Forecasting

Smetric uses a range of tools for cash flow forecasting and non-financial forecasting. But in this new age of digital transformation, we are now able to use machine learning/predictive analytics for some of our clients. While “predict” and “forecast” are synonyms in the dictionary, in the world of business analytics they are two very different kinds of activities.  What’s the difference?

Forecasting Vs Predictive Analytics

Forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.

Forecasting provides aggregate estimates based on past and current trends. Forecasting is very useful in businesses as it allows you to forecast cash flow, forecast sales, plan production and so on. If they are updated regularly and/or as things change, you won’t get caught out by surprises and have time to act.

Predictive analytics goes beyond standard forecasting by providing a predictive score for each customer or other organisational element. E.g. how likely is a customer to stop buying?  To do this, you need to know detailed attributes about the customer- age, location, previous purchasing history, payment history. You also need to have large amounts of historical data about similar users.

Cash Flow Forecasting

Cash flow is the lifeblood of all businesses. Cash flow forecasts can help predict upcoming cash surpluses or shortages to help you make the right decisions.  Here are the key reasons why a cash flow forecast is important:

1. 1. Identify potential shortfalls in cash balances in advance
   2. Make sure that the business can afford to pay suppliers and employees
   3. Spot problems with customer payments
   4. Plan for tax and new plant and equipment purchases

External stakeholders such as banks may require a regular forecast.

There are a number of software solutions for forecasting for simple businesses e.g. Futrli and Spotlight. We are experts in using Futrli, but for bigger or more complex businesses, we use Excel and Modano.  With Modano we are able to build and customise high quality financial models and automate time-consuming, high risk tasks such as model roll-forward, data import and the addition of categories.

Here is an example of what can be done in Power BI. Click on the dashboard you want to look at and drill down to further detail by using the filters in the top right hand corner of each page.

Non – Financial / Demand Forecasting

Combine non-financial data with financial data and get even more powerful, more accurate forecasts.
Most businesses have at least a few non-financial metrics that are key leading indicators. The volume and variety of this non-financial data is often specific to each industry and to what each business views as important. Non-financial data can include information about supply chain problems, product quality issues, digital marketing performance, customer calls, even the weather.

With rapid forecasting come improves responsiveness, providing a vital competitive advantage for those that are competing with disruptive new comers and the unpredictable, discontinuous change of the information age.

Financial Forecasting

Financial forecasting is the processing or estimating or predicting how a business will perform in the future. The most common type of financial forecast is an income statement, however, in a complete financial model all three statements are foretasted. In its simplest form, a financial projection is a forecast of future revenues and expenses. Typically, the projection will account for internal or historical data and will include a prediction of external market factors .

Please click on the below dashboard to view a Live demo.

Predictive Analytics/Machine Learning

Predictive analytics may provide opportunities to further improve your performance, once you are comfortable with the data from simple data analytics

Use predictive analytics to:

1. Predict sales based on historical conversion rates and current sales pipeline
2. Fraud detection
3. Campaign optimisation – model outcomes based on customer behaviour, preferences and profiles
4. Marketing and customer analytics – use information on what customers have done in the past to gauge what they will do in the future. Predict the likelihood of a customer to stop buying and therefore trigger a series of actions to keep them engaged.

How to get started?

There is no time like the present! Right now, there may be an opportunity we can leverage for better forecasting or predictive analytics in your business.

Get in touch and we’ll get started.

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