Originally published on JD Supra
I was recently a guest on Kristo Sween’s The BD Catalyst podcast, and like many conversations I’ve had this year, we got on the topic of AI.
Kristo asked: Where should MarComm professionals focus their time now, SEO or AIO?
My answer: Good content.
Here’s why.
Content existed long before the internet ever did. Search engines didn’t create SEO from scratch; they borrowed from all the good content before it.
Think about the origin of some of these best practices. Much of the way we write is based on printed mediums like newspapers, magazines, and journals. For example:
Optimization “rules” are also based on UI (user interface) and UX (user experience) principles:
These practices are not new, but the ability to game the system is.
That’s the distinction: choosing quality over gamification. If you’re only focused on SEO or AIO, you’re missing the entire point of having a content strategy. You’ll be chasing trends rather than creating content your audience wants to read. And that might get you the incoming traffic numbers you want, but it doesn’t build trust with your readers–they won’t be coming back for more.
Think of content as ice cream, and optimization as hot fudge. Ice cream is good on its own and can be consumed without a topping, but you wouldn’t eat the hot fudge by itself (I mean, in reality, maybe). They are tastier together than they are individually, but the topping only works because there’s something worth eating underneath it.
Which means don’t ignore SEO and AIO and all the other optimization acronyms—you need to know the space in which you’re operating. They can help you refine or focus what you’ve already created with your content strategy. But without good content, your keywords are just that – words. Words without value and an audience.
So if you’re feeling like you’re behind because you don’t have a firmwide optimization strategy, just remember…
Optimization can help good content travel farther, but it can’t make bad content worth reading.
With technology comes possibilities. And with endless possibilities comes overwhelm.
Where do you start?
It’s a great question, and one I can’t answer—where you start is up to you. But what I can do is give you a whole bunch of ideas to work with.
I’m lumping AI, agents, and automation together because they all involve a tool executing a directive without human intervention. Plus, they often feed into each other for an even more efficient workflow.
These suggestions are aimed at making your content workflows more efficient.
Consider your existing process rather than changing how you operate.
The basic concept here is to turn your handwritten notes into digital text.
Most genAI tools don’t transcribe audio files. There are dedicated ones, though I’m not going to touch on those here. If you work in-house, inquire about dictation/transcription tech—you may already have the technology to do this.
First thing is to capture the audio, of course. Make sure that you know how long you can talk for, otherwise you’ll be in the middle of a thought when it cuts out.
If you need to transcribe the audio, convert it using a free tool like AudioConvert.
You can automate pushing the transcribed audio content into another system (Word, Google Doc, Notion) by using a tool like Zapier. For example:
How you go about managing your process will depend on your existing tech stack.
First, you’ll want to figure out what your options are.
It’s easy to create one, particularly if you use MCPs to do it.
It doesn’t have to be complex, either. It can be a place where you keep your content drafts. Mine sits in Notion. It’s on the simplistic side: draft content, last edited date, target publication date, status. And I like Notion because I can use AI fields and agents in the tool itself, plus the MCP with Claude.
You can also explore direct connections to your website CMS. Proprietary systems will likely be more difficult, but WordPress is one that connects with most tools in some way.
Let’s say you finished a draft and want to send it off to the next person.
When drafts come back, use automation to update your content system.
The content process doesn’t end with publication.
Once it’s published, I usually like to keep the published URL alongside the draft content.
If you want more control over the process or have different processes, use a database structure to do it.
One way: create a new database in Notion (or have an MCP do it). Use an automation to pull new RSS feed items into the database (content, URL). Create fields that manage the process, like a status field and text fields for the content you’ll send. The text fields could be prompt-based or blank for your own input. So it could look like: new RSS item into Notion > the content is run through an AI prompt and output into a specific field > after editing, a status change triggers an email to a list.
Now, let’s say your content includes important “alerts” that get a different internal treatment. Add a select field in your Notion database that triggers a select prompt. A new RSS item appears in your database > you get an email to set the prompt > the field selection of the prompt type triggers an automation that runs the post content through a particular AI prompt > the output appears in a Notion text field > after editing, a status change triggers an email to a list.
