3 Sales Forecast Models To Help You Achieve A Better Cash Flow

Table of Contents

Why do you need to forecast sales?

Accurate forecasts with minimal errors are key to budgeting and predicting a company’s financial outlook. As the lifeblood of businesses, sales forecasting helps to determine the health level of cash flows. It allows you to have a snapshot of upcoming business expenses and revenue to determine the business strategy that can steer your company in the right direction. Here are three sales forecasting methods and when they should be used.

The Lead-Driven Model

When you analyse the value of each lead source to create a forecast, you are employing the Lead-Driven Model. Assigning a value to every lead source or lead type clarifies the probability of the leads transforming into actual revenue. Metrics involved include the historical leads per month in a set time period, the lead to customer conversation rate by source, and the average sales price by source, just to list a few.

This method is useful when you need to know how many leads are required in order to hit a pre-set revenue target within a certain time frame, which can be calculated by dividing the proposed revenue over the average lead value. The lead value per source can be calculated by multiplying the average sales price and the conversion rate from lead to customer.

It is important to note that the average sales cycle can differ from lead to lead, which renders an extra layer of analysis on time to purchase crucial. Strategy tweaks such as improvements made to the sales process, or discounts provided can alter conversion rates as well. SME loans, bridge loans, invoice financing or factoring to cope with the poor cash flow can directly affect working capital, which translates to changes in the lead generation strategy. Considering these qualitative factors, it will be better to look at data on a trailing 90 day period for accuracy.

Of course, not every lead source can be identified as an inbound one. That being said, do not throw the valuable data away, and categorise them together under ‘Others’ until a better solution can be found.

The Opportunity Creation Driven Model

This model is great when you want to predict the likelihood of sealing a deal based on the behavioural pattern or demographics. It helps you to prioritise who to connect with. Internal aspects of the potential client play a huge role in determining the success rate of clinching a deal and can be quantified through lead scoring. The process can be done by looking at the annual company revenue, the number of employees, the contact person’s role in the decision-making process, your past experience with the company, and more. Remember to not only focus on closed deals but also retained customers as well as those who tend to give referrals. There are many ways to classify and score leads. It can be done on a numerical scale (e.g: 1-100) and then sorted into alphabetical order (e.g: A – E) for easier reference.

The expected value of opportunity can hence be calculated by multiplying the average sale price with the close rate. In light of this, a well-defined criterion needs to be set for opportunity creation. Integrating a good data collection system into the sales team reporting pipeline will help. It can be useful to have a trustworthy programme to automate the opportunity scoring system too.

The Opportunity Stages Driven Model

This model forecasts the likelihood of an opportunity getting closed according to which stage your prospect is in in the sales process. Deal stages include appointment schedules, being qualified to buy, presentation delivery, contract provision, and more, and should map out the potential client’s early awareness stage to when the decision is finally confirmed. The probability to close assigned tends to be larger towards the tail end of the deal stages.

The expected revenue can be calculated by multiplying the deal amount with the probability of closing. Having a clear and detailed list of pre-requisites for a deal to be qualified enough to move on to the next stage is crucial. This optimises the accuracy of the data. Beware of outdated opportunities that may be nested in your pipeline for months which can affect your sales forecast. Update the database to keep it fresh.

It is always good to have both a conservative and optimistic forecast so that you know what to expect in both bad and good times. This way, you can be well-prepared even in the event that sales are slow, or if you reach an unexpected peak due to unforeseen opportunities.

Disclaimer: The information contained in this article is for general guidance purposes only and should not be regarded as a substitute for taking business advice.