Companies now have access to tons of customer data — but are they getting the most value out of it? In this episode of Customer Data Perspectives, host Isaac Sacolick sits down with Dale Tuck, Chief Information Technology Officer, Primerica, to explore how companies can transform their organizations to solve their biggest data challenges.
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Read the Transcript
Sacolick: Hello, everyone, welcome to this episode of Customer Data Perspectives. I am Isaac Sacolick, the president of StarCIO, really happy to have Dale Tuck here with me today. Dale is the CIO at Primerica and a good friend of mine. How’s it going Dale?
Tuck: I think it’s going fantastic. And thanks so much for having me and allowing me to share some of my perspectives on customer data.
Sacolick: You’re the Chief Information and Technology Officer, well, you got the longer title that I didn’t actually read off properly. And now we’ve been on stage together, we’ve had lots of conversations around data and analytics, you’ve had roles at Brambles, at Applied AI, you’ve worked in CRMs, and lots of different data technologies. Give everybody that two-minute background of Dale Tuck from that lens of managing all this data for these different companies.
Tuck: And I will mention Isaac, when you take the word CIO, and you may get CITO, what that really means is you’re also the guy that they call on when the projector doesn’t work. So that’s where it comes from. Yeah, so, very varied background. I started out in the supply chain space, with a global supply chain services company Brambles, you mentioned a moment ago. And as you can imagine, the clients that we served there – they had tons of data. And as a supply chain organization, that data was relevant to us, and the data we had was really relevant to them…a little bit later in my career, I launched Atlanta Applied Artificial intelligence. And actually, part of the reason that Atlanta Applied AI was born as a nonprofit, was actually to help with the adoption of artificial intelligence in Atlanta, which is where I live and really love living. And again there I started to get exposed to the fact that there is lots of data, but there’s not as much usable data, as people might suspect. So the goal of Atlanta AI was really to raise the awareness of what was going on, and how could Atlanta businesses really take advantage of this. Ironically, I got pulled out of Atlanta, I spent some time with Rocketdyne and Pratt and Whitney and other companies, but it didn’t really materialize. But the messaging and the learning were still there. And then more recently, moving into financial services. That’s been a real eye opener for me, because you know, the background of supply chain information, then artificial intelligence. And now seeing the myriad of transactions and requirements around managing data within a financial service industry has really given me a good perspective on not how we use data, but how should we use data? What are some of the things that are well-articulated data programs, specifically around customers, because we all want to grow our business, right? One they can do to accelerate the growth, and possibly even the profitability of an organization?
Sacolick: You know, what I love about your answer Dale, is you’re talking about profitability and growth. And so much of what I do is in the transformation space, and I remind everybody, there’s a lot of what we have to do just for compliance, there’s a lot of what we have to do to improve quality or efficiency. But if you’re going to do and go after some of the hard things, if you’re really going to go into artificial intelligence or machine learning, you got to have growth and customer at the top of the funnel. Because ultimately, you know, this stuff isn’t easy. I mean…Dale is one of the people I learned the most from around data and analytics, he’s worked at some of the biggest companies that have some of the biggest challenges. So Dale, let’s put some bookends on – what’s the easy stuff that people should know how to do today, particularly around customer data? And maybe what’s some of the hard stuff? That, you know, the vendors have been advertising for 10-15 years, but it’s still really hard to do.
Tuck: So I think the easy part of data is, it’s very easy to ignore it. That’s what a lot of people do. The best analogy, I can come up with data, it’s kind of the same as building a house, you know, when you if you build a house, you want all your bricks to be neatly lined up. And as you’re building that wall, you want the brick to be available, and bricks, a lot of data. Sadly, a lot of organizations today, the bricks aren’t in a neat little pile, they’re spread all over the yard, they’re spread all over the neighborhood that they’re spread down the road. So it actually impedes your ability to build the house or to build the capability or to get the information that you require. So I think again, it is very easy to ignore that.
