This sponsored episode of the Marketing Smarts podcast, featuring Neha Shah and Ruth Bolster from Salesforce Marketing Cloud, delves into various facets of using data and AI to enhance B2B customer experiences.

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The core of their discussion centers on the integration and intelligent utilization of data to forge stronger, more personalized connections with customers.

Here are some select highlights of this episode of Marketing Smarts:

Neha Shah

Neha emphasized the evolving expectations of B2B customers, who now demand better personalization based on the data companies have on them. She highlighted the need for marketers to stitch together customer experiences across different touchpoints, harnessing an array of data types to personalize interactions comprehensively.

She detailed how the surge in the number of data sources and the necessity of data integration across customer lifecycle stages demand a robust strategy if marketers are to personalize and enhance customer experiences effectively. Neha underlined the importance of leveraging AI alongside data to optimize marketing efforts and drive growth, particularly amid budget constraints and rising customer acquisition costs.

Ruth Bolster

Ruth underscored the expansion in the number and variety of applications and data sources used in B2B marketing. She explained that effective personalization hinges on harmonizing the available data, breaking down silos to create a unified view of customer interactions across different platforms and touchpoints.

She discussed real-time data's significance in adjusting marketing strategies on the fly to resonate with customers' current states or needs, advocating for AI's role in identifying patterns and automating responses to enhance customer journeys.

Both Neha and Ruth

Both guests elaborated on the interplay between AI and data in sculpting refined B2B marketing strategies, focusing on predictive and generative AI's potential to save time and enhance decision-making.

They addressed the challenges marketers face in unifying and analyzing disparate data sources, stressing the necessity for platforms that can consolidate data and facilitate AI-driven insights.

The conversation also touched on the importance of setting clear objectives and KPIs, ensuring cross-departmental collaboration, and maintaining a cycle of testing, learning, and iterating to capitalize on AI and data-driven marketing's benefits fully.

Select Takeaways

• The dialogue illustrated a clear road map for B2B marketers: Start with focused, small-scale experiments to integrate AI and data analytics, progressively expanding initial successes.
• Both guests advocated for a culture of continual learning and adaptation, urging marketers to stay abreast of technological advancements and regulatory and ethical developments.
• A success story—Grammarly's use of predictive AI in ABM campaigns—provided a tangible example of these strategies' impact, reinforcing the potential for AI and data to transform B2B marketing.

In essence, the discussion encapsulated a forward-thinking approach to B2B marketing, emphasizing the critical roles of data and AI in crafting customer-centric strategies that adapt dynamically to evolving preferences and behaviors as well as to each customer's journey.

Listen to the entire podcast or read the full transcript for all the critical details and insights.

You can listen to the entire show from the link above, or download the mp3 and listen at your convenience. Of course, you can also subscribe to the Marketing Smarts podcast in iTunes or via RSS and never miss an episode.

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This episode brought to you by Salesforce.

Salesforce is the customer company, helping companies connect with customers in a whole new way since 1999. Its pioneering formula of Data + AI + CRM + Trust helps companies embrace artificial intelligence across Customer 360, its complete portfolio of products that unites every team around the customer on an integrated, metadata-driven platform. Learn more at www.salesforce.com.

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"Marketing Smarts" theme music composed by Juanito Pascual of Signature Tones.

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Full Transcript: Creating Compelling Experiences With Data + AI for B2B Consumers, With Neha Shah & Ruth Bolster

George B. Thomas: I'm super excited because today we're back and we're creating compelling experiences with data, plus all of our friends, plus AI for B2B consumers. Today we are joined by a couple of guests.

Let me go ahead and explain what we're going to talk about today first. We are going to talk about connected experiences, we're going to talk about integrated data, we're going to talk about crucial lasting relationships and how to build them, and of course I'm going to ask a few questions along the way. We'll even do words of wisdom and maybe a #OneThing.

I'm not alone today. I have two guests with us, Neha and Ruth.

Neha Shah is the senior director of product marketing at Salesforce Marketing Cloud. In her role, she drives product positioning and solution messaging for Salesforce's B2B marketing offerings. She brings a strong background in technology and business to her role and has a proven track record of bringing solutions to market that help SaaS companies accelerate growth. She's passionate about leveraging data-driven techniques to drive superior customer engagement.

