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.
In the first two articles of this series (Part 1, Part 2), we covered contextualizing content data (understanding what data points matter and when) and mapping data (connecting those points across platforms to identify patterns of genuine interest). Now comes the payoff: packaging that intelligence into actionable steps for your lawyers.
This is where all your analysis becomes useful. The best data analysis in the world means nothing if your lawyers don’t understand it or act on it
What type of intelligence you package and how you do it will depend on a couple things:
Let’s go back to our scenario we created earlier in the series. Here’s a quick overview of the data analysis:
One of our key industries is retail. Quarter three review shows Company A, a national retailer, showed up in our results 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 as subscribers, and in our LinkedIn ad results. First, just a single data point on JD Supra with no name. Later, showing up with a name and title alongside individuals from that company who have read our DE&I-related content or subscribed to our newsletter. One contact fits our target profile: Jane Doe, senior employment counsel at Company A.
Research the individuals who fit the target profile. In this instance, do a general search for Jane Doe. The search engine results might tell me what organizations she participates in, what she might write about, speak about, awards received, where she went to college. This information can be used to find connection crossovers or “touchpoints” for direct outreach. Maybe Jane graduated from the same law school as one of our lawyers, or is an active participant in the same industry association we’re a member of.
Head to LinkedIn to find people connections. Expand your search pool to any individual name from that company who showed up in your data results. Note if there are any connections to any lawyers at your firm.
List possible connection methods. Start with an exhaustive list: who’s connected to Jane Doe or a company contact, and some of the basic facts about the company targets. To the best of your ability, order the list from most to least viable option.
Use data points to assemble the user journey. Based on the expanded scenario, I can assume that one person from the company initially read an article on JD Supra and likely shared our content or mentioned us to a colleague. Eventually, contacts signed up to receive JD Supra content and our email newsletter. Because the niche topic results started off with a single company interaction and expanded to others, we can guess that the content and topic resonated, and it was useful enough to share with others.
Compare relevant metrics across time. By relevant, I mean metrics that support the user journey intelligence. If you’ve been benchmarking data, you can aggregate the number of reads in that niche topic to show an increase over time, alongside when in the timeline they and others subscribed to the newsletter and interacted with LinkedIn content. If you’re going to use your charting skills, an exponential increase in reads from a company or company contact is the perfect place to use them.
Highlight milestones. Note major changes in behavior: this company went from one anonymous read on one DE&I article to content subscriptions on multiple platforms. The contacts reading our niche content went from one to five. This illustrates how this content strategy led to increased interest in our offering, reinforces why the group should continue to follow this content strategy, and adds to the credibility of the recommended actions.
Given that the scenario takes place in quarter three, the lawyers you’re presenting to should be somewhat familiar with the overall DE&I content strategy and the reasoning behind it.
I usually lead with a preview of the lead: the important, actionable intelligence from current clients and potential new targets who fit the profile.
Example: We should directly connect with Company A–they fit our target profile, and multiple people in their HR department have demonstrated interest in our DE&I work. I’ve found a few connections that are worth exploring to get on their radar.
Once you’ve given them a reason to listen, tell the story of how you arrived at that conclusion.
Example: In Q1, Company A read “Article 1.” Q2, Jane Doe read four of our six DE&I articles on JD Supra and signed up for our newsletter in April. She forwarded our Q3 newsletter three times; two HR department colleagues signed up for the newsletter. Across nine DE&I articles, we have 100 views from Company A; we now have three individual names who appear to have decision-making authority in this area. Information from our LinkedIn ads also shows that the content is being consumed by HR-related professionals on that platform.
Use visuals when it provides an impact, like a number increasing over time or a public interaction between our content and a key target.
After you explain your reasoning, start with your most viable connection options: direct connections.
Examples:
If direct connections don’t exist or aren’t viable, that’s where you can go further down the list of options.
For some direct connections, the next step is obvious. Outstanding questions may need to be answered before outreach happens, but the closer a firm contact is to the target contact, the easier it’ll be to find an entry point into getting this work.
Others might require more thought and strategy–this is where your general intelligence may be useful. The best-case for this scenario is probably a timely lawsuit or article.
Example: Company A announced it has formed a committee dedicated to implementing a DE&I program.
This is the perfect entry into direct outreach. The trick is having a BD-savvy lawyer who doesn’t mind making a pitch. Here’s how that outreach could go:
Jane Doe is active on LinkedIn. DE&I attorney Joe Harris volunteered to send Jane the following direct message:
Hi Jane, I read your company press release and wanted to congratulate your team on launching a DE&I program. Not an easy program to implement nationwide! Our team at Firm has advised national retailers in the execution of these programs, and we developed an implementation checklist that might be helpful as you roll this out. Happy to answer any questions you may have. Our firm actually writes a quarterly newsletter on the topic, if that’s ever of interest.
The intelligence and research you’ve gathered should be able to help you identify ‘hot’ targets and provide options that allow your lawyers to confidently pitch these targets at an opportune moment.
Content analytics for business development isn’t about tracking every metric or drowning in spreadsheets. It’s about a systematic approach to transforming the data your content already generates into genuine business intelligence:
Start small. Pick one strategic content initiative. Track data for your highest-priority targets. Map their engagement over one quarter. Package what you find and present it to one practice group. Build from there.
The intelligence is already there, waiting in your analytics platforms. The question is whether you’ll use it to transform your content program from a brand-building exercise into a genuine business development engine.
Don’t try to force AI. At the end of the day, it’s a piece of technology. You don’t implement a piece of technology just because everyone has it. You implement tech to solve a problem. So, assess how you go about writing or producing thought leadership.
Do you handwrite? Dictate? Use a ghostwriter?
Where do you keep your content drafts? How do you make edits? Do you have an internal approval process?
Consider improving your current workflow before changing behavior to conform to a tech tool.
Here are some ideas to take and adapt in your content workflow.
AI is great for deep research. Use GenAI 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:
Likely, the project management process for attorney content lies with the firm’s marketing department. But you can speed up the process by using a document repository, status field, and an automation to push your content draft to the next person in the workflow.
Other ideas:
The content process doesn’t end with publication.
Once it’s published:
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.