Retail Sales Forecasting: 10 Effective Methods for Today’s Retail Sales Teams
Sales are the driving force of any business and being able to accurately forecast your sales is essential in order to for your business to evolve. A proper sales forecast is the foundation of your sale strategy as it reveals valuable info into how your organization needs to plan in order to hit your profit targets, how much profit new products will bring or whether or not you need to hire more staff to reach your sales goals.
For a forecast to be as accurate as possible it needs to be based on as much statistical data as possible, and this is where things get tricky as hundreds of product SKUs may be spread across multiple locations and sales channels.
However, there are ways around this challenge. Stay with us as we are about to reveal the top 10 most effective methods for retail sales forecasting.
The Weighted Pipeline Technique
When you lack relevant statistical data, the best thing to do is to start with probability-based forecasting methods. The easiest to implement ‘probability-based’ method is the weighted pipeline technique. This method requires you to give each sales stage a percentage-based probability of closing. To calculate the weighted value, you multiply the probability of closing to the deal value. This process is repeated for every stage of the sales process, and while there might be some overestimations or underestimations in your forecast, they should weight themselves out. Given the fact that it is based on probability, the accuracy of this method is debatable, but it is a great starting point for a good forecast.
The Length Of The Sales Cycle Method
As the name suggests, the foundation of this method is the time that it usually takes a lead to convert. This method is very easy to implement. All you have to do is divide the average sales cycle, by the amount of time that a sales rep has been working on a lead. Keep in mind that different sales channels will have different cycle lengths, so you will have to separate your leads into different categories, such as referrals, cold emails and so on. Moreover, for this method to work, you need to be very consistent with tracking the first moment that the leads enter the sales pipeline.
The Intuitive Method
This method is based on the opinions of your sales reps. Obviously enough, this method is rather subjective, but you can improve it by building the confidence of your sales team. A great way to do this is to ask them to avoid 50% probabilities, and to motivate their answers.
The Test Market Analysis Technique
This method is ideal for new products and services. To implement this technique, you must segregate your market, and release the new product to a certain group of people. This will give you a glimpse of the market’s response to the new product, while also increase brand awareness. Moreover, this is a great way to gain some feedback, and maybe do some improvements before the official product release.
The Lead-driven Method
This method is more accurate than the previously discussed technique, but it does require more in-depth data such as the number of leads per months for a given period, the conversion rate by source, and the average sales price by source. To apply this method, you must assign each lead a value, based on the past behavior of similar leads. The challenge with this method is that the final forecast can be affected by changes in the lead generation strategy, so you must be very consistent with keeping your database updated with all the changes in the sales strategy.
Historical Data Techniques
This method relies heavily on historical business performance, assuming that future performance will at least equal the past performance. This method can also incorporate variables such as yearly growth rates, or anticipated growth rates based on recent investments such as new lead generation tools or the growth of the sales team. The problem with this method is that it can’t consider market changes, so it is best to only use historical forecasting as a benchmark for a more analytical forecast.
The Opportunity Stage Method
This method is also based on historical data, in order to estimate the rate of success of each stage of the sales pipeline. Based on that data, each stage will be assigned a percentage that represents the chances of closing the deal. Obviously enough, the further a lead is down the pipeline, the higher the conversion chances will be. Furthermore, the sales stages can differ for each company, based on individual lead generation and sales strategies.
The Opportunity Creation Model
This method uses demographic and behavioral data in order to predict which opportunities are more likely to convert. There are numerous variables that affect the conversion chances of an opportunity, from the size of the business to the decision power of the lead, previous interactions with your company, the leads decision-making process and so on. For this model to be accurate, you must consider the historical data for closed deals, but also for referrals or retained leads.
The Pipeline Technique
This method is a combination of the historical data and the opportunity stage methods. It is rather hard to implement, as it differs for each business, and it requires a lot of hard data. Ideally, you should work with a software solution to handle the complex calculations. The pipeline technique will calculate the chances of conversion of each lead, based on unique company indicators, such as the success rate of each individual sales representative or the individual value of each opportunity.
The Multivariable Approach
This is by far the most accurate forecasting solution, as it incorporates data from other forecasting techniques, such as the cycle length or the opportunity stage. Obviously enough, this approach is harder to implement, and it requires some religious data tracking. Moreover, this is not a method per-say, but a forecasting approach that can bring together numerous methods and complex analyses such as single equation models, cross-correlations, vector autoregressions and so on. The downside of this method is that it requires statistical data specialists as well as advanced analytical tools which can be quite expensive.
Curve’s Machine Learning Based Forecasts
Curve provides retailers and e-Merchants with an unbiased sales prediction technology that delivers clearer estimates of future sales and product inventory predictions.
Curve uses machine-learning based sales prediction technology, allowing companies to accurately forecast sales, products, and support requests, to increase revenue and optimize profitability. Our unique technology goes beyond traditional business intelligence, by recommending the right solutions based on use cases and customer segments.