Ruth Bolster is a product marketing manager at Salesforce Marketing Cloud. In her role, she drives product positioning and solution messaging for Salesforce's B2B Marketing products. She brings over eight years of sales, demand gen marketing, and product marketing experience to the role. She's a Salesforce certified account engagement specialist. Ruth is also a graduate of Vassar College.

Ladies and gentlemen, it is going to be a whirlwind of valuable information that you will be able to dive into. With you knowing that it's not just me, but two amazing guests, let's go ahead and get into the good stuff.

Again, we are diving into a great conversation. Dare I say a compelling conversation that is going to help you with the experiences, the data, the AI, the B2B. We have two special guests from Salesforce, Ruth and Neha. I am just over the moon for what we're going to talk about today, so let's go ahead and get into the good stuff.

By the way, you ladies can flip a coin and fight about who is going to answer, maybe both of you have answers for some of these questions. We're just going to add a ton of value to the listeners. Let's start with the fundamentals and work our way into the deep end of the pool.

The first question is, Can you explain the fundamental role of data pertaining to shaping customer experiences today? When I say today, I'm talking like you should know it's 2024, things have changed, experiences in today's B2B landscape.

Neha Shah: Thank you, George, for having us here. We're super excited to be here. I think this is such a great question when you talk about data because it is changing so significantly and so rapidly. When we think of, as you mentioned, today's B2B landscape, data plays a significant role, particularly in shaping customer experiences. When we talk about B2B customer expectations, they are evolving. As I talk to customers today, what they really want and what they really demand from B2B marketers is better personalization.

We at Salesforce do a lot of customer research. Based on our State of Connected Customer Report, which we recently launched, 80% of customers say that the experience they get with a company should be better considering the data the company collects about them. The question is what does it mean? Well, this means marketers must really connect the experiences across the customer's entire relationship and leverage the data that they have, personalizing that entire relationship.

I know we talked about B2B customers, but when we think about customers, they remember their last best experience, and that does not have to be B2B specific. I could be on Netflix the night before watching my favorite show, and Netflix is automatically recommending what other shows I would like. Then when I come to work, I don't forget about that experience, and that's what I expect from other companies that I interact with, B2B companies that I interact with at work.

The other big thing that plays a really important role is there has been a significant surge in the amount of data that businesses need to connect to that customer experience. If you look back, a decade ago for example, to create those connected customer experiences, B2B marketers are leveraging the marketing, the digital data, as well as the data they had in their CRM. However, in the past five years, there has been an explosion in the number of channels that B2B customers are using. Connecting to just CRM or just some of the digital is not enough.

You really need to connect with the entire customer lifecycle. So, they need to look at the service data, the transaction data, the behavior data, the preference data, the data that they have inside their system, outside their ecosystem. It could be anonymous, and they need to tie in once they know who the customer is.

My point is B2B marketers should leverage all of the data to deliver that personalized experience for the customer at the right moment on the right channel at the right time.

Ruth Bolster: Just to piggyback off what Neha just said, as B2B marketers we're using more applications to engage with our customers than ever before. Whether it's webinar apps, SMS apps, survey apps, different advertising channels, you name it, we're using it. The number of data sources that we use expands as we take into account those different customer touchpoints. If we have sales and service interacting with those customers, that brings in an entirely new set of data.

In some ways, this is incredibly helpful to marketers because as your company is engaging with customers across these different touchpoints, those customers are consensually sharing information about their preferences and their needs and their wants. In theory, it makes it easier for marketers to deliver these more personalized experiences because once you have this information, you can really tailor your content and your customer journeys to the customer's needs and where they are in the sales cycle.

But this really only works if you're harmonizing your data and you're removing those data silos. If you have your data locked up in different applications, or if your sales data or service data is not integrating with your marketing data, it's very difficult to actually leverage data for personalization. This is something that we are thinking about and something that B2B marketers should be thinking about as well.

George: So much good stuff in that section. Marketing Smarts listeners, did you hear that term connected experiences? Also, just this idea of the last best experience and that's what we set our expectations around. I have to ask you, Marketing Smarts listeners, are you lacking in data or have you had a surge in the data that you're looking through? I really have to ask, Is it in spreadsheets or in something like Salesforce CRM so you can actually do smart things with it?

Then I love, Ruth, that you talked about this idea of consensually giving data. Ladies and gentlemen, do you have a process set up where your audience can give you the data that you actually need? So much good in that section.

