Simplifying the Digital Customer Experience

Jim Jackson: It's like going to the doctor's office and you fill out the forms and it's like come on it's a digital world right? I should be able to give you all of this one time. At the end of the day our enterprise customers want a lot of the consumer experience that they get in their daily lives. They want it more automated. They don't want to have to integrate multiple different ecosystem partners. They expect that the experience from HP will be as simple as their everyday lives.

Kailey Raymond: Hello and welcome to Good Data, Better Marketing. I'm your host Kailey Raymond. In the realm of modern marketing the integration of AI and data analytics has undoubtedly revolutionized the approach businesses take towards customer engagement. Leveraging AI to curate and analyze vast amounts of data is now table stakes driving efficiencies across the enterprise. And obtaining good data that is also the right data for your business's needs is a prerequisite as it ensures that the insights drawn are both accurate and actionable. Companies like Hewlett Packard Enterprise are on the bleeding edge leading the charge and delivering the flexibility that companies need to run their most advanced AI use cases securely and responsibly. HPE's Jim Jackson and I discussed the importance of a customer-first approach principles for responsible AI adoption and building the right infrastructure for a seamless customer experience.

Kailey Raymond: Today, I'm joined by Jim Jackson Executive Vice President and Chief Marketing Officer for Hewlett Packard Enterprise. He oversees all aspects of marketing at HPE with emphasis on accelerating the digital transformation for the company to be the edge-to-cloud platform as a service company. Jim has over 25 years of IT industry experience and is passionate about the use of technology to inform customer-centric data-driven and digital-first marketing techniques across the entire business. Jim welcome to the show.

Jim Jackson: Thank you. Excited to be here.

Kailey Raymond: It should be really fun. I know that you've been with the HPE family for over two decades. Tell me about your career journey to get to be CMO of HPE.

Jim Jackson: Yeah most of my career has been in marketing. I love marketing. I love the fact that you get to do so many different things in marketing whether it's branding, data, digital, demand generation. There's so many different parts of it. I've always liked that. I started out in software so I was on the software side of things. I've been at Hewlett Packard Enterprise for over two decades as you said and I had the chance to work my way up from an individual contributor to CMO. I've had a chance to also see and do a lot of different things across marketing communications. I've had a chance to own different parts of marketing for areas like storage for high performance computing services. And I have always had a customer and partner first approach.

Kailey Raymond: Beautiful. You've seen a lot of different parts of the business and that cross training I feel like really kind of gives folks a full understanding of how all these different parts of the organization work fully well together as a well-oiled machine. I'm wondering how you might describe the marketing organization's role in building the customer experience in that journey.

Jim Jackson: I think one of the things that's really interesting about marketing is that we sit at the intersection of customers, sales and the BU. So we can bring a very unique perspective on what our customers need what motivates them what are their challenges what is their perception of us. So as we look at our company strategy and how we think about go-to-market marketing plays a very big role in that because again we're sitting on a lot of information and a lot of data that we can bring forward and help us make better decisions. I also spend a lot of time with customers and with partners. And when you do that you get the cold face of what's working how they really see you how they really perceive you and what challenges they're focused on and candidly where they're gonna spend money in the future. And that's what we're always trying to do is to bring those kinds of insights and those ideas forward. And then the other thing I think is important about marketing is you always have to push the envelope. I like change and trying new things and really view marketing as that catalyst that can try new ideas and that catalyst for growth as we're moving into a market which right now is very very, dynamic with AI with hybrid with so many different trends happening in the market.

Kailey Raymond: I had a boss once who'd said it starts with marketing. You're right. Sitting on that wealth of information and talking to those partners and customers the ability to ideate and innovate within marketing I think is a really unique thing and to be able to influence a lot of where the company is going. You mentioned AI you mentioned a couple of trends already. So I want to get your take as somebody who's really taking in a lot of this information from across the partners that we've talked about already. What are some of the trends that you're seeing that are impacting marketing and customer engagement that you're watching out for? 

