Cash flow is the foundation on which a successful business can be built. It keeps processes running, people employed and the lights on. But, all too often businesses take a laid-back approach to reporting and forecasting, using the same processes that have been in place since the business’ inception, which usually means the inputting of data into spreadsheets.
It’s an ‘if it ain’t broke’ approach that ignores the tangible benefits advancements in technology can bring to a business.
Using technology to improve these processes results in more accurate forecasts, which in turn will help businesses identify future shortfalls, or secure funding from banks or investors. This is especially important in the current global landscape of business instability caused by COVID-19.
Indeed, a cash flow forecast is only useful if it is accurate, and a business equipped with accurate forecasts will be able to run predictably, generate funding and make informed decisions on capital investment. Conversely, an inaccurate cash flow forecast can result in missed opportunities while the business had surplus cash in the bank.
At the very bleakest end of the spectrum, an inaccurate forecast could lead to overtrading and the end of the business. So, how do businesses improve cash flow forecasting, and how do they ensure it is accurate?
Typically, cash flow forecasts, future income and future expenses are completed in spreadsheets in monthly increments. The problem with this process is that the future is generated using data from the past, which means that by the time the forecast has been generated the data is no longer accurate.
Added to this is the fact that it takes time to assimilate data from many different sources in this way which causes further delays. To solve this, finance teams need to be able to see all the data from each department so that they can create an accurate and real-time data set.
This data set needs to be able to process a variety of data because companies generally process both product and service-based revenues or a combination of both. For example, a company might process rental and license revenues (product-based services) alongside maintenance revenues and consulting services (services-based revenues).
Any data set must be able to process the different structures of these models, including the different payment terms that each revenue stream can have.
Another challenge is volatility. Companies themselves (especially in the technology start-up sphere) are volatile, changing and upgrading their business models to stay ahead of the curve, so it makes sense that their revenues would also be volatile.
The best way to manage this is to ensure that all data has human oversight and is regularly reviewed, this will ensure that any projection is in line with the company’s strategy. Of course, volatility can come in other unexpected forms, the current pandemic has shown that while being prepared is vital, it cannot protect against every outcome.
The final, and greatest, challenge is revenue forecasting. Revenue generation crosses all departments: starting in marketing, it is then delivered by sales, realised by operations and, finally, measured by finance.
This creates multiple issues, the first of which is the task of collating the data, often in a complicated interlinking system of spreadsheets. The second issue is of disconnect between the departments, a lack of trust that means that instead of working together towards one goal, each department is focused on its own target.
As outlined above, there are two main problems to solve – the technology and the people. A business that implements new processes, the latest technology and the most recent gadgets may in fact see very minimal improvement if its employees are not on board. It is so important that any business initiating any kind of change ensures that its employees understand and are part of the process. Successfully changing the business culture will lead to successful adoption of new technologies and ultimately an improvement in processes.
In terms of technology, it really is time to do away with spreadsheets and embrace the new systems available. A key first step is the integration of the CRM with finance, this will give finance a direct insight into the active opportunities and therefore will generate more accurate cash flow forecasts.
This can be further enhanced by making use of AI to analyse historic data sets to predict future win rates and payments based on past activity. For example, projecting that those customers who were slow to pay in the past are likely to continue to be slow to pay in the future. Ultimately, the more integrated that all systems are (marketing, CRM, operations and finance) the more likely it is that the data will be of significant value to the business.
Overcoming these challenges will lead to accurate, real-time data sets which in turn will improve cash flow forecasting and ultimately have a positive impact on the business overall. The change management required may seem like quite an undertaking but there are tools to help along the way.
By Andy Campbell, Global Solution Evangelist at FinancialForce