Insurer That Relied On Flawed Decision Tree Analysis Hit With $7.2M Judgment For Rejecting Settlement Offers After Failed Mediation

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Decision Tree AnalysisThe dynamic present in personal injury mediations is fairly straightforward. The defendant’s insurer (or the defendant, if self-insured) will estimate the risk of a jury verdict for the plaintiff on liability, and discount the likely damages by that risk to determine a reasonable settlement range. On the other side of the table, the plaintiff’s attorney will try to raise the settlement range by persuading the insurer that the risk of liability, and/or the magnitude of the potential damages, are materially higher than the insurer has projected.

Among the uncertain factors both sides must weigh in their case evaluations are the degree of the plaintiff’s contribution, if any, to the accident that led to injury or death; the level of sympathy a jury will have for the plaintiff (or for the plaintiff’s survivors if there was a fatality); and the reputation of the judge for being either pro-plaintiff or pro-defense (if there are contested legal issues that may be a close call).

A recent Texas federal court decision offers a fascinating inside look at how this dynamic played out in a bicycle fatality case, including extensive, but flawed reliance on decision tree analysis.  See American Guarantee & Liability Ins. Co. v. ACE American Ins. Co., 2019 WL 4316531 (S.D. Tex. Sept. 11, 2019).

The Claim

The case concerns a claim by an excess insurer (AGLIC) that the primary insurer (ACE) breached its Stowers duty to accept a $2 million settlement offer within the limits of its coverage. For those readers unfamiliar with Texas law, the Stowers duty obligates insurers to accept reasonable settlement demands within policy limits (as evaluated under a reasonably prudent insurer standard).

As a result of ACE’s breach of that duty, a jury returned a verdict of nearly $40 million in a wrongful death case, which AGLIC ultimately settled for $9,750,000, resulting in a payout of $7,750,000 from its secondary layer.

The Facts

The accident at the heart of the case was tragic. Mark Braswell (“Braswell”) was bicycling when he hit the back of a parked landscaping truck owned by the Brickman Group (“Brickman”), and suffered fatal head injuries. Braswell was survived by his mother, his wife of twenty years, and two children — a 13-year old son, and a 9-year old daughter. Braswell’s survivors sued Brickman, and the driver of the truck, Guillermo Bermea (“Bermea”).

Brickman’s insurance coverage included a $500,000 deductible/self-insured retention (which was included within primary coverage limits), a $2 million primary business auto policy issued by ACE, a $10 million excess policy issued by AGLIC (to immediately follow the ACE policy), and a $40 million excess policy issued by Great American Insurance Company (which was not a party in the case because its layer was not ultimately triggered).

After the lawsuit was filed, Brickman tendered the deductible, and ACE took over the defense of the case and settlement negotiations.

The claims adjuster asked the attorney assigned to defend the case (a lawyer named Leibowitz) to prepare a pre-trial report to help the carriers evaluate the claim and prepare for mediation. The report concluded that defendants had a “very strong liability case” on the theory that Braswell was responsible for the accident because he was not paying attention when he was cycling. In support of this conclusion, the report cited compelling evidence that Braswell’s head was down (i.e., he was not looking ahead) when he struck the truck.

Still, there were also weaknesses in the defense case. First, the driver (Bermea) testified that although it was legal to have parked where he did, he believed it was dangerous to do so. Further, Bermea’s testimony concerning how long he had been parked was inconsistent, leading plaintiff’s counsel to argue that Bermea had stopped short, and left Braswell no time to react. Supporting plaintiff’s theory was the absence of cones around the truck at the time of the accident (which suggested that Bermea had just stopped; otherwise, he would have put out cones).

Separately, there was the sympathy factor. Braswell and his wife were firefighters, and Braswell had been cited for bravery for rescuing a double amputee from a burning building. Additionally, after Braswell’s death, his daughter began cutting herself, attempted to overdose, and spent a week in a mental hospital. She also frequently left notes for her father at the scene of the accident.