Let's dive in. Because it is 2024, there is this little thing, I'm pretty sure most people have heard of it, called AI. How is AI transforming how we actually understand and improve customer experiences in B2B marketing, and really pertaining to the data, the understanding the experience, how do these thread together?

Neha: That is such a great question. We just talked about data and the importance of it and improving B2B customer experience. When I think about data, AI goes hand in hand, it is equally important. I think of them as two sides of the same coin. They both are equally important, especially when we are looking at the B2B marketing landscape today.

One of the biggest challenges all of us in B2B marketing have is [that] we are expected to drive growth, but at the same time there are a lot of constraints and challenges. There are budget constraints, and we are saying that across all B2B marketing teams, whether you're SMB, enterprise, it is across all industries. It is real that we all are facing budget constraints. We talked a lot about data, but a lot of times that data is trapped in different islands within your company's ecosystem, the challenge is how do you get access to that high quality data.

The other big thing is, there's a lot of changes when it comes to cookie depreciation or privacy changes, GDPR and all of that. On top of all of this, the customer acquisition costs are continuing to rise. What we're seeing in B2B marketing is some of these constraints lead to overall campaign effectiveness being down.

We want to improve our customer experience, we want to do more with less, and one way to do that is by adopting AI if we want to deliver growth. If B2B marketers are trying to deliver growth and meet customer's demands, they can do that. AI is a tool that can help us do that. I talked about the survey earlier, but we are seeing that across marketers, 51% of B2B marketers are using not just predictive but also generative AI to save time on their everyday tasks. They are using it to create content, write copy, and analyze data.

I think that's the importance, it's the importance of predictive as well as generative AI that unlocks a new level of productivity for B2B marketers across the entire campaign lifecycle, but also helps transform customer experiences.

George: Love it so much. It's fun because you talked about predictive AI and generative AI. The creators and creatives have come out to play all over the Internet. When I think of this predictive but then also this idea of AI for analysis is the next level that I think marketers are going to need to get to.

Speaking of where we need to get to, what are some examples of how data-driven insights lead to more personalized experiences? Again, this comes down to let's treat them like humans because we're humans and we want to be a human-focused organization. What are some examples of how data-driven insights lead to more personalized B2B customer experiences?

Ruth: I can start with this. We know the more data you have the more precise you can be in your segmentation and that's going to lead to better customer experiences and it's going to lead to more personalized experiences because you can pull that cross-section of people and target that messaging.

As I mentioned earlier, you're really only able to use data to personalize experiences for customers if all of that data is fully integrated into your marketing. Again, I talked about this earlier, but if your sales data is locked up in your CRM and it's not connecting to your marketing automation platform, it's going to be difficult to actually use that to personalize your marketing journeys. The problem is only going to get more complicated as you pull in additional data sources.

Another example that we see a lot in B2B, especially in the high-tech industry, is product usage data. That's something that a lot of marketers are trying to pull in. Let's say hypothetically I work for a tech company and I have a freemium sales model, and my goal is to create a campaign that targets customers who are hitting the limit of their free usage with a goal of converting them to become paid subscribers. Of course, because I'm a B2B company, let's say I want to target everyone who is using a freemium product within a certain account.

In order for me to do that successfully, I need to get a cross-section of customers within a certain account who are hitting that product usage threshold. I can only do this if my data is not locked up in different silos and systems. I need to bring in my sales data, I need to bring in my product usage data. That's the only way I can pull that segment and I can be targeted. Otherwise, I'm going to need to be more generic in my outreach.

Neha: I think that's a really good point, Ruth. As I think about the data-driven insights and how it leads to personalized experiences, I think we can also extend to as marketers are starting to use generative AI capabilities.

The prompt or the content that you generate for your email, or for a subject line, or for a landing page form, any form of content that B2B marketers are generating, it's going to be more personalized, more dynamic, the more data it looks at. That's how they can personalize for that particular individual, or that particular use case, or that particular campaign. Data-driven insights, I agree, it is important to deliver those journeys, but also as you're thinking of creating content.

George: I love these ideas of better segmentation, integrated data, and the fact that you even talked about product usage. I think it paints a really good picture for the Marketing Smarts listeners to realize when we're talking about data, and even the reason for AI to look at it, is because we're talking about more data, very high level, better segmentation, to granular data, product usage. We're really having this under the narrative of an all-in-one area. Again, this could be something like Salesforce CRM that you're holding all of this information and being able to make heads or tails of it.