Jim Jackson: I mean when I think about customer and customer experiences we have to think more broadly. What is their complete set of engagements and interactions with a company and with a brand? And that's what our customers really are telling us right? I'm looking at you from the outside in I'm looking at every element of what you bring to the table. In terms of some of the big trends that we're seeing right now AI is huge for us because it has the power to transform every single industry out there. And I believe that it will. It's still early days especially in enterprise. Many of our customers candidly they're still trying to figure it out. And this is gonna be a multi-year build out that we'll all be able to take a part of and be on that journey with them. Data is another critical area. And getting your data into a format that you can use to make quick decisions and really being able to leverage as much of your data as possible. That's really, really important. And it's as you know this is not so much about data. It's how do I use data to derive insights that can impact the business and ultimately make better decisions for our customers.

Jim Jackson: Another big trend that we see is hybrid cloud. And if we were having this conversation four or five years ago it might be easy to say hey it's all going to go to the public cloud. But right now what we're saying is it is going to be a hybrid environment. Customers want applications running on-prem and colos and in a public cloud. And we have invested heavily in hybrid cloud. Another is data and networking at the edge. So when you think of edge computing and edge networking as more and more data is generated it needs to be acted on in real time at the edge. And these are really areas that we've been very, very focused on at Hewlett Packard Enterprise for the last several years. So what's good about it is our story and our message just continues to get deeper in each of these key trend areas and bring new innovations to our customers.

Kailey Raymond: That's beautiful. You've talked about a couple of points here. You've talked about AI you've talked about real-time. It's interesting. You've talked about data and kind of the cleanliness of that. I want to dig into all of those separately. It reminds me this quote I was talking to the CMO of Teradata a few months ago and she said everybody says data it's like oil but to her she says it's actually like water because it's everywhere but a lot of it's not usable. And so I think that especially in light of AI making sure that you do have good data to be able to leverage AI for good AI outcomes is something that we actually recently saw in a report that we ran. We ran a report called State of Personalization asking businesses how they're using AI. Of course personalization being a key use case there and 61% of brands that they were concerned about inaccurate data compromising the effectiveness of AI in particular for personalization. And so I know that you're at the bleeding edge of AI with your new partnership with NVIDIA. And I love a lot of the AI principles that HPE is investing in. Since 2019 I think you've had these core principles in place one of them being making sure that it's human focused making sure...

Kailey Raymond: Protecting privacy is another one. I guess I'd love your take on speaking to AI in particular as it relates to that balance that you can strike between technology human intervention where are we going here? 

Jim Jackson: That's a great question. I think when you think about AI it has the power to do so many things. And then the question boils down to is how do we leverage that in an effective way and help our customers go on the journey? So first of all you mentioned some of our principles and I think governance and adoption of AI ethically and responsibly is where we see a lot of customers struggling or lagging. What I mean is that we see them experimenting with Gen AI before they have some of the policies in place for scaling it into true production. And this is really important particularly as we help them on their journey. You mentioned at HPE we have developed five AI ethical principles and they're aligned to our mission which is to advance the way people live and work. That is our purpose as a company. So as we started on this journey we wanted to make sure that we had the right principles in place to drive the responsible development and use of AI. And those principles are Privacy enabled and secure. We believe that all AI systems should be designed and used to respect an individual's privacy.

Jim Jackson: Then to be secure and minimize the risk of errors. The second is human focus. You mentioned that AI systems should respect human rights and abide by the various applicable laws throughout their life cycle. So we need to think about AI systems being designed and used with mechanisms and safeguards such as the capacity for human determination or oversight so that we can support responsible use and prevent any misuse. Inclusive is the third one. AI systems should be designed and used to be inclusive to minimize things like harmful bias and ensure fair and equal treatment and access for individuals. Responsible is the fourth one. AI systems really need to be designed to be used responsibly need to have mechanisms in place to ensure accountability all around that. And then the last one we have is robust. AI systems should be subject to hazard based safety engineering approaches if you think about it that way throughout their life cycle so that you're building in quality testing and where possible all those technology safeguards to ensure that they function appropriately that you can minimize the risk of misuse and the impact of failure.

Jim Jackson: So we've done a lot in terms of just thinking about those core concepts. And then as it relates to customers and some of the things that we're seeing. Many of them are starting out in the cloud and then bringing a lot of their POCs on-prem. And with respect to some of those POCs we see some things to help our customers avoid common pitfalls. Things like the business use case needs to be well thought out with measurable KPIs that can be evaluated throughout the lifecycle of any AI POC. So focus on the problem at a small scale, deliver it, measure it, understand the potential impact then get feedback and then continue to scale it as you go forward. We talked about quality of your data. It cannot be understated whether it's structured unstructured formats from a multitude of sources. How do you think about that data life cycle, the data architecture, versioning and bringing it all together. We talked about governance. Literacy and training of your workforce is essential as you start thinking about again the successful adoption of Gen AI tools. I think human oversight is key to ensuring any generated response is accurate unbiased and appropriate for use certainly in any marketing materials that we're working on.