Finally, in defense counsel’s experience, the trial judge designated to hear the case “tended to favor the plaintiff side,” which meant that “close rulings” would likely go against the defendants. Defense counsel also held plaintiff’s counsel in high regard, and expected him to do an excellent job at trial.

Decision Tree Analysis

At this point, I’ll interject with a brief overview of decision tree analysis, which is a method of valuing lawsuits for purposes of deciding whether to settle or litigate. As we’ll see below, it appears defense counsel used some form of decision tree analysis to value Braswell’s case for purposes of mediation and settlement, but did not employ the technique correctly.

At bottom, assigning a value to a lawsuit represents decisionmaking under conditions of uncertainty. For example, there is uncertainty concerning the admissible evidence that will be available at trial to support a client’s positions, how a judge will rule on key motions (e.g., dismissal, summary judgment, evidentiary), how a jury will react to the evidence and the witnesses, and the measure of damages if liability is found.

The difficulty in valuing cases is exacerbated by the imprecise language that litigators often use to describe these uncertainties. For example, in the current case, defense counsel advised that the defendants had a “very strong” liability case, from which we can surmise that he believed it was “very likely” that a jury would either find that the defendants were not negligent, or that (even if defendants were negligent) Braswell caused the accident by biking with his head down.

Yet, defense counsel also conceded it was “possible” a jury might conclude that Braswell was not responsible for the accident because there was some evidence to suggest the truck driver stopped short. It was also acknowledged that sympathy for the plaintiffs “might possibly” affect the jury’s deliberations. Finally, defense counsel also recognized that the trial judge was pro-plaintiff, which meant there was a “good chance” he would rule in favor of the plaintiffs on any close calls.

It should be clear that phrases like “very strong,” “very likely,” “possible,” “might,” and “good chance” are highly imprecise, and will mean different things to different people. For example, does “very strong” mean a 90% likelihood of prevailing, or an 80% likelihood of prevailing? The lawyer who provides the assessment might be thinking “80%” while the client interprets “very strong” as “90%,” which means the client perceives the litigation as meaningfully less risky than the lawyer intends to convey.

To ensure that counsel and client are on the same page concerning the likely outcome of a case, a method is needed to convert imprecise qualitative assessments into unambiguous quantitative probabilities that can be mathematically combined to yield an expected value for the case to appropriately guide decisionmaking about settlement.

Decision tree analysis is such a method. The technique:

  • breaks down a case into key binary uncertainties (e.g., will a jury find the defendant was negligent or not, will the judge admit this evidence or not);
  • assigns probabilities to the alternative outcomes for each uncertainty (e.g., 75% chance of finding negligent, 25% chance of finding not negligent);
  • combines the multiple probabilities using a “tree” format (using the multiplication rule of statistics described below), and
  • applies those probabilities to potential damage awards in a weighted fashion to calculate an expected value for the case.

That’s certainly a mouthful. But let’s consider an example. In the case under discussion, we might say there is a 40% chance that a jury will find that the driver was not negligent when he stopped the truck on the road (how we get 40% is discussed further below; for now we’ll focus on the math).

Moreover, even if a jury were to find the driver was negligent (60% chance), and it proceeds to the question of comparative fault, there is a 50% chance that the jury will find that Braswell was more than 50% responsible for the accident (which would bar recovery under Texas state law).

Using decision tree analysis, we can combine those probabilities as follows: as noted, a 40% chance of “not negligent” necessarily means a 60% chance of “negligent.” But even if there’s a 60% chance a jury will find the defendants were negligent, there’s still a 50% chance in that scenario of completely barring recovery based on Braswell’s comparative fault, which means we need to multiply the 60% by 50% (under the multiplication rule of statistics, which states that the probability that A (“negligence”) and B (“more than 50% responsibility”) both occur is equal to the probability that A occurs times the conditional probability that B occurs given that A occurs). That calculation yields a 30% chance of barring recovery based on comparative fault (even if the defendants are found negligent).

