'd work with at my organization. So we have a super strong
Paula Chiocchi: Mm.
Kyle Pobur: set of offerings here at Blend. We have brilliant data scientists, data engineers, a BI specialty. So anything working on measurement and reporting to tell that story of, hey, this is how everything's going that we've put into place. That's super important from the back end of saying, hey, this is what we've done. And then obviously we have a growing AI practice here at Blend, but having those subject matter experts within our organization that are leaders across the industry help me tell that story better than better than I ever could. I you know love being part of this team and allowing them to be able to do that. But it's it's really breaking things down and explaining it into easily digestible ways because sometimes we're dealing with CMOs that aren't very data savvy, but they understand the big picture and what is important for the entire organization, especially being part of that C-suite. But there are times where we do get into the technical aspects of what we're doing and you know people's eyes kind of glaze over, and you can see that, you know. In conversations where it's like, you guys are super smart, this is great, this all sounds good, but I I don't really understand it. And that's where our team does a really good job of breaking it down and saying, This is what this means to you, and this is why this should be important to you. And if you go down this path, this is solving for this goal, but also these are the next three things that you can go after as far as an initiative to to modernize your organization. That's that's really where We see potential roadblocks, but it's something that our organization does does pretty well.
Paula Chiocchi: Remember years ago we had a client say, please come to us with duck and bunny slides that we that we can take to upper management.
Kyle Pobur: Yeah. I like the I like the you to use the method of breaking it down to the explain it like I'm five type situation. So
Paula Chiocchi: Yeah.
Kyle Pobur: Like let's let's start there, then we can go into all the technical aspects of you know, where is the data being stored, how is it interacting, how can we how can we query it and interact with it? and especially with with all the advances with with AI and LLMs these days. It's you know, how can how can I instead of just reacting to what a dashboard is saying, how can I have a conversation with that dashboard and really say, Okay, I'm trying to put together a presentation for my internal team, what it would be The most important four insights that I can pull out of this dashboard rather than you know taking a couple screenshots and saying, hey, these are our main KPIs that these were hit, kind of take it to that next level down and really start to tell a story behind it because that's that's the biggest problem at the end of our day is we know what we do here at Blend creates a ton of value for our organizations that we work with, and that's reflected amongst the you know the logos that we have on our website across the Fortune 500s, but it's really trying to bring in those people. people that that aren't convinced yet and take them from that, hey, I'm curious about this, I know this is something that we need to do. How can we go to that next level of of you know advancing through using this technology and using our services?
Paula Chiocchi: So I think a lot of companies feel like they're they are data driven, okay? And and so what's a scenario where the belief doesn't actually match the reality that you've seen?
Kyle Pobur: Hmm, that is a good question. thankfully I don't run into too much of that with my clients. we do have a pretty robust front door policy of, you know, evaluating who we want to work with. so anyone that does end up, you know, that I end up interacting with is typically bought into that or or has subscribed to that type of that type of thought process of, hey, we're saying we're data dri driven and we need to back it up with some actual actions. but it's those are opportunities again. It's it comes down to to education and you know going back to the example of like, hey we we get 200 data attributes on our our clients how can we use those and it's it's really breaking it down of saying you know where are you you getting the pressures internally within your organization our our model here at Blend is very collaborative and very invested in the success of the people that we work with so it's really breaking down like hey what are your organization's goals for your immediate team not that not the entire group And then what are your personal goals on you know how how do you act successful in your role? And you're working with me. I'm invested in, you know, saying if Paula, if you were if you were my client, it's like Paula, what what are your end-of-year goals? And some of them are tied to you know financial numbers, whether that's revenue or margin or how many new signups that we get there, and then really kind of taking that step back of saying, okay, this is the end goal, this is what should be pasted behind your computer that you look at every single day. For 2026, how can we use data to help you out with that? Is it driving you know, some organizations are all about driving that top of funnel? so it's like, hey, there could be a business that's trying to go out and get all their small and medium businesses to adopt their software. Hey, we're really concerned with getting just our call center to answer the phone or to make their phone ring over the course of the day. And some some organizations that's the metric that they care about, where it's hey, we believe in. our call center and if we can get a business owner on the line with our group, you know, they can they can go into the deep dive of this is why you would benefit from working with us. And you know, so it's some people I work with, it's hey, just make the phone ring. We, you know, conversions we're not gonna hold you to. That's our team's job once we get to there. And then there's other ones where it's like, hey, we'll track the 10 top of funnel metrics, but at the end of the day, when it comes down to it's lifetime value against customer acquisition. Cost that we really care, and it's really focused on that ratio of of what the costs are. but even if somebody does start in that in that way where it's like going back to your original question of, hey, we're data-driven, this is what we're doing, we're open to testing, we're only really focusing on that number. We can really kind of back it up and say, okay, if that is the number that you're focused on, where are people falling off in the customer experience journey? Where are those. those pain points where do you see people enter all their information and then no longer pursue the rest of that conversation with your organization to become a customer and kind of diagnose those things by looking at the the whole picture but understanding that end goal is really where I like to start and then it's either you know build a build a game plan backwards or forwards in order to meet those goals and that's that's that's really where you can start to point to them and say all right everyone's everyone Out there saying, hey, we're a data driven organization, these are the data points that you need to care about in order to get to your finish line.