Jim Jackson: So those are just some of the things that we're thinking about here. Again, it is such a topic with so much opportunity for us to delve into and talk about.

Kailey Raymond: A lot of what you're talking about right now is starting with process. It's starting with use case. It's starting with the end result of why you'll be using which seems simple enough like you should have that concept down but sometimes I think it can get lost by the shiny new toy. So it is a really good reminder to come back to this and do it in a privacy-enabled human-focused inclusive responsible way. Those are critically important because I was listening to your keynote recently and one of the quotes that struck me was 94% of the leaders in IT are saying that AI has increased enterprise security risks. And so making sure that you're doing this in a way that protects privacy and is enhancing security is critically important to being able to deliver for your customers at the end of the day. I'm wondering if you have any examples that you can kind of share about the things that you're excited about that either you yourselves at HPE or your customers are doing with AI at the moment? 

Jim Jackson: Lots of different things happening right now in across AI but maybe let me start from more of a marketing perspective because it's an area certainly that I can go a little bit deeper. If you think about how we're thinking about it and leveraging it across HP marketing first of all everything that we do starts with how do we treat data responsibly? And then it's one of our core principles as we think about things. But today we curate close to 300 terabytes of customer data signals that refreshes on a daily basis. And that's both within and outside of HPE. And those data sets span across first party data sources such as HP.com for example pipeline data, e-commerce, telemetry data. We also invest in marketing and third party data sets coming from data providers for things like digital intent, competitive installations, technographics and other types of media agencies. And then our in-house agency and we brought a lot of our data science team in-house. So we've got I don't know 15 data scientists that I fund within marketing. They integrate all of that data they leverage it and they build predictive propensity models that enable us to identify the top 5 to 10% of accounts that are most likely to buy products from us and solutions from us in the next three to six months.

Jim Jackson: And then what we can do is start to target our sales and our marketing teams obviously to go after those accounts that you know can bring things like ABX ABM together and be much more targeted because we see the opportunity to do that. So across marketing we've embedded the propensity models into our demand generation workflows spanning things like digital media targeting, event invitations our customer innovation centers so invitations for that as well as partner marketing. So lots of different things here and bringing it all together across marketing and sales. And I think that's a really good example of how we think about it. You asked for examples maybe another one that I would highlight is chat. That's a good one. On HP.com we obviously have chatbots everyone does. And they're connected to other downstream systems to help our customers with information that they require. They're obviously in a sales motion or trying to get more information but we don't think it's gonna be a one-stop shop to answer all of our customer needs.

Jim Jackson: So let's assume that a customer is trying to activate a product and they require some critical information. She might start engaging with our chatbot which will have access to some information but probably not to all the confidential systems with activation codes and everything that's needed to get that up and running. So then we can then obviously bring in the option of a human specialist and tech support agent who can help them get what they need. So those are just a couple of examples. Again, I think we can get more specifically into some other things that we're focused on in marketing around use cases. It would be more around things like productivity and cost efficiencies and improving the customer experience. So an example around productivity for us we're experimenting with large language models to help our marketing teams to help them ideate and create content for various uses. Could be blogs, could be solution-based, could be research summaries, could be ad copy, web copy, could be an EDM, it could be an invitation for an event for example.

Jim Jackson: And we've seen a productivity gain there and we're now continuing to build on that as we go forward. In a lot of ways what I've told my team is think about this as a very smart intern. Let that person you will expect them to give you a very good draft but make sure that you also double check it for completeness and I think what you'll see is as we go forward we'll continue to bring in more and more of training that model so that obviously we'll spend less and less time.

Jim Jackson: Double checking it. Another thing that we did recently at our HPE Discover event, which was great for us, but we needed a way to connect our customers and partners that visit our CICs, our customer innovation centers, to our best subject matter experts. And I really have in the back of my mind, AI can help us get our top, top people. And unfortunately, you know, you're talking about them maybe on two hands, right? People who can just communicate with customers in an impactful way, deliver the whole breadth of the portfolio. So how do you scale that? 