Let's keep diving in. There's data and then there's this thing that we hear in our space, especially as marketers, real-time data. How important is real-time data in modifying and enhancing customer experiences, and what role with this real-time data does AI play in that conversation?

Ruth: This is an excellent question. I'm going to say that real-time data doesn't just enhance customer experiences. I'd argue that it's crucial to forming those lasting customer relationships.

Typically, in B2B we talk about real-time data in terms of the idea of just in time messaging. Let's say I have a customer who is up for contract renewal. You can use real-time data in order to put them on a just in time up-sell campaign, that type of thing. But real-time data goes beyond this to ensure that customers feel like they're being treated as individuals and that they're prioritized and that they're valued.

Another good example of this is if someone has a service case open. In this case, you would use that real-time data to move them off of a campaign and maybe pause their outreach until that service case is resolved. You don't want to be sending a customer offers if they're really upset with your service or they have an issue with a product, you really want to be pausing that communication.

From a customer perspective, this really helps them feel like they're valued and more than just a consumer or just a number who is going through this generic campaign. Again, it's only possible if you're adjusting journeys based on this real-time data.

To add to this, I think AI really has the potential to revolutionize this. It can help marketers spot patterns and data that they might not be able to see without doing a lot of manual analysis or number crunching. A really good example of this, and we have this in our product Marketing Cloud Account Engagement, is this idea of engagement frequency scoring.

As B2B demand gen marketers, we have probably all been in a situation where we're running multiple campaigns and there's overlap in terms of campaign recipients. This can lead to some customers being oversaturated with emails. We know no one wants to be emailed too much.

Typically, what a demand gen marketer would do in this situation is spin up a bunch of suppression lists and hope for the best. But if you're leveraging predictive AI, like engagement frequency scoring, the predictive AI can determine whether or not someone is getting too many emails and assign them a score of oversaturated. The marketer can then pull everyone who is considered to be oversaturated and put them on a suppression list.

It makes this idea of spinning up suppression lists more of an exact science instead of just hoping for the best. Ultimately, this means that your customer is not going to be inundated with emails, it makes for a better customer relationship, and it's just a really good example of how AI can take the guesswork out of your real-time data and just give you a result.

Neha: I think that's a really good point. When we think about real-time data, a quote that comes to mind is strike when the iron is hot. That is super important.

If I am a prospective customer checking something out on the website, it would be great if there was a real-time alert to a respective sales leader so they can reach out with personalized outreach to that customer. Timing is important because I am looking for the product or the solution, or I have questions right now and that's why I'm checking out your website, that's why I've downloaded the e-book, or that's why I've attended the webinar. Whatever those signals are, passing them in real-time across departments is equally important because that's what delivers a real customer expectation and experience that customers are looking for.

When we think about AI, it would be great at this time, if I'm a sales rep, I get this is from your top account, pay attention to them, or this is a very highly engaged lead that you can work them into a customer, they are looking for these questions. If all those signals can come, I think AI can really help power that. That's the power of real-time data marrying with AI, and doing that across the customer journey across departments. I think that's the power.

George: So many good words in that section. I love the fact that in that last section we heard things like crucial to build lasting relationships, because that's what it's all about if we're doing business right. I love this idea of just-in-time offers, treating them as an individual, and that they feel valued, because you're invoking emotions when we start to hear words like that in the experiences that we're providing. I love, too, there's a couple of internal things we can start to think about where it helps marketers spot, was the phrase you used, and it's at the end of that's what is important.

Then this idea of engagement frequency. Marketing Smarts listeners, if you heard the words engagement frequency and your head turned to the side a little bit, Google it, learn about it. Your prospects, leads, and customers will thank you in the future. Neha, you said internal notifications and best next actions in your section. I know a bunch of sales teams that if we could say we could take the guesswork out of their daily lives, they would probably love us that much more as marketers.

Let's keep diving down into the good stuff. Can we discuss how predictive analytics looks at and anticipates customer needs and behaviors for B2B marketers and how that would actually impact our roles, our days, and our customer experiences?

Neha: That's such a great question. When I think of predictive analytics, my whole thing is predictive analytics really helps marketers surface insights about their customers without the manual analysis. We just talked about real-time. What predictive analytics also does is helps us as marketers act on them quickly.

