Decision-making is as much an art as a science

CFOs and their teams have always worked hard to supply and analyse the data their companies need to be able to make solid, fact-based decisions. However, finance departments have long been constrained by basic forecasting techniques.

More often than not, the underlying data collection process is time consuming and error-prone, and the result often falls short of three main requirements:

  • Depth: even in a mythical perfectly integrated single-ERP organisation, forecasts are drawn from aggregate figures, obscuring the original information on which
  • Scope: supporting data is mainly historical and collected within the organisation (changes in sales patterns during previous periods, production costs, etc.) whereas exogenous factors (change in consumption behaviour, competitor positioning, legal changes, etc.) may impact the organisation to a much larger extent.
  • Quality: when little data is readily available, finance executives will tend to make assumptions based on their experience, gut-feeling or whatever may make the figures palatable to the audience they are intended to be presented to. Results may also be biased by a lack of data ‘freshness’.

Not only is the underlying data unsatisfactory, but its processing is suboptimal. All these approximate figures end up being copied and pasted from spreadsheet to spreadsheet and undergo many manual transformations. For instance, applying the Excel ‘trend’ function (linear regression) on historical figures helps create forecasts that seem ‘about’ right.

This approach is obviously misleading:

  • It’s a case of rubbish in, rubbish out. Regardless of the quality of the forecasting process, if the data is not detailed, sufficient, relevant and up-to-date, the result will be inadequate.
  • The future is not a mere repeat of the past. Making the assumption that “all other things will remain equal” when drawing a trend line is an over-simplification of the reality of the economic environment given that its main characteristic is volatility.
  • No lessons are learned from previous errors and therefore there’s no scope for improvement to the next forecasting cycle.

Data Science means that automated and accurate forecasting and financial modelling is today within every CFO’s reach.

Digitisation now gives access to more granular and diverse data – be that quantitative or qualitative, structured or unstructured – about present conditions or past situations and their outcomes.

Any data set that may help describe, explain, predict or even determine a company’s positioning can now be stored, updated and processed. This is the case whether that data has been sourced from an internal system or from the world outside (documents exchanged with business partners, information about the industry and macro-economic environment, relevant comments on social networks, weather conditions, etc.).

This 360° view creates an opportunity to discover correlations between the collected data and the figures tracked by finance executives in their modelling activity. But to derive valuable knowledge from data diversity, the trend line methodology is not sufficient.

For the process of discovery to take place, this newly found trove of data needs to be mined with Machine Learning technology.

To put it simply, Machine Learning is the automated search for correlations or patterns within vast amounts of data. Once a statistically significant correlation is identified with a high degree of certainty, it may be applied to new data to predict an outcome.

Let’s take a simple example. Assume you are the CFO of a company that sells goods to other businesses and you want to anticipate your Customer Payment behaviour to prevent delays and accelerate your total inbound cash flow.

Taking another approach

The traditional way would be to look at your past transactions and payment experiences with every significant customer and infer a probable date of payment for each of them.

But if you take another approach and look closer at your data, you may well find that your customer payment behaviours are not always consistent across time, that your historical view is missing some essential explanatory information about the customer’s behaviour that may or may not be specific to their relationship with your company. You end up shooting in the dark.

Wouldn’t your cash-in forecasts be much better if you had also correlated the actual time your customers took to pay you in the past, with detailed information about those transactions?

Information about each customer such as:

  • their payment behaviour with other companies
  • their ordering frequency
  • the date of their first order
  • the date of their previous order
  • whether there’s a contract between your customer and your company or not
  • applicable payment terms

Information about each transaction such as:

  • the amount of the invoice
  • the type of invoiced products (direct vs. Indirect, strategic/ commodity)
  • whether there was a PO or not
  • the accuracy of customer business data on the invoice (PO number, analytical code)
  • whether delivery of goods was acknowledged or not
  • what dunning process was applied to generate payment
  • Information about the industry and macro-economic situation such as growth or the level of short term interest rates

In theory, you cannot be sure that this model will perform well until:

  • You have run a Machine Learning algorithm on your own data, looking for predictive rules that relate each payment behaviour to the detailed information of the corresponding transaction.
  • You have tested the predictive power of those rules on a set of examples.

In fact, the forecast is likely to be much more accurate than with the traditional methodology, provided that the data you fed the algorithm with were representative of your entire customer base.

Which leads us to the next question: can I find all this information about my past transactions while making sure they are representative?

Well, you can’t. Or, at least, not on your own.

Unfortunately, most of this information may not be readily available internally, either because you’ve never collected them so far or they are not flowing through your existing Order-to-Cash process. For instance, it is unlikely that you know whether your customers pay their other suppliers late or not.

But SaaS platforms can capture most of this information for you, by, for instance, accumulating detailed information about hundreds of millions of transactions with millions of customers, representing hundreds of billions of pounds’ worth of revenue. The Machine Learning software will then be able to discover the predictive rules and apply them to your own invoices to forecast their likely payment dates.

Predicting customer payment dates is just a start.

If inbound cash flows can be accurately deduced, so can most other key metrics, such as revenue, for instance, provided the data is available.

CFOs are the ultimate source of truth in an organisation. They manage the skilled resources who translate facts into numbers and confer them credibility. They are therefore the most legitimate and best equipped to tap from as many diverse data sources as available, leveraging the power of Data Science to accurately forecast what comes next and thus gain marketing insight and competitive advantage for their company.

Thus, with their augmented capabilities, CFOs are now poised to be the digital pilots of today’s new data-driven organisations.

Jean-Cyril Schütterlé, ‎VP Product & Data science at Sidetrade
Image source: Shutterstock/everything possible