Using Machine Learning to Conduct Sales Forecasting
As the future of machine learning slowly transforms the present, models and algorithms are increasingly becoming more powerful, flexible and scalable. They’re also perfectly capable of being adapted into any industry, regardless of how they’re used and to what end.
An example of that is the use of machine learning in sales forecasting. When product catalogs expand in volume and variables become more complex, machine learning algorithms make the process of sales prediction easier and more real-time in the following ways:
Analyzing Sales Variables
Machine learning algorithms can be used to sift through data dumps of prices and stocks as well as to conduct analyses of traffic to certain products and pages or identifying trending products. Thanks to such analyses, retail and e-retail firms can identify which products are likely to perform well and what measures to take to ensure the success of other offerings now and in the future. Some of the variables that directly affect sales are:
- Price of products
- Supply of products
- Market trends
- Demand for products
- Marketing tactics
Revising Compensation
Sales compensation is a powerful driving force in motivating sales employees to achieve targets. However, what’s often seen in companies is that sales targets can be unachievable or based on incorrect metrics that can hamper top employees’ performances, even cause them to leave. Machine learning systems can help to identify Key Performance Indicators (KPIs) based not only on past performance but also on the overall performance of the company and external influential factors. Here are some ways in which machine learning training can set better sales goals:
- Setting achievable targets
- Adapting the right frequency for revision of metrics
- Identifying the ideal incentive
- Implementing compensation and revisions
Identifying and Maintaining Benchmarks
Benchmarks are ideal scenarios that firms use as a target to meet or emulate. Machine learning algorithms can be leveraged to identify these benchmarks using the aforementioned sales indicators as well as past data dumps of employee performance and business targets. Benchmarks are just that– they needn’t be used if it’s business as usual. But to stay ahead of competitors and identify winning strategies, it’s essential that a firm has a goal to work towards and an ideal situation to use as a comparison. Upon failing to meet benchmarks, companies can turn the lens inwards to identify loopholes in the sales cycle, demotivation in employees or products or services that have fallen out of favor.
Maximizing Sales
A data dump is the most important asset to a machine learning algorithm, an arsenal of sorts. This arsenal can be used to predict prime prices that are attractive to customers yet profitable for the company. They can also be used to upsell, cross-sell and recommend stock-ups to avoid going out of stock and losing out on potential sales. Future sales can also be predicted; this, in turn, can be used to drive investments into departments or services and advise marketing strategies to maximize the bang for their marketing buck.
Carrying out A/B Testing
A/B testing is crucial for firms who do not know what marketing strategy will work or what products will do well in the market despite initial research, however thorough. Machine learning can conduct such tests without running too much risk to the business or demanding human resources and attention.
Conclusion
Machine learning has permeated every industry today, so much so that every good machine learning course explores the benefits of technology across fields. By using machine learning algorithms and persevering through trials, businesses can transition into higher performances, better sales, and more impressive profit margins.