The 40% we generated above, plus the 30%, equals 70%, which means there’s a 70% chance of a defense verdict either because a jury finds the defendants were not negligent, or (negligence notwithstanding) finds that Braswell was more than 50% at fault.

Finally, for the sake of simplicity, let’s assume that the remaining 30% balance of what might happen at trial translates into a full recovery for the plaintiff (in fact, that recovery would likely be adjusted downward depending how the jury apportions fault, assuming that Braswell was less than 50% responsible for the accident, but more than 0% responsible).

With our percentages in hand, let’s look at damages. If the potential jury verdict (assuming liability) is in the range of $6-$8 million (both economic and non-economic damages), we would multiply that range by the 30% probability of a plaintiff verdict, which means an expected value for the case of $1.8 million to $2.4 million (i.e., 30% x $6-$8 million).

Here’s a very simple “decision tree” graphic  illustrating the calculations above:

Just to be clear, this analysis is not a prediction about how the case will come out if tried. Instead, it is a weighted probability assessment stating that if this case was tried ten times (or a hundred times) before a jury in this particular county before this particular judge, 7 out of 10 of those trials should result in a defense verdict.

It’s like flipping a coin. I know the odds of heads is 50/50, but I certainly cannot predict whether an individual flip will be heads or tails. But if I flip the coin one hundred times, it’s likely the coin will land on heads around 50 times, and on tails around 50 times.

Avoiding Garbage-In, Garbage Out

A crucial question, of course, is how one arrives at the percentages used above. We can’t just pick percentages out of thin air because “garbage in” results in “garbage out,” and the expected value we calculated would be meaningless for purposes of decisionmaking.

Instead, as decision tree analysis pioneer Marc Victor explains*, we need to develop a list of reasons why each of the alternative outcomes for each binary uncertainty is more or less likely. For example, the reasons why a jury might find the defendants were “not negligent” in the case at hand is that the truck was parked legally, and the weight of the evidence suggests that Bermea did not stop short.

On the other hand, the jury might find that defendants were negligent because Bermea conceded it was still “dangerous” to park where he did (even if legal), and there is some evidence that Bermea might have stopped short. And perhaps Bermea won’t come across as a credible witness, given his inability to remember key details about the accident. And of course, there’s the sympathy factor, and the skilled plaintiff’s counsel.

Weighing all of those considerations, a reasonable trial attorney might conclude there is a 40% chance of a jury finding not negligent, and a 60% chance of a jury finding negligence. The trial attorney would then similarly develop a list of reasons for why (or why not) a jury would find that Braswell was more than 50% responsible for the accident.

Updating a Decision Tree Analysis as a Case Progresses

 Victor stresses that the probabilities used in a decision tree are not static numbers, but must constantly be updated as a case progresses, and more information concerning the likely resolution of various uncertainties becomes available.

For example, defendants can try the case before a mock jury, and solicit their feedback on the evidence and testimony they found most compelling. As discussed below, defense counsel used a mock jury for that purpose in this case, but seemingly failed to update the percentages in their decision tree analysis to account for the feedback received from the mock jurors.

Back to the Case: Defense Counsel’s Decision Tree Analysis

With that background, it’s clear from the court’s decision that ACE’s defense counsel performed a decision tree analysis of some sort when evaluating the case:

Leibowitz prepared a Case Summary and Evaluation on August 10, 2016 (the “August Memo”). In the August Memo, Leibowitz estimated the range of a potential verdict to be between $6,000,000 and $8,000,000. He said that the plaintiffs’ expert put economic damages between $2.85 million and $3.365 million, which was “certainly reasonable.” He stated:

Based upon all the foregoing, we believe this is a defensible case on behalf of Brickman. We believe that it is likely that the jury will find that Defendants were not negligent. However, even if a jury were to find that Defendants were negligent, we believe that a jury would find a significant amount of contributory negligence on the part of Mr. Braswell with a very good chance of Plaintiff’s negligence exceeding 50%. As you know, if a jury were to determine that Plaintiff’s contributory negligence exceeded 50% he would be barred from any recovery. At this time, we believe that if we tried this case 10 times that we would get a finding of no negligence on behalf of Defendants or a verdict where Plaintiff’s negligence exceeds 50%, 7 out of 10 times. If Plaintiff’s negligence does not exceed 50%, we believe that in most cases a jury would find Plaintiff’s negligence to be in the range of 30-50%.