Let me talk maybe through two examples that were very interesting to me and kind of just highlight how easy it is to ignore data. One, I was working on a system to actually develop our marketing clouds for our business. And in this particular business, we wanted to develop multiple marketing clouds. So it was on behalf of what we call a base shop. And one of the things you need when you’re developing a marketing cloud is you need a client’s email address. But in this company, what had happened is, over the years, we had many systems that, you know, the intent of those systems was to gather client information. And what had happened is over the years, all of those systems had a client email input screens. So when it came time for me to say, Okay, we’re going to use the client email address, the response was, Well, which one? I’m going, Oh, we’ve got more than one? And the answer was, yeah, we got many client email addresses. So you can imagine the consternation of me thinking, how hard can it be to get a client email address, and then actually realizing we have multiple of them. So in that particular case, we actually had to come up with an algorithm that said, if you’re in this system, and you’re in this system, and it’s the same, let’s use that one, or alternatively, if you and this one and this one, and not in this one, but I see you over here…So that algorithm took us actually months to put together so that we could we could solve that problem and actually use the correct client address. And, and while it’s focused around customer, Isaac, that analogy works across a myriad of data types within an organ organization. So that was one of the ones that gave me a couple of gray hairs.
The other one was more related to some regulatory requirements that we had. And we were forced to pull some data out of our system. And the same mentality kicks in again, how hard can this be? And ironically, it really fell back into that ETL process that you discussed in your first book. But it’s extraction, transformation, and then loading into the destination system. Everybody thinks that process is easy. But in this particular example, the transformation required inputs that we didn’t have in our data. So even the idea of grabbing data out and maybe modifying it, appending the data, enriching the data with third-party sources, sometimes you just don’t have it there to be able to do that. So that became another hard problem. And guess what we had to do again? We had to create a proxy, which was basically an algorithm that said, for that piece of data that isn’t giving us the output that we require, this algorithm will fill in the blanks, and we actually got it to within 1 percent of the answer we needed. So as of right now, in my journey, I say that there are very few easy data problems. But there’s lots of complex data problems, but they are all solvable. As long as you don’t jump too quickly. You got to take the time to ponder what you’re trying to achieve, have a look at the assets, the building blocks that you have, the bricks that you have. And if you give yourself that, that leeway, and some of that time, the solutions do materialize. And I’ll talk a little bit about that if we get a chance through this podcast.
Sacolick: Dale, it’s a great answer. Actually, my favorite part of your answer is that somebody in your organization is going to think and say, Well, how hard can that be, right? It seems like such an easy problem. But I know when you go back to the email example, you know, I have a StarCIO email address, I have a Gmail email address. And then underneath that, I have about two dozen aliases, right, for different purposes or for different needs. And now you’re a marketer, you’re trying to reach me, you send something to the wrong email address, it’s getting buried in a folder that I’m never going to see. Right? You send it to the right email address, and I’m probably going to have it at the top and probably have a look at it. And then I have, you know, entire mailboxes that I don’t even look at anymore.
Tuck: I was gonna say, and while some people might be listening to this guy, well, you know, ours is better – even if you just get a handful of those email addresses wrong. Or even worse, you send it to multiple email addresses. It’s particularly in CRM, you want it to feel very personal. Hey, Isaac, you know, happy birthday. So you know, it’s coming up on the anniversary of picking your favorite contract. If you’re doing that to, you know, 15 different email accounts, and the person knows that, you’ve removed that personal connection. And that’s really what we’re trying to create with CRM. And with marketing automation, we’re trying to make it feel like there’s a human being who’s connecting with another human being to try and make that human being’s life better. So your example is spot on.
Sacolick: Yeah. So let’s jump into CRM a little bit. You’re, again, one of the major experts. Having managed a CRM team. I’ve managed CRM teams before. But we’ve been doing CRM for two decades, right? And so, you know, the CFO, the CMO thinks, you know, I have my CRM, it’s like the ERP for working with customers. It has everything in it, it has all the data in it. Why is this stuff so hard? So let’s talk about CRM from the perspective of attracting customers, retaining them growing, you know, growing it and penetrating new markets, things like that. What’s missing here? Why has been so hard over the last two decades? Why are we still talking about it?
Tuck: Yeah, I think we’re going to be talking about it forever. If you look at the MarTech space, the marketing technology space, there were 7000 companies out – it’s insane…for me, I like to think in terms of models. And I guess this is where you put me at a disadvantage because you know me so well. So you know, all the buttons to push. But I’ve been involved in migrations from legacy systems into brand new, top-tier CRMs. And they’re difficult for all the reasons that we outlined a little bit earlier.