Make use of having your content in one location.
Originally published on Legal Sales & Service Organization (LSSO) on April 2, 2026
In the first article of this series, we covered how to contextualize content data—understanding where it comes from, how to segment it, when to pull it, and which data points matter most for business development. But here’s the reality: even the most valuable individual data point (contact information) rarely tells you enough to warrant action.
The real power of analytics comes from mapping: connecting data points across platforms and over time to see patterns that reveal genuine prospect interest. This is where analytics move from interesting to genuinely powerful.
It’s somewhat difficult to explain in the abstract, so let’s go through an example that illustrates the concept. (This assumes that you’re not using technology that aggregates data for you.)
The basic scenario: Your firm sends out a quarterly newsletter dedicated to a niche topic.
Before we dive into mapping, let’s establish the context for our example scenario.
First, put the marketing and data into context by asking yourself some questions. And note, these are questions you can ask yourself before publishing anything: thinking through these aspects can add value to the results.
Let’s get more specific and strategic on the scenario so we can walk through how to map it.
Your firm sends out a quarterly newsletter dedicated to diversity, equity, and inclusion in large corporate workplaces. The email contains the entirety of the quarterly articles with links to the JD Supra version and the author biographies on your firm’s website. It’s sent to a curated list of current firm clients and individuals who opt in directly via the firm’s website and email signatures. Since it’s a business development priority for your Labor & Employment group, your attorneys are producing more thought leadership on the same or similar subjects that you’re doing paid campaigns on LinkedIn with the newsletter content.
Here’s one way of approaching the data analysis for this scenario:
Once you’ve got your data points, pay attention to where they intersect or lead. As above, this is just one way to go about mapping.
Start with key targets. Look for key individual contacts and company names in your email marketing tool and JD Supra reader statistics.
Gather anecdotal evidence. Chances are that if you have practice group initiatives, you’re having regular group meetings. Make it a habit to ask about interactions when specific content goes out: did you forward the quarterly email to anyone, and did you get a response? Did a key target connect with you on LinkedIn? Did you post the article(s) on your LinkedIn profile, and did you see any engagement or an increase in engagement (likes, comments, shares)?
Check for patterns (or the absence of them): patterns are easier to spot across time, which is why I recommend saving raw data. Increases in click rates over time are a good sign that the content is resonating, for example. Look for industries, job titles, and company and individual names you don’t already have on your key targets. Is the content performing well on one platform over another? Which delivery method produces the most wanted result?
Understand the journey: the next steps someone takes after consuming your content and/or the direct results. Some journeys are obvious–content > email open > bio click > direct outreach. Attorney’s LinkedIn post > post engagement > direct outreach. Email open > JD Supra reads > direct outreach. Others aren’t as clear, or take time to see. For example, a company you weren’t targeting appears several times in your reader data > company contact subscribes to your quarterly newsletter > contact interacts with your newsletter over time > attorney plans direct contact on LinkedIn.
Let’s see how this plays out with our real scenario:
One of our key industries is retail. Upon our Q3 review, I’ve recorded Company A, a national retailer, for the third quarterly review this year. They were not on our initial target list, but they’ve appeared on our JD Supra reader analytics, email newsletter subscriber list, and in our LinkedIn ad results.
Here’s what the mapped data reveals:
Mapped data tells a story that isolated metrics can’t. Jane Doe’s name from a single JD Supra read would never warrant outreach. But when you connect that read to email subscriptions, newsletter forwards, colleague involvement, and consistent LinkedIn engagement over three quarters, you’ve identified a pattern of organizational interest worth acting on.
Now that you’ve identified and mapped these patterns, the final step is packaging this intelligence in a way that motivates your lawyers to act. In the next article, we’ll explore how to translate data patterns into compelling, actionable outreach opportunities.
Originally published on Legal Sales & Service Organization (LSSO) on February, 16, 2026
Content data is everywhere, scattered across website analytics, email platforms, social media dashboards, and CRM systems. Every article published, every email opened, every download tracked adds to the pile. No longer are we starved for intelligence; we’re overwhelmed by its abundance.