Leibowitz concluded that the case had a settlement value in the range of $1.25-$2 million. (emphasis added).

Three comments:

First, Liebowitz assigned a settlement value in the range of $1.25-$2 million. However, as noted above, if Liebowitz assessed a 70% chance of a defense verdict, and total economic and non-economic damages were $6-$8 million, then the expected value of the case would be $1.8-$2.4 million (substantially higher than the range identified by Liebowitz).

The source of this discrepancy is not clear. It appears that Liebowitz might have been proposing a “low ball” settlement range below the expected value of the case. Alternatively, he may simply have made an ad hoc adjustment to the expected value based on intuition without disclosing what factors drove that modification.

In either case, he should have better explained his thinking since the $1.25-$2 million range specified became the de facto standard by which ACE subsequently evaluated all of plaintiff’s settlement offers even though (i) this range was well below the expected value of the case according to Liebowitz’s own initial assessment of the likelihood of a defense verdict (70%), and magnitude of the potential damages ($6-$8 million), and (ii) subsequent developments in the case dramatically increased the likelihood of a plaintiff verdict and rendered this settlement range even more out of line with expected value.

Second, readers will notice that Liebowitz used qualitative assessments such as “likely” and “very good chance.” But he concludes with a quantitative assessment — a 70% chance of a defense verdict. So the question is, how did he get from “A” (qualitative assessments”) to “B” (quantitative prediction)? The court’s recital of the facts indicates that defense counsel was aware of the weaknesses in the case. But it’s not clear how those weaknesses were incorporated into the analysis. Again, unhelpful because (as will be shown) as the case progressed and new information became available, there was no mechanism to easily update the case’s expected value by adjusting relevant percentages.

Third, readers of our analysis above will note that we tried to arrange the probabilities to reach the same 70% probability of a defense verdict that Liebowitz calculated. But to do that, we had to assign only a 40% chance of a jury finding not negligent. We could have instead assigned a 60% chance of “not negligent,” and then a 25% chance of finding that Braswell was more than 50% responsible for the accident (40% (negligent) x 25% (more than 50% responsible) = 10%).

But Liebowitz indicated it was “likely” a jury would find that the defendants were “not negligent” and there was a “very good chance of Plaintiff’s negligence exceeding 50%.” In this author’s mind “likely” and “very good chance” both mean meaningfully greater than 50%. But there is no way mathematically to arrive at a total probability of 70% for a defense verdict where the likelihood of “no negligence” and “comparative fault exceeding 50%” are both greater than 50%. So, again, it’s not clear how Liebowitz arrived at his 70% quantitative prediction based on the qualitative assessments that he shared.

ACE’s Internal Valuation

More mystifying was how ACE’s internal team valued the case. Here is how the Court described their analysis:

Adamo and Albin calculated the settlement value at $600,000. Adamo testified that he reached this number by multiplying ACE’s policy limit of $2 million by 30%, Leibowitz’s estimate of the likelihood of a plaintiff verdict. Albin agreed with this analysis. (Tr. 60-61; Albin Depo). Smith, on the other hand, agreed that the settlement value reached by ACE was $600,000, but disagreed that they reached that value by calculating 30% of the $2 million policy limit. He said that they reached the value by “looking at ground up what the value of the case was.” (Tr. 576-577; Smith Live). However, the ACE claim log clearly states that ACE believes that “we have an approximately 70% chance of a defense verdict . . . Therefore, we are reserving at 30% of our limits and will attempt to settle with plaintiff up to that amount.