I’ve also been involved in Greenfield where we’ve been able to build something from the ground up. So I’ll talk about some of the things on the migration, but it’s vision that matters, you have to bring a voice to the vision. And that’s sometimes difficult, because the vision of CRM is sometimes a little a little bit nebulous, that data matters, you have to have access to your data. So when I talk about data, again, I use it back to the reference of building blocks of a house. Data not only informs your decision making engine, whatever engine you use to make decisions, but data is also a capability that you use when you build new applications. CRMs have genuinely been intensive – Salesforce has to capture a lot of data, you’ve got to figure out how you minimize that. And data can help by providing – I’m going to use a phrase – “next best action.” Data can help us really automate and leapfrog things that a human used to do if we use it appropriately and if we don’t just try and go for the prize of “oh, I installed the system,” that should never be the goal of a CIO.
It should be how does that system deliver the maximum value, because the value will be remembered long after the missed deadline is forgotten. Principles matter. And so what I mean by principles, I’ll give you one example. In one of the CRM implementations we did, the principle we used was two clicks. If my agent or my rep can’t enable a campaign in two clicks for his client base – bearing in mind, I’ve got all of his clients data, so I know who’s having a birthday, etc. – then that’s not good enough. So we focused a lot on the principle that it needed to be super, super user friendly for our sales force. And when you look at the research, you’ll see lots of people say the reason that a lot of CRMs fail is because it puts too much, you know, too much work on the sales guy. So those principles.
Another principle was, there should never be more data on the screen that can fit on a horizontal mobile phone, which meant that we had to design these screens really, really carefully to be able to click through it and get the additional data, we also have to fight. And this happens everywhere in IT at the moment. I’m migrating from one system to the other, the first inclination is just to copy what you have and put it in the new and you’ve got to blow that puppy up. It doesn’t accelerate your timeline, that’s for sure. But just taking what was old and migrating it to the new, you’re losing a lot of fidelity and a lot of opportunities to do something better. And change management comes into these conversations all the time. On some of the newest stuff that I’ve done. There was one model we put together. And I wish I had the slide with me here at home. But it was selecting a model that said, I’ve got to have the right contact, so who am I talking to? I’ve got to have the right content for that content. For that contact, I’ve got to have the right context. So why am I reaching out with that content to that contact? Am I going through the right channel, I mean, maybe this person responds better to text messages, or to social media posts or to an email, or maybe even a phone call, or you know, pushing an agent to say, hey, it’s time for you to find this person, because that’s their preferred method, or actually send them a postcard. So if the right contact the right context, the right content through the right channel, and then having the right controls in place.
Sacolick: You’re sounding like a CMO right now.
Tuck: Right. But again, I think IT is not about technical vanity and building cool stuff. Although I love that part of IT. It’s about being an asset to our business. And too often IT are the order takers. And that’s the wrong place to be because you got a lot of engineering horsepower in there. The last one I mentioned, and it doesn’t really fit into my method, but it’s closing the loop. So too often we create these programs that engage the audience or engage clients, but we don’t close the loop and say, what did that just give me? You know, what was the value of that campaign that I just sent out now? So we talked about click rates and open rates and drop offs and all of those things, but it’s really about – I sent a campaign out to engage with somebody to have them do a specific action – can I look in my system two, three, four weeks later? Did that action prove valuable? And what was the value of that action?If we start building that into our CRM solutions with MarTech that’s really in tune with what drives business growth and what drives profitability there’ll be nothing stopping them. Absolutely nothing stopping them.
Sacolick: I’m laughing because I had two conversations with other CIOs over the last couple of days, completely different contexts. And one of them, we were talking about how bad it is to do a cloud migration lift and shift – taking all the problems in your old system, bring it to the new system, like it’s magically going to be better, different, more impactful for your business, if you’re in a different system, but doing the same thing, that doesn’t work. And then, you know, the big problem I see in the CRM is typically brought been brought up through that light, or no governance phase, everybody has to have their field, in the contact record, in the deal record, in the account record, you know, four or five different lines of business, over 15 years, different people come and go. All of a sudden, you have a form that has 75 fields in it with no data quality against it.
