Using data to become a strategic forecaster
An interview with Alexander Mansfield, Financial Director at Jackson Lees.
Alexander Mansfield’s key priority as Financial Director of Jackson Lees is to ensure his team supports the 15 legal teams across the business. To do this, he and his team rely on data from different departments, to reliably forecast how the business is faring from a financial perspective.
To help with this, Jackson Lees recently brought in a data manager, to help collate all of the data the organisation has.
“It’s one thing to have all of this data, but it’s very difficult to analyse and use it productively,” says Alexander.
The first step in making sure the data can be used to help the finance team, is to ensure it is accurate and fit for purpose.
“If you don’t have confidence, a completeness of the population or the accuracy of the information within, you cannot reliably draw any inferences from the data,” he explains, emphasizing that the integrity of the underlying data needs to be rigorously tested.
As his team rely on information that others have entered onto the system – there is a chance for human error which would render the data useless; this can have a big impact because just a small proportion of incorrect data inputted into the system can make the finance team have an incorrect idea of where the business is, and prevent it from forecasting accurately.
One of the pieces of work that the organisation is working on is looking at the different channels used by a client, before opting for the firm to take on their case.
“That wholly relies on the people who are opening the cases picking the right channel – which is initially a training issue, and then we utilise a set of mandatory fields to ensure that the right data is captured at the right time, avoiding blanks,” Alexander explains.
The issue can be that by forcing people to not put in any blanks they may pick an option with the least path of resistance, which may not be the right decision.
However, if the data is inputted correctly, then the firm is able to analyse what are the average case lengths and what are the average fees are. It can then accurately forecast when a case will generate fees in the future, and also get a better idea of how long it will be until a client pays, and when will those fees arrive in cash, essentially enabling his team to forecast billing and future cash collection. If this works, his team will apply the averages to the historical data and compare the model to what actually happened.
“This will enable us to fine tune the model so that we have confidence that the assumptions in the model make sense compared to the actual data,” Alexander says.
An added benefit will be that the firm would be better able to plan in regards to capacity.
“If we know which cases have been opened and how much time is normally spend on average on these, you’re able to judge whether the firm has the right amount of people and the necessary time to be able to do the work,” he says.
This means if the firm has a huge number of cases it can quickly redeploy people from other departments, or scale up the workforce in order to cope – and equally it can scale down in quieter periods.
In addition, the organisation can use the insight it gleans from this data to set targets for departments.
“By knowing the average cases and fees we can they say to our marketing team that we need them to generate x amount of leads because they know that the leads will generate cases which generate fees – it’s a holistic cycle,” he says.
How automation and AI could play a part
While Alexander doesn’t believe that automation or AI will play a part in the firm in the near future, he believes a big step would be when the firm is able to keep track of milestones within each case.
“If you can split cases into four sections then you can keep track of when they’ve been achieved. You can then do reporting by exception so that the manager or supervisor can get automatically flagged for their attention when a case hasn’t hit a key milestone in time,” he says.
“You don’t really want to know about the 99% of cases that are running smoothly, you want to use the data and expectations to track the 1% that need your attention,” he adds.
By picking up the outliers, problems can be mitigated at an earlier stage, before they can start impacting on billing, forecasting and cashflow.
“It all depends on analysing the historic data, forecasting forwards and having the systems in place to automate these points,” he says.
There’s likely to be a lot of work at Jackson Lees with regard to data in the coming years, and in order to help Alexander become a strategic forecaster for the business, he will have to work closely with the data manager and other departments to get this right.
- Accuracy of underlying data is critical
The data, including fees, dates, duration and information about which channel the client used in order to opt for working with Jackson Lees, all has to be accurate in order to make inferences. Training staff, and going back to test the inferences are key.
- Forecasting can help with capacity and targets, as well as financials
If the data is right, it can help the firm to forecast cashflow, billing and other financials. It also provides the firm with insight into whether it needs to scale up or scale down the workforce depending on the workload. The finance team can also tell the marketing department how many leads it requires.
- Flagging outliers automatically can help the firm to keep on track
Splitting cases into four different milestones and enabling the technology to automatically flag if a case is running behind, can help managers to ensure cases are always on time and mitigate any issues at an earlier stage before they impact billing, forecasting and cashflow.
To read the full report ‘Digital transformation and strategic forecasting in a legal finance function: four FDs give us their take’ commissioned in partnership with Katchr please click below.View Article