The obvious error here is that ACE’s internal team multiplied their policy limit by 30%. But the policy limit represents ACE’s exposure, not the potential damages. As noted, under a decision tree analysis, the correct approach would have been to multiply the potential damages by the probability of a plaintiff’s verdict to determine the expected value of the case.

The Mock Juries 

To obtain better certainty about the strengths and weaknesses of the defendant’s case, ACE convened two mock juries. In one mock jury, four of eight mock jurors assigned 10% liability to Braswell and 90% to Bermea, three assigned 90% liability to Braswell and 10% to Bermea, and one assigned 60% liability to Braswell and 40% to Bermea. The other mock jury rendered a defense verdict assigning 100% of responsibility to Braswell.

What led to these conclusions? The mock jurors heard testimony that Brickman’s driver did not break any laws. Yet, even pro-defendant mock jurors felt that the truck was dangerously parked.

Additionally, several jurors believed the plaintiff’s theory that the truck must have stopped short or cut Braswell off so that he could not react in time. As a result, the mock juror report advised that “[i]t will be important to make clear to jurors that there is no evidence that Mr. Bermea cut off Mr. Braswell or stopped suddenly in front of him.”

Finally, and perhaps most critically, the mock jury did not hear evidence about Braswell’s daughter cutting herself, her attempted suicide, her hospitalization in the aftermath of her father’s death, or the fact that she left notes for her father near the accident site. Apparently, it was assumed that the judge would not permit such evidence to be admitted (even though he was pro-plaintiff).

A few days after the mock jury, a member of ACE’s internal team sent an email to Leibowitz and others stating that, “I feel for settlement purposes that this is more of a six figure case than a seven figure case.”

It is difficult to understand how anyone could reach such a conclusion. The mock juries had indicated that plaintiff’s “stopped short” theory was persuasive, and therefore the probability of a jury finding “no negligence” clearly went down. That would reduce the likelihood of a defense verdict below 70%, and increase the expected value of the case. Yet, no one appears to have questioned the assessment in the ACE email. The likely reason is that the feedback from the mock juries was not used to update the probabilities in the original decision tree analysis.

First Mediation 

A mediation was held, but was unsuccessful because the offers were too far apart with plaintiff initially demanding $7.5 million (which unrealistically assumed a virtually 100% likelihood of plaintiff prevailing), and defendants initially offering only $100,000 (which unrealistically assumed a virtually 100% likelihood of defendant prevailing). We have previously discussed how bracketing and other impasse-breaking techniques can be deployed to to start reducing such a large gap, but apparently no such techniques were employed.

However, a different mediator approached by AGLIC later shared that plaintiff’s counsel was “a formidable opponent” and “has a huge presence in Houston, very well regarded personally and professionally.” The mediator also confirmed that the trial judge “tended to lean towards the plaintiff” on discretionary rulings.

A local Houston attorney subsequently expressed the view to AGLIC that “a Harris County jury would never decide that Braswell was 51% or more responsible because of the sympathy factor.”

AGLIC shared this information with ACE, but Leibowitz later testified that he had already incorporated these factors into his original analysis.

Second Mediation

 A second mediation was also unsuccessful. The mediator advised AGLIC, however, that he thought the case was worth $2 million. Whereupon, AGLIC urged ACE to settle within ACE’s policy limits; ACE refused.

First Settlement Offer

Immediately after the second mediation, plaintiff’s counsel sent defense counsel a Stowers demand for the $2 million limit of the ACE policy. Based on the decision tree analysis set forth above, that was an extremely reasonable settlement offer — well within the expected value of the case ($1.8-$2.4 million) even before taking into account the additional information subsequently provided by the mock juries and discussions with the mediators and local Houston attorney.

