As discussed in our previous articles, we identified where opportunities existed throughout our review process which can directly impact our bottom line and contain our total cost of review.
To recap, we first identified the two hidden cost drivers of review, throughput and rework, that can secretly cause review budgets to escalate. Secondly, we identified how an effective project ramp-up and successful review management led to cost reductions of up to approximately 30%. Lastly, we identified how proper reviewer optimization and coding consistencies can increase the number of documents reviewed by 30%, thus leading to further cost savings.
Individually, we can see that each one of our choices throughout the review led to notable savings over the life of the case. In today’s exercise, we will look back at all the opportunities we previously identified and the choices we made. This leads us to our final question: What happens when we put it all together?
Establishing the Numbers
Let’s recap the assumptions we will continue to work from. In this scenario, we have 100,000 documents that need to be reviewed, with 15,000 documents needing to be re-reviewed (reworked) at a review throughput rate of 40 docs/hour, at a $42/hour rate. With these rework and throughput rates accounted for, this results in 115,000 effective documents reviewed.
Putting It All Together
So, what happens when you put it all together? If we optimize our review team and effectively manage their work throughout the project, utilizing technology such as batching strategies and coding consistencies, what’s the net impact of that in economic terms?
The “Perfect” Review Setup
Let’s start with our review team. In the example above, we see that having an efficient review team that has been properly trained results in the number of documents reviewed increasing by 6 additional documents/hour. Just by being strategic on who you pick for your review team will lower the number of documents that need to be reworked by 12%. This shows how small tweaks to your review project from the beginning can lower the overall cost of the budget by almost 20%!
However, reducing review costs doesn’t stop at the beginning of the project. To consistently reduce review costs, monitoring your review team throughout the process becomes increasingly important. Using reports and statistics to actively monitor your team will show you how many documents reviewers are coding per hour, as well as the overall pace of the project.
Let’s say, for example, you have six reviewers on a project and the standard norm for reviewers is roughly 40-50 documents an hour. Like the example below, you may have one rockstar reviewer that is reviewing 90 docs/hour, have some reviewers who are reviewing in the 40-45 range, one reviewer that is a little bit below the bar, and two reviewers that are way below the average.
In managing this team, you may choose to coach reviewer 1, cut reviewers number 2 and 3 since they are below average, and flag reviewer 6 to peek at later to make sure that their coding is consistent.
If you were to make this decision to cut reviewers 2 and 3 in this scenario, although the weeks to complete the review may increase by 1.3 weeks, the throughput of the reviewers is also increasing from 40 documents/hour up to 53 documents/hour.
Even though this sample review project was extended for an additional week, it still allowed for a decrease in costs. By reducing the team, it increased documents reviewed to 53 from 40, aka 13 more documents an hour, and lowered rework by 6% which is a 30% reduction on total cost.
Fine Tuning the Review Process
So, you have your review team, but you want to see what you can do to get the review moving quicker. Tech strategies like persistent highlighting – not just of privilege documents that hit on a privilege term, but also those that hit on one of the agreed upon terms, can help the review team identify the terms quickly. Rather than just batching out documents for a review based on hits, it’s best to think about how to batch out more strategically to reduce costs.
As you can see in this example, by doing something as simple as batching strategically, you can increase the number of documents reviewed by 30%, lower the quality control by 3% and in this example, lower the cost of review by $30k.
The last cost driver in review deals with coding consistency. This refers to the ways we can keep coding consistent and thus reduce how many documents that may need to be re-reviewed because of coding inconsistencies.
By leveraging techniques such as streamlining the coding panel, utilizing dashboards, and engaging active learning, you can capture potential issues and inconsistencies in real time before they spiral into a huge problem. In this example, although the number of documents reviewed did not change, the percentage of rework dropped by almost 10%. This also lowered the total cost of review by almost $10K.
The End Is in Sight
Now it’s time to reap the fruits of our labor. So, let’s move into our review budget and look at how these strategic, incremental changes to your project reduce risks of inconsistencies that can affect the quality and costs of your review.
Throughout our project, we saw several reductions through these techniques. However, each of these are separate tactics, and if we were to subtract all the percentage reductions we found in our review budget, we would end up with diminishing returns. What we do see though, are substantial savings. Looking at all of these techniques together, if you are looking at 100,000 documents to be reviewed using a throughput of 60 docs/hour, and your rework goes from 15% to 5%, then you’re now only reviewing 105,000 effective documents, and your budget substantially decreases from $120,000 to $73,000.
Now, we live in a world where we have to find people who can do this work and who are skilled at it. Paying these reviewers $42/hour may be ambitious, so we must determine what number we would be willing to pay our contract reviewers under the strategic workflow that gets us to the same price so that we can reach a middle ground.
Running the numbers, we found that this number would be $69/hour. So, if we’re really strategic and pay a reviewer $69/hour, we will come out to about the same place as if we were to be less strategic and going for cheaper reviewers. Obviously, paying $69/hour isn’t necessarily always going to be the case, and the right dollar amount is usually somewhere in the middle of $42/hour and $69/hour.
This is also a good framework to use when needing to explain why choosing a reviewer with more expertise would actually benefit the client and the team.
Predictable, Certain, and Stress Free
This scenario goes against the belief that if we want to reduce the budget, we’re going to have to give up on quality. The truth is that if you run a good process, you can win on quality and cost at the same time.
We’ve seen that these same techniques are going to result in a reduced error rate and increased quality, so less of your documents will be miscoded. There is a cost to poor quality, and as we continue to use these techniques, quality is going to improve. The attorneys, the client, and everyone involved will have much less stress about the process because we will know what our assumptions are, what our milestones are that we need to hit to make our budget and deadline. We’ll know in week one, week two, and week three of the review whether we’re on target or falling behind, and if we’re falling behind, and can get ahead of it by communicating what has changed with the client from our expectations, or communicating with opposing counsel on what has changed. This allows us to not be subject to the mercy of document review and to start to gain control over the process resulting in a much better overall experience.
If you have an upcoming review project and are interested in learning more about Acorn’s review services, feel free to contact us at Info@AcornLS.com.
