The Enterprise Tech Year – Marketing

The central theme in marketing this year was how to implement generative AI into marketing and sales technology. Yes, there were other conversations, but they all seemed to come back to the use of AI. It took a better part of the year, but we finally started to see some solid examples of AI for more than writing content.
Everything, whether AI-related or not, all came back to creating better customer experiences across the buyer journey. It’s less about shiny object syndrome and more about how we use content, including audio and video, and how we measure performance, all in the service of the customer. And, to be honest, it feels like it’s about time.
Here are my picks of the best diginomica content for 2024.
There is value in leveraging generative AI for content, and most companies are cutting their teeth on this technology by using it for content creation. But are there better uses that will help marketers in more important ways.
Why? We are well into generative AI, and most discussions are still around using it to write content. To be fair, that capability has improved dramatically. But we are also finally starting to see the things I was looking for – using generative AI to take on more of the operational tasks of marketers (and sales).
Instead of relying on pre-built reports, why can’t marketers ask questions and have AI do the hard work of analyzing and pulling everything together? And then enable the marketer to select which insights to act on and help create the framework of a campaign to make that happen. Sure, there’s content creation in there, but it’s only part of what happens, and it’s not the first thing.
Why? I was starting to believe we might not see some exciting use cases for generative AI in marketing. Until I learned what Bloomreach was building. This was the first time I saw a copilot (Bloomreach calls them ‘pilots’) take on the bulk of the work of setting up a campaign end to end. The marketer tells the AI what they want, and the AI goes away and sets everything up, leveraging data about customers, from past campaigns, and more. The marketer can review, modify, and press publish. It’s every marketer’s dream because it allows them to focus on the important work – understanding customers and creating campaigns and experiences that work.
Custom tool handling is an opportunity for software vendors, Penn said. He said if a martech vendor can expose its services to the models, then the models can intuit when to use that tool. For example, a model can build an API to connect to Demandbase to pull in intent data. Or it could connect to Zoominfo to pull in a contact’s information.
Why? Most martech vendors are still in the copilot phase of generative AI, and that’s okay. But things are changing fast. Agents that do the bulk of a task end to end are gaining in popularity, especially the ones that can connect to other agents to perform a series of tasks. And then there’s custom tooling, which we haven’t heard much about from marketing vendors but which holds a huge opportunity because it builds on the popular LLMs everyone uses. Wait five more minutes – I’m sure a new way to leverage generative AI will appear and capture our attention.
We keep talking about how B2B buying behaviors have fundamentally changed. We hear everyone talking about moving from MQL-centric marketing to signal-based marketing, the importance of looking at different intent signals and what’s the propensity to engage further, the propensity to buy, the propensity to renew, looking at that using AI to drive forecasting around stuff like that. But I just don’t think that we are addressing it properly if we don’t have content intelligence baked into our tech stack and baked into our overall go-to-market strategy.
Why? We keep saying that content is king, but do we truly monitor and track its performance in relation to the customer experience? Too often, marketers create and publish content but don’t know how it impacts how customers engage with the brand or how that content drives greater engagement and conversion. And so they keep creating more content. Content intelligence empowers marketers to understand the value of their content better. When they do that, they can prove that their content is king.
Perhaps the most overlooked reality of effective content capabilities is that there is no single right answer for what they should look like. Tech stacks and organizational structures alike are most effective when they are defined by the unique requirements and priorities of a given business or organization. And in all cases, effective internal communication and content operations play an instrumental role.
Why? You can manage your content effectively if you have the best content management system. Not really. The best content management system depends on your business’s unique requirements, which means you have to spend time understanding those requirements. Also critical are governance policies. I think this piece aligns with the one before—you have to treat our content as a critical business asset, and until you do, your experiences won’t be great.
I’ve worked in enterprise software for a long time, and understanding what the experience a person is having as they move through the products is, is a constant learning curve, especially for something like Salesforce, where there’s so many different kinds of users using this in different ways. So finding the common things that really tie it together has been a good and fun challenge of this role in the last 18 months.
Why? Every piece of software that’s been around for any amount of time goes through design changes. This piece provides insights into how to best think about updating the user experience using Salesforce as an example. There is a lot to think about, including visual elements, user experience, and not breaking the customization that existing customers have made. It may not be easy, but it is possible.
I think another thing that’s going to happen is basically the floor of the quality of videos is going to go up. It’s going to be easier for the average person to make a video that they’re proud of. I think it’s going to take cultural time for people to get used to the AI avatar thing. I think that’s going to take longer to actually proliferate, but I think that the tools to aid in production are going to happen very, very rapidly.
Why? AI is not only impacting text-based content; it’s also driving change in video and audio production, democratizing what was once the purview of graphic designers and video production companies. We’re also seeing a rise in the use of avatars and image and video generators. The technology is cool, and the opportunities are many. Let’s just be sure not to lose our humanity in the process of all this AI.
Attribution isn’t as easy as saying that for every dollar you spend on a specific channel, x number of leads can be obtained. That’s because it’s rare that a person sees an ad in one channel and then automatically converts. And it’s even more rare that they’ve only seen your brand in a single channel. There is no straight line to conversion.
Why? Attribution has become a dirty word in marketing because determining it is challenging at the best of times. Marketing impact modeling is a different way to determine what marketing tactics are helping drive conversions. You can look at each channel involved and leverage predictive technology to learn how each channel supports the journey. It doesn’t require a straight-line journey, which is good because that path to purchase doesn’t exist.
According to Salesloft’s Chief Product Officer, Ellie Fields, the traditional funnel is broken, and it’s hard to argue with her. Buyers don’t proceed in the nice orderly fashion that’s typically defined (awareness, consideration, decision, customer – or some form of that funnel/journey). Instead, they are all over the place.
That’s even more true for B2B buying teams. Fields argues that Marketing and Sales have operated in silos, and it has been a disservice to the buyer because they are basically dropped from one team to the other with little to no explanation of why, resulting in the buyer often feeling like they are starting from the beginning when they engage Sales.
Why? I share this one because it’s a good example of how marketing and sales technology are coming together to support the buyer journey. Instead of each group doing their own thing, they can see across the tech stack and, as a result, have access to the customer data needed to create the right experiences—whether on the marketing side or when sales reach out for a conversation.
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