Jeffrey MacIntyre
Scalable content personalization systems create huge value for businesses. Like most valuable endeavors, they're really hard to do well.
Jeffrey MacIntyre orchestrates the activities - terminology and taxonomy work, metadata strategy, information architecture, and more - that help businesses build content operations that deliver the customer-focused experiences that consumers expect.
We talked about:
two trends he sees that are driving content strategy for customer experience: product thinking and the need for better IA in large enterprises
the opportunity that better IA affords to build a competitive moat around your business
how structured, semantically meaningful content and enriched metadata enable better personalization
his take on the "personalization gap"
his prediction that something like the Hippocratic oath will emerge in the near future to address ethical issues around AI
the importance of a cross-functionally and collaboratively generated controlled vocabulary
his advocacy for up-skilling content design professionals with better taxonomy and metadata skills
how "cowboy taxonomy" work fits in his practice
his assertion that the best way to advance collaboration is a robust test and learn program
how information structure provides a backstop for AI
a shout-out to Marcia Bates for her insights about "berry picking"
how good metadata can prevent the accumulation of "experience debt"
the benefits of Leidy Klotz's notion of "subtraction"
Jeffrey's bio
A personalization optimist and information retrieval obsessive, Jeffrey MacIntyre is an independent consultant focused on "shovel-ready" solutions to personalized, automated, and simplified customer experiences. He writes Bucket List, a newsletter of tales from the trenches of his consultancy, Bucket Studio. He speaks widely on the IA for AI — the role of information science in shaping connected experiences — and runs Bucket Brigade, the only community for those who design such experiences.
Connect with Jeffrey online
Video
Here’s the video version of our conversation:
https://youtu.be/krcZnNgnJFI
Podcast intro transcript
This is the Content Strategy Insights podcast, episode number 189. Crafting the personalized content experiences that consumers expect nowadays is not an easy job. To assemble those made-to-order interactions, you need to first align your internal teams on the terminology you use and organize it around a clear metadata strategy, and then you need to structure and categorize your content so that you can create engaging experiences at scale. Jeffrey MacIntyre specializes in orchestrating content operations like these.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 189 of the Content Strategy Insights Podcast. I am really happy today to welcome to the show Jeffrey MacIntyre. Jeffrey is an independent personalization consultant. He's well known in the industry as "the personalization optimist." He's the principal and founder at a studio, Bucket Studio, which is his agency. He also runs the Bucket Brigade community, which we might talk about a little bit in here. He's also a founding member of the Consortium for Personalization Professionals. So you're catching the theme of personalization here, but tell the folks a little bit more about what's going on these days, Jeffrey.
Jeffrey:
Yeah, absolutely, and thanks, Larry, for having me on - a big fan of the pod. So what's happening is we've got two interesting developments in my mind. We have the ascendancy and design circles of product thinking, so a lot of people wanting to really skillfully design and deliver really discreet user flows within a customer journey and do growth hacking along it and really measure and understand success, sense, and respond. That's trend one. And then trend two, particularly in the enterprise, but not alone, we have very large sprawling sites that are ripe for rationalization, particularly now with AI whereby people are taking stock right now of, how do I want to steward this gargantuan site of mine and what would an essential customer experience? If I took it down to the bones, what would this house look like? And that's where IA really comes in. Obviously, we know AI is going to be a delivery mechanism for this, but we need to structure the data, and that's why sometimes I talk in bumper stickers about IA before AI.
Larry:
I would love to elaborate on that a little bit because I think I get what you mean, but I want to hear your take on why is information architecture crucial to AI?
Jeffrey:
I think that there's a few reasons. Obviously, the one that's well known in the practitioner community is the rather large and clear present danger or risk to your brand and to your legal office and legal counsel around hallucinations and other mis-firings or over fittings that AI can produce. I think that this is well known and well diagnosed, so I don't want to spend too much time on it. Let's talk about the upside of structuring all of the things. The upside is that if you are a company or a brand that's been producing content over a long period of time, you actually have a new moat. If you can rationalize your site and make it digestible and machine consumable enough, you can have a moat around your products and places. Because we're looking at the real time evolution of information retrieval. It's happening for us right now where we're seeing search alongside a co-pilot UI alongside navigation.
Jeffrey:
And I think the Walmart iOS app is a great example of this where all these things are coming together and it's not too hard to see in five years time that you're probably going to go down to just a conversational input query, query and response mechanism for traversing large sites and products and experiences. Now, where do the semantics come in? The semantics come in where you want that AI to traverse your information product and data successfully and effectively, maybe consistently. I put an asterisk on that, but nevertheless, you want to be able to baseline what quality is. And as a personalization consultant, I feel like I've got a little bit of a competitive advantage in that we've been working under existing legislation for years, GDPR being only among the better known pieces of that legislation, which requires data destructibility, requires traceability, requires working backwards from algorithmically generated decisions so that I can pull my data out of this service if I'm a California resident or if I'm a European resident.
Jeffrey:
And those rights and those legal standards are going to follow in AI. So I think that there's a lot of upside both from a brand perspective of having that competitive moat, because imagine you've got a co-pilot on your site that knows your content better than Google, better than Bing. This is a very different way of thinking about the power of your content when it's effectively structured.
Larry:
And can you talk a little bit, just dive a little bit deeper on that? Because I get what you're saying. Because you're going to have this unique corpus that you're drawing from, whereas the big search engine, they're looking at the whole internet. You have this unique focus on your own thing, plus you know what you want to do. It's like content strategy. You know who you are and what you're about, what you have to say and have crafted that. Tell me about how the semantic tooling can amplify that and enable it.
Jeffrey:
I think that there's just the existing structuring of content that you want the AI to respect and acknowledge so that it's not pulling in false positives. If you query, "I want to see all the white papers," which is a broad and unrealistic query, "I want to see all the white papers on computing," or, "I want to see all the white papers on cloud computing in the last quarter about Kubernetes," now you're starting to get narrower, narrower, narrower. That result really should be using essentially TF-IDF or nearest neighbor or some very sophisticated algorithms so that we're pulling really relevant results. But if we're also dealing with an ontology, or if we got a knowledge graph or a knowledge base in the backdrop, even just we have a taxonomy, an effective working taxonomy or polyhierarchy, we know that results that are put in these branches are relevant and others are going to be of diminished or lesser importance.
Jeffrey:
So that's just the ranking and stuff, because, again, with AI, a lot of the queries are coming down to, how can you synthesize knowledge for me very quickly? And we run into these problems of synthetic data and misleading results. But it doesn't change the human craving for, hey, can you just summarize a whole bunch of stuff for me and give me the one true answer? That's going to be a garbage in, garbage out thing, but it's not going to stop us humans from doing that. And so we've got to use structure to reinforce and have those guardrails.
Larry:
As you say that I'm reminded that computers behave differently now than we're used to them behaving, this hallucination and creativity on the part of computers. They've always been reliable computing machines just doing a lot of fancy stuff. And back to what you said about semantics, it sounds like semantics is the thing that can add to the number-crunch-ey part of these things, which the LLMs are just predicting what people are going to say. And you mentioned ontologies and knowledge graphs, even a simple knowledge base with a taxonomy so you're ascribing with that metadata, you're ascribing characteristics to this content that you want to make sure people get. Stitch that together for me how content people can use that to amplify this treasure trove that they're hiding behind that they now have behind a moat.
Jeffrey:
Yeah,