Tuck: I’m gonna tell you another analogy. So in one of the jobs I was in, I asked to take over IT finance, because I believe if you have a good handle on IT finance, it helps you foster the right relationships with other leaders in the organization. And what happened was, they had a data problem, everything was managed on large Excel spreadsheets, and it was just all over the place. So the impact of that for me was I wasn’t able to answer questions very, very simply, it was actually very difficult for me to even get the answers to very basic questions. So I’ll draw the correlator that we implemented one of the leading IT finance management platforms. And the individual that implemented it for me, was an expert in IT finance. The result? Going from weeks to get an answer to sometimes I get answers within minutes to complex questions about: how are we spending money? What is our unit cost of x? What is our unit cost of y? What is the difference if I move this load from on prem to the cloud? And while that sounds like, Oh, it’s a strong finance guy? No, it was actually just having the data available to ask those questions. Because I like to get lots of impressions before I form an opinion. But if just to get one impression takes you three days, guess what – you don’t have many inputs to make the decisions you need to make. So just one example of how taking stuff out of you know, unmanaged Excel and thoughtfully executing that in a well-articulated or well-put-together finance management system has changed my decision making ability, at least on the finance side.
Sacolick: You’re bringing a whole new dimension to this discussion around how much more real time, our decisions have to make, and how many more decisions we’re making, but also the fact that we’re dealing with multiples of systems, right? You know, all the activity that our customers are doing are not just happening in the CRM, or in our Customer Support System, or in our web systems, or in the 100 marketing systems that you’re using. They’re happening in a lot of different places, they’re happening more in real time. I don’t want to wait and make a decision today on data, that’s one or two weeks old, that doesn’t work for me, I don’t want to work on it when there’s known data quality issues. I want to be able to put better data quality in. But there’s one aspect of this that you have a really unique perspective on and that’s this notion of a customer. And a customer in some businesses, you know, in retail, it’s the person coming in to buy something in my store or my website. But you’ve worked in some very complex value chain marketplaces with lots of different people that you can call customer that you’re collecting data on that you’re providing services to. So give us a flavor of what that looks like.
Tuck: So it’s funny. I actually view everybody that isn’t in IT actually even view some of the people within IT, of being my customers. And that’s really my human capital management side is, how can I help my peers and teammates go out and kind of replicate what I’m doing? But coming back specifically to the question. In all of the environments that I’ve been in, one of the biggest challenges that organizations have had is, who is the customer, and what do I need to know about that customer so that I can have the absolute best engagement with that customer that I can? And we have this concept of customer master data, but that’s only one aspect of it. I used a phrase a little bit earlier – next best action – and that’s something that I’m currently very passionate about because I do believe we can use some of the data tools, whether it’s, you know, AI, or ML regression, etc. – you pick your favorite vernacular around artificial intelligence. But if we have a good understanding of who our customer is, and we understand – and you use the phrase that I love – the interactions that that person has had – whether it’s within our ecosystem, or outside of our ecosystem, that we can buy through various means (there’s a lot of data aggregators out there that you can buy) what is this person been up to? If you can marry who that person is, and what about them you need to know with those external datas, you’re able to start doing the magic of being able to talk to that person in the right context at the right channel at the right time, with the right message. And that will drive up the engagement. And that will then make that customer more sticky.
And you know, sometimes it isn’t just about making a sale, it’s about just building a stronger relationship with that individual. Even simple things, like being able to identify a birthday and having the system send out a personalized email, Hey, Isaac, happy birthday, I haven’t seen you in a while. All of those things can be generated through data, enriching that data, and then using some of these tools. And it goes all the way from that simple example, all the way to some of the examples. And actually, sadly, not all of them are from the U.S.. But where you’re able to use the system to recommend the next best interaction. So I’ve done this with you, what’s the next best thing I can do? Because it’s probably 1,000 things that I could do with a client. But the system is getting to the level now where it’s able to start suggesting, hey, the next best interaction with this person might be this or it might be that.
Sacolick: I just want to pause here, because you’re giving everybody an incredible hint on how to think about these systems. When you say the next best interaction, now we’re thinking of a machine learning approach? And, you know, we tend to think heuristically, like, we can come up with what that next action is, but you’re bringing in, there’s a ton of data out there, there’s a ton of context. When you’re talking to an individual customer, I might be a customer, but I also may be a supplier, I might be an influencer, right. I might, you know, in different situations, I’m playing different roles, but I’m still the same physical person. And so when you’re writing an algorithm, what’s the next thing that should show up on the screen? You’re a customer service rep, what’s the next question that you should be asking someone? You’re a sales rep – I have certain things that I bought with you, I have certain things that I have bought with you –what’s the context that might allow you to say, look, you got one shot at selling me something new?