It was thus not surprising that AGLIC’s representative demanded that ACE accept:

As you know, our mutual insured’s defense counsel evaluation of the Braswell claims includes a risk of a verdict in excess of the Chubb 2M primary limit. Defense counsel also evaluates the Braswell case for settlement up to your 2M limit. In other words, defense counsel does not opine that a defense verdict is a certainty or that an adverse verdict will not exceed Chubb’s primary limit. There is no such certainty given a variety of factors including, but not limited to: the well-regarded reputation and success of Mr. Mithoff; the expectation that Judge Sandill will favor plaintiffs, as he usually does, including on evidentiary issues; the $2.8M-$3.365M lost earning claim (deemed ‘certainly reasonable’ by defense counsel); respective consortium claims of the surviving spouse and two children; a conscious pain and suffering claim; sympathy factor for the surviving family; and the remote chance of a 51% fault allocation on the late Mr. Braswell by a Harris County jury. Therefore, Zurich agrees with defense counsel that a gross verdict could be as high as $8 million (if not more), which supports that the $2 million Stowers demand is reasonable.

ACE, however, refused to accept, and eventually countered with a “high/low offer” of $250,000/$2 million, meaning if the jury ruled against plaintiff, they would get no less than $250,000, while if the jury ruled against the defendants, they would pay no more than $2 million. Plaintiff declined.

The Trial Gets Underway

Meanwhile, the trial got underway. The judge promptly ruled against the defense on several key evidentiary issues. He:

  • excluded evidence that the Brickman truck was parked legally (which had been a strong defense argument that had been compelling to the mock jury);
  • permitted the introduction of evidence about the psychological state and self-destructive actions of Braswell’s daughter following her father’s death; and
  • permitted testimony that was seemingly hearsay that the truck had stopped short.

Subsequently, the judge permitted a verdict form to list “soft” damages such as mental anguish separately for each of Braswell’s survivors. At closing argument, plaintiff’s counsel requested $10 million in general damages.

All of these rulings sharply increased the likelihood of a plaintiff verdict on liability, and increased the potential magnitude of the damages. Nevertheless, as the jury began deliberating, plaintiff’s counsel made a “high/low offer” to defense counsel of $1.9 million/$2 million.

After ACE rejected that proposal, plaintiff’s counsel renewed his offer to settle for the ACE policy limits of $2 million. ACE rejected this offer as well, and countered with a high/low of $650,000/$2 million, which plaintiff declined.

ACE’s representatives later testified that they continued to believe there was only a 30% probability of a plaintiff verdict — an obviously stale and outdated assessment in light of all the subsequent developments in the case (i.e., new information) and at trial (i.e., the judge’s evidentiary rulings in favor of plaintiff).

The Verdict

The jury returned a verdict of $39,960,000.00, apportioning 68% responsibility to the defendants and 32% to Braswell. The court entered judgment for $27,712,598.90 against the defendants after adjusting for comparative fault.

AGLIC ultimately took control of the settlement negotiations post-verdict after ACE tendered $2 million. AGLIC settled for $9,750,000, of which AGLIC paid $7,750,000. AGLIC then sued ACE for damages arising from ACE’s breach of its Stowers duty to accept plaintiff’s earlier reasonable settlement offers within the policy limits.

After a bench trial, the Court ruled in favor AGLIC, and awarded it a $7.2 million judgment against ACE. Clearly the right result.

Takeaway

The takeaway from this fascinating case is plain. Decision tree analysis is an incredibly valuable tool that, when properly used, can help a party accurately gauge the reasonableness of settlement offers, and decide whether to accept or reject them.  But when used incorrectly (such as by failing to update the probabilities when new information becomes available), it can lead a party to make unreasonable choices.

*Source: Marc B. Victor, Litigation Risk Analysis & ADR (PDF)

[View source.]

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    • Right to Correct Information: You may ask that we make corrections to any information we hold, if you believe such correction to be necessary.
    • Right to Restrict Our Processing or Erasure of Information: You also have the right in certain circumstances to ask us to restrict processing of your personal information or to erase your personal information. Where you have consented to our use of your personal information, you can withdraw your consent at any time.

