Sales Analytics/Benchmarking Podcast

Artificial Intelligence for Marketing

As AI continues to evolve, its impact on sales processes and strategies is becoming more evident. Only about 20% of the CROs that Alexander Group recently surveyed have fully implemented and are actively using AI tools to support their Marketing functions. Another 40% are in a kind of trialing process. Primarily they’re using generative AI to generate marketing content, but many are also investing in some of the more strategic and machine learning-driven tools, like lead scoring and qualification and customer sentiment analysis, just to name a few.

Kevan Savage of Alexander Group is joined by Armin Kakas from Revology Analytics to share insights on how AI is utilized in the Marketing function.

Deima Tankus: Hi everyone. And welcome to the second installment in our artificial intelligence for the commercial organization series. My name is Deima Tankus. I’m an associate consultant in our analytics and research benchmarking practice here at the Alexander Group. And for the last year or so, my team and I have been working to really understand how AI is transforming the commercial organization and how companies are getting ahead of this in their go-to-market strategy.

So our last release focused on the impact of AI on sales productivity and profitability. And today we’re pivoting to the marketing space. We know from recent executive surveys that AI investment is a top marketing priority in 2024. And this is really the year of investment and experimentation. We know that only about 20% of the CROs that we surveyed have already fully implemented and are actively using AI tools to support their Marketing functions. Another 40% are in this kind of trialing process. Primarily they’re using generative AI to generate marketing content, but many are also investing in some of the more strategic and machine learning-driven tools, like lead scoring and qualification and customer sentiment analysis, just to name a few.

To help us elaborate on this today, I’m joined by two experts who will give us a much deeper look into some of these AI use cases and to explain how AI investments are accelerating commercial growth through the Marketing practice.

I’d like to welcome Kevin Savage, principal and Marketing practice lead here at the Alexander group, who’s helped a lot of companies focus and refine their marketing strategy. Kevin, I’m really excited for your strategic perspective. Thank you for joining us.

And we also have a familiar voice on. Joining us once more is Armin Kakas, founder and managing partner at Revology Analytics, who’s going to give us his technical perspective on how companies are choosing and implementing these AI tools. Armin, it’s great to see you again and thank you for joining.

All right. So before we go into these AI accelerators, let’s first start a bit broad with the marketing strategy.

It seems to me that conversations about marketing strategy are starting to shift much more toward the topic of customer experience across the whole buyer journey and not just the kind of pre-sales activities. I want to hear from both of you. Maybe let’s start with Kevin. What is your sense of today? What the remit of the marketing function is and where it starts and where it stops.

Kevan Savage: Yeah, most marketers would attest to it. They get tasked with the whole journey. Now. That being said, some marketing organizations are equipped to support that in terms of their maturity and some are not.

But what we do see is, a lot of emphasis, even in the least mature organizations on at least the pre-sale part of the journey and where we see more advanced sophistication is pre, during and post-sale optimization of that experience. And so to give you a couple of quick examples, and then I’ll turn it over to Armin and get his perspective.

In the post-sale kind of elements that we see with organizations, we’ll see a lot of focus around knowledge hubs, education centers, areas where they can, advise customers or aggregate content without creating interactions in the organization to self serve content and support and things along those lines.

And then in the pre-sale side. A lot more focus around personalization, right? In terms of elevating and aggregating content that’s more targeted for their, prospective buyers or audiences and a lot more sophistication happening in that space. But, Armin, what’s your perspective on the the remit of marketing and the responsibility around the journey?

Armin Kakas: Yeah no, this is really good. It’s fairly similar. I think for especially in kind of B2C organizations where marketing is the primary revenue driver. So think like e-commerce, retailers, nonprofits, SaaS companies, marketers are absolutely responsible for the entire customer journey. I think they should be responsible again, given kind of the tech stack.

It’s not just about customer acquisition and churn prevention anymore, right? It’s the many industries, I think the opportunity is much more about how do I capitalize on all this prospect and customer data to drive as much revenue as possible and do it profitably. What we see in B2B environments especially those with longer lead cycles with a lot more human touch in the sales process.

Yeah, that delineation between kind of sales and marketing is much stronger and the lines are less blurred, but I think even in those instances, marketing can influence at least all the stages in the customer lifecycle.

Deima Tankus: Let’s click into one of these marketing practices. One that is classically within the scope of marketing, and that is developing qualified pipeline. Kevin, what would you say are some of the basics of qualified pipeline development that marketers have to get right?

Whether or not they’re enabling with artificial intelligence.