Tuck: I love the fact that that takes three minutes where you can explain it in 15 seconds, which is your secret skill. I want to add one more lane on top of that. And again, you’re probably going to accuse me of being more of a CMO. But on all of these journeys, what we’ve tried to do is instill what’s the value of that interaction? And I can’t go into details. But in prior organizations, what we’ve done is there are 52 transactions we do or interactions we do with a client. One of those interactions might be a stepping stone interaction. So once you get the first piece, you can get the second piece, maybe converting a prospect to a lead, or a lead to a prospect depending on your favorite vernacular. Understanding how much that is worth is one of the bigger inputs into your engine, because there’s some transactions that might be worth $1. There are other transactions that might be worth $10,000. So in that engine with an AR saying, what’s the next best thing for me to do that person, the profitability of that role, of that of that interaction is really, really important. And it’s actually something that I’ve brought with me along my career.
I want to touch on one other thing quickly and I know we’ve only got limited time. But on this journey, I was also looking at this concept that, you know, people explained, which is, oh, we’ve got tons of data, it must be valuable. And I think that’s flawed. I think within organizations today, there is a lot of data. But there are these kinds of strategic data assets that people need to get their arms around. And I wrote a LinkedIn article about it because I was so passionate about it. It’s not all your data, there’s just pockets of that data when you append it, when you merge it with other data, it starts to push you in the right direction. So data can help you do analytics. It can help you build better applications because all the bricks are there and available for the users to use. But there’s kind of this third piece where it can start guiding you to what your next big business opportunities are gonna be. I’ll give you two very quick examples. Red Roof Inn did some work on analytics, using flight patterns and weather patterns to figure out when passengers may be stranded. And they use that in their marketing. And they increased their occupancy by about 12 percent. Another famous example – UPS. Some guy was sitting there going, man, I’m sitting so long. Yeah, if this traffic light is right now, what if I go right, maybe, maybe I can get around quicker. So they upended their logistics system, their route management system, with their delivery. And they created this, this asset, which took thousands of tons of carbon out of the air and delivered packages much more quickly. And that was a really good idea of somebody smart sitting down, pondering and going, Wow, these are strategic data assets? How do I use them a little bit better?
Sacolick: I think it goes back to what you said earlier of having really good vision, focusing on problems that really matter. It’s one of the things I talked to data scientists about all the time. There’s more problems, that we can apply our data, if you’re spending your time in the right area, because we know it’s layered, right? Once we solve one problem, that leads to 10 more questions. And you’re describing, for those of you who don’t know how neural networks work, you’re going to decide feature vectors that upfront, right, and you’re talking about, which of my data is most important for us to use in those feature vectors? Because if I throw 1,000 parameters, Google can do that. But maybe Isaac can’t do that. I don’t have a big enough machine, big enough budget to throw at it. And, which of the decisions are we going to weigh higher, which are giving him the right formula to think about it?
But I want to get to one thing – I think our audience would kill me if I didn’t ask a Chief Information Technology Officer about technology. I want to make sure we get into this question of technology. What are you using, what architectures, and technologies are in customer data in particular? Give me a sense of using data lakes using data marts, planetary databases, search engines?
Tuck: So we’re just following that typical path. And that’s kind of what I’ve always done. You get stuck in this conundrum, where you go, do I fix everything that I see that’s not working appropriately? Or do I find a way to wrap and renew – you do something new that eventually envelops the old. So, we have always invested in the concept behind a data lake, a data mart, a data puddle, if you want to just dump your data somewhere and make it all messy. What I found to be our secret sauce here is a new technology. It’s not really a new technology. But it’s a virtualization technology. So imagine an organization that has many datasets all over the place. So your referential integrity is not there, the data is not normalized, it doesn’t link up nicely. To try and go and fix all of that, while there are applications interacting with that data, there are reports being run off that data, is incredibly difficult.