You can make a request to exercise any of these rights by emailing us at privacy@jdsupra.com or by writing to us at:

Privacy Officer
JD Supra, LLC
10 Liberty Ship Way, Suite 300
Sausalito, California 94965

You can also manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard.

We will make all practical efforts to respect your wishes. There may be times, however, where we are not able to fulfill your request, for example, if applicable law prohibits our compliance. Please note that JD Supra does not use "automatic decision making" or "profiling" as those terms are defined in the GDPR.

  • Timeframe for retaining your personal information: We will retain your personal information in a form that identifies you only for as long as it serves the purpose(s) for which it was initially collected as stated in this Privacy Policy, or subsequently authorized. We may continue processing your personal information for longer periods, but only for the time and to the extent such processing reasonably serves the purposes of archiving in the public interest, journalism, literature and art, scientific or historical research and statistical analysis, and subject to the protection of this Privacy Policy. For example, if you are an author, your personal information may continue to be published in connection with your article indefinitely. When we have no ongoing legitimate business need to process your personal information, we will either delete or anonymize it, or, if this is not possible (for example, because your personal information has been stored in backup archives), then we will securely store your personal information and isolate it from any further processing until deletion is possible.
  • Onward Transfer to Third Parties: As noted in the "How We Share Your Data" Section above, JD Supra may share your information with third parties. When JD Supra discloses your personal information to third parties, we have ensured that such third parties have either certified under the EU-U.S. or Swiss Privacy Shield Framework and will process all personal data received from EU member states/Switzerland in reliance on the applicable Privacy Shield Framework or that they have been subjected to strict contractual provisions in their contract with us to guarantee an adequate level of data protection for your data.

California Privacy Rights

Pursuant to Section 1798.83 of the California Civil Code, our customers who are California residents have the right to request certain information regarding our disclosure of personal information to third parties for their direct marketing purposes.

You can make a request for this information by emailing us at privacy@jdsupra.com or by writing to us at:

Privacy Officer
JD Supra, LLC
10 Liberty Ship Way, Suite 300
Sausalito, California 94965

Some browsers have incorporated a Do Not Track (DNT) feature. These features, when turned on, send a signal that you prefer that the website you are visiting not collect and use data regarding your online searching and browsing activities. As there is not yet a common understanding on how to interpret the DNT signal, we currently do not respond to DNT signals on our site.

Access/Correct/Update/Delete Personal Information

For non-EU/Swiss residents, if you would like to know what personal information we have about you, you can send an e-mail to privacy@jdsupra.com. We will be in contact with you (by mail or otherwise) to verify your identity and provide you the information you request. We will respond within 30 days to your request for access to your personal information. In some cases, we may not be able to remove your personal information, in which case we will let you know if we are unable to do so and why. If you would like to correct or update your personal information, you can manage your profile and subscriptions through our Privacy Center under the "My Account" dashboard. If you would like to delete your account or remove your information from our Website and Services, send an e-mail to privacy@jdsupra.com.

Changes in Our Privacy Policy

We reserve the right to change this Privacy Policy at any time. Please refer to the date at the top of this page to determine when this Policy was last revised. Any changes to our Privacy Policy will become effective upon posting of the revised policy on the Website. By continuing to use our Website and Services following such changes, you will be deemed to have agreed to such changes.

Contacting JD Supra

If you have any questions about this Privacy Policy, the practices of this site, your dealings with our Website or Services, or if you would like to change any of the information you have provided to us, please contact us at: privacy@jdsupra.com.

JD Supra Cookie Guide

As with many websites, JD Supra's website (located at www.jdsupra.com) (our "Website") and our services (such as our email article digests)(our "Services") use a standard technology called a "cookie" and other similar technologies (such as, pixels and web beacons), which are small data files that are transferred to your computer when you use our Website and Services. These technologies automatically identify your browser whenever you interact with our Website and Services.