When we think about predictive analytics that can help improve customer needs, some of them we mentioned earlier, but it could be send time optimization. You want to analyze, and that needs to be analyzed at individual level. George, you might wake up and you might be checking your emails in the mornings, and there might be a different time that I need to reach out to Ruth based on her past behavior and engagement with the content that we have been sending. I think that's really important.

The other type of predictive analytics that we alluded to earlier was understanding the scoring of different prospects. You and I both could be on the website, but who is more important or who is more valuable to me as a customer, or who is looking for the right data? Analyzing and surfacing those insights without having to do the manual work is where the power of predictive analytics comes in, when it comes to anticipating customer needs and behavior. Just looking at the engagement data that we have about a customer, marrying it with any of the other data that we have across the ecosystem about that customer or prospect, and putting out those insights.

Ruth: If I could also add, opportunity for predictive AI is for account insights. This is crucial for anyone who might be doing an ABM campaign. What the predictive AI can do here is comb through all of your data, spot patterns, determine if a particular account fits that pattern, and whether or not it looks like an account that has historically gone on to become closed one, this surfaces accounts so that you don't have to spend hours in spreadsheets and doing all of that manual planning. It helps you identify what the low hanging fruit is and where you might want to be spending your efforts.

Again, I think the key with predictive AI is that it really takes that manual work and that guesswork out of the equation because it allows you to better understand your customer data. With account scoring, for example, you could take the time to go through all of your account data and try to determine who is likely to convert, but we know that's going to take days or maybe weeks in reports and spreadsheets and planning meetings. AI just surfaces that for you.

George: So good. As I listened to you talk about that last section, both of you, my mind goes to this idea of you mean customers have a journey that they're taking, and we could be scoring it and paying attention to it on our website. Of course, every good marketer knows that we love to nerd out on a good customer journey map. Historically, that might have been a little bit difficult.

When we have this data, when that data is creating a customer journey, how does integrating AI with the customer journey mapping provide a more comprehensive understanding of this B2B buyer's journey that these humans are going through?

Ruth: That's a great question. I think customer journey mapping is a great opportunity to leverage your predictive AI in particular. The predictive AI, in some ways, can help add guardrails to the customer journey so that you can better understand how the customer is going to be interacting throughout the flow that you set up.

When you are building our customer journeys, you want to keep a couple of different factors in mind where AI can particularly help with this. One factor is whether a customer is highly engaged or needs additional nurturing. If you bring predictive lead scoring into the equation, this will help you send those leads who need additional nurturing on different paths than someone who is super engaged and ready to buy. It's going to be personalizing the journey in that way.

We talked about being oversaturated with emails. You'll factor in a pause or slowdown in the customer journey for customers who might be getting too many emails. Again, this is where AI with engagement frequency scoring can really help with that. You might also want to think about whether or not someone is likely to open your email at a particular time. If you're using predictive AI tools like send time optimization, you can use AI to make sure that the emails within that customer journey are being sent when that customer is most likely to open it.

Again, using AI to account for these factors is going to lead to a better experience for the customer when they enter into the journeys. It's also going to help you as a marketer have a better understanding as to how customers are experiencing the journey. When you set it up with AI in mind, it's going to give you some guardrails and it's going to give you some touchpoints as to how a customer might be experiencing those emails and those messages.

Neha: I think I would like to add to that. It's great. When we talk to a lot of marketers, most B2B think of customer journey as the funnel, it's like what's top of the funnel and how to convert them. I think we should start expanding that to beyond converting them into customers. I think the entire customer journey should be about not just how I convert a lead into a customer, but how can I convert a customer into our champion.

That's where the example that Ruth mentioned earlier about how marketing can work with service. Even though I'm a customer, if I have a service case open, I should be treated differently in terms of the outreach and marketing efforts than somebody else. The idea about the customer journey is a long journey, let's think of it beyond the funnel and let's think of it as how can we make them our champions and leverage all of the tools we have for that and integrate AI to build and enhance even after they become our customer.

George: I like that so much. Let's jump forward a little bit. We have folks who are like yes data, yes AI, yes we need a tool to do that, but they're going to run into some common challenges. I call them hurdles. Some other folks might call them potholes. Whatever gets you stuck, you use the terminology that you want. The question that I want to dive into now is what are some of the common challenges that you've seen B2B marketers face in leveraging AI and data together to do what we're talking about today, and how can those humans overcome these challenges, hurdles, and potholes?