So we use a concept called data virtualization. And it is another platform, and that’s part of my strategy is to implement a platform that goes on top of it. And the best description is, it kind of shines a flashlight on all of that data. Once you’ve got the data in the virtualization platform, then the legacy data stays there and it carries on feeding the systems that it needs to feed. But the data virtualization becomes your new source for data for analytics, data for applications, data for just running ad hoc queries. And the beauty of that is it abstracts away all that hideousness, which is sitting within IT, that IT does a very good job of managing, and it creates (I use the word baskets, it’s the simplest way for me to explain it to my kids) baskets of customer data, of product data have transactional data. And my strategy has been to make that layer available for business consumers. So I want to do the technology side of things. I want to make the data available, but I want the business to be able to interact with that data in a very easy way to get clean, scrubbed, and described data. And we’ll talk about that in a little bit in a moment.
So data marts, data lakes, after the behind the scenes – data virtualization sits on top of that. And as projects are demanding data, we’re using that project as the pool to create the next basket of data. So I mentioned earlier, a client was one of them. So we have a clean client basket of client data, which can then be consumed by anybody. We even use it for APIs. And then on top of that, we’re trying to standardize on a fairly common brand that has a dashboarding tool. And that seems to be working really, really well.
But the secret sauce of all of that was that middle virtualization layer, because without that you can’t create the pool from the business, you can’t help them bring voice to the vision. Hey, guys, we said, we’re going to make it easier for you to get data out of our system. And then all of a sudden, they see this webpage. And it’s got these baskets that are describing data that they need every single day. But now I have to write an SQL query that uses NFF subject matter expertise, because all of that data is described.
And so I’m going to just say one more thing about data virtualization. Imagine bringing a partner in, you’re trying to maybe offshore some work, or get some lower cost work done. It’s very difficult today, especially in complex environments and organizations to do that. Because you have to have this SME knowledge, where is the data? What does this mean? Data virtualization has the power to put those baskets together, and it also means it’s easy for me to bring external people in. I can ramp up new employees far more quickly, when I go, here’s all our client data, here’s all our contract data, here’s all that. So it makes everything better. It isn’t measured in months, though. That’s the challenge. It’s measured in years, it takes years to get these institutionalized and operational hours. But again, the goal, the goal is worth it. Because you’re speeding up your organization with every new project with every new bit of work you’re doing, you’re increasing the velocity of getting new capabilities added to our business, which is ultimately what they want from us.
Sacolick: Yeah. You know, what I like about what you’re describing here is this notion of a flashlight. You know, I sometimes joke that CIOs used to know more about the box that stored the data than what the data was inside of it. And I think that’s changed a lot over the last few years. But you know, in every organization, there’s a few people who are the subject matter experts around their datasets. And then the reality is we need a lot more people, if it’s 100 people, it’s 1,000 people – next year, it’s 10,000 people after that. Being versatile to work with our data, we have to do it more real time. It’s not just our employees, or it’s our contractors. And so now I got policies put in place around that. I don’t want to have to rebuild the same basket five times, five different ways.
Tuck: And you know how often that happens, Isaac.
Sacolick: Everybody has a secret sauce to write a SQL query and has their secret tool to put it in there. And you’re saying, Well, you know, how do we get away from this? Because we’re going to be using our data for the next 10 years in different ways. Give me a sense of talent around this, right? It’s not like data is a new story here. And it’s not like, you can just go and hire people with TensorFlow talent. Tomorrow. So what’s your secret sauce around data? And then we’ll do one wrap up question.
Tuck: Yeah, I don’t know if I’ve necessarily got a secret sauce, but I will give you my philosophy. I wanted to share one. One comment that a prior CIO that I worked for made and she said, knowledge is power, shared knowledge is even more powerful. And I’ve loved that phrase. So making more of our data available in a consumable way for our business, it’s actually the right thing to do.
But let me talk about talent. So right now, I think it’s a great time to talk about talent and those things. So really, my primary goal is retention. I have so many incredibly talented individuals, I don’t want to lose them. And with remote working, there’s a lot of pressure on opportunities, even outside of Atlanta. So, back to my model. We’ve developed a model and the first part of the model, and I’m stealing from CRMs. So it’s the four Ps. And the first P is pay. Are we really looking after that person? You know, are they a market related comp comp level, all things considered? The second one is purpose. Does this person feel like they’re working for a company that gives them purpose? And I’m fortunate, you know, within the supply chain arena, I was with a company that was really concerned about the environment, in financial services, with a company that was really concerned about helping American families become financially independent. So I’ve been blessed with a purposeful career.