Kevan Savage: Yeah, it’s a great question. There’s a lot in that question, but it starts with really good prospect identification and qualification, right? In terms of what’s your product market fit, who are your ideal customer profiles? And more importantly, how do you use artificial intelligence and data to give you signals around whether you’re identifying the right prospects aligned to your growth objectives. Then from there, it’s an easier exercise, right? In terms of how do you use AI to accelerate other things like progression through the pipeline in terms of: I generated a qualified lead. It was handed off to sales, and I’m now routing that lead to the right representative and with the right level of intent. But if you don’t get the first part right? Then essentially you’re creating a lot of demand and qualified pipeline or unqualified pipeline that simply never gets fulfilled in the organization.

Deima Tankus: Armin, how are you seeing companies support these best practices with AI?

Armin Kakas: Our clients typically come from manufacturing, retail and distribution. Those are the heavy concentrations and they’re largely bifurcated in terms of their marketing acumen or their insights driven decision making acumen. So the ones on the left side that typically do a good job of capitalizing on these AI assisted tools for their pipeline optimization, they’re typically B2C. They have a large volume of transactions, short sales cycles.

But again, even with B2B companies with these longer sales cycles, they can also take advantage of these AI driven pipeline development ops and optimization tools. We see more and more companies taking advantage of LLM. So large language models like enabled marketing SaaS solutions that automate many aspects of your pipeline development. So there’s literally tools out there nowadays that you can subscribe to that do anything from building your prospect list, enriching that data, deploying hyper personalized content deploying trigger based workflow sequences. And they do it at scale, right? That used to be the job of many people.

On the more sophisticated side, so we do have some. Companies that are employing marketing knowledge graphs, which is this fancy term for a graph database, but basically houses all your sales and marketing and customer interaction data. And through these knowledge graphs companies can actually optimize not just the method of the customer communication, but the timing, the content they can understand the impact of the marketing efforts.

They can ask key questions about other marketing efforts, which is the best combination of my marketing channels or campaigns and in what sequence that are the best for conversions for my customer segments. On the attribution models you’ve mentioned, we’re actually seeing something interesting. We are seeing more and more companies become interested in marketing mix versus attribution modeling.

So attribution modeling was like all the rage in the last decade. And I think marketers are realizing there’s a heavy amount of, signal loss and just general noise in the data. And this whole concept of marketing mixed modeling is making a comeback and it’s especially for companies that want to understand the impact of the marketing efforts on their pipeline in an omni channel world, right?

So maybe the last thing that I mentioned on this piece I think for many companies it’s not unusual to see, one billion dollar companies that have, grown in organically historically, they’re typically the ones that are struggling with this whole concept of how do I leverage AI for marketing, right?

Or how do I leverage AI for sales or any of my commercial efforts? And so I see a lot of companies in this kind of upper end of the mid market, $500 million all the way to a billion, sometimes $2 billion, they’re without a data warehouse. They’re surviving on Excel sheets. And they’re dealing with all these difficulties, let alone understanding the impact of the marketing expense.

I think there’s a host of companies out there that are responsible for a bulk of kind of this economy that still need to take advantage of this.

Kevan Savage: Yeah, Armin you’re hitting on a good point. One of the things that we’ve seen work well in organizations where they’re leveraging AI for, if marketing’s setting the stage for where we focus our strategy, who do we try to target, we’ll see things like tracking deals backwards, and how customers interacted pre, during, and at close in the deal lifecycle and stages, so they can replicate that, do that lookalike modeling. And it’s also a great kind of converging exercise between the marketing and the sales teams to understand where AI can take really good application in terms of developing more qualified pipeline.

Armin Kakas: 100%. Yep. Agreed.

Deima Tankus: That’s great. Let’s cover one more marketing responsibility and one that might not be traditionally under the marketing umbrella but is pretty ripe with opportunity to drive growth through in the marketing world. I want to talk about cross- and upselling to existing accounts.

Kevin, where do you think marketers can make an impact in this activity? That might be traditionally very seller led.

Kevan Savage: Yeah, cross-selling and upselling is massive for B2B organizations, right? And one of the challenges we see is marketing tends to get tasked with solely new logo acquisition.

Our point of view is, yeah, do that. But then as you do that drive the expansion plays, drive the renewal plays, drive the cross-sell/upsell bundling, if you will. And an example of that is, there are all kinds of predictive algorithms and other types of personalized things that can be done to drive either automated post-sale interaction. So everybody shops on Amazon, right? What happens when you order something? You get automatically upsold on recommended products.

But there’s also an opportunity to understand your install base and look at other adjacent cross-sell and upsell opportunities. As an example, we’ll have industrial manufacturers that are looking at pumps, runtime, length of runs things along the age of the equipment itself to drive things like warranty replacements, upgrades, trade in, trade outs, things along those lines that require different marketing and sales motions, but believe it or not, get very dismissed in lots of organizations.