Ruth: That's a great question. I mentioned this before, but having your data trapped in different silos is probably the biggest challenge that marketers face when leveraging AI. We know that for both generative and predictive AI it's really only as good as the data that it has access to. In order to train these models, it really needs access to your data, it needs a complete picture of your data.

Of course, if you have your data in different applications, for example, your sales and service teams are not carrying data with you, it's really difficult for the AI model to get that complete picture when training the model. This means that AI outputs will not reflect the full picture of your business, and this is especially true with predictive AI. If you're trying to do anything AI based, like in terms of AI based behavior scoring with a goal of understanding which leads are most likely to convert, and the AI doesn't have access to your sales data, it can't really make those predictions. In this case, it really important to get all of your data in one platform so that you can use the AI successfully.

Of course, this also parlays into issues of trust. If you're sharing your data with an AI model, you need to make sure that you're protecting that data. That's something that we think about a lot at Salesforce in terms of data masking, in terms of making sure that customer data and proprietary data aren't just being leaked into the ether, if you will.

Neha: Yes. Ruth talked a lot about data and how if data is trapped and in silos that impacts how we can leverage AI, but one of the other things that we are seeing with B2B marketers is they're using AI to really do personalization at scale. It's also important not just to have the right data, but the right user interface. Leverage AI from systems, from companies, where everyone in your marketing organization can use it. An example, like we talked about creating segments. A lot of companies, a lot of marketers, it's a technical thing, you have to write inquiries, it takes time, and then the whole aspect of real-time goes away.

Think about it, now with generative AI, the possibilities are endless. What if a marketer could go and type that in a natural language prompt? That democratizes AI. It's important to have all of the data, but it's also important to think how can we democratize so that everyone can use it. I think with generative AI we have that power to really optimize the user interface for folks to use it and have that access in natural language prompts to create content, or to create segments, or some of these complex use cases that required technical knowledge in the past.

George: So good. It's fun because we're having this conversation and we've really been laying down the framework thus far to get to this next question, which I think is really important because we need to know what success looks like. There's where we're at and there's where we're trying to go. Maybe you could share a success story or two where AI and data significantly improved a B2B customer experience that you've seen happen.

Ruth: This is another great question. I've had the privilege of working with one of our customers, Grammarly, and doing a presentation with them at Dreamforce back in 2022. I'm sure everybody listening is familiar with Grammarly. They're a Cloud-based typing assistant, they review grammar, they detect plagiarism, and overall they help people to be better writers. What you might not know is they have a very strong B2B arm that focuses on getting their software into businesses and schools at the account level.

They leverage these extensive ABM campaigns and they often scale out their ABM efforts using traditional demand generation tactics, so they're pretty advanced in terms of how they're using marketing technology in order to personalize. As part of this, they leverage predictive Einstein AI extensively, starting with Einstein account identification. They use this to surface the accounts that are most engaged, but they also use this information to gut check whether their target accounts are being sufficiently engaged as part of their ABM efforts.

Having that score front and center helps them know if they are being successful with their ABM efforts. They also use Einstein behavior and lead scoring in order to identify leads within those accounts for further nurturing. They leverage tools like send time optimization to make sure emails are at the top of their customer's inboxes.

In Grammarly's case, they're extensively using predictive AI to help provide a more white glove experience for their target accounts in their ABM campaigns. They do this by personalizing send times, using the scoring to understand where they should be spending their efforts and where they should be going that extra mile in order to offer that extra personalization.

George: So good. By the way, I love me some Grammarly. I'm just going to throw that out there. It's probably one of my most used favorite tools. Speaking of tools, I'm super curious, what tools and technologies are essential for the B2B marketers, the Marketing Smarts audience that's listening, when they're looking to leverage AI and data for their strategies? This might be a complete volleyball set and you guys spike it conversation. What tools are necessary for them to do these strategies that we've been talking about today?

Neha: We've spoken a lot about some of these tools in our discussion so far. We talked a lot about untrapping the data. As an organization, as a B2B company, the data is trapped in a lot of different silos. How do you untrap it? A customer data platform, something like our Salesforce Data Cloud, that's essential because what that does is brings all of this data together to create that single view so we can truly understand whether it is your customer or understand all of your customers in a particular account because that's important for B2B marketers.

