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.

Context Considerations

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.

• Is the list targeted? Open rates don’t have much value if it’s the wrong audience.
• How did the audience subscribe? Click rates mean more when the audience was interested enough to subscribe themselves vs being automatically added to a list.
• How did they receive the email? Email marketing tools are generally used to mass blast, and that’s fine. Depending on the size of your list and how important the topic is to your internal folks, forwarding the designed email with a short message to a specific client about how it impacts them specifically is so much more effective than a mass blast with generalized information
• What content is included in the email? Most newsletters include a text teaser with a link to the full piece of content on their website. Click rates could be high because of curiosity, not relevance. If you’ve got the full content contained within the email and the user still clicked, there’s a higher likelihood that it resonated for them.
• Where are users directed? If it’s to your website, consider conversions or landing pages with a specific call to action (like a form). I used to direct users to the article’s JD Supra version for higher-value data points.

Approaching the Data Analysis

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:

• Review data monthly, quarterly, and yearly. Monthly reviews may help you shape what you put in the quarterly newsletter. Give the email at least a week before you look at data.
• Pair data with internal strategy. Make sure you know (1) who your important clients are in this audience, specifically companies where you don’t already do this work, and (2) prospective companies that fit your target profile. Specific contacts aren’t necessary.
• Be clear on whether there are demographics at play. Maybe your attorneys are only licensed to do this work in Minnesota, or they’ve noticed a pattern of DE&I programs being rolled out in a specific industry.
• Extract data from email newsletters, JD Supra reader analytics, and LinkedIn campaigns. Include anecdotal evidence specific to content performance: direct outreach that mentions the content, new LinkedIn connections made after someone engaged with a post, etc.
• Benchmark data points: benchmarking means establishing baseline metrics (average click rate, typical readership numbers) so you can identify meaningful changes over time.
• Document! Particularly when you first start tracking over time. Data points may pop in and out of relevance, so keep your raw data spreadsheets and have a notes document where you record your unedited thoughts/observations.

Connecting the Data Points

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.

• Email: filter by clicks so you’re not searching contacts who didn’t perform an action; note which CTA they clicked (article link vs attorney bio, which has more weight in this scenario). Of those contacts, which ones have multiple opens, clicks, or forwards?
• JD Supra: filter content by relevant DE&I articles, check readership. Do those target company names or contacts appear? What about their colleagues? Are they reading content related to DE&I or a topic that fits into a different firm practice group?
• LinkedIn: review the campaign’s data to see if these targets appear.

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.

The Power of Mapping: An Example

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:

• Q1: One anonymous read of a DE&I article on JD Supra from Company A (no name included).
• Q2: Jane Doe (Senior Employment Counsel at Company A) read four of our six DE&I articles on JD Supra and subsequently subscribed to our newsletter in April.
• Q3: Jane forwards our Q3 newsletter three times; two HR department colleagues subscribe to the newsletter.
• Across nine articles over three quarters: 100 total views of our DE&I articles from Company A; three individual names who appear to have decision-making authority.

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:

1. Contextualizing: understanding where to find it, when to find it, and what it’s actually telling you. 
2. Mapping: connecting data points across platforms to see broader patterns. 
3. Packaging: translating gathered intelligence into potential opportunities for your lawyers.

This article covers the first phase: contextualizing data. Future articles will tackle mapping and packaging.

Contextualizing Data

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.

Where the data is from

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. 

How you’re segmenting the content

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. 

When the data is pulled

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.

What data points to look at

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:

• Vanity metrics. This includes email open rates, post impressions, link clicks, likes, website page views–metrics that only tell you someone scrolled past or viewed it. 
• Time-based metrics. Time spent on a website page, how long a video or podcast was viewed/listened to–information that could indicate whether your content resonated with your target audience.
• Action-based metrics. Web and social conversions, video/audio completion rates, downloads, shares/forwards usually mean your content was relevant enough that your audience took the next step. 
• Basic demographic information. Location, industry, and other categorical data points tell you if you’re hitting the right audience.
• Contact information. The holy grail of BD data: job titles, company names, even individual names, can be accessed in tools like LinkedIn and third-party tools like JD Supra and HubSpot. 

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.