Jim Jackson: And one of our best sellers actually is our CEO. So what we did is working with our services organization and NVIDIA, as well as some other partners, we built out an interactive Gen AI hologram, which we affectionately ended up calling Antonio Nearly. It was really cool. And we armed the underlying models with the knowledge of the entire HPE portfolio of services and solutions. We built a rag solution using our trusted enterprise AI stack. It was a huge hit.

Jim Jackson: Now, it's still early and we have a lot of work to do to build it out. And we still have to obviously have some supervision around it, stuff like that. But these are just some of the use cases that we're starting to see. And it again, I think, runs the gamut by different industries and different areas where we see all kinds of AI use cases.

Kailey Raymond: You just touched on some incredible examples that kind of go across the swath of channels and kind of different teams and just kind of reiterate what you would use, what we just talked about in terms of the use cases. You're talking about ABM, which is really an entire GTM structure. I love that you're enabling your field team, your sales team, and your marketing team all with the same information to be able to speak the right language together, because rowing all in the same cadence is extremely important when you're doing that. I love my favorite example, though, has to be Antonio Nearly. That is great.

Kailey Raymond: It's also like, well, such a unique activation to be able to actually feel like you're speaking to a CEO in person. But then to be able to power it with a real language model, how did you test that to make sure that any question that somebody asked, you'd feel confident in the response? What did that look like? 

Jim Jackson: Yeah, well, that was one of the things I was really talking to the team, especially coming into Discover, 'cause you have a lot of people and they could ask it the wrong question, so to speak. And it had a lot built in where if you asked a question that was kind of outside, let's say somebody asked a question about politics, it would just basically say, I'm here to talk to you about HPE's value prop and the solutions that we bring to the market. So we were able to build those things in.

Jim Jackson: Like I said, we had adult supervision there. We had people there just making sure. And over time, as we continue to scale this and build it, obviously, the model will get more and more tuned and trained, and we'll have a higher degree of confidence. But you're right. Those are exactly the kinds of things you have to think about as you're doing something like this and be really thoughtful. Back to our kind of core principles that we talked about earlier.

Kailey Raymond: 100%. And a great example of how humans are still fully needed in this process of developing AI solutions. And so I love that as an example. And I'm wondering if you have thoughts that you'd be willing to share on what the future of AI and customer experience looks like. Anything that you're excited for or predictions that you wanna put out into the world? 

Jim Jackson: Yeah. I think as it relates to customer experience, I mean, one of the things that I guess as I kind of look at some of the behaviors that I'm seeing today, with respect to AI customer adoption, I think of it as more of a bell curve, right? 10% of the customers that we're dealing with, they're really just beginning to understand their data, their compliance, their legal, their governmental aspects, their governance aspects, rather. All the things associated with it.

Jim Jackson: We have probably 80% who are in the middle, who are figuring it out. They're doing proof of concepts. They're starting to get a feel for AI and what it can or can't deliver. And the key questions that we hear from a lot of them here are things like, will it deliver better productivity? Well, what about top line growth? How much should I invest here? How do I track ROI? Do I have the right skill set? So, a whole bunch of things there.

Jim Jackson: And then we're starting to see 10% of customers that they really have fully integrated AI into their operational workflows. They're monetizing it today, and they're really starting to get a lot of scale out there, right, as they start thinking about some of those huge opportunities. I think it's gonna be a little bit specific by different verticals where we're gonna see some of those big breakthroughs in AI. But most of it, I'm a baseball fan, so I would characterize a lot of this as still relatively early innings.

Kailey Raymond: I fully agree with you. And that kind of maturity curve that you're talking about and just the story around data in general that we're kind of hovering around and the example of AI kind of being the primetime example of that, I think really highlights how hard a lot of this is. You have to really invest in a flexible, adaptable, but robust system that has the ability to make sure that your data is clean and that you trust it. What do you see as that biggest challenge as it relates to building that journey towards customer experience? I know data is obviously kind of one of those. Anything else you wanna add? 

Jim Jackson: Well, data is a huge part of it because, obviously, without the right data, it's really hard to make the right decisions and to trust those decisions. Again, when I come back to customer experience, I think a couple of things are really, really important here. And that's, first of all, really understanding what is the experience that your customer is currently having with you. The reason I say that is sometimes we can look internally and look at a bunch of dashboards and everybody can say they're green, but then the customer might say, yeah, but I don't feel that experience from, right? 