Paula Chiocchi: So there's clearly a difference between having insights and actually making decisions. What needs to change inside an organization to make that leap, do you think?
Kyle Pobur: Buy in and trust, I think, within the organization. So some of the areas and again I'm really fortunate to deal with with clients that are bought in on on our way of doing work, but every once in a while you'll see something where it's it's it's almost I don't want to use the word selling, but A lot like going down that same storytelling path to where it's someone that I'm working with that isn't the end all be all decision maker, like they have bosses within their organization, whether that's the CEO or the president or anything like that, just taking it that next layer up of saying, All right, here's all of the insights that we can provide from you for these last six months of campaigns. Here's fifteen things that happened. We saw a a bump in performance. In month two, we saw a slight dip in three. here's some issue, or here are some things that may have caused those. These are the different data sources that we've used throughout the campaign. That might end up into a list of 20 insights, and then it's really taking that group of insights and distilling it down to: hey, here's the top three, or here's the top five action items that we can make going forward to improve those insights going. So it's, you know, whether we get a bump in a couple percentage points in our conversion rates or whether we're it could be the flip side, we could be more efficient. Say our conversion rates are going up and we decide, hey, our marketing spend can actually go a little bit lower because we're getting more juice out of what we what we're actually putting into play here. So it it's really allowing our people to be prepared and be successful for those internal conversations that we always can't be a part of. Our team doesn't Great job of prevent presenting information to our key contacts that are that are tied to our work, but it's those next steps, internal conversations of saying, hey, we've been working with Blend, this is all the great stuff we've been doing, and allow them to speak as educated on everything that we are internally in their organization. I think that's really where it goes from insights to action items, because if we can build that trust not only with the people that we're interacting with. On a daily basis, but also their senior leadership within their organization, they're gonna say, Hey, that worked. What's next? What can we do next year? You know.
Paula Chiocchi: Yeah. Yeah. So it sounds like what you do is you're also helping your clients evolve from just dashboards and reporting into true decision making systems. That's what one of the things that bring brings to the table.
Kyle Pobur: Exactly, exactly. And and it it goes back to that point I was making earlier where it's like we don't get want people reacting with their data. We almost want people having conversations with their data. And whether that's in a more of a traditional way where, you know, we're having a thirty to sixty minute meeting and a conversation, just going through some dashboards and saying, Hey, this is interesting here or whether it is that next step of AI enablement where like you can actually type into the dashboard and have those conversational style interactions, that's the next step of where where it's really going. But you know, in the meantime, yeah, for
Paula Chiocchi: your your clients open to the AI?
Kyle Pobur: A a lot of a lot of them are. Some of them are are pushing for it quite hard. So it is just something that it's it's another speed play. It's hey, how can instead of clicking through four different tabs and making sure I'm looking at the right things, you know, can I just have have some of those insights automatically pulled from me? Or we have a new offering here at Blend, it's branded as Clarity and it actually interacts with dashboards to where it could be like, Hey, I'm trying to put a together a presentation for Our CMO and our CEO, here's what their general profiles look like, here's their background, here's what they care about, here's how sophisticated they are in the data landscape. And our offering will go in and pull out. Here are the top three things that are on fire right now that we should discuss immediately. Here are three long-term goals, and here are things that we should focus on, but they aren't something that the is is urgent for us this to discuss, but put it on the roadmap. And really trying to automate some of those some of those decision-making things with intelligent applications that that can really just take large amounts of data and distill it down into those actionable points that you were talking about.
Paula Chiocchi: And it makes your job a little bit easier and faster also to be able to incorporate that. And then I'm I'm I'm s really surprised sometimes when I talk to people and they just say that they're just they don't know how to use the AI or they're concerned about using the I because it really is a time saver.