You mentioned CRM, but a connected CRM and marketing automation system is important. We talked about the emphasis on connected customer journey, delivering that consistent experience across the customer journey. Think of CRM and marketing automation, but think how it can be connected because then you can execute any B2B marketing strategy, whether you're thinking ABM, which Ruth just talked about, which is account based marketing, whether you're thinking about broad based demand gen marketing, inbound marketing, or you want to think about opportunity based marketing and really think about all of the key buyers and influencers in a particular opportunity and how you nurture them and convert them into customers.

I think those two are very critical, I would say those are foundation tools. As you thinking about these tools, also think about do they have capabilities around predictive analytics, do they have capabilities around generative AI, does it allow your teams to use those newer technologies in conjunction with bringing all of the data together.

Then it's also the choice. We talked about customer journey. A customer determines their journey, not a marketer. A customer decides, "I want to open this email," and then, "I'll go to the website." There's no way a marketer can plan this is what I want my customers to do as the next 10 steps. They are in control of their journey. So, also looking at a tool that can really help you personalize those experiences across those channels.

I don't mean just digital channels. It's also human channels. A customer could be attending your webinar, but the next time he interacts is with a sales rep. Making sure that both your sales team and your marketing team knows what those conversations are and what the updates are to the conversation is important because that's what the experience should be irrespective of the channel the customer chooses.

Then with any marketing strategy or with any business strategy, it is so important to measure the effectiveness, the return on investment, the impact that you're driving to the top and the bottom line of your business. Also, most importantly, as a marketer, I need to know where do I double down my efforts and where can I scale back. If suddenly I have more budget, where should I invest and am I making those data-driven decisions? Tomorrow, if there's a budget cut, where do I prioritize what's going to drive my business? The most important thing toward the end is attribution, analytics, understanding where and how my campaigns are performing.

I feel like these tools are very essential, very critical to not just harness the power of AI and data that we talked about, but also for improving the overall business analytics, as well as customer experience. It's win-win. The customer wins with the amazing personalized experience that they get and then you're also driving positive impact to the business.

George: So good. I wondered if we would make it through this episode without talking about ROI and us marketers always wanting to make sure that we're getting what we need out of what we've invested in. I love the fact that we talked about how as marketers we draw straight lines, or maybe a good way to say it is we give them chopsticks, but they hand us back a bowl of spaghetti, that's what we get back from them as far as how they actually went through what we took time to design.

Let's fast-forward. We just talked about tools. Let's talk about skills. What skills should marketing professionals develop to better utilize this fast-paced, coming at your face AI and all of this data that's rushing in, like we talked about at the beginning with the surge of data? What skills should marketing professionals develop to better utilize AI and data in crafting these customer experiences closer to the bowl of spaghetti that we're going to get anyway?

Neha: We're talking a lot about AI and data. When I think of skills, I think of it as three different buckets:

 

• One is what do I need from a technical perspective because we're talking about all of this data and AI.
• The second thing is analytical skills. All of this data and AI is there, how do I make sense of it?
• The third is the creative component in terms of how do I creatively leverage this so that my next campaign is more creative or I'm creatively adapting to my changing customer behavior or customer journey.

 

When I think of those three areas, we talked a lot about data, so it's very important that marketing professionals of today's generation double up the strong data analysis skills, like the ability to identify trends, patterns, and correlate and inform that in your marketing strategy is going to be critical as we think about delivering those amazing customer experiences.

We talked a lot about AI, so also familiarizing in terms of how does these AI algorithms work so that we can leverage the AI-powered tools that are available, the newer technologies that are coming. I think that's very important. It's also important to keep an eye on what are the evolving ethical and regulatory changes. AI is great, but there's also guardrails that we all need to have. You also need to respect a customer's privacy preferences and how they want to be engaged, so just understanding at a broader level what are the ethical and regulatory, having awareness around that is going to be important.

The most important, I feel like, are the two big ones around adaptability and continuous learning. With AI and data, the marketing is constantly evolving. How can I learn and be adaptable and stay involved in the latest trends, the technologies, the best practices? How can I as a marketer be up to speed on what does generative AI mean for my customers, how does it impact the campaigns that I'm running, how can I make it better and adapt to my customer's needs?

We talked about journey of a customer across the entire lifecycle, so collaboration and communication. I cannot emphasize how important that is. When we think about collaboration, it's not just collaborating within your marketing org, but how you're collaborating with your sales teams, with your service teams, with your technology team who are helping power some of these AI tools. I think that becomes more and more critical because we are thinking across the customer journey and we're thinking of delivering those personalized experiences across the entire journey. As I mentioned, when we talk about journey, it's not just bring them as a new customer, but we actually want them as our advocate, so it's throughout the lifecycle with our company.