What do we do with it all?
Try to understand it, of course—to put it together and follow the journey where it leads. Depending on your firm’s tech stack and department makeup, this can be relatively easy or difficult. Most tools provide charts and graphs that better visualize what the numbers might be telling you; some tools aggregate and provide an analysis for you (like JD Supra’s integration with Intapp or a tool like HubSpot); there are even tools that only focus on client intelligence. Still, it’s important to understand what’s at play and how to use its intelligence.
There are three phases of gathering and leveraging content analytics for BD:
This article covers the first phase: contextualizing data. Future articles will tackle mapping and packaging.
The foundation for using analytics for BD intelligence is understanding the context surrounding it.
Think of email marketing data: the numbers don’t matter if you’re sending to the wrong audience. There’s more to analytics than highs and lows.
Let’s take a look at some contextual factors when it comes to thinking about data analytics for BD.
Consider what platform it’s coming from: Google Analytics, social media, email marketing, paid advertising, or syndicated partners. Likely, your target audience isn’t spread across all of those; you’ll want to look more closely at the data associated with the platform that has the highest concentration of your intended audience. For many of us in professional services, that’s LinkedIn or our website (closer to the sales part of the funnel).
Expand your data extraction beyond your firm’s digital properties to your attorneys who are sharing it. For example, ask your attorneys to share their LinkedIn post statistics. These numbers may shape some of your understanding.
Don’t forget to gather anecdotal evidence! Ask your attorneys at regular intervals if they’ve seen an increase in engagement or had targets reach out. These actions are really what you’re trying to enact.
Not every piece of content warrants a deep analysis, and what’s worthy depends on what matters to your internal clients and your resource capacity. The good news is that there’s no right answer, and there are lots of ways to segment and prioritize.
First, you’ll get better results if you’re posting consistently in your target segments. While you can gather intelligence from one-off articles or advertisements, without averages across time, you don’t have anything else to check against. Isolated data points can lead you down rabbit holes—a single viral post might attract the wrong audience, or an outlier metric might suggest a trend that doesn’t actually exist.
Consider your firm’s strategic priorities: often, these align with practice group initiatives. Sometimes, they might be a niche subject, but the important piece is that for strategic initiatives, you want a consistent effort. Think of content for BD as building a relationship without direct contact.
Most content on the internet lives beyond the initial post date. The question is: when do you pull data for intelligence? Timing considerations vary by platform and content type—which is another layer to the segmentation decisions above.
Social media posts have a shorter shelf life. Typically, you pull statistics within days because the algorithm is the one serving up content to users. Social platforms may be searchable, but they’re not as robust or weighted as search engines or indexed sites. I know LinkedIn tends to have a 3-week cycle for posts, so that would be a good time to pull data.
Campaigns are typically measured by the length of the campaign, as they disappear once the ad period is over. If campaigns are tied to a strategic initiative, they’re often aggregated over time.
Content syndicators like JD Supra and firm websites don’t have expiration dates and are searchable, so consider these data points at set intervals. Look at all content on a quarterly and yearly basis. For content related to campaigns or strategic initiatives, I suggest adding one week and one month intervals in addition to the quarterly and yearly reviews.
Different data points carry different weight for business development purposes, and understanding their relative value is key to efficient analysis.
Here’s a list of common data points found in content platforms, starting with the least useful:
Even contact information—the most valuable single data point—rarely tells you the whole story on its own. You wouldn’t want to recommend your lawyer contact someone who only interacted with one piece of content. Your lawyer would have much better luck at forming a relationship with the contact armed with more information. Data-informed outreach beats blind outreach every time.
Understanding context is the foundation—knowing where your data comes from, how to segment it, when to pull it, and which data points actually matter. But individual data points, even contact information, rarely tell the complete story on their own.
The real power comes from connecting these data points across platforms and over time to identify patterns of genuine interest.
In the next article, we’ll explore how to map data to transform isolated metrics into actionable business intelligence.