Kyle Pobur: Certainly. Certainly does. It is. It is. Yeah. It's where we s where where I personally have seen it and I'm trying to get my team into the same way of thinking is anything that we're doing that's repeatable that you could pretty much do nah, I don't want to say with your eyes closed because that's that wouldn't be accurate, but something that you do on a monthly basis. Use those systems in but do it in a responsible way that is you know, we we're Uploading client data to anything. So it's more it's more like internal processes of looking at results and numbers where where I really see it. but you yeah, so there is a we we go through training, new trainings that come out every single week of like, hey, best practices, these are the absolute no's, these are the things you should be using it for, and then here's some new skills that you can use. But yeah, it's really it's it's it's a speed play at the end of the day, and it's saying, Okay, I'm looking at a something that used to be in an Excel space. Spreadsheet that is across 14 different tabs that would take a long time to go through. And okay, I'm gonna type out manually this is what these results mean, or we're seeing a bump in performance from adding a Facebook or a meta treatment, and it is significant enough to outweigh the additional cost. Something like that, you would have to go through and click in on the individual tabs, whether it's an Excel or Tableau or Power BI, and really pull that information out. And this is just speeding that up to where it's just like. There's no sensitive data in here, it's all results. How can how can we help you know make decisions faster on this? Or how can we pull insights that might not have been visible just due to the you know the small nuances and differences?
Paula Chiocchi: Efficiencies are amazing.
Kyle Pobur: Yeah. And then yeah, and then at the end of the day, like the simple stuff like, hey, just take all these insights and put into a PowerPoint for me. It's just used to take, you know, o about an hour to put together a polished deck and now it's it's something that takes a fraction of that time. So again, like we talked about at the start, it's if you're it's a new tool and if if you're not using the tools that are available out there, it's it's gonna be easy to get left behind. Yep.
Paula Chiocchi: I was gonna say it's gonna be easy to get left behind. Can y can you share a practical example where AI or advanced analytics actually influenced the real business decision?
Kyle Pobur: Hmm, there's plenty of them out there and I think of a good one to share with you here. I would say most recently we just did a QBR with a large B2B mailer. They do small and business small and medium business mailings to get people to sign up for their software service and just did recently did like i've said a a big q brand where you know normally we're showing anywhere from seven to ten total views across a a performance of of of an area and or or performance of a campaign and for q br's we produce anywhere between like twenty to thirty different views because there's different breakdowns On geos, there's different breakdowns on industries, there's different breakdowns on hey, who is actually responding and who is this the offers resonating to. so what we were able to do is once we had the the raw data, just the numbers on these are the industries, these are the states that converted, it was able to easily put together a a breakdown of by region of the country what are the top industries that are responding to these offers and what that led to. To is hey, here is a breakdown of in the southeast, these are the top 10 industries that performed. And then we could also look at it from the flip side of like we mailed a ton of these industries and we didn't hardly get any type of response out of it. Now, if you compare that to the Pacific Northwest, there are you know a couple of the industries, business professional services, I think were popping across the board, but there are some different ones that were showing up in the top 10 and the bottom 10 as far as response. Rate above a certain threshold, we were able to take that and say, hey, this is our next recommendation for hey, if it's in these states, we eliminate these SIC codes. If it's in these states, these are the ones that we want to include. So it created an actionable efficiency play, analyzing a bunch of results. That, I mean, when we look at the the industry codes and some of the DMA values or the the the zip code values that we then map to those regions, we're talking In over 5,000, over 10,000, sometimes over 12,000 rows of results that we used to have to manually look through. Now we can take those and say, hey, here's a clean PDF with no sensitive information included into it. How can we make this actionable to increase the efficiency of our campaigns? And at the end of the day, like my job isn't to maximize the amount of mail that someone is sending. My job is to get you the results at the end. Most efficient way possible. So if I can say, hey, we're mailing 100,000 pieces a week and we can bring that down to 50,000 by making these tweaks and still see the same results, that's a cost savings of half for an effort from my clients while getting those same results. And hey, then if you want to maintain that same level of marketing spend, you increase that and you should expect the same the same level of end results at the end the day. yeah. Like I said, I I'm I'm very fortunate to work with some clients that are open to testing and you know, they they don't like getting complacent, same as same as me. So yeah, they you know, we could we're very very dynamic in our our planning and that that just speaks to myself and the team that is underneath me that I'm I'm I'm getting more familiar with these these different projects that we can make dynamic decisions. So it's like, Hey, this source isn't working or we need to change our order selection criteria to focus on These areas, these geographic areas, these SIC codes, and so forth. And or or could be something as simple as like, hey, we're seeing these titles are all of a sudden responding at a higher rate. Let's go find those people rather than just the highest ranking person at that business that could be a better target. So it's all it's all a very iterative process. It's you know, I I d I would never consider myself a scientist, but I've always been very drawn to science growing up in my entire life. of hey there you should always be testing a new hypothesis. You should always be learning something new. And if you're not then you know you get stagnant and you'll eventually get left in the past, no matter how good your your strategy is. it just goes back to the point I just landed on is if you aren't learning and you aren't trying to explore something new out there, you you just end up getting into that stagnant place. So just to answer your question, if you're if you're not doing those things out in your daily life, if you're not trying to you know, do continuing education efforts of taking new trainings, learning new skills, learning new tools and how to use them, you'll just get left to the past.