I think those are some of the important skills as we think about utilizing AI and data and how that can help craft better customer experiences.

George: Nice. I have one more question that you knew I was going to ask, and then I have a question after that that you didn't know I was going to ask, so there's a little curveball for you. Let's go ahead and hit this last question before I ask you the secret question of this podcast episode.

We've heard what success looks like, and we've gone through all of the things around data and journeys and AI. How the heck do we get started? What are the initial steps that you would recommend for B2B marketers just starting out with AI and data-driven strategies, what do they have to do, what do they have to be thinking about?

Neha: When I look at all of the changes, all of the new technologies that are coming out in the market, it is, frankly speaking, very overwhelming. Where do I get started? I think what I have seen is I've always found success in starting small. Think of that one segment, one campaign, one target audience, one webinar that you want to improve. Think about starting small and test it, because there will be a lot of learning. As we talked about, you have to be adaptable and improve on it. Starting small and testing it is the key thing.

Secondly, we talked about a lot of this in terms of tools, making sure you have the right tools, making sure your CRM and marketing automation systems are connected, making sure you have the right customer data platform that can help you get access to all of this data, because frankly speaking, if you don't have all of the data, your AI outputs, your experiences that you're delivering to customers are not going to be accurate because it is literally what you put in. Garbage in will give you garbage out. What goes in is what comes out.

Making sure that you have the right tools and then also as an organization you have readiness, you have access to that data and, as Ruth mentioned earlier, it's not trapped in different silos. How do we break the silos to actually see the impact of your campaigns that you're doing with AI and data-driven strategies. I think that's how to start and the readiness, but I think as you're thinking about this, it is very important to establish some KPIs so you know what success looks like or what you're aiming for and where you fall after the small test segment that you do, so you know which levers to improve on. I always love that aspect.

Then we talked a lot about customer experience across journeys. If you're just doing it within your small teams, that's not going to define success and you're not going to be able to measure it. Also, ensuring that this is a priority across departments and you have the right collaboration across teams is going to be important because that's what real success will look like. You could do the best thing in marketing, but if a customer has a bad experience with a sales team member because he wasn't aware about all of the things they've engaged in, that's going to derail all of the efforts. So, ensuring that there is collaboration across teams, the goals are identified, and everyone is leaning in is going to be important.

I always keep this in mind, iterate and learn. The first campaign is going to be successful, maybe not, you haven't met your KPIs, but what were some of the learnings and how can we iterate and continue to get better at it. I think that's going to be the most important thing as to this is a journey. Along with a customer journey, this is also a B2B marketing journey and how do we keep improving at every stage.

George: We've had multiple rewind points, but I hope that the Marketing Smarts listeners rewind and at least listen to that section for a second time, make sure they have their notepad and pen out or maybe a whiteboard and a marker to take some notes. So, here's the secret question. I'm going to give it to you in two ways, you can choose which way you want to go. Here's the thing. If you could boil down today's conversation into #OneThing that you hope they take away, or are there words of wisdom that you want to share? You can do a #OneThing or words of wisdom. What say you?

Ruth: I have some words of wisdom talking about AI. I have the privilege of speaking with marketers all over the country, and I feel like the biggest misconceptions are always around AI. People will come up to me and say AI is only for enterprise companies, or AI is only for people who are super mature in their marketing. If anyone takes anything away from today's conversation, it's that these are all misconceptions.

AI is really designed to be your assistant. It's designed for everyone from small teams to larger teams, but people who just need that extra assistance to get things done. It's designed to take a lot of the manual work out of the process, whether that means it's going to save you six hours of going into Excel sheets and trying to analyze your data or it's going to save you an afternoon of drafting email copy and iterating on that. It's designed to save you time and it's designed for everyone. I really want people to think of AI as their marketing assistant and not some scary thing that's inaccessible.

Neha: I would like to end with that. I love that idea, Ruth, but I also want to emphasize the importance of data. Untrapping that data across your organization is going to be important because data is the foundation for AI. The more data you have, the more data you have about your customer, the better your AI is going to be. As I said, they go hand in hand. As Ruth mentioned, AI is accessible to everyone. Please ensure that your data is accessible to you so that you can leverage it to generate better AI.

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Published on March 28, 2024