Effective Sales Forecasting: 5 Tips to Improve Your Predictions
Why Sales Forecasting Is Key In Business?
Before we dive into the tips, let’s take a moment to focus on a key question. Why is sales forecasting important? In truth, it’s not important. It’s crucial – because it helps you predict the revenue you can expect to generate within a certain timeframe. You can then use this information to make smart decisions based on historical data: choices that will impact the whole sales process.
You must use facts to guide you instead of placing your faith in blind hope. What’s more, sales forecasting enables you to predict potential sales problems in the near-term. By being aware, your team can take steps to prevent them – or at least, to mitigate their effects.
Qualitative Vs. Quantitative: Key Differences In Sales Forecasting Methods
In reality, sales forecasting is a broad term. It includes two primary methods – qualitative and quantitative – but what’s the difference? Let’s compare and contrast using the table below and the explanations that follow.
| Method | Description |
|---|---|
| Qualitative | A method based mainly on the opinion of potential customers or company executives. It’s not as accurate as quantitative forecasting. |
| Quantitative | A data-centric method, which makes it more accurate than a qualitative approach. |
5 Tips for Improving Your Sales Forecasting
We now know why sales forecasting is crucial. And we’ve covered the methods you can use. It’s time to dive into the details that will have a real impact on your business: our five tips for improving your sales forecasting process.
1. Prepare your data properly
Bad quality data is the leading reason behind a missed forecast. And it’s little surprise: how can you expect an accurate prediction without properly prepared data?
To achieve the most accurate forecasts, you need to prepare your data correctly and have experience building such solutions. That’s why many executives outsource software development to companies specializing in Big Data, with DLabs.AI being one such specialist.
2. Use historical data to train your models
The more data you have, the more accurate your predictions will be. Historical data is essential for training your models and improving their performance over time.
3. Leverage machine learning algorithms
Machine learning algorithms can be used to analyze large datasets and identify patterns that may not be immediately apparent to human analysts. By leveraging these algorithms, you can gain valuable insights into customer behavior and make more informed decisions.
4. Monitor and adjust your models
Your models are not set in stone. They need to be regularly monitored and adjusted to ensure they remain accurate and effective. By doing so, you can stay ahead of the competition and make data-driven decisions.
5. Use data visualization to communicate insights
Data visualization is a powerful tool for communicating insights to stakeholders. By using data visualization, you can help your team understand complex data and make more informed decisions.
Sales Forecasting – A Case Study
In recent months, DLabs.AI has been building a customized CRM solution for a large company selling various types of clothing from multiple brands. This client has tens-of-millions of customers, generating vast swathes of data to process weekly.
The problem is that the client is migrating and extending its data and analytics tools to Amazon AWS-based Big Data solutions. We’re supporting the Data and Analytics team in its Consumer Lifecycle Management solutions, mainly in automating the gathering and preparing of customer data, modeling consumption habits, and periodic performance reporting related to the implemented models and processes.
Our approach is to gather and prepare data on an AWS Redshift cluster. This consists of building a huge table with millions of rows (the customers) and thousands of columns (the features of each customer) called Customer State Vector (CSV) that is used to feed the models. The AWS Redshift cluster meets the extreme performance and scalability requirements.
The modeling processes are developed in AWS SageMaker, which provides a wide array of ready-to-use Machine Learning models and a data processing pipeline integrating all the processing steps – allowing the data scientist to focus on the model itself and its performance on the modeled data.
We expect to improve the model’s accuracy by at least a few percent, which, given this involves millions of customers, represents a significant uplift.
Conclusion
Sales forecasting is a crucial aspect of any business, and getting it right can make all the difference in terms of revenue, profitability, and competitiveness. By following these five tips, you can improve your sales forecasting process and make more informed decisions about your business.
FAQs
Q: What is sales forecasting?
A: Sales forecasting is the process of predicting future sales revenue based on historical data and market trends.
Q: Why is sales forecasting important?
A: Sales forecasting is important because it helps you predict the revenue you can expect to generate within a certain timeframe, allowing you to make smart decisions about your business.
Q: What are the different methods of sales forecasting?
A: The two primary methods of sales forecasting are qualitative and quantitative.
Q: What is the main difference between qualitative and quantitative sales forecasting?
A: The main difference between qualitative and quantitative sales forecasting is that qualitative forecasting is based on opinion and intuition, while quantitative forecasting is based on data and analysis.