The next one is their passion. And this is where the data side of it comes in. If I’ve got some data architects, and if I give them an SOW to write, or something to do that isn’t related they go, “I don’t know if you’re using me appropriately.” So we try and make sure that we’re using those people appropriately and letting them live their passion. And the last P, just to close it out, is around preferences. If I’ve got a data engineer, and he lives in, you know, Chicago, and he wants to stay in Chicago, thankfully, these days, we can do that. And they want to start work an hour later and work a little bit later in the evening. So we’re also trying to meet people’s preferences. And I’m hoping through those four Ps, I can not only retain, but when I share that with recruits, I can also recruit people. So wow, this is a company that seems to have their act together, they’re gonna pay me fairly, they’re going to leverage my passions, what I want to do, they’re going to let me live my preferences. And by the way, they’ve got a really cool mission in terms of society.
Sacolick: That’s an incredible answer. What I love about it is when I throw that question out at a lot of folks, I get the gripes about how hard it is to hire people. You know, I can’t find cloud, I can’t find security, I can’t find a digital marketer, all the top skill sets come off the list. And I’m like, Well, what are you doing internally, right? You got talent there, you’re always, you know, challenged to retain that talent, under all kinds of circumstances, you’re always challenged to make sure they like what they’re working on, or they’re always stretching their capabilities, so that they’re always learning. You got it covered in all four pieces.
Tuck: I’ll add one more thing to it. And that’s learning and development. So we launched, we call it an advanced learning program. And that was part of a recruit from within strategy that I had, because we’re always posting new jobs. And there’s people inside that just have a few months training or a certificate or a couple of certificates – they could apply for those jobs that have been posted. So the whole recruit from within spawned a learning program, multiple channels, security, cloud, all the things you mentioned, API’s backend for frontend. But we’re trying to target it so that they qualify for jobs that we’re posting externally. And that’s getting a little bit more sticky right now as well. So I think you hit the nail on the head.
Sacolick: You know, Dale, you’re CMO, Chief Technology Officer, analytics and ML background, and now you’re going into HR territory.
Tuck: Hey, that’s what makes great companies, is great people. And it’s actually one of my pillars in my current strategy is getting closer to the business planning and executing flawlessly. The third pillar is managing our costs appropriately. And the fourth one is pushing people up late, let’s invest in our people.
Sacolick: I’m gonna have you do my digital Trailblazer keynotes cuz you got all the right recipes in there. I have one last question for us. You’ve been great. A lot of really good insights here. But I’ve been asking the same question to all of the guests of Customer Data Perspectives. What do you want as an easy button?
We talked about all the nuances of some of the hard things, some of the things that we all have to do around talent, all the technologies that you’re putting in place, but you’re the expert. So give a shout out to our vendors and our suppliers and who we’re getting technology from. Where do you need an easy button in the next couple of years?
Tuck: So I’m going to argue that I found one of the easy buttons with a virtualization platform, but I’m gonna take your lead in terms of vendors and suppliers. The easy button for me with onboarding a vendor or supplier is somebody that doesn’t just show me the PowerPoint, but actually says, I’m willing to invest in understanding your environment. So it’s not my easy button. But if you’re a vendor of your supplier, you think you’ve got some secret sauce, what I’ve shared with you today has piqued your interest, then my easy button to push to come in and help me on my journey is: understand my problem. And I find it so funny that so many times people will throw these solutions at me and expect me to find the problem. I’ve been in five companies and you guys see a lot more you come in and tell me what you’ve seen and what you can do to help me make this place better. So I think even there, Isaac, you know, vendors and partners that have that mindset of, let me understand before I put a solution in place, they can be my easy button to make things better.
Sacolick: That’s a great answer to the 9,000 logos on the MarTech landscape. Exactly right. Know the customer problem and then present the solution. Dale, this has been awesome. Thank you for being a part of Customer Data Perspectives, a lot of really good insights here. AndI can’t wait to break bread and have a beer with you again soon.
Tuck: Now, absolutely. Isaac and just want to thank you for all the support you’ve given me over the years. Your big books have been fantastic in shaping my thinking as well. And you know, good luck. Happy to have a follow up and share with you how we’ve made progress along this journey but thank you for having me.
Sacolick: Absolutely. Thanks again everybody for joining us for customer data perspectives and I can’t wait to see you in the next episode. Have a great day.