Kailey Raymond: Oh, yeah.

Jim Jackson: You have to look at it end to end and make sure that everything is integrated, that it's all working together. We think about some high-level guiding principles like consistency, convenience, quality, efficiency. And then some of the broader trends that we're also looking at is things like digital customer experience for scale. So how do we start to bring that digital element in? We've talked a lot about AI here and it's AI integrations that are fueled by data-driven insights. Again, more aspirational, but moving that direction. Opportunity for increased personalization.

Jim Jackson: And one of the things that's frustrating is if you have a problem and then you come back and you have to start all over. It's like going to the doctor's office and you fill out the forms and it's like, come on, it's a digital world. I should be able to give you all of this one time and then just update whenever I have to come in. So we're thinking about all of those kinds of things as we bring it forward and as we start to think about it. At the end of the day, our enterprise customers want a lot of the consumer experience that they get and that they get in their daily lives. They want it more automated.

Jim Jackson: They don't wanna have to integrate multiple different ecosystem partners. They expect that the experience from HPE will be as simple as their everyday lives. There's work to do 'cause we have a lot of complexity that we're bringing. But we are investing in a lot of different technologies to make it easier for them to get that experience. The other thing I would highlight here is increasingly you have to talk about the customer experience in the language of their vertical or industry 'cause you have unique challenges, whether it's speed, regulatory, other requirements that you have to bring forward. And again, I do think this is where AI has the power to transform every industry going forward. It's something that I truly believe is gonna have a huge impact.

Kailey Raymond: I fully agree with you. And I think that one of the things that you talked about as it relates to consumers and that expectation that's bleeding into the B2B side of the business, the catalyst behind that, I think oftentimes is speed matters nowadays. People are used to having information at their fingertips on any different channel. And you're right, that expectation of like, you should know me is certainly there. I think IDC came out with this survey recently that said that 50% of execs say that data loses the value that it has within hours, but not everybody can actually access that data.

Kailey Raymond: It's like a very small percentage of people that can access that data in real time, which I think highlights this acute lack of alignment in an organization to be able to meet that expectation that is driven by sure, the consumer experiences that we all have in a B2B lens. So it's not necessarily an easy place that we are right now today, but AI is helping.

Jim Jackson: It's definitely not easy. And I think a lot of times people talk about Good Data, but we like to actually step back and say, let's talk about right data. And some of the key things you have to think about is completeness, accuracy, consistency, uniqueness, integrity, bringing all of that together. And a lot of our discussions, particularly around AI with customers, they wanna talk about AI, they wanna talk about different POCs, but many times it gets down to a data first modernization discussion.

Jim Jackson: How can you first help me get my data together into a place where I can make the right decisions more quickly? And again, Good Data is ultimately data that we trust and that we can leverage to make those better business decisions. And it's not easy. It's challenging.

Kailey Raymond: 100%. And another quote from Antonio, data will be recorded in your balance sheet. I think that is such a powerful quote to really highlight the importance of getting this right. So I'm wondering, you know, we've talked about a couple of these examples. You talked a little bit about ABX, you talked a little bit about chat, productivity. Do you have any other examples or programs you would wanna highlight with how you are leveraging Good Data or right data as how you kind of describe it to influence your marketing strategies and tactics at HPE? 

Jim Jackson: I think at the end of the day, marketing today is really all about data. I mean, we certainly do a lot on the creative side of things, but data is such a big part of the kinds of things that we have to do and how we think about delivering that better customer experience. And again, you know, if you just think about it back to what we talked about earlier, Good Data is ultimately data that we can trust and that we can leverage to make different or to make better business decisions ultimately.

Jim Jackson: And I think where people get into problems is there's really no such thing, at least that I've seen a perfect data. The question we really have to ask ourselves is, is it good enough to make a decision? So for example, a customer out there might say that their install-based data footprint is sketchy 'cause they have a number of, we'll say, IT issues, but it's probably still good enough to serve as one of multiple different input sources to predict if someone is likely to buy from them in the next three to six months.

Jim Jackson: That's an example of something that we would think about in terms of data. You know, and when you just think also about the level of precision, like you could use Google Maps, for example, to navigate to a friend's house and you're okay if it navigated you a few meters farther than expected, right? Worst case scenario, you turn around and you're there. But if you're in the logistics or the food delivery business, that's gonna delay your rider leading to a lower CSAT score, right? Ultimately, you have now frustrated somebody.