Armin Kakas: I completely agree Kevin. So this is probably one of the biggest untapped opportunities that I see for companies to drive organic growth. And it’s still relatively underutilized, at least in many B2B organizations that I see. And so to the extent that you have the data which most organizations do like doing these cross-sell and upsell recommendations, they can be easily automated. They can easily be served up to the sales team in a very digestible way, actionable matter that actually aligns with their sales workflows. And like you mentioned, the ease of algorithms that are out there, it’s very easily accessible. They’re relatively democratized nowadays. You have turnkey SaaS solutions that actually integrate with most of your CRM systems for certain industries, like subscription based industries, to optimize your post acquisition efforts. But again, it’s relatively underutilized in many companies that I see.

Kevan Savage: Yeah, and for our tech colleagues out there, we are definitely seeing AI identify other partner cross-sell and upsell plays that can be executed as well, especially where they’re building out kind of partner and channel ecosystems where you have integration partners being very critical to your business or integrations. As someone subscribes to your product or your service, how do you actually make that post sale kind of set of recommended integrations very seamless, easy for them to do business with? We see that as a unique differentiator for some of the tech companies that are more advanced out there.

Armin Kakas: Yeah. Like for a lot of these efforts, the timing is also really important. So not just, which customers, what products, what services that you want to proselytize and upsell them on, but when do you actually reach out to these customers? It’s really important either because of seasonality or past behavioral patterns that the algorithms identify, but not as you can do that also very easily.

So again, I would just urge organizations, especially the ones that don’t have upselling cross-sell either quasi automated or automated as part of their sales workflows to do it. It’s a huge organic revenue increase opportunity.

Deima Tankus: One use case that I think has not really been mentioned very much in this podcast, but it’s one that we’re seeing the first foray of people using gen AI is to generate this marketing content.

There’s a big push, 60%, I think of the companies that we surveyed were using gen AI for this purpose. And there’s a big push for everyone to use this technology to tweak their message and, a hundred different ways for a hundred different customer personas.

If in the next two years, that utilization goes from 60% of companies to 90% of companies, suddenly everyone’s using this technology. What’s going to happen to the competitive advantage of using AI? And how will certain companies and their brands continue to stand out in this kind of sea of hyper personalized messaging.

Armin Kakas: I’ll take a somewhat contrarian view, and I’ll tell you my opinion. But 1st of all, I think we’re quite far from having everybody use AI for their marketing efforts. I think 2nd of all, I don’t think we have AI. I think we have machine learning that we rebranded as AI for marketing materials and boardroom conversations. But that doesn’t sound besides the point of this question, I think the huge, portion of this economy, not just in the U. S., but we have clients in Western Europe as well.

They’re driven by these mid market companies that I mentioned before, right? They’ve not even made the transition from these clunky databases and Excel spreadsheets to things like data warehouses and dashboards. And I think it’ll be a long time before these companies adopt AI for their marketing efforts of scale.

But, suppose a hypothetical scenario 90% use gen AI. I think we’ll reach a point where customers become immune to this type of personalization. I think everyone is doing it and it becomes indistinguishable from a human outreach. And I think we, Simply assume that everything is AI at that point, which is quite dangerous in terms of the impact of the messaging.

And I think we’ll lose some effectiveness unless we’re talking about hyper personalization that delivers some tangible value to the customer, like a promotion or a pricing offer. But I think the ones that will be differentiated will be the ones that actually use AI to augment their existing human creativity and strategic capability and vision and emotional intelligence for the customer. I think that at that point, that’s what becomes the differentiator in my opinion.

Kevan Savage: You’re hitting on a couple points that I was thinking about too. Armin. And, I think a common misconception around AI for content development is that it always has to be new.

And the reality is that companies out there are sitting on tons of internal content that if they figure out just how to make that relevant and customer facing become a competitive differentiator on one side of the spectrum. On the other side of the kind of futuristic spectrum, we have clients as an example that are thinking about how do I leverage AI to drive very specific demo delivery where my technical engineer or my sales engineer is showing up on a call and AI is running the demo for a customer and they’re answering the specific call it more humanized and kind of time and relevant focused in depth customer needs and or sets of requirements where they’ve brought the expertise outside in and inside out. So I think there’s a huge opportunity to leverage AI for customer facing content development, but there’s also applications to take your content to customers. That AI can help your colleagues accelerate their kind of efficiency and productivity around to Armin’s point.

Armin Kakas: Absolutely.

Last topic, I want to take a couple of minutes to talk about impact to jobs in our last podcast. We brought up the idea that companies are starting to rebalance their workforces as they invest more into AI. And honestly, over the last couple of months, we’re continuing to see news about this.

