A recent survey by Big Hand found that 96 percent of firms reward partners for profit, but only 41 percent share profit data. Those sharing data often limit the metrics to standard rates, realization, collections and margins, focusing partner attention on revenue maximization at the potential expense of client value. Further, limited profit data also introduces the risk of poor decisions with unintended consequences such as sub-optimizing partner profit.
The following scenarios illustrate how this may happen:
Consider two scenarios for the same client matter, each scenario providing the same service quality and result. In scenario 2, the partner worked 100 fewer hours, and the associate worked 150 more hours to compensate for a learning curve. Scenario 2 also assumed a lower realization, with the client asking for a reduced negotiated rate because of fewer partner hours.
Scenario 1 appears to outperform scenario 2 because it checks all the conventional boxes: highest rates, realization, net income and margin. However, if your strategy is to maximize profit per equity partner (PPEP), scenario 2 significantly outperformed scenario 1.
| Associate | Partner | Matter Summary | |
|---|---|---|---|
| Hours | 500 | 250 | 750 |
| Standard Rate | $515 | $725 | $585 |
| Realization | 80% | 94% | 86% |
| Collected Rate | $412 | $682 | $502 |
| Collections | $206,000 | $170,375 | $376,375 |
| Less: expenses | (125,000) | (43,750) | (168,750) |
| Net Income | $81,000 | $126,625 | $207,625 |
| Margin | 55.2% |
| Associate | Partner | Matter Summary | |
|---|---|---|---|
| Hours | 650 | 150 | 800 |
| Standard Rate | $515 | $725 | $554 |
| Realization | 75% | 94% | 80% |
| Collected Rate | $386.25 | $681.50 | $441.61 |
| Collections | $251,063 | $102,225 | $353,288 |
| Less: expenses | (162,500) | (26,250) | (188,750) |
| Net Income | $88,563 | $75,975 | $164,538 |
| Margin | 46.6% |
While scenario 2 resulted in a lower net Income, it required fewer partner hours. The return per partner hour increased from $831 to $1,097, calculated by dividing net income by partner hours. Return per partner hour is like earnings per share (EPS) used when evaluating the relative shareholder profitability of public companies. While scenario 1 earned a higher net income, scenario 2 created the highest return for partner sweat equity due to higher leverage.
| Scenario 1 | Scenario 2 | |
| Hours | 750 | 800 |
| Standard Rate | $585 | $554 |
| Realization | 86% | 80% |
| Collected Rate | $502 | $442 |
| Collections | $376,375 | $353,288 |
| Less: expenses | (168,750) | (188,750) |
| Net Income | $207,625 | $164,538 |
| Margin | 55.2% | 46.6% |
| Leverage | 2:1 | 4.3:1 |
| Return per Ptn Hr | $831 | $1097 |
| PPEP | $1,245,750 | $1,645,375 |
This simple analysis also demonstrates that if similar gains could be achieved on all the partner’s matters, two alternatives are created that were not previously evident: First, as the return per partner hour increases, the partner could work 365 fewer billable hours and earn the same PPEP under scenario 1 assumptions. Those hours could be directed to other client value activities, such as no-cost, on-site client visits, training and mentoring associates, marketing for new work, etc.
Alternatively, the partner could decide to hold billing rates in the coming year and still earn a considerably higher PPEP. Of course, an in-between option could be a combination of both. This example illustrates the power of benchmarking and conducting what-if scenarios.
If you are uncertain about the profit potential for leverage, consider that last year's Am Law results reported the top 100 firms' average leverage was 3.97 compared to 2.15 for the second 100. An artificial intelligence (AI) key influencer analysis of the top 100 firms found that leverage was the most significant profit metric driving comparative partner profit of the top 100 firms. Leverage is challenging to improve over the short term but must be on the radar for firms that want to obtain a competitive advantage to attract profitable clients and top talent.
Sharing limited data may encourage poor decisions. Too great a focus on billing rates and margins might encourage responsible partners to delegate to other partners rather than associates to maximize billing rates and lower per-hour costs. Expenses are lower when delegating to partners because partner compensation is not an expense but a distribution of net income, like the Am Law annual survey.
The following section suggests measures, benchmarks, segments and what-if capabilities that a comprehensive profit dashboard should include. The example dashboard provides new insights typically found only in difficult-to-create ad hoc Excel analysis.
Below is an example of a benchmarking dashboard that drills down to practice, responsible partner, client/matter, timekeeper, office, etc. A user can further segment data by clients that exceed or are below firm average PPEP and further segment clients by annual revenue.
If a benchmark result reveals a metric underperforming firm average, a user can move the slider to observe the impact improved performance would have. If improving a low-performing metric is challenging, the user could adjust other metrics to seek the desired outcome.
In this example, the finance group was highlighted in the practice table, which triggered the waterfall table to show how each metric contributed to the practice group's strong performance. The tables on the right show each metric's per hour and total dollar variances. The only negative variance for this practice was leverage at .72 compared to the firm average of .98. Achieving the firm average leverage would increase PPEP by $82K.