How We Use Cookies and Other Tracking Technologies

We use cookies and other tracking technologies to:

  1. Improve the user experience on our Website and Services;
  2. Store the authorization token that users receive when they login to the private areas of our Website. This token is specific to a user's login session and requires a valid username and password to obtain. It is required to access the user's profile information, subscriptions, and analytics;
  3. Track anonymous site usage; and
  4. Permit connectivity with social media networks to permit content sharing.

There are different types of cookies and other technologies used our Website, notably:

  • "Session cookies" - These cookies only last as long as your online session, and disappear from your computer or device when you close your browser (like Internet Explorer, Google Chrome or Safari).
  • "Persistent cookies" - These cookies stay on your computer or device after your browser has been closed and last for a time specified in the cookie. We use persistent cookies when we need to know who you are for more than one browsing session. For example, we use them to remember your preferences for the next time you visit.
  • "Web Beacons/Pixels" - Some of our web pages and emails may also contain small electronic images known as web beacons, clear GIFs or single-pixel GIFs. These images are placed on a web page or email and typically work in conjunction with cookies to collect data. We use these images to identify our users and user behavior, such as counting the number of users who have visited a web page or acted upon one of our email digests.

JD Supra Cookies. We place our own cookies on your computer to track certain information about you while you are using our Website and Services. For example, we place a session cookie on your computer each time you visit our Website. We use these cookies to allow you to log-in to your subscriber account. In addition, through these cookies we are able to collect information about how you use the Website, including what browser you may be using, your IP address, and the URL address you came from upon visiting our Website and the URL you next visit (even if those URLs are not on our Website). We also utilize email web beacons to monitor whether our emails are being delivered and read. We also use these tools to help deliver reader analytics to our authors to give them insight into their readership and help them to improve their content, so that it is most useful for our users.

Analytics/Performance Cookies. JD Supra also uses the following analytic tools to help us analyze the performance of our Website and Services as well as how visitors use our Website and Services:

  • HubSpot - For more information about HubSpot cookies, please visit legal.hubspot.com/privacy-policy.
  • New Relic - For more information on New Relic cookies, please visit www.newrelic.com/privacy.
  • Google Analytics - For more information on Google Analytics cookies, visit www.google.com/policies. To opt-out of being tracked by Google Analytics across all websites visit http://tools.google.com/dlpage/gaoptout. This will allow you to download and install a Google Analytics cookie-free web browser.

Facebook, Twitter and other Social Network Cookies. Our content pages allow you to share content appearing on our Website and Services to your social media accounts through the "Like," "Tweet," or similar buttons displayed on such pages. To accomplish this Service, we embed code that such third party social networks provide and that we do not control. These buttons know that you are logged in to your social network account and therefore such social networks could also know that you are viewing the JD Supra Website.

Controlling and Deleting Cookies

If you would like to change how a browser uses cookies, including blocking or deleting cookies from the JD Supra Website and Services you can do so by changing the settings in your web browser. To control cookies, most browsers allow you to either accept or reject all cookies, only accept certain types of cookies, or prompt you every time a site wishes to save a cookie. It's also easy to delete cookies that are already saved on your device by a browser.

The processes for controlling and deleting cookies vary depending on which browser you use. To find out how to do so with a particular browser, you can use your browser's "Help" function or alternatively, you can visit http://www.aboutcookies.org which explains, step-by-step, how to control and delete cookies in most browsers.

Updates to This Policy

We may update this cookie policy and our Privacy Policy from time-to-time, particularly as technology changes. You can always check this page for the latest version. We may also notify you of changes to our privacy policy by email.

Contacting JD Supra

If you have any questions about how we use cookies and other tracking technologies, please contact us at: privacy@jdsupra.com.

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This website uses cookies to improve user experience, track anonymous site usage, store authorization tokens and permit sharing on social media networks. By continuing to browse this website you accept the use of cookies. Click here to read more about how we use cookies.