Deima Tankus: What do you guys think from a marketing perspective, as a AI investments, help companies refine their marketing decisions and things like automating lead scoring and uncovering more customer personas and optimizing copy. What do you think is going to happen to the day to day of the marketer?

Is it going to become much more technical? How do you think it’s going to change?

Kevan Savage: Yeah, maybe I’ll jump in the starting point. To give you an analogy. I compare this to when things like marketing automation were first introduced, it was like, wow, there’s this really cool new technology that marketers should use.

And then when you bring it into the organization, the marketing team says something like, I don’t know how to do this. I’m going to hire an integration partner. Or I need to find a unicorn out there in terms of to help me drive this strategy. Fast forward to today, most of your digital marketing team members actually know like a ton about marketing automation systems. It’s part of the way they work. Is it perfect? No. But It’s just part of the talent profile and competency that’s there today. And it’s part of the expectation.

So I think of AI the same way, which is it’s probably early stage in terms of adoption and understanding kind of the technical specifics of it, but some of your more modern marketers are very sharp and how to use these technologies to advance company strategy.

So the recommendation there would be, it’s probably not changing jobs per se, but it’s providing new talent opportunities for some of your more advanced marketers that are out there today that are maybe more technical in nature. So do you identify those opportunities for your colleagues versus thinking about just hiring that in?

Armin Kakas: Yeah, Kevin, I completely agree. It’s highly unlikely that the job will disappear of the market. I think it’s going to be crucial. I think when most Corporate roles that we’ve seen, I think it’ll transform, right?

I think we’ll expect this transformation of the role itself and the skill sets. But we’ve seen this transformation a little bit in sales and marketing, right? The jobs are involving even in sales, right? You’re expected to be much more of a strategic advisor who leverages insights derive whether it’s AI or advanced analytics to actually solve customer problems. And so for marketers, it’s all about leveraging those insights and tools to actually guide the decision making and the campaign direction. And one thing that I think will enable marketers to become better at is just experimentation at scale, right?

Right now, it’s a very kind of resource intensive thing to do. But you’ll be able to do that at scale in a much more kind of diverse and surgical manner. But the marketers will become much more technical. And so there’s going to be a higher degree of digital literacy to be working effectively with these AI tools.

And, I would go as far as saying that a foundational understanding of data science and AI and machine learning. We’ll actually be somewhat critical for a strong marketer in the future. Like this new era guys, and not just in marketing, but in, many commercial roles and, even in finance, it’ll require people who are adaptable and lifelong learners, right?

It’s, it’s no different than you needing some complex surgery and you’re going to a double board certified surgeon, they’re required to invest in their continuous education seminars, latest tools, et cetera. And I think that will be the same thing for marketers. And, in terms of the lines blurring between sales and marketing, I think Marketers will continue to have this requirement to understand the sales process fairly in depth.

I think vice versa. I think in some industries, the lines will be blurred, right? I’m thinking SaaS and DTC but in others we talked about long cycle B2B sales. I think that delineation will still be fairly apparent, but I agree with Kevin. I think being technical being analytical is going to be incredibly helpful in this field because quite frankly, I think marketers will be the way organizations to grow revenues and grow them profitably leveraging customer data, right? And they’re going to be much better much better position than sales in that sense.

Kevan Savage: Yeah, one other quick point to add and this is more on the customer experience and kind of the creative side of the marketing responsibility.

Where AI will benefit more testing and iteration and optimization in the marketplace. It will also make the differentiation of really strong creative more difficult because there will be a lot more out there that kind of points of differentiation. The true spirit of creativity will take a lot more data to make it differentiated.

So I think those are some things that we expect to see. We’re already seeing them. You can see them in Superbowl commercials and whatnot in other formats. But the needs for brand to really think about their creative differentiation, leveraging these capabilities and their talent, it’s going to be pretty paramount as well.

Armin Kakas: Totally agree.

Deima Tankus: Absolutely. All right. Thank you, Kevin and Armin for your great insights. We learned a lot in this episode. We learned that, the responsibility of the marketer is stretching across the whole buyer journey and that AI tools are helping that stretch feel more attainable and scientific. We learned about the ways in which qualified pipeline development can be strengthened through AI investment and how Marketers can partner with sales to drive that cross and upselling within existing accounts. And then we got Armin and Kevin’s perspective on the AI impact to marketing jobs. Both showing the potential for scale and improving accuracy and also talking about how that the jobs are going to be evolving.

We hope that you enjoyed this conversation. Please continue to look out for more research on AI impacts to the commercial organization and to the go-to-market strategy. And thank you for listening.

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