Jim Jackson: So I think it is, again, dependent on the business use case and the vertical that you're in. And again, in marketing, we're doing a lot of different things here to understand that customer journey, leveraging data to optimize that experience for them at every single step along the way, because ultimately what we want to do is make it easier for them to get the right information quickly so that they can make the right decision, hopefully on our technology, obviously, for the future.

Kailey Raymond: I wanna dig in there. Anything interesting or surprising that you've learned about that customer experience and journey as you started to map that out? Any unique insights that you'd be willing to share? 

Jim Jackson: I mean, I think a lot of times what we're able to understand is what content is really working. Like a lot of times everybody thinks they have great content. And when you become data-driven, you actually can start to see the flow through customers on your digital journey. And you can understand where is the data actually not working, because you're losing them or they're not willing to take an action that you think they should take, I.e. Self-qualifying and saying, I'll give you information so that I can get additional assets and those kinds of things.

Jim Jackson: So one of those things that we've seen is that it's really enabled us to come back to the business or back to certainly product marketing and really reinforce, hey, we're losing people here or we have an issue with the value prop. So that's something that I think earlier in my career, we spent a lot of time debating and arguing with people. And now we're very precise. We can say, ah, if you look at it, actually, customers are spending a lot of time here. They're clearly consuming a lot of this data. They get to this stage and we're not seeing that same level. So we have a content issue.

Jim Jackson: That's a good example of some of the things that we're doing. The other thing is that we may be working with an account where we see a set of engagements happening around one part of our portfolio, whereas digitally, we see other people within that account who are looking at other parts of our portfolio. That's a great way for us to now triangulate that and come back to our sales teams and say, you might wanna also look at conversations in these areas because clearly there's some interest. Obviously, this is all with the right data privacy and all of that. But those are the kinds of things where when you start to use data, it becomes really insightful.

Kailey Raymond: Yeah, that's a beautiful example of intent and the ability to drive a solution as opposed to a product sale. Making sure that you're really elevating the conversation to the problems that can be solved as opposed to this kind of point solution that somebody might be taking a look at.

Kailey Raymond: I have a question, switching topics for you, which is, I think that we've been talking a lot about these tactics that you've been doing internally. I'm wondering if you have any folks that you look to that you're like, that's a great customer experience. Any inspiring brands, companies come to mind? 

Jim Jackson: The one I would probably highlight is Apple, in my opinion. When I think of my iPhone, they make it easy to move to the next generation of hardware. All of your data, all your apps essentially transfer. You might have to go in and update some passwords and those kinds of things, but in general, it's a pretty seamless experience now. I remember 10 years ago, it was a lot harder, it took a lot longer, and they said you're gonna have to go back in, but they make it much simpler today. They even also charge the phone or iPad or whatever you're buying so that you can start immediately.

Jim Jackson: And I think it's those little touches that really play to the customer experience 'cause let's be honest, when you get something and it's now connected to the internet, you wanna go start researching, you wanna start playing with it. So I think that Apple, in my opinion, has done a good job.

Kailey Raymond: I fully agree.

Jim Jackson: I'd be interested in your perspective on that one.

Kailey Raymond: Oh no I think is doing it right? 

Jim Jackson: Yeah.

Kailey Raymond: Yeah, it's a good question. One of my favorites is Delta. I think that the experience that they've been able to bridge to consumers with their app is unlike most airlines and they connect that all the way through into their lounge experiences. So they've been able to develop a real omni-channel perspective to this and even the flight attendants will go up to you if you're sitting in your seat and you have some membership with them and they'll greet you by name. And so the connection that they've created from digital to in-person, I think is something that brands can really look to and be envious about. So that's one that I think is really good. I also think that Chase is pretty good with their digital applications for sure.

Jim Jackson: I love that example because it's so powerful when somebody calls you by your name or if they say, welcome back, here's what you looked at last time, here are some things we think you might be interested in, right? Those kinds of things go a long way 'cause you feel like they already kind of know you and they're using your time wisely.

Kailey Raymond: Dives back to that personalization and the idea around real-time they were talking about earlier is that a lot of that information, the timeliness of it, is critically important to having the right conversation. So you've got to get all those things right.