The regulatory practice earned the lowest PPEP of any practice, with negative variances in all but the leverage category.
| $317 | $278 |
| Firm Standard Rate | Standard Rate |
| 91.8% | 82.9% |
| Firm Realization | Realization Percentage |
| $291 | $230 |
| Firm Collective Rate | Collective Rate per Hour |
| $148 | $156 |
| Firm Expense Rate | Cost Rate per Hour |
| $142 | $74 |
| Firm Profit Hour | Profit per Hour |
| 0.98 | 1.02 |
| Firm Leverage | Leverage |
| $282 | $149 |
| Firm Return per Partner Hour | Return per Partner Hour |
| $619,930 | $314,222 |
| Firm PPEP | PPEP (annualized) |
The regulatory group served 265 clients. Filtering for high-profit clients identified 82 that collectively returned an average PPEP of $822K. On the surface, one might assume there is not much more the firm can do, but given the competition for clients, a high-level dashboard review should be conducted to determine if any of these clients are at risk of being raided.
Another sort identified 142 clients with average revenue of $4.2K, resulting in a negative PPEP. An initial reaction might be to let these clients go; however, the data tables also revealed that clients in this segment obtained services from 3.7 other practices besides regulatory. Drilling down to these clients' matters by practice showed that the PPEP for matters served by other practice groups was either near or above the firm average PPEP.
A firm with these results should work with the practice group leader and client-responsible partners to determine the next steps. For example, should the regulatory practice be considered a loss leader and accept low profits, or should positive steps be taken to convince clients that regulatory services are as valuable as other practices and billing rates should reflect this fact?
Lawyer productivity has been a drag on partner profit in the 12 years leading up to COVID. Average monthly billable hours between 2007 and 2019 fell from 134 to 122, representing a decrease of 144 hours annually. Productivity is again an issue in 2023 for many firms.
The PPEP Net Production Variance chart shows that if the average timekeeper met their billable hour target, the firm PPEP would increase by $7,099, represented by the vertical black line. The net variance is not a revenue projection but the out-of-pocket cost for over-capacity determined by calculating hourly cost differences between budgeted and actual billable hours. The number represents the net effect on PPEP at the end of the bars. Gray bars represent the aggregate impact of timekeepers below budget, and the blue bars are those that exceeded budgeted hours.
Optimizing lawyer capacity in a multi-office law firm has long been challenging, as partners prefer to work with lawyers in the same office. If there was one positive that came from COVID, it taught firms how to work virtually. With this experience and firm infrastructure created to support remote working, there is a real opportunity to improve productively if lawyers across offices can learn of each other's capabilities and available bandwidth.
The experience dashboard below can facilitate this effort.
The first choice the user makes is the practice area. After that, sorts are exhaustive, pulling data from finance, document management, and nonconfidential data in HR systems. In this example, sorts were by timekeeper titles (filtered for associates), timekeeper names (sorted by total hours of experience in the practice area) then client names (also sorted by hours).
The timekeeper cards on the right are drillable to a timekeeper bio, hyperlinks to blogs posted by the timekeeper, previous work product and projected time availability in the coming week, month and three-month periods. The green box across the top can include additional dynamic information that adjusts as sorts are refined.
Unintended consequences can result when firms do not provide profit information or limit data to rates, realization and margin. Incomplete profit metrics focused on revenue maximization could support client assertions that law firms’ financial interests are not aligned with theirs. Don’t wait until clients move to lower-cost providers when tools are available to improve partner profit and client value simultaneously.
Distributing profit information empowers firm leaders to become better business managers, focused on metrics that will ensure long-term success and giving them tools that will allow them to be more reactive to client-changing needs. The benchmarks in the data direct the user to explore variances for improvement opportunities. What-if abilities allow users to evaluate different scenarios from which to pick the best or to discuss options with clients to agree on win-win strategies.
Standard profit dashboards are being incorporated into many financial accounting and practice management tools for law firms. As Steve has demonstrated, dashboards focusing on standard rates, realization, collections and margins, while a step in the right direction, can lead to the equivalent of putting blinders on a horse, limiting its view and leading the firm to make myopic financial decisions. Artificial intelligence (AI) analysis of law firm profitability has revealed that leverage is an underutilized and underappreciated metric, one that is ignored by standard profit dashboards. Steve has proposed incorporating leverage and what-if scenario analysis into profitability dashboards, driving down the metrics beyond standard benchmark analysis and into deeper analysis that can result in an increase of profit per equity partner, and an increase in available time for equity partners to use in ways that further benefit the partner and the firm.
Furthermore, this new dashboard analysis can reveal clients who are at risk of moving to lower-cost competitors, seeking higher client value that are not being addressed in the traditional dashboard analysis which are focused on traditional law firm metrics. While dashboard metrics seek to make law firm leaders into better business managers, limiting your analysis to traditional dashboard metrics can result in unintended consequences to the firm in ways the firm may not even realize, such as failing to optimize lawyer capacity, failure to achieve optimal leverage, and ignoring client value with a resultant loss of alignment of firm and client objectives. Building a better dashboard can achieve all this by using what-if scenario analysis, AI analysis and drilling into metrics that reveal deeper information on law firm financials, allowing the firm to make much better decisions as compared to traditional dashboards.