Jim Jackson: Exactly. Yep.

Kailey Raymond: Do you have an example of a favorite data-based campaign that you've run in your career? 

Jim Jackson: I would highlight there was a competitor that was acquired and there was a lot of uncertainty in the minds of customers regarding their product roadmap. Our business unit GM came to us in marketing and said, I wanna get ahead of this right? And we were able to get the addressable set of accounts who were IB customers for a competitor's product. We got this from third-party data, so it was available that we could subscribe to.

Jim Jackson: And then we were able to essentially look at the overlap of how many of those accounts were showing digital intent, both on our website, third-party websites, etcetera. And that then helped us to start to narrow down or really focus where we saw higher propensity to buy. So again, back to that propensity to buy. How do we give our field more intelligence and be more targeted? Then what we did is we essentially overlaid that information on how many of those accounts have transacted with HPE or adjacent products or solutions.

Jim Jackson: And then this helped us really start to think, how do we zero in HPE sales, our partners who might have warm relationships, we'll say, or some kind of ongoing dialogue? And then we built it into a compelling offer. Obviously, we leveraged our marketing engine as well. This became a playbook for campaigns going forward. So it enabled us to leverage data and then really have much more of a database campaign model. And that was a couple of years ago. It's something that we've kind of replicated the mechanics of that multiple times over.

Kailey Raymond: I love that ABM-driven campaign that you're describing right there. Yeah, that's one of our favorites too. We now leverage Crossbeam, which is a really fun one to identify within your partner ecosystem, the overlap of customers there and can really easily layer on intent on top of that to create really targeted lists and make sure we're enabling our sales team with that right message so that all the different tactics can kind of sing together at the same time. Love it. Last question for you, Jim. You've been a wealth of information today. I really appreciate your time. And I wanna ask you, if you had any recommendations to somebody that's looking to uplevel their customer experience strategies, what would it be? 

Jim Jackson: First of all, I would say that there's no one SKU that you can buy. You can't just go out and say, give me your customer experience strategy SKU and it's gonna fix everything. A lot of it starts with data. How do you think about getting your data right so that you actually understand what the actual experience that your customers have and you've got all that concept? 

Jim Jackson: Operations and processes, how do you then bring those things in and think about that as well? How do you make sure that you have the right technology and infrastructure? That's gonna be very, very important. It plays a key role in enabling that customer experience and ultimately that end-to-end perspective. Then back to the models and the deployment, particularly as you start thinking about AI and scaling that going forward. So these are some of the things as we start to engage with more and more customers and look at various plays that we have and steps and processes and engagements, helping them go on that journey.

Jim Jackson: Again, it's back to what we talked about earlier. You have to really look at the full end-to-end customer experience that a customer has with you today. And the other thing I would just say that's important is sometimes we try to all wanna solve it immediately. I don't think you can eat the elephant in one bite. If you can think about the steps and customer experience and where you're at today and where you're going, build a very clear roadmap and just be very clear. These are the things that we're gonna go focus on and then communicate the timelines, if you will.

Jim Jackson: And if you're very, very clear in that and you use the right data, to your point, you think about personalization and you think about that customer experience at every single step and you set the right expectations, you will move the needle. That's been the experience that I've seen. A lot of complexity out there. It's hard to do, but to many of the questions that you've asked today, it is so powerful. And We're all learning. We're learning every single day.

Jim Jackson: We're getting better and better. And I'm excited about what the future holds with AI, with hybrid, with edge, and how do you think about data to ultimately make it easier for all of us to get what we need quickly in the most simple way. And finally, maybe that's the final point I'll end on here. What I hear from a lot of customers is, make my experience simpler, please. Make it simple. I just wanna be able to get the information or the capabilities I need so that I can focus on the business outcome that I have to go drive.

Kailey Raymond: I think that's always a good one to make sure we're orienting ourselves around is, is it as easy and as simple as it can possibly be, but your point around step changes and making sure that you're not putting the cart before the horse. I think you talked about data, processes, infrastructure, and then activation. That's a nice little maturity curve for folks to take a look at. Jim, thank you so much for being here. It was so much fun to chat with you today.

Jim Jackson: Thank you. I had a blast chatting with you. Thank you so much. Great questions. And yeah, it was excellent. I actually learned a lot as well just from your questions.

Kailey Raymond: I'm so glad to hear it. Thanks again.

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